From d9bf7a03bb3d29c4dc90f3cca4a17d0def8f63a7 Mon Sep 17 00:00:00 2001
From: byungheon-jeong <b1jeong@ucsd.edu>
Date: Thu, 24 Sep 2020 21:34:57 +0000
Subject: [PATCH] addition of the scripts
---
.../ImageLoader-checkpoint.ipynb | 18555 ++++++++++++++++
.../OGimplementation-checkpoint.ipynb | 7254 ++++++
.../historyEvaluater-checkpoint.ipynb | 256 +
.../testing-checkpoint.ipynb | 591 +
scripts/ImageLoader.ipynb | 18555 ++++++++++++++++
scripts/OGimplementation.ipynb | 7374 ++++++
scripts/e5_2048Training.py | 304 +
scripts/historyEvaluater.ipynb | 256 +
scripts/testing.ipynb | 814 +
scripts/trainingScript.py | 259 +
10 files changed, 54218 insertions(+)
create mode 100644 scripts/.ipynb_checkpoints/ImageLoader-checkpoint.ipynb
create mode 100644 scripts/.ipynb_checkpoints/OGimplementation-checkpoint.ipynb
create mode 100644 scripts/.ipynb_checkpoints/historyEvaluater-checkpoint.ipynb
create mode 100644 scripts/.ipynb_checkpoints/testing-checkpoint.ipynb
create mode 100644 scripts/ImageLoader.ipynb
create mode 100644 scripts/OGimplementation.ipynb
create mode 100644 scripts/e5_2048Training.py
create mode 100644 scripts/historyEvaluater.ipynb
create mode 100644 scripts/testing.ipynb
create mode 100644 scripts/trainingScript.py
diff --git a/scripts/.ipynb_checkpoints/ImageLoader-checkpoint.ipynb b/scripts/.ipynb_checkpoints/ImageLoader-checkpoint.ipynb
new file mode 100644
index 0000000..d2ad692
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/ImageLoader-checkpoint.ipynb
@@ -0,0 +1,18555 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\")\n",
+ " \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import tensorflow as tf\n",
+ "from os import listdir\n",
+ "from os.path import isdir, join, isfile\n",
+ "from numpy import asarray\n",
+ "from numpy import save\n",
+ "import itertools\n",
+ "import shutil \n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mygrouper(n, iterable):\n",
+ " args = [iter(iterable)] * n\n",
+ " return ([e for e in t if e != None] for t in itertools.zip_longest(*args))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mypath = \"/userdata/kerasData/preloaded/subsets/set2\"\n",
+ "savepath = \"/userdata/kerasData/flowDirectory\"\n",
+ "onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = list(mygrouper(10, onlyfiles))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dimentionsEE={}\n",
+ "\n",
+ "for fire in onlyfiles:\n",
+ " rhoice = random.choice(os.listdir(mypath + \"/\"+ fire))\n",
+ " cur = Image.open(mypath+\"/\"+fire+\"/\"+rhoice)\n",
+ " dimentionsEE[fire] = cur.size\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 1 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "firstTrigger = True \n",
+ "count = 0\n",
+ "fireCount = 0\n",
+ "test_label=[]\n",
+ "train_label=[]\n",
+ "validation_label=[]\n",
+ "\n",
+ "onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# \"201710 26-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ "train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ "train, validation = train_test_split(train, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ "print(len(train), len(test), len(validation))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pixelSize = {}\n",
+ "leftout=[]\n",
+ "\n",
+ "def load_dataset(datasetPath, outputPath):\n",
+ " \n",
+ " firstTrigger = True \n",
+ " count = 0\n",
+ " fireCount = 0\n",
+ " test_label=[]\n",
+ " train_label=[]\n",
+ " validation_label=[]\n",
+ " \n",
+ " mypath = datasetPath\n",
+ " onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# \"20171026-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ " train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " train, validation = train_test_split(train, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " print(len(train), len(test), len(validation))\n",
+ "\n",
+ " for fire in test:\n",
+ " if not os.path.exists(f\"{outputPath}/test\"):\n",
+ " os.makedirs(f\"{outputPath}/test\")\n",
+ " os.makedirs(f\"{outputPath}/test/fire\")\n",
+ " os.makedirs(f'{outputPath}/test/nonfire')\n",
+ " fireCount +=1\n",
+ " print(f'{fire} - test fire number {fireCount}')\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"/test/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"/test/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " count +=1\n",
+ " print(count)\n",
+ " if \"+\" in element:\n",
+ " test_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " test_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in train:\n",
+ " if not os.path.exists(f\"{outputPath}/train\"):\n",
+ " os.makedirs(f\"{outputPath}/train\")\n",
+ " os.makedirs(f\"{outputPath}/train/fire\")\n",
+ " os.makedirs(f'{outputPath}/train/nonfire')\n",
+ " print(f\"{fire} - train-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"/train/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"/train/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " print(count)\n",
+ " count += 1\n",
+ "\n",
+ " if \"+\" in element:\n",
+ " train_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " train_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in validation:\n",
+ " print(f\"{fire} - validation-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " # pixelSize.setdefault(fire, set([]))\n",
+ " if not os.path.exists(f\"{outputPath}/validation\"):\n",
+ " os.makedirs(f\"{outputPath}/validation\")\n",
+ " os.makedirs(f\"{outputPath}/validation/fire\")\n",
+ " os.makedirs(f'{outputPath}/validation/nonfire')\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " print(count)\n",
+ " count += 1\n",
+ " dst1 = outputPath+\"/validation/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"/validation/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " if \"+\" in element:\n",
+ " validation_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " validation_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 1 1\n",
+ "20170609_FIRE_sm-n-mobo-c - test fire number 1\n",
+ "1\n",
+ "2\n",
+ "3\n",
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+ "20171021_FIRE_pi-e-mobo-c - train-fire number 1\n",
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+ "20170520_FIRE_pi-w-mobo-c - validation-fire number 1\n",
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+ ]
+ }
+ ],
+ "source": [
+ "load_dataset(\"/userdata/kerasData/preloaded/subsets/set2\", \"/userdata/kerasData/preloaded/flowDirectory4\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pixelSize = {}\n",
+ "leftout=[]\n",
+ "\n",
+ "def load_dataset(datasetPath, outputPath):\n",
+ " \n",
+ " firstTrigger = True \n",
+ " count = 0\n",
+ " fireCount = 0\n",
+ " test_label=[]\n",
+ " train_label=[]\n",
+ " validation_label=[]\n",
+ " \n",
+ " mypath = datasetPath\n",
+ " onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# \"20171026-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ " train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " train, validation = train_test_split(train, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " print(len(train), len(test), len(validation))\n",
+ "\n",
+ " for fire in test:\n",
+ " if not os.path.exists(\"/userdata/kerasData/preloaded/flowDirectory/test\"):\n",
+ "# os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test\")\n",
+ " os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test/fire\")\n",
+ " os.makedirs('/userdata/kerasData/preloaded/flowDirectory/test/nonfire')\n",
+ " fireCount +=1\n",
+ " print(f'{fire} - test fire number {fireCount}')\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"test/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"test/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " count +=1\n",
+ " print(count)\n",
+ " if \"+\" in element:\n",
+ " test_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " test_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in train:\n",
+ " if not os.path.exists(\"/userdata/kerasData/preloaded/flowDirectory/train\"):\n",
+ " # os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test\")\n",
+ " os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/train/fire\")\n",
+ " os.makedirs('/userdata/kerasData/preloaded/flowDirectory/train/nonfire')\n",
+ " print(f\"{fire} - train-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"train/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"train/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " print(count)\n",
+ " count += 1\n",
+ "\n",
+ " if \"+\" in element:\n",
+ " train_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " train_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in validation:\n",
+ " print(f\"{fire} - validation-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " # pixelSize.setdefault(fire, set([]))\n",
+ " if not os.path.exists(\"/userdata/kerasData/preloaded/flowDirectory/validation\"):\n",
+ " # os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test\")\n",
+ " os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/validation/fire\")\n",
+ " os.makedirs('/userdata/kerasData/preloaded/flowDirectory/validation/nonfire')\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " print(count)\n",
+ " count += 1\n",
+ " dst1 = outputPath+\"validation/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"validation/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " if \"+\" in element:\n",
+ " validation_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " validation_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "138 44 35\n",
+ "20191001_FIRE_smer-tcs9-mobo-c - test fire number 1\n",
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+ ]
+ }
+ ],
+ "source": [
+ "mypath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "savepath = \"/userdata/kerasData/preloaded/flowDirectory/\"\n",
+ "# endPath = \"/userdata/kerasData/imageData/\"\n",
+ "\n",
+ "# Xtrain, Xtest, Xvalidation, Ytrain, Ytest, Yvalidation, pixels, count, classWeight = load_dataset(mypath, savepath)\n",
+ "load_dataset(mypath, savepath)\n",
+ "# Xvalidation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test = pixels\n",
+ "\n",
+ "for key, value in test.items():\n",
+ " print(f\"{key} : {value.pop()}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Registering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NON_FIRE_PATH = \"/userdata/kerasData/Robbery_Accident_Fire_Database2/Robbery\"\n",
+ "FIRE_PATH = \"/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire\"\n",
+ "CLASSES = [\"Non-Fire\", \"Fire\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TRAIN_SPLIT = 0.75\n",
+ "TEST_SPLIT = 0.25\n",
+ "INIT_LR = 1e-2\n",
+ "BATCH_SIZE = 64\n",
+ "NUM_EPOCHS = 50\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL_PATH = os.path.sep.join([\"~/output\", \"pyimage_fire_detection.model\"])\n",
+ "TRAINING_PLOT_PATH = os.path.sep.join([\"~/output\", \"training_plot.png\"])\n",
+ "SAMPLE_SIZE = 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pixelSize = {}\n",
+ "# leftout=[]\n",
+ "\n",
+ "# def load_dataset(datasetPath):\n",
+ "# testX = []\n",
+ "# trainX = []\n",
+ "# validationX = [] \n",
+ " \n",
+ "# test_label = []\n",
+ "# train_label = []\n",
+ "# validation_label = []\n",
+ " \n",
+ "# firstTrigger = True\n",
+ " \n",
+ "# count = 0\n",
+ "# fireCount = 0\n",
+ "\n",
+ "# mypath = datasetPath\n",
+ "# onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# # onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# # \"20171026-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ "# train, test = train_test_split(onlyfiles, test_size = 0.25, train_size = 0.75, shuffle=True, random_state = 2100)\n",
+ "# train, validation = train_test_split(train, test_size = 0.25, train_size = 0.75, shuffle=True, random_state = 2100)\n",
+ "# print(len(train), len(test), len(validation))\n",
+ "\n",
+ "# for index,testsplit in enumerate(list(mygrouper(10, test))):\n",
+ "# testX = []\n",
+ "# for fire in testsplit:\n",
+ "# fireCount +=1\n",
+ "# print(f'{fire} - fire number {fireCount}')\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# count +=1\n",
+ "# print(count)\n",
+ "# if \"+\" in element:\n",
+ "# test_label.append(1)\n",
+ "# else:\n",
+ "# test_label.append(0)\n",
+ "\n",
+ "# fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# pixelSize[fire].add(fire_im.size)\n",
+ "# try:\n",
+ "# fire_im = fire_im.resize((2048,1536))\n",
+ "# except Error:\n",
+ "# print(fire)\n",
+ "# leftout.append(fire)\n",
+ "# break\n",
+ "\n",
+ "# inArrayim = np.asarray(fire_im) \n",
+ "# inArrayim = inArrayim/255\n",
+ "\n",
+ "# # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # image = cv2.resize(image, (128,128))\n",
+ "# testX.append(inArrayim)\n",
+ "# name = f\"testX_{index}.npy\"\n",
+ "# save(name, testX)\n",
+ " \n",
+ "# a = fireCount\n",
+ " \n",
+ "# # for fire in train:\n",
+ "# # print(f\"{fire} - train-fire number {fireCount - a +1}\")\n",
+ "# # fireCount+=1\n",
+ "# # pixelSize.setdefault(fire, set([]))\n",
+ "# # for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# # print(count)\n",
+ "# # count += 1\n",
+ "\n",
+ "# # if \"+\" in element:\n",
+ "# # train_label.append(1)\n",
+ "# # else:\n",
+ "# # train_label.append(0)\n",
+ " \n",
+ "# # fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# # pixelSize[fire].add(fire_im.size)\n",
+ "# # fire_im = fire_im.resize((2048,1536))\n",
+ "# # inArrayim = np.asarray(fire_im)\n",
+ "# # inArrayim = inArrayim/255\n",
+ "# # # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # # image = cv2.resize(image, (128,128))\n",
+ "# # trainX.append(inArrayim) \n",
+ " \n",
+ "# # # a = fireCount \n",
+ "\n",
+ "# # a = 0\n",
+ "# # for fire in validation:\n",
+ "# # print(f\"{fire} - validation-fire number {fireCount - a +1}\")\n",
+ "# # fireCount+=1\n",
+ "# # pixelSize.setdefault(fire, set([]))\n",
+ "# # for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# # print(count)\n",
+ "# # count += 1\n",
+ "\n",
+ "# # if \"+\" in element:\n",
+ "# # validation_label.append(1)\n",
+ "# # else:\n",
+ "# # validation_label.append(0)\n",
+ " \n",
+ "# # fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# # pixelSize[fire].add(fire_im.size)\n",
+ "# # fire_im = fire_im.resize((2048,1536))\n",
+ "# # inArrayim = np.asarray(fire_im)\n",
+ "# # inArrayim = inArrayim/255\n",
+ "\n",
+ "# # # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # # image = cv2.resize(image, (128,128))\n",
+ "# # # print(validationX)\n",
+ "# # validationX.append(inArrayim) \n",
+ " \n",
+ "# # print(fireCount)\n",
+ " \n",
+ "\n",
+ "# # save(\"trainX.npy\", trainX)\n",
+ "# # save(\"testX.npy\", testX)\n",
+ "# # save(\"validationX.npy\", validationX)\n",
+ " \n",
+ "# trainY = tf.keras.utils.to_categorical(np.array(train_label), num_classes=2)\n",
+ "# testY = tf.keras.utils.to_categorical(np.array(test_label), num_classes=2)\n",
+ "# validationY = tf.keras.utils.to_categorical(np.array(validation_label), num_classes = 2)\n",
+ " \n",
+ "# save(\"trainY.npy\", trainY)\n",
+ "# save(\"testY.npy\", testY)\n",
+ "# save(\"validationY.npy\", validationY)\n",
+ " \n",
+ "# # labels = np.append(trainY, testY, validationY)\n",
+ "# labels = np.vstack((trainY, testY))\n",
+ "# labels = np.vstack((labels, validationY))\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ "# save(\"classWeight.npy\", classWeight)\n",
+ "\n",
+ "# # return np.array(trainX, dtype=\"float32\"), np.array(testX, dtype=\"float32\"), np.array(validationX, dtype=\"float32\"), trainY, testY, validationY, pixelSize, count, classWeight\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.8, shuffle=True, random_state=200)\n",
+ "\n",
+ "# count = 0\n",
+ "# countTest = 0\n",
+ "\n",
+ "# for fire in train:\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ train):\n",
+ "# count +=1\n",
+ "# if \"+\" in element:\n",
+ "# label = 1\n",
+ "# label = tf.keras.utils.to_categorical(label, num_classes=2)\n",
+ "# width, height = Image.open(datasetPath + \"/\"+ train+ \"/\" +element).size\n",
+ "# print(width*height)\n",
+ "# print(datasetPath + \"/\"+ element + \"/\" + element)\n",
+ "\n",
+ "# for fire in test:\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# countTest +=1\n",
+ "# if \"+\" in element:\n",
+ "# label = 1\n",
+ "# label = tf.keras.utils.to_categorical(label, num_classes=2)\n",
+ "# width, height = Image.open(datasetPath + \"/\"+ element).size\n",
+ "# print(datasetPath + \"/\"+ element + \"/\" + element)\n",
+ "# print(count, countTest)\n",
+ "\n",
+ "# image = cv2.resize(image, (128,128))\n",
+ "# trainX.insert(image)\n",
+ "# to_categorical(labels)\n",
+ "\n",
+ "\n",
+ "# def load_dataset(datasetPath):\n",
+ "# # grab the paths to all images in our dataset directory, then\n",
+ "# # initialize our lists of images\n",
+ "# imagePaths = os.listdir(datasetPath)\n",
+ "# trainXList = []\n",
+ "# testXList = []\n",
+ "# testX = np.array([])\n",
+ "# trainY = np.array([])\n",
+ "# trainY = np.array([])\n",
+ "# testY = np.array([])\n",
+ "\n",
+ "# testI = 0 \n",
+ " \n",
+ "# # loop over the image paths\n",
+ "# for directories in imagePaths:\n",
+ "# tempF= []\n",
+ "# tempNF = []\n",
+ " \n",
+ "# for element in os.listdir(datasetPath + \"/\"+ directories):\n",
+ "# if re.search(\".jpg\", element):\n",
+ "# image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# image = cv2.resize(image, (128,128))\n",
+ "# if \"+\" in element:\n",
+ "# tempF.append(image)\n",
+ "# else:\n",
+ "# tempNF.append(image)\n",
+ " \n",
+ "# tempF = np.array(tempF, dtype=\"float32\")\n",
+ "# tempNF = np.array(tempNF, dtype=\"float32\")\n",
+ " \n",
+ "# fireLabels = np.ones((tempF.shape[0],))\n",
+ "# nonFireLabels = np.zeros((tempNF.shape[0],))\n",
+ "# data = np.vstack([tempF, tempNF])\n",
+ "# labels = np.hstack([fireLabels, nonFireLabels])\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ " \n",
+ "# #print(labels)\n",
+ " \n",
+ "# data /= 255\n",
+ "\n",
+ "# (t_trainX, t_testX, t_trainY, t_testY) = train_test_split(data, labels,\n",
+ "# test_size=0.2, random_state=42)\n",
+ " \n",
+ "# trainXList.append(t_trainX)\n",
+ "# testXList.append(t_testX)\n",
+ "# print(t_trainY.shape, trainY.shape)\n",
+ " \n",
+ "# if trainY.size == 0:\n",
+ "# trainY = t_trainY\n",
+ "# testY = t_testY\n",
+ "# else:\n",
+ "# trainY = np.append(trainY, t_trainY, axis = 0)\n",
+ "# testY = np.append(testY, t_testY, axis = 0)\n",
+ "\n",
+ " \n",
+ "# trainX = np.vstack(trainXList)\n",
+ "# testX = np.vstack(testXList)\n",
+ "# trainY = np.hstack(trainYList)\n",
+ "# testY = np.hstack(testYList)\n",
+ " \n",
+ "# labels = np.append(trainY, testY)\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ " \n",
+ "# print(trainX.shape, testX.shape, trainY.shape, testY.shape)\n",
+ " \n",
+ "# return trainX, testX, trainY, testY, classWeight\n",
+ " \n",
+ "# # load the image and resize it to be a fixed 128x128 pixels,\n",
+ "# # ignoring aspect ratio\n",
+ "# # image = cv2.imread(imagePath)\n",
+ "# # image = cv2.resize(image, (128, 128))\n",
+ " \n",
+ "# # add the image to the data lists\n",
+ "# # data.append(image)\n",
+ "\n",
+ "# # return the data list as a NumPy array\n",
+ "# # return np.array(data, dtype=\"float32\")\n",
+ "\n",
+ "# labels = np.append(trainY, testY)\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ "# classWeight\n",
+ "\n",
+ "# from numpy import asarray\n",
+ "# from numpy import save\n",
+ "# from numpy import load\n",
+ "\n",
+ "# try:\n",
+ "# fireData = load(\"firedata1.npy\")\n",
+ "# nonFireData = load(\"nonfiredata1.npy\")\n",
+ "# except IOError:\n",
+ "# print(\"Loading...\")\n",
+ "# fireData = load_dataset(FIRE_PATH)\n",
+ "# nonFireData = load_dataset(NON_FIRE_PATH)\n",
+ "# save(\"firedata1.npy\", fireData)\n",
+ "# save(\"nonfiredata1.npy\", nonFireData)\n",
+ "\n",
+ "# fireLabels = np.ones((fireData.shape[0],))\n",
+ "# nonFireLabels = np.zeros((nonFireData.shape[0],))\n",
+ "\n",
+ "# data = np.vstack([fireData, nonFireData])\n",
+ "# labels = np.hstack([fireLabels, nonFireLabels])\n",
+ "# data /= 255\n",
+ "# data.shape\n",
+ "\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ "\n",
+ "# im = Image.open(\"/userdata/kerasData/images/hpwren.ucsd.edu/HWB/HPWREN-FIgLib/20180614-Hope-wc-e-mobo-c/1529002400_+01440.jpg\")\n",
+ "# a = np.asarray(im)\n",
+ "# a = a/255\n",
+ "# # cv2.imread(\"/userdata/kerasData/images/hpwren.ucsd.edu/HWB/HPWREN-FIgLib/20180614-Hope-wc-e-mobo-c/1529002400_+01440.jpg\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# testX = []\n",
+ "# trainX = []\n",
+ "# validationX = []\n",
+ "# pixelSize = {}\n",
+ "# datasetPath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "# savepath = \"/userdata/kerasData/preloaded\"\n",
+ "\n",
+ "# firstTrigger = True\n",
+ "\n",
+ "# count = 0\n",
+ "# fireCount = 0\n",
+ "# test_label = []\n",
+ "# train_label = []\n",
+ "# validation_label = []\n",
+ "# finfin = np.array([])\n",
+ "\n",
+ "# for index,test in enumerate(onlyfiles):\n",
+ "# for fire in test:\n",
+ "# testX= []\n",
+ "# fireCount +=1\n",
+ "# print(f'{fire} - fire number {fireCount}')\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# count +=1\n",
+ "# print(count)\n",
+ "# if \"+\" in element:\n",
+ "# test_label.append(1)\n",
+ "# else:\n",
+ "# test_label.append(0)\n",
+ "# fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# pixelSize[fire].add(fire_im.size)\n",
+ "# try:\n",
+ "# fire_im = fire_im.resize((2048,1536))\n",
+ "# except Error:\n",
+ "# print(fire)\n",
+ "# leftout.append(fire)\n",
+ "# break\n",
+ "\n",
+ "# inArrayim = np.asarray(fire_im) \n",
+ "# inArrayim = inArrayim/255\n",
+ "\n",
+ "# # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # image = cv2.resize(image, (128,128))\n",
+ "# testX.append(inArrayim)\n",
+ "# name = f\"\n",
+ "# print(\"DONE\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/.ipynb_checkpoints/OGimplementation-checkpoint.ipynb b/scripts/.ipynb_checkpoints/OGimplementation-checkpoint.ipynb
new file mode 100644
index 0000000..b0a8ffe
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/OGimplementation-checkpoint.ipynb
@@ -0,0 +1,7254 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import BatchNormalization\n",
+ "from tensorflow.keras.layers import SeparableConv2D\n",
+ "from tensorflow.keras.layers import MaxPooling2D\n",
+ "from tensorflow.keras.layers import Activation\n",
+ "from tensorflow.keras.layers import Flatten\n",
+ "from tensorflow.keras.layers import Dropout\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\") \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\")\n",
+ " \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import tensorflow as tf\n",
+ "from os import listdir\n",
+ "from os.path import isdir, join, isfile\n",
+ "from numpy import asarray\n",
+ "from numpy import save\n",
+ "import itertools\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\")\n",
+ " \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import pandas as pd\n",
+ "import keras"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def is_jpg(filename):\n",
+ " try:\n",
+ " i=Image.open(filename)\n",
+ " return i.format =='JPEG'\n",
+ " except IOError:\n",
+ " return False\n",
+ " \n",
+ "def loadDataARRAY(savePath):\n",
+ " pixelSize = {}\n",
+ "\n",
+ " testX = []\n",
+ " trainX = []\n",
+ " validationX = []\n",
+ "\n",
+ " test_label = []\n",
+ " train_label = []\n",
+ " validation_label = []\n",
+ "\n",
+ " nonfirecount = 0\n",
+ " fireCount = 0 \n",
+ " TRAIN_PATH = \"/userdata/kerasData/preloaded/recreate_2/train\"\n",
+ "# TEST_PATH = \"/userdata/kerasData/preloaded/recreate_2/test\"\n",
+ " VALIDATION_PATH = \"/userdata/kerasData/preloaded/recreate_2/validation\"\n",
+ "\n",
+ " train_fire = os.listdir(f\"{TRAIN_PATH}/fire\")\n",
+ " train_nonfire = os.listdir(f\"{TRAIN_PATH}/nonfire\")\n",
+ "\n",
+ "# test_fire = os.listdir(f\"{TEST_PATH}/fire\")\n",
+ "# test_nonfire = os.listdir(f\"{TEST_PATH}/nonfire\")\n",
+ "\n",
+ " validation_fire = os.listdir(f\"{VALIDATION_PATH}/fire\")\n",
+ " validation_nonfire = os.listdir(f\"{VALIDATION_PATH}/nonfire\")\n",
+ "\n",
+ "# print(f\"train: {len(train_fire)} FIRES {len(train_nonfire)} NONFIRES, test: {len(test_fire)} FIRES {len(test_nonfire)} NONFIRES, validation: {len(validation_fire)} FIRES {len(validation_nonfire)} NONFIRES\")\n",
+ " i = 0\n",
+ "\n",
+ "\n",
+ " for path in train_fire:\n",
+ " fire = f\"{TRAIN_PATH}/fire/{path}\"\n",
+ " if is_jpg(fire):\n",
+ " fireCount +=1\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ " fire_im = Image.open(fire)\n",
+ " pixelSize[fire].add(fire_im.size)\n",
+ " fire_im = fire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(fire_im) \n",
+ " inArrayim = inArrayim/255\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} TRAIN\")\n",
+ " trainX.append(inArrayim)\n",
+ " train_label.append(1)\n",
+ "\n",
+ " for path in train_nonfire:\n",
+ " nonfire = f\"{TRAIN_PATH}/nonfire/{path}\"\n",
+ " if is_jpg(nonfire):\n",
+ " nonfirecount +=1\n",
+ " pixelSize.setdefault(nonfire, set([]))\n",
+ " nonfire_im = Image.open(nonfire)\n",
+ " pixelSize[nonfire].add(nonfire_im.size)\n",
+ "\n",
+ " nonfire_im = nonfire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(nonfire_im) \n",
+ " inArrayim = inArrayim/255\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} TRAIN\")\n",
+ " trainX.append(inArrayim)\n",
+ " train_label.append(0)\n",
+ "\n",
+ "# for path in test_fire:\n",
+ "# fire = f\"{TEST_PATH}/fire/{path}\"\n",
+ "# if is_jpg(fire):\n",
+ "# fireCount+=1\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# fire_im = Image.open(fire)\n",
+ "# pixelSize[fire].add(fire_im.size)\n",
+ "# fire_im = fire_im.resize((128,128))\n",
+ "# inArrayim = np.asarray(fire_im)\n",
+ "# inArrayim = inArrayim/255\n",
+ "# # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # image = cv2.resize(image, (128,128))\n",
+ "# shape = inArrayim.shape\n",
+ "# if(shape == (128, 128, 3)):\n",
+ "# print(f\"{path} TEST\")\n",
+ "# testX.append(inArrayim)\n",
+ "# test_label.append(1)\n",
+ "\n",
+ "# for path in test_nonfire:\n",
+ "# nonfire = f\"{TEST_PATH}/nonfire/{path}\"\n",
+ "# if is_jpg(nonfire):\n",
+ "# nonfirecount +=1\n",
+ "# pixelSize.setdefault(nonfire, set([]))\n",
+ "# nonfire_im = Image.open(nonfire)\n",
+ "# pixelSize[nonfire].add(nonfire_im.size)\n",
+ "# nonfire_im = nonfire_im.resize((128,128))\n",
+ "# inArrayim = np.asarray(nonfire_im) \n",
+ "# inArrayim = inArrayim/255\n",
+ "# shape = inArrayim.shape\n",
+ "# if(shape == (128, 128, 3)):\n",
+ "# print(f\"{path} TEST\")\n",
+ "# testX.append(inArrayim)\n",
+ "# test_label.append(0)\n",
+ "\n",
+ " for path in validation_fire:\n",
+ " fire = f\"{VALIDATION_PATH}/fire/{path}\"\n",
+ " if is_jpg(fire):\n",
+ " print(f\"{path} VALIDATION\")\n",
+ " fireCount+=1\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ "\n",
+ " fire_im = Image.open(fire)\n",
+ " pixelSize[fire].add(fire_im.size)\n",
+ " fire_im = fire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(fire_im)\n",
+ " inArrayim = inArrayim/255\n",
+ " # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ " # image = cv2.resize(image, (128,128))\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} VALIDATION\")\n",
+ " validationX.append(inArrayim)\n",
+ " validation_label.append(1)\n",
+ "\n",
+ " for path in validation_nonfire:\n",
+ " nonfire =f\"{VALIDATION_PATH}/nonfire/{path}\"\n",
+ " if is_jpg(nonfire):\n",
+ " nonfirecount +=1\n",
+ " pixelSize.setdefault(nonfire, set([]))\n",
+ " nonfire_im = Image.open(nonfire)\n",
+ " pixelSize[nonfire].add(nonfire_im.size)\n",
+ " nonfire_im = nonfire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(nonfire_im) \n",
+ " inArrayim = inArrayim/255\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} VALIDATION\")\n",
+ " validationX.append(inArrayim)\n",
+ " validation_label.append(0)\n",
+ "\n",
+ " save(f\"{savePath}rere_trainX.npy\", trainX)\n",
+ "# save(f\"{savePath}testX.npy\", testX)\n",
+ " save(f\"{savePath}rere_validationX.npy\", validationX)\n",
+ " \n",
+ " trainY = np.array(train_label)\n",
+ " testY = np.array(test_label)\n",
+ " validationY = np.array(validation_label)\n",
+ " \n",
+ " save(f\"{savePath}rere_trainY.npy\", trainY)\n",
+ "# save(f\"{savePath}testY.npy\", testY)\n",
+ " save(f\"{savePath}rere_validationY.npy\", validationY)\n",
+ " \n",
+ " obj = [fireCount, nonfirecount]\n",
+ " labels = np.append(trainY, testY)\n",
+ " labels = np.append(labels, validationY)\n",
+ " labels = to_categorical(labels, num_classes=2)\n",
+ " classTotals = labels.sum(axis=0)\n",
+ " print(classTotals, obj)\n",
+ " \n",
+ " classWeight = classTotals.max() / classTotals\n",
+ " save(f\"{savePath}rere_classWeight.npy\", classWeight)\n",
+ " return trainX, testX, validationX, trainY, testY, validationY"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "167.jpg TRAIN\n",
+ "855.jpg TRAIN\n",
+ "869.jpg TRAIN\n",
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+ "insidecity_urb584.jpg VALIDATION\n",
+ "tallbuilding_art181.jpg VALIDATION\n",
+ "street_par125.jpg VALIDATION\n",
+ "opencountry_nat511.jpg VALIDATION\n",
+ "forest_bost190.jpg VALIDATION\n",
+ "forest_natu707.jpg VALIDATION\n",
+ "tallbuilding_urban982.jpg VALIDATION\n",
+ "mountain_n18009.jpg VALIDATION\n",
+ "mountain_n737041.jpg VALIDATION\n",
+ "insidecity_boston305.jpg VALIDATION\n",
+ "mountain_sharp74.jpg VALIDATION\n",
+ "insidecity_a462055.jpg VALIDATION\n",
+ "highway_bost158.jpg VALIDATION\n",
+ "opencountry_n251011.jpg VALIDATION\n",
+ "insidecity_urb844.jpg VALIDATION\n",
+ "opencountry_urb969.jpg VALIDATION\n",
+ "mountain_nat78.jpg VALIDATION\n",
+ "opencountry_land290.jpg VALIDATION\n",
+ "insidecity_art641.jpg VALIDATION\n",
+ "mountain_nat1003.jpg VALIDATION\n",
+ "opencountry_natu138.jpg VALIDATION\n",
+ "highway_art885.jpg VALIDATION\n",
+ "forest_for84.jpg VALIDATION\n",
+ "mountain_land223.jpg VALIDATION\n",
+ "coast_nat1091.jpg VALIDATION\n",
+ "highway_gre50.jpg VALIDATION\n",
+ "mountain_land161.jpg VALIDATION\n",
+ "tallbuilding_art219.jpg VALIDATION\n",
+ "mountain_n480098.jpg VALIDATION\n",
+ "coast_nat910.jpg VALIDATION\n",
+ "mountain_nat801.jpg VALIDATION\n",
+ "highway_gre470.jpg VALIDATION\n",
+ "highway_gre473.jpg VALIDATION\n",
+ "tallbuilding_city50.jpg VALIDATION\n",
+ "street_gre11.jpg VALIDATION\n",
+ "highway_gre125.jpg VALIDATION\n",
+ "coast_natu815.jpg VALIDATION\n",
+ "forest_natu169.jpg VALIDATION\n",
+ "highway_gre474.jpg VALIDATION\n",
+ "[2688. 1268.] [1281, 2688]\n"
+ ]
+ }
+ ],
+ "source": [
+ "trainX, testX, validationX, trainY, testY, validationY = loadDataARRAY(\"/userdata/kerasData/preloaded/recreate/loaded_arrays/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "savePath = \"/userdata/kerasData/preloaded/loaded_arrays/recreate_2/trainrere_\"\n",
+ "\n",
+ "testX = np.array([])\n",
+ "testY = np.array([])\n",
+ "\n",
+ "save(f\"{savePath}testX.npy\", testX)\n",
+ "save(f\"{savePath}testY.npy\", testY)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0., 1.],\n",
+ " [0., 1.],\n",
+ " [0., 1.],\n",
+ " ...,\n",
+ " [1., 0.],\n",
+ " [1., 0.],\n",
+ " [1., 0.]], dtype=float32)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def loadData(pathToFiles):\n",
+ " Xtrain = np.load(f\"{pathToFiles}trainX.npy\")\n",
+ "# Xtest = np.load(f\"{pathToFiles}testX.npy\")\n",
+ " XValidation = np.load(f\"{pathToFiles}validationX.npy\")\n",
+ " \n",
+ " Ytrain = np.load(f\"{pathToFiles}trainY.npy\")\n",
+ "# Ytest = np.load(f\"{pathToFiles}testY.npy\")\n",
+ " YValidation = np.load(f\"{pathToFiles}validationY.npy\")\n",
+ " \n",
+ " classWeight = np.load(f\"{pathToFiles}classWeight.npy\")\n",
+ " return Xtrain, XValidation, Ytrain, YValidation, classWeight\n",
+ "\n",
+ "mypath = \"/userdata/kerasData/preloaded/loaded_arrays/recreate_3/\"\n",
+ "Xtrain, Xvalidation, Ytrain, Yvalidation, classWeight = loadData(mypath)\n",
+ "\n",
+ "Ytrain = to_categorical(Ytrain, num_classes=2)\n",
+ "Yvalidation =to_categorical(Yvalidation, num_classes=2)\n",
+ "\n",
+ "Yvalidation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(994, 2)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Yvalidation.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class FireDetectionNet:\n",
+ " @staticmethod\n",
+ " def build(width, height, depth):\n",
+ " # initialize the model along with the input shape to be\n",
+ " # \"channels last\" and the channels dimension itself\n",
+ " model = Sequential()\n",
+ " inputShape = (height, width, depth)\n",
+ " chanDim = -1\n",
+ " \n",
+ " model.add(SeparableConv2D(16, (7, 7), padding=\"same\",\n",
+ " input_shape=inputShape))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(32, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(Flatten())\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # second set of FC => RELU layers\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # softmax classifier\n",
+ " model.add(Dense(2))\n",
+ " model.add(Activation(\"softmax\"))\n",
+ "\n",
+ " # return the constructed network architecture\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from keras import backend as K\n",
+ "\n",
+ "def f1(y_true, y_pred):\n",
+ " \n",
+ " def recall(y_true, y_pred):\n",
+ " \"\"\"Recall metric.\n",
+ "\n",
+ " Only computes a batch-wise average of recall.\n",
+ "\n",
+ " Computes the recall, a metric for multi-label classification of\n",
+ " how many relevant items are selected.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ " def precision(y_true, y_pred):\n",
+ " \"\"\"Precision metric.\n",
+ "\n",
+ " Only computes a batch-wise average of precision.\n",
+ "\n",
+ " Computes the precision, a metric for multi-label classification of\n",
+ " how many selected items are relevant.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ " precision = precision(y_true, y_pred)\n",
+ " recall = recall(y_true, y_pred)\n",
+ " return 2*((precision*recall)/(precision+recall+K.epsilon()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define the initial learning rate, batch size, and number of epochs\n",
+ "INIT_LR = 1e-2\n",
+ "BATCH_SIZE = 64\n",
+ "NUM_EPOCHS = 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<tensorflow.python.keras.losses.BinaryCrossentropy at 0x7efd7830b470>"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bincross = tf.keras.losses.BinaryCrossentropy(\n",
+ " from_logits=False, label_smoothing=0,\n",
+ " name='binary_crossentropy'\n",
+ ")\n",
+ "\n",
+ "bincross"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/preloaded/madeModels/OGRUN_I1/assets\n",
+ "Epoch 1/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.7305 - accuracy: 0.6809 - precision_1: 0.6809 - recall_1: 0.6809 - f1: 0.6809\n",
+ "Epoch 00001: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.7297 - accuracy: 0.6812 - precision_1: 0.6812 - recall_1: 0.6812 - f1: 0.6818 - val_loss: 0.6095 - val_accuracy: 0.6660 - val_precision_1: 0.6660 - val_recall_1: 0.6660 - val_f1: 0.6611\n",
+ "Epoch 2/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.5209 - accuracy: 0.7516 - precision_1: 0.7516 - recall_1: 0.7516 - f1: 0.7494\n",
+ "Epoch 00002: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.5209 - accuracy: 0.7516 - precision_1: 0.7516 - recall_1: 0.7516 - f1: 0.7494 - val_loss: 0.5999 - val_accuracy: 0.6509 - val_precision_1: 0.6509 - val_recall_1: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 3/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4861 - accuracy: 0.7602 - precision_1: 0.7602 - recall_1: 0.7602 - f1: 0.7587\n",
+ "Epoch 00003: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 18s 399ms/step - loss: 0.4861 - accuracy: 0.7602 - precision_1: 0.7602 - recall_1: 0.7602 - f1: 0.7587 - val_loss: 0.5990 - val_accuracy: 0.6700 - val_precision_1: 0.6700 - val_recall_1: 0.6700 - val_f1: 0.6650\n",
+ "Epoch 4/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4336 - accuracy: 0.8026 - precision_1: 0.8026 - recall_1: 0.8026 - f1: 0.8022\n",
+ "Epoch 00004: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.4336 - accuracy: 0.8026 - precision_1: 0.8026 - recall_1: 0.8026 - f1: 0.8022 - val_loss: 0.6292 - val_accuracy: 0.6650 - val_precision_1: 0.6650 - val_recall_1: 0.6650 - val_f1: 0.6602\n",
+ "Epoch 5/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4169 - accuracy: 0.8085 - precision_1: 0.8085 - recall_1: 0.8085 - f1: 0.8071\n",
+ "Epoch 00005: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.4169 - accuracy: 0.8085 - precision_1: 0.8085 - recall_1: 0.8085 - f1: 0.8071 - val_loss: 0.4673 - val_accuracy: 0.7575 - val_precision_1: 0.7575 - val_recall_1: 0.7575 - val_f1: 0.7646\n",
+ "Epoch 6/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4313 - accuracy: 0.7999 - precision_1: 0.7999 - recall_1: 0.7999 - f1: 0.7969\n",
+ "Epoch 00006: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.4313 - accuracy: 0.7999 - precision_1: 0.7999 - recall_1: 0.7999 - f1: 0.7969 - val_loss: 0.9378 - val_accuracy: 0.6841 - val_precision_1: 0.6841 - val_recall_1: 0.6841 - val_f1: 0.6934\n",
+ "Epoch 7/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3919 - accuracy: 0.8137 - precision_1: 0.8137 - recall_1: 0.8137 - f1: 0.8148\n",
+ "Epoch 00007: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 404ms/step - loss: 0.3919 - accuracy: 0.8137 - precision_1: 0.8137 - recall_1: 0.8137 - f1: 0.8148 - val_loss: 0.6346 - val_accuracy: 0.7354 - val_precision_1: 0.7354 - val_recall_1: 0.7354 - val_f1: 0.7432\n",
+ "Epoch 8/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3824 - accuracy: 0.8223 - precision_1: 0.8223 - recall_1: 0.8223 - f1: 0.8233\n",
+ "Epoch 00008: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.3824 - accuracy: 0.8223 - precision_1: 0.8223 - recall_1: 0.8223 - f1: 0.8233 - val_loss: 0.4394 - val_accuracy: 0.7867 - val_precision_1: 0.7867 - val_recall_1: 0.7867 - val_f1: 0.7930\n",
+ "Epoch 9/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3946 - accuracy: 0.8116 - precision_1: 0.8116 - recall_1: 0.8116 - f1: 0.8128\n",
+ "Epoch 00009: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.3946 - accuracy: 0.8116 - precision_1: 0.8116 - recall_1: 0.8116 - f1: 0.8128 - val_loss: 0.4116 - val_accuracy: 0.8129 - val_precision_1: 0.8129 - val_recall_1: 0.8129 - val_f1: 0.8037\n",
+ "Epoch 10/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3718 - accuracy: 0.8268 - precision_1: 0.8268 - recall_1: 0.8268 - f1: 0.8251\n",
+ "Epoch 00010: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.3718 - accuracy: 0.8268 - precision_1: 0.8268 - recall_1: 0.8268 - f1: 0.8251 - val_loss: 0.4110 - val_accuracy: 0.8169 - val_precision_1: 0.8169 - val_recall_1: 0.8169 - val_f1: 0.8223\n",
+ "Epoch 11/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3636 - accuracy: 0.8337 - precision_1: 0.8337 - recall_1: 0.8337 - f1: 0.8337\n",
+ "Epoch 00011: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.3636 - accuracy: 0.8337 - precision_1: 0.8337 - recall_1: 0.8337 - f1: 0.8337 - val_loss: 0.4239 - val_accuracy: 0.8119 - val_precision_1: 0.8119 - val_recall_1: 0.8119 - val_f1: 0.8174\n",
+ "Epoch 12/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3433 - accuracy: 0.8464 - precision_1: 0.8464 - recall_1: 0.8464 - f1: 0.8454\n",
+ "Epoch 00012: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.3433 - accuracy: 0.8464 - precision_1: 0.8464 - recall_1: 0.8464 - f1: 0.8454 - val_loss: 0.3578 - val_accuracy: 0.8350 - val_precision_1: 0.8350 - val_recall_1: 0.8350 - val_f1: 0.8252\n",
+ "Epoch 13/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3383 - accuracy: 0.8461 - precision_1: 0.8461 - recall_1: 0.8461 - f1: 0.8468\n",
+ "Epoch 00013: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 404ms/step - loss: 0.3383 - accuracy: 0.8461 - precision_1: 0.8461 - recall_1: 0.8461 - f1: 0.8468 - val_loss: 0.3457 - val_accuracy: 0.8370 - val_precision_1: 0.8370 - val_recall_1: 0.8370 - val_f1: 0.8418\n",
+ "Epoch 14/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3388 - accuracy: 0.8427 - precision_1: 0.8427 - recall_1: 0.8427 - f1: 0.8442\n",
+ "Epoch 00014: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.3388 - accuracy: 0.8427 - precision_1: 0.8427 - recall_1: 0.8427 - f1: 0.8442 - val_loss: 0.4213 - val_accuracy: 0.8139 - val_precision_1: 0.8139 - val_recall_1: 0.8139 - val_f1: 0.8047\n",
+ "Epoch 15/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3281 - accuracy: 0.8516 - precision_1: 0.8516 - recall_1: 0.8516 - f1: 0.8505\n",
+ "Epoch 00015: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.3281 - accuracy: 0.8516 - precision_1: 0.8516 - recall_1: 0.8516 - f1: 0.8505 - val_loss: 0.3311 - val_accuracy: 0.8461 - val_precision_1: 0.8461 - val_recall_1: 0.8461 - val_f1: 0.8506\n",
+ "Epoch 16/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3294 - accuracy: 0.8478 - precision_1: 0.8478 - recall_1: 0.8478 - f1: 0.8459\n",
+ "Epoch 00016: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.3294 - accuracy: 0.8478 - precision_1: 0.8478 - recall_1: 0.8478 - f1: 0.8459 - val_loss: 0.3488 - val_accuracy: 0.8350 - val_precision_1: 0.8350 - val_recall_1: 0.8350 - val_f1: 0.8398\n",
+ "Epoch 17/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3654 - accuracy: 0.8295 - precision_1: 0.8295 - recall_1: 0.8295 - f1: 0.8270\n",
+ "Epoch 00017: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.3654 - accuracy: 0.8295 - precision_1: 0.8295 - recall_1: 0.8295 - f1: 0.8270 - val_loss: 0.4036 - val_accuracy: 0.8159 - val_precision_1: 0.8159 - val_recall_1: 0.8159 - val_f1: 0.8213\n",
+ "Epoch 18/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3874 - accuracy: 0.8264 - precision_1: 0.8264 - recall_1: 0.8264 - f1: 0.8257\n",
+ "Epoch 00018: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.3874 - accuracy: 0.8264 - precision_1: 0.8264 - recall_1: 0.8264 - f1: 0.8257 - val_loss: 0.5043 - val_accuracy: 0.7495 - val_precision_1: 0.7495 - val_recall_1: 0.7495 - val_f1: 0.7422\n",
+ "Epoch 19/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.3452 - accuracy: 0.8469 - precision_1: 0.8469 - recall_1: 0.8469 - f1: 0.8469\n",
+ "Epoch 00019: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.3450 - accuracy: 0.8464 - precision_1: 0.8464 - recall_1: 0.8464 - f1: 0.8454 - val_loss: 0.3459 - val_accuracy: 0.8461 - val_precision_1: 0.8461 - val_recall_1: 0.8461 - val_f1: 0.8359\n",
+ "Epoch 20/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3168 - accuracy: 0.8561 - precision_1: 0.8561 - recall_1: 0.8561 - f1: 0.8549\n",
+ "Epoch 00020: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.3168 - accuracy: 0.8561 - precision_1: 0.8561 - recall_1: 0.8561 - f1: 0.8549 - val_loss: 0.4748 - val_accuracy: 0.7857 - val_precision_1: 0.7857 - val_recall_1: 0.7857 - val_f1: 0.7920\n",
+ "Epoch 21/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3271 - accuracy: 0.8582 - precision_1: 0.8582 - recall_1: 0.8582 - f1: 0.8595\n",
+ "Epoch 00021: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.3271 - accuracy: 0.8582 - precision_1: 0.8582 - recall_1: 0.8582 - f1: 0.8595 - val_loss: 0.7284 - val_accuracy: 0.7545 - val_precision_1: 0.7545 - val_recall_1: 0.7545 - val_f1: 0.7617\n",
+ "Epoch 22/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3168 - accuracy: 0.8589 - precision_1: 0.8589 - recall_1: 0.8589 - f1: 0.8602\n",
+ "Epoch 00022: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.3168 - accuracy: 0.8589 - precision_1: 0.8589 - recall_1: 0.8589 - f1: 0.8602 - val_loss: 0.3633 - val_accuracy: 0.8229 - val_precision_1: 0.8229 - val_recall_1: 0.8229 - val_f1: 0.8281\n",
+ "Epoch 23/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3224 - accuracy: 0.8554 - precision_1: 0.8554 - recall_1: 0.8554 - f1: 0.8551\n",
+ "Epoch 00023: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 472ms/step - loss: 0.3224 - accuracy: 0.8554 - precision_1: 0.8554 - recall_1: 0.8554 - f1: 0.8551 - val_loss: 0.3564 - val_accuracy: 0.8310 - val_precision_1: 0.8310 - val_recall_1: 0.8310 - val_f1: 0.8213\n",
+ "Epoch 24/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3052 - accuracy: 0.8637 - precision_1: 0.8637 - recall_1: 0.8637 - f1: 0.8624\n",
+ "Epoch 00024: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.3052 - accuracy: 0.8637 - precision_1: 0.8637 - recall_1: 0.8637 - f1: 0.8624 - val_loss: 0.5038 - val_accuracy: 0.7958 - val_precision_1: 0.7958 - val_recall_1: 0.7958 - val_f1: 0.8018\n",
+ "Epoch 25/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3008 - accuracy: 0.8678 - precision_1: 0.8678 - recall_1: 0.8678 - f1: 0.8664\n",
+ "Epoch 00025: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.3008 - accuracy: 0.8678 - precision_1: 0.8678 - recall_1: 0.8678 - f1: 0.8664 - val_loss: 0.3331 - val_accuracy: 0.8561 - val_precision_1: 0.8561 - val_recall_1: 0.8561 - val_f1: 0.8457\n",
+ "Epoch 26/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2969 - accuracy: 0.8678 - precision_1: 0.8678 - recall_1: 0.8678 - f1: 0.8690\n",
+ "Epoch 00026: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2969 - accuracy: 0.8678 - precision_1: 0.8678 - recall_1: 0.8678 - f1: 0.8690 - val_loss: 0.3532 - val_accuracy: 0.8400 - val_precision_1: 0.8400 - val_recall_1: 0.8400 - val_f1: 0.8447\n",
+ "Epoch 27/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2963 - accuracy: 0.8723 - precision_1: 0.8723 - recall_1: 0.8723 - f1: 0.8726\n",
+ "Epoch 00027: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.2963 - accuracy: 0.8723 - precision_1: 0.8723 - recall_1: 0.8723 - f1: 0.8726 - val_loss: 0.4195 - val_accuracy: 0.8078 - val_precision_1: 0.8078 - val_recall_1: 0.8078 - val_f1: 0.8135\n",
+ "Epoch 28/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3012 - accuracy: 0.8654 - precision_1: 0.8654 - recall_1: 0.8654 - f1: 0.8667\n",
+ "Epoch 00028: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.3012 - accuracy: 0.8654 - precision_1: 0.8654 - recall_1: 0.8654 - f1: 0.8667 - val_loss: 0.3587 - val_accuracy: 0.8410 - val_precision_1: 0.8410 - val_recall_1: 0.8410 - val_f1: 0.8311\n",
+ "Epoch 29/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2915 - accuracy: 0.8699 - precision_1: 0.8699 - recall_1: 0.8699 - f1: 0.8693\n",
+ "Epoch 00029: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2915 - accuracy: 0.8699 - precision_1: 0.8699 - recall_1: 0.8699 - f1: 0.8693 - val_loss: 0.6721 - val_accuracy: 0.7646 - val_precision_1: 0.7646 - val_recall_1: 0.7646 - val_f1: 0.7568\n",
+ "Epoch 30/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2918 - accuracy: 0.8737 - precision_1: 0.8737 - recall_1: 0.8737 - f1: 0.8739\n",
+ "Epoch 00030: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2918 - accuracy: 0.8737 - precision_1: 0.8737 - recall_1: 0.8737 - f1: 0.8739 - val_loss: 0.3187 - val_accuracy: 0.8571 - val_precision_1: 0.8571 - val_recall_1: 0.8571 - val_f1: 0.8613\n",
+ "Epoch 31/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2863 - accuracy: 0.8744 - precision_1: 0.8744 - recall_1: 0.8744 - f1: 0.8738\n",
+ "Epoch 00031: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2863 - accuracy: 0.8744 - precision_1: 0.8744 - recall_1: 0.8744 - f1: 0.8738 - val_loss: 0.3012 - val_accuracy: 0.8712 - val_precision_1: 0.8712 - val_recall_1: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 32/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2897 - accuracy: 0.8716 - precision_1: 0.8716 - recall_1: 0.8716 - f1: 0.8736\n",
+ "Epoch 00032: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2897 - accuracy: 0.8716 - precision_1: 0.8716 - recall_1: 0.8716 - f1: 0.8736 - val_loss: 0.3208 - val_accuracy: 0.8541 - val_precision_1: 0.8541 - val_recall_1: 0.8541 - val_f1: 0.8437\n",
+ "Epoch 33/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2718 - accuracy: 0.8806 - precision_1: 0.8806 - recall_1: 0.8806 - f1: 0.8825\n",
+ "Epoch 00033: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2718 - accuracy: 0.8806 - precision_1: 0.8806 - recall_1: 0.8806 - f1: 0.8825 - val_loss: 0.3303 - val_accuracy: 0.8481 - val_precision_1: 0.8481 - val_recall_1: 0.8481 - val_f1: 0.8525\n",
+ "Epoch 34/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2945 - accuracy: 0.8782 - precision_1: 0.8782 - recall_1: 0.8782 - f1: 0.8758\n",
+ "Epoch 00034: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2945 - accuracy: 0.8782 - precision_1: 0.8782 - recall_1: 0.8782 - f1: 0.8758 - val_loss: 0.3336 - val_accuracy: 0.8622 - val_precision_1: 0.8622 - val_recall_1: 0.8622 - val_f1: 0.8662\n",
+ "Epoch 35/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2833 - accuracy: 0.8761 - precision_1: 0.8761 - recall_1: 0.8761 - f1: 0.8755\n",
+ "Epoch 00035: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.2833 - accuracy: 0.8761 - precision_1: 0.8761 - recall_1: 0.8761 - f1: 0.8755 - val_loss: 0.3613 - val_accuracy: 0.8471 - val_precision_1: 0.8471 - val_recall_1: 0.8471 - val_f1: 0.8516\n",
+ "Epoch 36/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2781 - accuracy: 0.8765 - precision_1: 0.8765 - recall_1: 0.8765 - f1: 0.8784\n",
+ "Epoch 00036: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.2781 - accuracy: 0.8765 - precision_1: 0.8765 - recall_1: 0.8765 - f1: 0.8784 - val_loss: 0.3463 - val_accuracy: 0.8421 - val_precision_1: 0.8421 - val_recall_1: 0.8421 - val_f1: 0.8320\n",
+ "Epoch 37/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2809 - accuracy: 0.8796 - precision_1: 0.8796 - recall_1: 0.8796 - f1: 0.8788\n",
+ "Epoch 00037: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.2809 - accuracy: 0.8796 - precision_1: 0.8796 - recall_1: 0.8796 - f1: 0.8788 - val_loss: 0.3737 - val_accuracy: 0.8410 - val_precision_1: 0.8410 - val_recall_1: 0.8410 - val_f1: 0.8457\n",
+ "Epoch 38/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2863 - accuracy: 0.8785 - precision_1: 0.8785 - recall_1: 0.8785 - f1: 0.8770\n",
+ "Epoch 00038: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.2863 - accuracy: 0.8785 - precision_1: 0.8785 - recall_1: 0.8785 - f1: 0.8770 - val_loss: 0.4955 - val_accuracy: 0.7867 - val_precision_1: 0.7867 - val_recall_1: 0.7867 - val_f1: 0.7930\n",
+ "Epoch 39/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3064 - accuracy: 0.8620 - precision_1: 0.8620 - recall_1: 0.8620 - f1: 0.8633\n",
+ "Epoch 00039: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.3064 - accuracy: 0.8620 - precision_1: 0.8620 - recall_1: 0.8620 - f1: 0.8633 - val_loss: 0.5747 - val_accuracy: 0.7857 - val_precision_1: 0.7857 - val_recall_1: 0.7857 - val_f1: 0.7773\n",
+ "Epoch 40/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2839 - accuracy: 0.8772 - precision_1: 0.8772 - recall_1: 0.8772 - f1: 0.8782\n",
+ "Epoch 00040: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2839 - accuracy: 0.8772 - precision_1: 0.8772 - recall_1: 0.8772 - f1: 0.8782 - val_loss: 0.3338 - val_accuracy: 0.8441 - val_precision_1: 0.8441 - val_recall_1: 0.8441 - val_f1: 0.8486\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 41/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2837 - accuracy: 0.8823 - precision_1: 0.8823 - recall_1: 0.8823 - f1: 0.8842\n",
+ "Epoch 00041: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2837 - accuracy: 0.8823 - precision_1: 0.8823 - recall_1: 0.8823 - f1: 0.8842 - val_loss: 0.4031 - val_accuracy: 0.8209 - val_precision_1: 0.8209 - val_recall_1: 0.8209 - val_f1: 0.8262\n",
+ "Epoch 42/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2841 - accuracy: 0.8709 - precision_1: 0.8709 - recall_1: 0.8709 - f1: 0.8721\n",
+ "Epoch 00042: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 439ms/step - loss: 0.2841 - accuracy: 0.8709 - precision_1: 0.8709 - recall_1: 0.8709 - f1: 0.8721 - val_loss: 0.2990 - val_accuracy: 0.8732 - val_precision_1: 0.8732 - val_recall_1: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 43/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2883 - accuracy: 0.8799 - precision_1: 0.8799 - recall_1: 0.8799 - f1: 0.8783\n",
+ "Epoch 00043: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2883 - accuracy: 0.8799 - precision_1: 0.8799 - recall_1: 0.8799 - f1: 0.8783 - val_loss: 0.3019 - val_accuracy: 0.8692 - val_precision_1: 0.8692 - val_recall_1: 0.8692 - val_f1: 0.8730\n",
+ "Epoch 44/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2645 - accuracy: 0.8806 - precision_1: 0.8806 - recall_1: 0.8806 - f1: 0.8807\n",
+ "Epoch 00044: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.2645 - accuracy: 0.8806 - precision_1: 0.8806 - recall_1: 0.8806 - f1: 0.8807 - val_loss: 0.3290 - val_accuracy: 0.8581 - val_precision_1: 0.8581 - val_recall_1: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 45/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.8851 - precision_1: 0.8851 - recall_1: 0.8851 - f1: 0.8834\n",
+ "Epoch 00045: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2677 - accuracy: 0.8851 - precision_1: 0.8851 - recall_1: 0.8851 - f1: 0.8834 - val_loss: 0.2748 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 46/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8937 - precision_1: 0.8937 - recall_1: 0.8937 - f1: 0.8928\n",
+ "Epoch 00046: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.2681 - accuracy: 0.8937 - precision_1: 0.8937 - recall_1: 0.8937 - f1: 0.8928 - val_loss: 0.5146 - val_accuracy: 0.7938 - val_precision_1: 0.7938 - val_recall_1: 0.7938 - val_f1: 0.7998\n",
+ "Epoch 47/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2672 - accuracy: 0.8906 - precision_1: 0.8906 - recall_1: 0.8906 - f1: 0.8915\n",
+ "Epoch 00047: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.2672 - accuracy: 0.8906 - precision_1: 0.8906 - recall_1: 0.8906 - f1: 0.8915 - val_loss: 0.3063 - val_accuracy: 0.8632 - val_precision_1: 0.8632 - val_recall_1: 0.8632 - val_f1: 0.8525\n",
+ "Epoch 48/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3050 - accuracy: 0.8661 - precision_1: 0.8661 - recall_1: 0.8661 - f1: 0.8665\n",
+ "Epoch 00048: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.3050 - accuracy: 0.8661 - precision_1: 0.8661 - recall_1: 0.8661 - f1: 0.8665 - val_loss: 0.9220 - val_accuracy: 0.7314 - val_precision_1: 0.7314 - val_recall_1: 0.7314 - val_f1: 0.7393\n",
+ "Epoch 49/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2934 - accuracy: 0.8713 - precision_1: 0.8713 - recall_1: 0.8713 - f1: 0.8733\n",
+ "Epoch 00049: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 404ms/step - loss: 0.2934 - accuracy: 0.8713 - precision_1: 0.8713 - recall_1: 0.8713 - f1: 0.8733 - val_loss: 0.3066 - val_accuracy: 0.8662 - val_precision_1: 0.8662 - val_recall_1: 0.8662 - val_f1: 0.8701\n",
+ "Epoch 50/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2797 - accuracy: 0.8744 - precision_1: 0.8744 - recall_1: 0.8744 - f1: 0.8746\n",
+ "Epoch 00050: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2797 - accuracy: 0.8744 - precision_1: 0.8744 - recall_1: 0.8744 - f1: 0.8746 - val_loss: 0.3326 - val_accuracy: 0.8491 - val_precision_1: 0.8491 - val_recall_1: 0.8491 - val_f1: 0.8389\n",
+ "Epoch 51/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2728 - accuracy: 0.8775 - precision_1: 0.8775 - recall_1: 0.8775 - f1: 0.8768\n",
+ "Epoch 00051: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2728 - accuracy: 0.8775 - precision_1: 0.8775 - recall_1: 0.8775 - f1: 0.8768 - val_loss: 0.3189 - val_accuracy: 0.8501 - val_precision_1: 0.8501 - val_recall_1: 0.8501 - val_f1: 0.8398\n",
+ "Epoch 52/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2658 - accuracy: 0.8875 - precision_1: 0.8875 - recall_1: 0.8875 - f1: 0.8893\n",
+ "Epoch 00052: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 469ms/step - loss: 0.2658 - accuracy: 0.8875 - precision_1: 0.8875 - recall_1: 0.8875 - f1: 0.8893 - val_loss: 0.3493 - val_accuracy: 0.8491 - val_precision_1: 0.8491 - val_recall_1: 0.8491 - val_f1: 0.8535\n",
+ "Epoch 53/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2670 - accuracy: 0.8837 - precision_1: 0.8837 - recall_1: 0.8837 - f1: 0.8812\n",
+ "Epoch 00053: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.2670 - accuracy: 0.8837 - precision_1: 0.8837 - recall_1: 0.8837 - f1: 0.8812 - val_loss: 0.3189 - val_accuracy: 0.8581 - val_precision_1: 0.8581 - val_recall_1: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 54/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2514 - accuracy: 0.8961 - precision_1: 0.8961 - recall_1: 0.8961 - f1: 0.8960\n",
+ "Epoch 00054: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2514 - accuracy: 0.8961 - precision_1: 0.8961 - recall_1: 0.8961 - f1: 0.8960 - val_loss: 0.2678 - val_accuracy: 0.8873 - val_precision_1: 0.8873 - val_recall_1: 0.8873 - val_f1: 0.8906\n",
+ "Epoch 55/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2730 - accuracy: 0.8861 - precision_1: 0.8861 - recall_1: 0.8861 - f1: 0.8870\n",
+ "Epoch 00055: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.2730 - accuracy: 0.8861 - precision_1: 0.8861 - recall_1: 0.8861 - f1: 0.8870 - val_loss: 0.3307 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8486\n",
+ "Epoch 56/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2606 - accuracy: 0.8972 - precision_1: 0.8972 - recall_1: 0.8972 - f1: 0.8970\n",
+ "Epoch 00056: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.2606 - accuracy: 0.8972 - precision_1: 0.8972 - recall_1: 0.8972 - f1: 0.8970 - val_loss: 0.3089 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 57/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2548 - accuracy: 0.8910 - precision_1: 0.8910 - recall_1: 0.8910 - f1: 0.8901\n",
+ "Epoch 00057: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2548 - accuracy: 0.8910 - precision_1: 0.8910 - recall_1: 0.8910 - f1: 0.8901 - val_loss: 0.3091 - val_accuracy: 0.8602 - val_precision_1: 0.8602 - val_recall_1: 0.8602 - val_f1: 0.8643\n",
+ "Epoch 58/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.9003 - precision_1: 0.9003 - recall_1: 0.9003 - f1: 0.9010\n",
+ "Epoch 00058: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2411 - accuracy: 0.9003 - precision_1: 0.9003 - recall_1: 0.9003 - f1: 0.9010 - val_loss: 0.3301 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 59/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2617 - accuracy: 0.8903 - precision_1: 0.8903 - recall_1: 0.8903 - f1: 0.8902\n",
+ "Epoch 00059: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.2617 - accuracy: 0.8903 - precision_1: 0.8903 - recall_1: 0.8903 - f1: 0.8902 - val_loss: 0.2925 - val_accuracy: 0.8803 - val_precision_1: 0.8803 - val_recall_1: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 60/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.8954 - precision_1: 0.8954 - recall_1: 0.8954 - f1: 0.8936\n",
+ "Epoch 00060: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2483 - accuracy: 0.8954 - precision_1: 0.8954 - recall_1: 0.8954 - f1: 0.8936 - val_loss: 0.4151 - val_accuracy: 0.8219 - val_precision_1: 0.8219 - val_recall_1: 0.8219 - val_f1: 0.8271\n",
+ "Epoch 61/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2508 - accuracy: 0.8882 - precision_1: 0.8882 - recall_1: 0.8882 - f1: 0.8882\n",
+ "Epoch 00061: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 439ms/step - loss: 0.2508 - accuracy: 0.8882 - precision_1: 0.8882 - recall_1: 0.8882 - f1: 0.8882 - val_loss: 0.2866 - val_accuracy: 0.8763 - val_precision_1: 0.8763 - val_recall_1: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 62/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2462 - accuracy: 0.8948 - precision_1: 0.8948 - recall_1: 0.8948 - f1: 0.8947\n",
+ "Epoch 00062: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2462 - accuracy: 0.8948 - precision_1: 0.8948 - recall_1: 0.8948 - f1: 0.8947 - val_loss: 0.2964 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 63/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2469 - accuracy: 0.8958 - precision_1: 0.8958 - recall_1: 0.8958 - f1: 0.8957\n",
+ "Epoch 00063: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2469 - accuracy: 0.8958 - precision_1: 0.8958 - recall_1: 0.8958 - f1: 0.8957 - val_loss: 0.3025 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8809\n",
+ "Epoch 64/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2506 - accuracy: 0.8916 - precision_1: 0.8916 - recall_1: 0.8916 - f1: 0.8907\n",
+ "Epoch 00064: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2506 - accuracy: 0.8916 - precision_1: 0.8916 - recall_1: 0.8916 - f1: 0.8907 - val_loss: 0.4311 - val_accuracy: 0.8169 - val_precision_1: 0.8169 - val_recall_1: 0.8169 - val_f1: 0.8223\n",
+ "Epoch 65/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2571 - accuracy: 0.8930 - precision_1: 0.8930 - recall_1: 0.8930 - f1: 0.8930\n",
+ "Epoch 00065: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2571 - accuracy: 0.8930 - precision_1: 0.8930 - recall_1: 0.8930 - f1: 0.8930 - val_loss: 0.2865 - val_accuracy: 0.8692 - val_precision_1: 0.8692 - val_recall_1: 0.8692 - val_f1: 0.8730\n",
+ "Epoch 66/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2440 - accuracy: 0.8927 - precision_1: 0.8927 - recall_1: 0.8927 - f1: 0.8926\n",
+ "Epoch 00066: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2440 - accuracy: 0.8927 - precision_1: 0.8927 - recall_1: 0.8927 - f1: 0.8926 - val_loss: 0.2677 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8818\n",
+ "Epoch 67/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2509 - accuracy: 0.8937 - precision_1: 0.8937 - recall_1: 0.8937 - f1: 0.8936\n",
+ "Epoch 00067: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.2509 - accuracy: 0.8937 - precision_1: 0.8937 - recall_1: 0.8937 - f1: 0.8936 - val_loss: 0.2798 - val_accuracy: 0.8803 - val_precision_1: 0.8803 - val_recall_1: 0.8803 - val_f1: 0.8838\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 68/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2478 - accuracy: 0.8906 - precision_1: 0.8906 - recall_1: 0.8906 - f1: 0.8906\n",
+ "Epoch 00068: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.2478 - accuracy: 0.8906 - precision_1: 0.8906 - recall_1: 0.8906 - f1: 0.8906 - val_loss: 0.2882 - val_accuracy: 0.8753 - val_precision_1: 0.8753 - val_recall_1: 0.8753 - val_f1: 0.8643\n",
+ "Epoch 69/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2375 - accuracy: 0.9010 - precision_1: 0.9010 - recall_1: 0.9010 - f1: 0.9008\n",
+ "Epoch 00069: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.2375 - accuracy: 0.9010 - precision_1: 0.9010 - recall_1: 0.9010 - f1: 0.9008 - val_loss: 0.3044 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 70/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2402 - accuracy: 0.9041 - precision_1: 0.9041 - recall_1: 0.9041 - f1: 0.9030\n",
+ "Epoch 00070: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2402 - accuracy: 0.9041 - precision_1: 0.9041 - recall_1: 0.9041 - f1: 0.9030 - val_loss: 0.2677 - val_accuracy: 0.8783 - val_precision_1: 0.8783 - val_recall_1: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 71/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2397 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.9003\n",
+ "Epoch 00071: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2397 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.9003 - val_loss: 0.3360 - val_accuracy: 0.8642 - val_precision_1: 0.8642 - val_recall_1: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 72/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2379 - accuracy: 0.9044 - precision_1: 0.9044 - recall_1: 0.9044 - f1: 0.9033\n",
+ "Epoch 00072: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2379 - accuracy: 0.9044 - precision_1: 0.9044 - recall_1: 0.9044 - f1: 0.9033 - val_loss: 0.2828 - val_accuracy: 0.8783 - val_precision_1: 0.8783 - val_recall_1: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 73/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2373 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.9003\n",
+ "Epoch 00073: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2373 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.9003 - val_loss: 0.5421 - val_accuracy: 0.8109 - val_precision_1: 0.8109 - val_recall_1: 0.8109 - val_f1: 0.8164\n",
+ "Epoch 74/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2533 - accuracy: 0.8903 - precision_1: 0.8903 - recall_1: 0.8903 - f1: 0.8876\n",
+ "Epoch 00074: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2533 - accuracy: 0.8903 - precision_1: 0.8903 - recall_1: 0.8903 - f1: 0.8876 - val_loss: 0.2971 - val_accuracy: 0.8702 - val_precision_1: 0.8702 - val_recall_1: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 75/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2454 - accuracy: 0.8986 - precision_1: 0.8986 - recall_1: 0.8986 - f1: 0.8993\n",
+ "Epoch 00075: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 459ms/step - loss: 0.2454 - accuracy: 0.8986 - precision_1: 0.8986 - recall_1: 0.8986 - f1: 0.8993 - val_loss: 0.3675 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 76/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2278 - accuracy: 0.9034 - precision_1: 0.9034 - recall_1: 0.9034 - f1: 0.9014\n",
+ "Epoch 00076: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2278 - accuracy: 0.9034 - precision_1: 0.9034 - recall_1: 0.9034 - f1: 0.9014 - val_loss: 0.2823 - val_accuracy: 0.8783 - val_precision_1: 0.8783 - val_recall_1: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 77/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2299 - accuracy: 0.9089 - precision_1: 0.9089 - recall_1: 0.9089 - f1: 0.9095\n",
+ "Epoch 00077: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 443ms/step - loss: 0.2299 - accuracy: 0.9089 - precision_1: 0.9089 - recall_1: 0.9089 - f1: 0.9095 - val_loss: 0.3098 - val_accuracy: 0.8732 - val_precision_1: 0.8732 - val_recall_1: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 78/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2379 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8986\n",
+ "Epoch 00078: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.2379 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8986 - val_loss: 0.3045 - val_accuracy: 0.8803 - val_precision_1: 0.8803 - val_recall_1: 0.8803 - val_f1: 0.8691\n",
+ "Epoch 79/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2330 - accuracy: 0.9017 - precision_1: 0.9017 - recall_1: 0.9017 - f1: 0.9023\n",
+ "Epoch 00079: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2330 - accuracy: 0.9017 - precision_1: 0.9017 - recall_1: 0.9017 - f1: 0.9023 - val_loss: 0.2788 - val_accuracy: 0.8803 - val_precision_1: 0.8803 - val_recall_1: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 80/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2223 - accuracy: 0.9058 - precision_1: 0.9058 - recall_1: 0.9058 - f1: 0.9055\n",
+ "Epoch 00080: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2223 - accuracy: 0.9058 - precision_1: 0.9058 - recall_1: 0.9058 - f1: 0.9055 - val_loss: 0.3405 - val_accuracy: 0.8551 - val_precision_1: 0.8551 - val_recall_1: 0.8551 - val_f1: 0.8594\n",
+ "Epoch 81/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2229 - accuracy: 0.9082 - precision_1: 0.9082 - recall_1: 0.9082 - f1: 0.9070\n",
+ "Epoch 00081: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.2229 - accuracy: 0.9082 - precision_1: 0.9082 - recall_1: 0.9082 - f1: 0.9070 - val_loss: 0.2864 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 82/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2237 - accuracy: 0.9065 - precision_1: 0.9065 - recall_1: 0.9065 - f1: 0.9053\n",
+ "Epoch 00082: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2237 - accuracy: 0.9065 - precision_1: 0.9065 - recall_1: 0.9065 - f1: 0.9053 - val_loss: 0.2704 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8721\n",
+ "Epoch 83/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2255 - accuracy: 0.9113 - precision_1: 0.9113 - recall_1: 0.9113 - f1: 0.9092\n",
+ "Epoch 00083: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.2255 - accuracy: 0.9113 - precision_1: 0.9113 - recall_1: 0.9113 - f1: 0.9092 - val_loss: 0.3003 - val_accuracy: 0.8803 - val_precision_1: 0.8803 - val_recall_1: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 84/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2281 - accuracy: 0.9055 - precision_1: 0.9055 - recall_1: 0.9055 - f1: 0.9052\n",
+ "Epoch 00084: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2281 - accuracy: 0.9055 - precision_1: 0.9055 - recall_1: 0.9055 - f1: 0.9052 - val_loss: 0.2698 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8789\n",
+ "Epoch 85/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2073 - accuracy: 0.9161 - precision_1: 0.9161 - recall_1: 0.9161 - f1: 0.9166\n",
+ "Epoch 00085: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.2073 - accuracy: 0.9161 - precision_1: 0.9161 - recall_1: 0.9161 - f1: 0.9166 - val_loss: 0.2778 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 86/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2171 - accuracy: 0.9110 - precision_1: 0.9110 - recall_1: 0.9110 - f1: 0.9106\n",
+ "Epoch 00086: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.2171 - accuracy: 0.9110 - precision_1: 0.9110 - recall_1: 0.9110 - f1: 0.9106 - val_loss: 0.2825 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8789\n",
+ "Epoch 87/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2360 - accuracy: 0.9023 - precision_1: 0.9023 - recall_1: 0.9023 - f1: 0.9021\n",
+ "Epoch 00087: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 439ms/step - loss: 0.2360 - accuracy: 0.9023 - precision_1: 0.9023 - recall_1: 0.9023 - f1: 0.9021 - val_loss: 0.3046 - val_accuracy: 0.8793 - val_precision_1: 0.8793 - val_recall_1: 0.8793 - val_f1: 0.8682\n",
+ "Epoch 88/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9056 - precision_1: 0.9056 - recall_1: 0.9056 - f1: 0.9056\n",
+ "Epoch 00088: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2235 - accuracy: 0.9056 - precision_1: 0.9056 - recall_1: 0.9056 - f1: 0.9056 - val_loss: 0.2824 - val_accuracy: 0.8843 - val_precision_1: 0.8843 - val_recall_1: 0.8843 - val_f1: 0.8877\n",
+ "Epoch 89/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2217 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9089\n",
+ "Epoch 00089: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.2217 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9089 - val_loss: 0.3302 - val_accuracy: 0.8632 - val_precision_1: 0.8632 - val_recall_1: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 90/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2172 - accuracy: 0.9082 - precision_1: 0.9082 - recall_1: 0.9082 - f1: 0.9070\n",
+ "Epoch 00090: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 454ms/step - loss: 0.2172 - accuracy: 0.9082 - precision_1: 0.9082 - recall_1: 0.9082 - f1: 0.9070 - val_loss: 0.3142 - val_accuracy: 0.8692 - val_precision_1: 0.8692 - val_recall_1: 0.8692 - val_f1: 0.8730\n",
+ "Epoch 91/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2174 - accuracy: 0.9099 - precision_1: 0.9099 - recall_1: 0.9099 - f1: 0.9113\n",
+ "Epoch 00091: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 472ms/step - loss: 0.2174 - accuracy: 0.9099 - precision_1: 0.9099 - recall_1: 0.9099 - f1: 0.9113 - val_loss: 0.4681 - val_accuracy: 0.8370 - val_precision_1: 0.8370 - val_recall_1: 0.8370 - val_f1: 0.8418\n",
+ "Epoch 92/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2241 - accuracy: 0.9048 - precision_1: 0.9048 - recall_1: 0.9048 - f1: 0.9028\n",
+ "Epoch 00092: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2241 - accuracy: 0.9048 - precision_1: 0.9048 - recall_1: 0.9048 - f1: 0.9028 - val_loss: 0.2686 - val_accuracy: 0.8994 - val_precision_1: 0.8994 - val_recall_1: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 93/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2179 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9058\n",
+ "Epoch 00093: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.2179 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9058 - val_loss: 0.2499 - val_accuracy: 0.8984 - val_precision_1: 0.8984 - val_recall_1: 0.8984 - val_f1: 0.8867\n",
+ "Epoch 94/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2141 - accuracy: 0.9061 - precision_1: 0.9061 - recall_1: 0.9061 - f1: 0.9067\n",
+ "Epoch 00094: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2141 - accuracy: 0.9061 - precision_1: 0.9061 - recall_1: 0.9061 - f1: 0.9067 - val_loss: 0.3144 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 95/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2273 - accuracy: 0.9110 - precision_1: 0.9110 - recall_1: 0.9110 - f1: 0.9106\n",
+ "Epoch 00095: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2273 - accuracy: 0.9110 - precision_1: 0.9110 - recall_1: 0.9110 - f1: 0.9106 - val_loss: 0.2661 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8955\n",
+ "Epoch 96/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2027 - accuracy: 0.9155 - precision_1: 0.9155 - recall_1: 0.9155 - f1: 0.9150\n",
+ "Epoch 00096: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.2027 - accuracy: 0.9155 - precision_1: 0.9155 - recall_1: 0.9155 - f1: 0.9150 - val_loss: 0.3660 - val_accuracy: 0.8612 - val_precision_1: 0.8612 - val_recall_1: 0.8612 - val_f1: 0.8652\n",
+ "Epoch 97/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2181 - accuracy: 0.9103 - precision_1: 0.9103 - recall_1: 0.9103 - f1: 0.9117\n",
+ "Epoch 00097: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 469ms/step - loss: 0.2181 - accuracy: 0.9103 - precision_1: 0.9103 - recall_1: 0.9103 - f1: 0.9117 - val_loss: 0.2731 - val_accuracy: 0.8873 - val_precision_1: 0.8873 - val_recall_1: 0.8873 - val_f1: 0.8760\n",
+ "Epoch 98/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2070 - accuracy: 0.9086 - precision_1: 0.9086 - recall_1: 0.9086 - f1: 0.9065\n",
+ "Epoch 00098: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 465ms/step - loss: 0.2070 - accuracy: 0.9086 - precision_1: 0.9086 - recall_1: 0.9086 - f1: 0.9065 - val_loss: 0.2742 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 99/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2163 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9098\n",
+ "Epoch 00099: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 457ms/step - loss: 0.2163 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9098 - val_loss: 0.2609 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 100/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2117 - accuracy: 0.9113 - precision_1: 0.9113 - recall_1: 0.9113 - f1: 0.9118\n",
+ "Epoch 00100: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.2117 - accuracy: 0.9113 - precision_1: 0.9113 - recall_1: 0.9113 - f1: 0.9118 - val_loss: 0.3211 - val_accuracy: 0.8642 - val_precision_1: 0.8642 - val_recall_1: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 101/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2102 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9159\n",
+ "Epoch 00101: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.2102 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9159 - val_loss: 0.2793 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 102/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2042 - accuracy: 0.9155 - precision_1: 0.9155 - recall_1: 0.9155 - f1: 0.9150\n",
+ "Epoch 00102: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.2042 - accuracy: 0.9155 - precision_1: 0.9155 - recall_1: 0.9155 - f1: 0.9150 - val_loss: 0.3096 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 103/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2169 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9098\n",
+ "Epoch 00103: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 476ms/step - loss: 0.2169 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9098 - val_loss: 0.3281 - val_accuracy: 0.8672 - val_precision_1: 0.8672 - val_recall_1: 0.8672 - val_f1: 0.8711\n",
+ "Epoch 104/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2068 - accuracy: 0.9120 - precision_1: 0.9120 - recall_1: 0.9120 - f1: 0.9134\n",
+ "Epoch 00104: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 496ms/step - loss: 0.2068 - accuracy: 0.9120 - precision_1: 0.9120 - recall_1: 0.9120 - f1: 0.9134 - val_loss: 0.2694 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 105/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2113 - accuracy: 0.9103 - precision_1: 0.9103 - recall_1: 0.9103 - f1: 0.9082\n",
+ "Epoch 00105: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2113 - accuracy: 0.9103 - precision_1: 0.9103 - recall_1: 0.9103 - f1: 0.9082 - val_loss: 0.3734 - val_accuracy: 0.8541 - val_precision_1: 0.8541 - val_recall_1: 0.8541 - val_f1: 0.8437\n",
+ "Epoch 106/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2000 - accuracy: 0.9161 - precision_1: 0.9161 - recall_1: 0.9161 - f1: 0.9175\n",
+ "Epoch 00106: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.2000 - accuracy: 0.9161 - precision_1: 0.9161 - recall_1: 0.9161 - f1: 0.9175 - val_loss: 0.2836 - val_accuracy: 0.8843 - val_precision_1: 0.8843 - val_recall_1: 0.8843 - val_f1: 0.8877\n",
+ "Epoch 107/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1944 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9212\n",
+ "Epoch 00107: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1944 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9212 - val_loss: 0.2603 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 108/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1961 - accuracy: 0.9189 - precision_1: 0.9189 - recall_1: 0.9189 - f1: 0.9193\n",
+ "Epoch 00108: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.1961 - accuracy: 0.9189 - precision_1: 0.9189 - recall_1: 0.9189 - f1: 0.9193 - val_loss: 0.2855 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 109/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2042 - accuracy: 0.9179 - precision_1: 0.9179 - recall_1: 0.9179 - f1: 0.9183\n",
+ "Epoch 00109: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.2042 - accuracy: 0.9179 - precision_1: 0.9179 - recall_1: 0.9179 - f1: 0.9183 - val_loss: 0.3695 - val_accuracy: 0.8551 - val_precision_1: 0.8551 - val_recall_1: 0.8551 - val_f1: 0.8594\n",
+ "Epoch 110/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2121 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9081\n",
+ "Epoch 00110: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 501ms/step - loss: 0.2121 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9081 - val_loss: 0.3826 - val_accuracy: 0.8612 - val_precision_1: 0.8612 - val_recall_1: 0.8612 - val_f1: 0.8652\n",
+ "Epoch 111/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1905 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9242\n",
+ "Epoch 00111: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.1905 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9242 - val_loss: 0.3912 - val_accuracy: 0.8632 - val_precision_1: 0.8632 - val_recall_1: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 112/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1869 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9256\n",
+ "Epoch 00112: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1869 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9256 - val_loss: 0.4811 - val_accuracy: 0.8330 - val_precision_1: 0.8330 - val_recall_1: 0.8330 - val_f1: 0.8379\n",
+ "Epoch 113/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2053 - accuracy: 0.9203 - precision_1: 0.9203 - recall_1: 0.9203 - f1: 0.9172\n",
+ "Epoch 00113: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 445ms/step - loss: 0.2053 - accuracy: 0.9203 - precision_1: 0.9203 - recall_1: 0.9203 - f1: 0.9172 - val_loss: 0.3557 - val_accuracy: 0.8742 - val_precision_1: 0.8742 - val_recall_1: 0.8742 - val_f1: 0.8779\n",
+ "Epoch 114/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2092 - accuracy: 0.9117 - precision_1: 0.9117 - recall_1: 0.9117 - f1: 0.9078\n",
+ "Epoch 00114: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.2092 - accuracy: 0.9117 - precision_1: 0.9117 - recall_1: 0.9117 - f1: 0.9078 - val_loss: 0.3495 - val_accuracy: 0.8652 - val_precision_1: 0.8652 - val_recall_1: 0.8652 - val_f1: 0.8691\n",
+ "Epoch 115/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1985 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9176\n",
+ "Epoch 00115: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 462ms/step - loss: 0.1985 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9176 - val_loss: 0.2985 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 116/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1865 - accuracy: 0.9220 - precision_1: 0.9220 - recall_1: 0.9220 - f1: 0.9206\n",
+ "Epoch 00116: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 510ms/step - loss: 0.1865 - accuracy: 0.9220 - precision_1: 0.9220 - recall_1: 0.9220 - f1: 0.9206 - val_loss: 0.4011 - val_accuracy: 0.8441 - val_precision_1: 0.8441 - val_recall_1: 0.8441 - val_f1: 0.8486\n",
+ "Epoch 117/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2063 - accuracy: 0.9141 - precision_1: 0.9141 - recall_1: 0.9141 - f1: 0.9128\n",
+ "Epoch 00117: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.2063 - accuracy: 0.9141 - precision_1: 0.9141 - recall_1: 0.9141 - f1: 0.9128 - val_loss: 0.2565 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 118/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1998 - accuracy: 0.9224 - precision_1: 0.9224 - recall_1: 0.9224 - f1: 0.9201\n",
+ "Epoch 00118: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1998 - accuracy: 0.9224 - precision_1: 0.9224 - recall_1: 0.9224 - f1: 0.9201 - val_loss: 0.2720 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 119/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2036 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9195\n",
+ "Epoch 00119: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.2036 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9195 - val_loss: 0.2915 - val_accuracy: 0.8732 - val_precision_1: 0.8732 - val_recall_1: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 120/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1855 - accuracy: 0.9244 - precision_1: 0.9244 - recall_1: 0.9244 - f1: 0.9256\n",
+ "Epoch 00120: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.1855 - accuracy: 0.9244 - precision_1: 0.9244 - recall_1: 0.9244 - f1: 0.9256 - val_loss: 0.2989 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8662\n",
+ "Epoch 121/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2013 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9196\n",
+ "Epoch 00121: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.2013 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9196 - val_loss: 0.2565 - val_accuracy: 0.8843 - val_precision_1: 0.8843 - val_recall_1: 0.8843 - val_f1: 0.8877\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 122/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1976 - accuracy: 0.9134 - precision_1: 0.9134 - recall_1: 0.9134 - f1: 0.9147\n",
+ "Epoch 00122: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 476ms/step - loss: 0.1976 - accuracy: 0.9134 - precision_1: 0.9134 - recall_1: 0.9134 - f1: 0.9147 - val_loss: 0.2809 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 123/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2036 - accuracy: 0.9210 - precision_1: 0.9210 - recall_1: 0.9210 - f1: 0.9213\n",
+ "Epoch 00123: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 445ms/step - loss: 0.2036 - accuracy: 0.9210 - precision_1: 0.9210 - recall_1: 0.9210 - f1: 0.9213 - val_loss: 0.2908 - val_accuracy: 0.8793 - val_precision_1: 0.8793 - val_recall_1: 0.8793 - val_f1: 0.8828\n",
+ "Epoch 124/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1965 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9177\n",
+ "Epoch 00124: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.1965 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9177 - val_loss: 0.2782 - val_accuracy: 0.8763 - val_precision_1: 0.8763 - val_recall_1: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 125/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1903 - accuracy: 0.9186 - precision_1: 0.9186 - recall_1: 0.9186 - f1: 0.9181\n",
+ "Epoch 00125: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1903 - accuracy: 0.9186 - precision_1: 0.9186 - recall_1: 0.9186 - f1: 0.9181 - val_loss: 0.2731 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 126/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1806 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9286\n",
+ "Epoch 00126: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.1806 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9286 - val_loss: 0.2853 - val_accuracy: 0.8823 - val_precision_1: 0.8823 - val_recall_1: 0.8823 - val_f1: 0.8711\n",
+ "Epoch 127/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1929 - accuracy: 0.9217 - precision_1: 0.9217 - recall_1: 0.9217 - f1: 0.9220\n",
+ "Epoch 00127: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 490ms/step - loss: 0.1929 - accuracy: 0.9217 - precision_1: 0.9217 - recall_1: 0.9217 - f1: 0.9220 - val_loss: 0.2516 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 128/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1987 - accuracy: 0.9224 - precision_1: 0.9224 - recall_1: 0.9224 - f1: 0.9210\n",
+ "Epoch 00128: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 491ms/step - loss: 0.1987 - accuracy: 0.9224 - precision_1: 0.9224 - recall_1: 0.9224 - f1: 0.9210 - val_loss: 0.3152 - val_accuracy: 0.8622 - val_precision_1: 0.8622 - val_recall_1: 0.8622 - val_f1: 0.8662\n",
+ "Epoch 129/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1896 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188\n",
+ "Epoch 00129: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1896 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188 - val_loss: 0.2915 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8779\n",
+ "Epoch 130/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1756 - accuracy: 0.9255 - precision_1: 0.9255 - recall_1: 0.9255 - f1: 0.9266\n",
+ "Epoch 00130: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.1756 - accuracy: 0.9255 - precision_1: 0.9255 - recall_1: 0.9255 - f1: 0.9266 - val_loss: 0.2472 - val_accuracy: 0.9105 - val_precision_1: 0.9105 - val_recall_1: 0.9105 - val_f1: 0.9131\n",
+ "Epoch 131/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1937 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9196\n",
+ "Epoch 00131: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.1937 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9196 - val_loss: 0.2820 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8770\n",
+ "Epoch 132/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1869 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9230\n",
+ "Epoch 00132: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 482ms/step - loss: 0.1869 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9230 - val_loss: 0.2679 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8809\n",
+ "Epoch 133/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1867 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9228\n",
+ "Epoch 00133: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 474ms/step - loss: 0.1867 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9228 - val_loss: 0.2688 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 134/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1723 - accuracy: 0.9268 - precision_1: 0.9268 - recall_1: 0.9268 - f1: 0.9271\n",
+ "Epoch 00134: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 498ms/step - loss: 0.1723 - accuracy: 0.9268 - precision_1: 0.9268 - recall_1: 0.9268 - f1: 0.9271 - val_loss: 0.2522 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 135/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9332\n",
+ "Epoch 00135: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1677 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9332 - val_loss: 0.4041 - val_accuracy: 0.8471 - val_precision_1: 0.8471 - val_recall_1: 0.8471 - val_f1: 0.8516\n",
+ "Epoch 136/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1886 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9254\n",
+ "Epoch 00136: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1886 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9254 - val_loss: 0.2641 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 137/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1791 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9213\n",
+ "Epoch 00137: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.1791 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9213 - val_loss: 0.2651 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8955\n",
+ "Epoch 138/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1821 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9242\n",
+ "Epoch 00138: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 448ms/step - loss: 0.1821 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9242 - val_loss: 0.2705 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 139/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1700 - accuracy: 0.9324 - precision_1: 0.9324 - recall_1: 0.9324 - f1: 0.9317\n",
+ "Epoch 00139: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 473ms/step - loss: 0.1700 - accuracy: 0.9324 - precision_1: 0.9324 - recall_1: 0.9324 - f1: 0.9317 - val_loss: 0.2754 - val_accuracy: 0.8913 - val_precision_1: 0.8913 - val_recall_1: 0.8913 - val_f1: 0.8799\n",
+ "Epoch 140/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1759 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9297\n",
+ "Epoch 00140: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 465ms/step - loss: 0.1759 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9297 - val_loss: 0.2920 - val_accuracy: 0.8783 - val_precision_1: 0.8783 - val_recall_1: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 141/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1729 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9296\n",
+ "Epoch 00141: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.1729 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9296 - val_loss: 0.2542 - val_accuracy: 0.9014 - val_precision_1: 0.9014 - val_recall_1: 0.9014 - val_f1: 0.9043\n",
+ "Epoch 142/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1713 - accuracy: 0.9310 - precision_1: 0.9310 - recall_1: 0.9310 - f1: 0.9286\n",
+ "Epoch 00142: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.1713 - accuracy: 0.9310 - precision_1: 0.9310 - recall_1: 0.9310 - f1: 0.9286 - val_loss: 0.2582 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8809\n",
+ "Epoch 143/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1905 - accuracy: 0.9224 - precision_1: 0.9224 - recall_1: 0.9224 - f1: 0.9210\n",
+ "Epoch 00143: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.1905 - accuracy: 0.9224 - precision_1: 0.9224 - recall_1: 0.9224 - f1: 0.9210 - val_loss: 0.2589 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 144/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1884 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9246\n",
+ "Epoch 00144: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1884 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9246 - val_loss: 0.3563 - val_accuracy: 0.8702 - val_precision_1: 0.8702 - val_recall_1: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 145/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1732 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9280\n",
+ "Epoch 00145: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 503ms/step - loss: 0.1732 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9280 - val_loss: 0.2810 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 146/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1690 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9332\n",
+ "Epoch 00146: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 483ms/step - loss: 0.1690 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9332 - val_loss: 0.2832 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 147/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1775 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9251\n",
+ "Epoch 00147: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1775 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9251 - val_loss: 0.2519 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 148/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1667 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9286\n",
+ "Epoch 00148: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1667 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9286 - val_loss: 0.2346 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 149/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2147 - accuracy: 0.9048 - precision_1: 0.9048 - recall_1: 0.9048 - f1: 0.9045\n",
+ "Epoch 00149: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 455ms/step - loss: 0.2147 - accuracy: 0.9048 - precision_1: 0.9048 - recall_1: 0.9048 - f1: 0.9045 - val_loss: 0.2961 - val_accuracy: 0.8692 - val_precision_1: 0.8692 - val_recall_1: 0.8692 - val_f1: 0.8584\n",
+ "Epoch 150/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1694 - accuracy: 0.9310 - precision_1: 0.9310 - recall_1: 0.9310 - f1: 0.9295\n",
+ "Epoch 00150: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1694 - accuracy: 0.9310 - precision_1: 0.9310 - recall_1: 0.9310 - f1: 0.9295 - val_loss: 0.2276 - val_accuracy: 0.9044 - val_precision_1: 0.9044 - val_recall_1: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 151/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1576 - accuracy: 0.9306 - precision_1: 0.9306 - recall_1: 0.9306 - f1: 0.9317\n",
+ "Epoch 00151: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 460ms/step - loss: 0.1576 - accuracy: 0.9306 - precision_1: 0.9306 - recall_1: 0.9306 - f1: 0.9317 - val_loss: 0.2567 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8828\n",
+ "Epoch 152/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1547 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9400\n",
+ "Epoch 00152: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 499ms/step - loss: 0.1547 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9400 - val_loss: 0.2478 - val_accuracy: 0.8994 - val_precision_1: 0.8994 - val_recall_1: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 153/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1888 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188\n",
+ "Epoch 00153: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1888 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188 - val_loss: 0.4254 - val_accuracy: 0.8451 - val_precision_1: 0.8451 - val_recall_1: 0.8451 - val_f1: 0.8496\n",
+ "Epoch 154/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1743 - accuracy: 0.9279 - precision_1: 0.9279 - recall_1: 0.9279 - f1: 0.9281\n",
+ "Epoch 00154: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.1743 - accuracy: 0.9279 - precision_1: 0.9279 - recall_1: 0.9279 - f1: 0.9281 - val_loss: 0.2752 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 155/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1707 - accuracy: 0.9306 - precision_1: 0.9306 - recall_1: 0.9306 - f1: 0.9309\n",
+ "Epoch 00155: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1707 - accuracy: 0.9306 - precision_1: 0.9306 - recall_1: 0.9306 - f1: 0.9309 - val_loss: 0.3995 - val_accuracy: 0.8632 - val_precision_1: 0.8632 - val_recall_1: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 156/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1780 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9298\n",
+ "Epoch 00156: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.1780 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9298 - val_loss: 0.2953 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 157/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1588 - accuracy: 0.9324 - precision_1: 0.9324 - recall_1: 0.9324 - f1: 0.9317\n",
+ "Epoch 00157: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 508ms/step - loss: 0.1588 - accuracy: 0.9324 - precision_1: 0.9324 - recall_1: 0.9324 - f1: 0.9317 - val_loss: 0.2314 - val_accuracy: 0.9074 - val_precision_1: 0.9074 - val_recall_1: 0.9074 - val_f1: 0.9102\n",
+ "Epoch 158/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1789 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9281\n",
+ "Epoch 00158: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 490ms/step - loss: 0.1789 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9281 - val_loss: 0.5614 - val_accuracy: 0.8058 - val_precision_1: 0.8058 - val_recall_1: 0.8058 - val_f1: 0.8115\n",
+ "Epoch 159/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1646 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9307\n",
+ "Epoch 00159: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.1646 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9307 - val_loss: 0.2706 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.8857\n",
+ "Epoch 160/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.1548 - accuracy: 0.9396 - precision_1: 0.9396 - recall_1: 0.9396 - f1: 0.9396\n",
+ "Epoch 00160: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1545 - accuracy: 0.9396 - precision_1: 0.9396 - recall_1: 0.9396 - f1: 0.9397 - val_loss: 0.2799 - val_accuracy: 0.8913 - val_precision_1: 0.8913 - val_recall_1: 0.8913 - val_f1: 0.8799\n",
+ "Epoch 161/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1654 - accuracy: 0.9300 - precision_1: 0.9300 - recall_1: 0.9300 - f1: 0.9302\n",
+ "Epoch 00161: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.1654 - accuracy: 0.9300 - precision_1: 0.9300 - recall_1: 0.9300 - f1: 0.9302 - val_loss: 0.3061 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 162/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1740 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9297\n",
+ "Epoch 00162: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1740 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9297 - val_loss: 0.2499 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 163/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1694 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9304\n",
+ "Epoch 00163: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 488ms/step - loss: 0.1694 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9304 - val_loss: 0.2265 - val_accuracy: 0.9165 - val_precision_1: 0.9165 - val_recall_1: 0.9165 - val_f1: 0.9189\n",
+ "Epoch 164/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1585 - accuracy: 0.9327 - precision_1: 0.9327 - recall_1: 0.9327 - f1: 0.9320\n",
+ "Epoch 00164: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 495ms/step - loss: 0.1585 - accuracy: 0.9327 - precision_1: 0.9327 - recall_1: 0.9327 - f1: 0.9320 - val_loss: 0.2414 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 165/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9389 - precision_1: 0.9389 - recall_1: 0.9389 - f1: 0.9399\n",
+ "Epoch 00165: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.1557 - accuracy: 0.9389 - precision_1: 0.9389 - recall_1: 0.9389 - f1: 0.9399 - val_loss: 0.2484 - val_accuracy: 0.9115 - val_precision_1: 0.9115 - val_recall_1: 0.9115 - val_f1: 0.9141\n",
+ "Epoch 166/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1554 - accuracy: 0.9375 - precision_1: 0.9375 - recall_1: 0.9375 - f1: 0.9368\n",
+ "Epoch 00166: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.1554 - accuracy: 0.9375 - precision_1: 0.9375 - recall_1: 0.9375 - f1: 0.9368 - val_loss: 0.2444 - val_accuracy: 0.9064 - val_precision_1: 0.9064 - val_recall_1: 0.9064 - val_f1: 0.8799\n",
+ "Epoch 167/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1505 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9374\n",
+ "Epoch 00167: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.1505 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9374 - val_loss: 0.2789 - val_accuracy: 0.8913 - val_precision_1: 0.8913 - val_recall_1: 0.8913 - val_f1: 0.8945\n",
+ "Epoch 168/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1690 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9290\n",
+ "Epoch 00168: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.1690 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9290 - val_loss: 0.2912 - val_accuracy: 0.8984 - val_precision_1: 0.8984 - val_recall_1: 0.8984 - val_f1: 0.9014\n",
+ "Epoch 169/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1564 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9366\n",
+ "Epoch 00169: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 476ms/step - loss: 0.1564 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9366 - val_loss: 0.9310 - val_accuracy: 0.7616 - val_precision_1: 0.7616 - val_recall_1: 0.7616 - val_f1: 0.7686\n",
+ "Epoch 170/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1468 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9358\n",
+ "Epoch 00170: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 507ms/step - loss: 0.1468 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9358 - val_loss: 0.2551 - val_accuracy: 0.9105 - val_precision_1: 0.9105 - val_recall_1: 0.9105 - val_f1: 0.9131\n",
+ "Epoch 171/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1675 - accuracy: 0.9320 - precision_1: 0.9320 - recall_1: 0.9320 - f1: 0.9313\n",
+ "Epoch 00171: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1675 - accuracy: 0.9320 - precision_1: 0.9320 - recall_1: 0.9320 - f1: 0.9313 - val_loss: 0.2882 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8789\n",
+ "Epoch 172/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1774 - accuracy: 0.9272 - precision_1: 0.9272 - recall_1: 0.9272 - f1: 0.9266\n",
+ "Epoch 00172: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1774 - accuracy: 0.9272 - precision_1: 0.9272 - recall_1: 0.9272 - f1: 0.9266 - val_loss: 0.2508 - val_accuracy: 0.9034 - val_precision_1: 0.9034 - val_recall_1: 0.9034 - val_f1: 0.9062\n",
+ "Epoch 173/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1526 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9363\n",
+ "Epoch 00173: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1526 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9363 - val_loss: 0.2476 - val_accuracy: 0.9044 - val_precision_1: 0.9044 - val_recall_1: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 174/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1765 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9254\n",
+ "Epoch 00174: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.1765 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9254 - val_loss: 0.2596 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 175/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1478 - accuracy: 0.9386 - precision_1: 0.9386 - recall_1: 0.9386 - f1: 0.9378\n",
+ "Epoch 00175: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 464ms/step - loss: 0.1478 - accuracy: 0.9386 - precision_1: 0.9386 - recall_1: 0.9386 - f1: 0.9378 - val_loss: 0.3862 - val_accuracy: 0.8662 - val_precision_1: 0.8662 - val_recall_1: 0.8662 - val_f1: 0.8701\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 176/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1647 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9295\n",
+ "Epoch 00176: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 494ms/step - loss: 0.1647 - accuracy: 0.9293 - precision_1: 0.9293 - recall_1: 0.9293 - f1: 0.9295 - val_loss: 0.2455 - val_accuracy: 0.9034 - val_precision_1: 0.9034 - val_recall_1: 0.9034 - val_f1: 0.8916\n",
+ "Epoch 177/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1632 - accuracy: 0.9337 - precision_1: 0.9337 - recall_1: 0.9337 - f1: 0.9330\n",
+ "Epoch 00177: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.1632 - accuracy: 0.9337 - precision_1: 0.9337 - recall_1: 0.9337 - f1: 0.9330 - val_loss: 0.2572 - val_accuracy: 0.9034 - val_precision_1: 0.9034 - val_recall_1: 0.9034 - val_f1: 0.9062\n",
+ "Epoch 178/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1617 - accuracy: 0.9372 - precision_1: 0.9372 - recall_1: 0.9372 - f1: 0.9373\n",
+ "Epoch 00178: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.1617 - accuracy: 0.9372 - precision_1: 0.9372 - recall_1: 0.9372 - f1: 0.9373 - val_loss: 0.2966 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 179/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1622 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9371\n",
+ "Epoch 00179: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.1622 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9371 - val_loss: 0.2844 - val_accuracy: 0.8984 - val_precision_1: 0.8984 - val_recall_1: 0.8984 - val_f1: 0.8867\n",
+ "Epoch 180/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1636 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9307\n",
+ "Epoch 00180: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 507ms/step - loss: 0.1636 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9307 - val_loss: 0.2614 - val_accuracy: 0.9085 - val_precision_1: 0.9085 - val_recall_1: 0.9085 - val_f1: 0.9111\n",
+ "Epoch 181/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1597 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9337\n",
+ "Epoch 00181: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 467ms/step - loss: 0.1597 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9337 - val_loss: 0.2812 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 182/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1687 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9354\n",
+ "Epoch 00182: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 26s 557ms/step - loss: 0.1687 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9354 - val_loss: 0.2459 - val_accuracy: 0.9105 - val_precision_1: 0.9105 - val_recall_1: 0.9105 - val_f1: 0.9131\n",
+ "Epoch 183/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1612 - accuracy: 0.9375 - precision_1: 0.9375 - recall_1: 0.9375 - f1: 0.9368\n",
+ "Epoch 00183: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.1612 - accuracy: 0.9375 - precision_1: 0.9375 - recall_1: 0.9375 - f1: 0.9368 - val_loss: 0.2431 - val_accuracy: 0.8984 - val_precision_1: 0.8984 - val_recall_1: 0.8984 - val_f1: 0.9014\n",
+ "Epoch 184/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1550 - accuracy: 0.9413 - precision_1: 0.9413 - recall_1: 0.9413 - f1: 0.9414\n",
+ "Epoch 00184: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.1550 - accuracy: 0.9413 - precision_1: 0.9413 - recall_1: 0.9413 - f1: 0.9414 - val_loss: 0.2658 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8955\n",
+ "Epoch 185/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.9393 - precision_1: 0.9393 - recall_1: 0.9393 - f1: 0.9393\n",
+ "Epoch 00185: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 473ms/step - loss: 0.1473 - accuracy: 0.9393 - precision_1: 0.9393 - recall_1: 0.9393 - f1: 0.9393 - val_loss: 0.3129 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 186/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1626 - accuracy: 0.9337 - precision_1: 0.9337 - recall_1: 0.9337 - f1: 0.9322\n",
+ "Epoch 00186: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.1626 - accuracy: 0.9337 - precision_1: 0.9337 - recall_1: 0.9337 - f1: 0.9322 - val_loss: 0.2368 - val_accuracy: 0.9074 - val_precision_1: 0.9074 - val_recall_1: 0.9074 - val_f1: 0.9102\n",
+ "Epoch 187/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1723 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9281\n",
+ "Epoch 00187: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 470ms/step - loss: 0.1723 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9281 - val_loss: 0.2612 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8770\n",
+ "Epoch 188/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1353 - accuracy: 0.9458 - precision_1: 0.9458 - recall_1: 0.9458 - f1: 0.9467\n",
+ "Epoch 00188: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 494ms/step - loss: 0.1353 - accuracy: 0.9458 - precision_1: 0.9458 - recall_1: 0.9458 - f1: 0.9467 - val_loss: 0.2989 - val_accuracy: 0.8954 - val_precision_1: 0.8954 - val_recall_1: 0.8954 - val_f1: 0.8984\n",
+ "Epoch 189/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1399 - accuracy: 0.9472 - precision_1: 0.9472 - recall_1: 0.9472 - f1: 0.9480\n",
+ "Epoch 00189: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 466ms/step - loss: 0.1399 - accuracy: 0.9472 - precision_1: 0.9472 - recall_1: 0.9472 - f1: 0.9480 - val_loss: 0.2590 - val_accuracy: 0.9064 - val_precision_1: 0.9064 - val_recall_1: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 190/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1546 - accuracy: 0.9424 - precision_1: 0.9424 - recall_1: 0.9424 - f1: 0.9433\n",
+ "Epoch 00190: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.1546 - accuracy: 0.9424 - precision_1: 0.9424 - recall_1: 0.9424 - f1: 0.9433 - val_loss: 0.2669 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 191/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1438 - accuracy: 0.9444 - precision_1: 0.9444 - recall_1: 0.9444 - f1: 0.9427\n",
+ "Epoch 00191: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.1438 - accuracy: 0.9444 - precision_1: 0.9444 - recall_1: 0.9444 - f1: 0.9427 - val_loss: 0.2762 - val_accuracy: 0.9034 - val_precision_1: 0.9034 - val_recall_1: 0.9034 - val_f1: 0.9062\n",
+ "Epoch 192/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1454 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9366\n",
+ "Epoch 00192: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.1454 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9366 - val_loss: 0.2514 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 193/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1493 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380\n",
+ "Epoch 00193: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 24s 518ms/step - loss: 0.1493 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380 - val_loss: 0.2736 - val_accuracy: 0.8994 - val_precision_1: 0.8994 - val_recall_1: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 194/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1439 - accuracy: 0.9416 - precision_1: 0.9416 - recall_1: 0.9416 - f1: 0.9416\n",
+ "Epoch 00194: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 481ms/step - loss: 0.1439 - accuracy: 0.9416 - precision_1: 0.9416 - recall_1: 0.9416 - f1: 0.9416 - val_loss: 0.2389 - val_accuracy: 0.9095 - val_precision_1: 0.9095 - val_recall_1: 0.9095 - val_f1: 0.9121\n",
+ "Epoch 195/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1439 - accuracy: 0.9402 - precision_1: 0.9402 - recall_1: 0.9402 - f1: 0.9402\n",
+ "Epoch 00195: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1439 - accuracy: 0.9402 - precision_1: 0.9402 - recall_1: 0.9402 - f1: 0.9402 - val_loss: 0.3037 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 196/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1366 - accuracy: 0.9417 - precision_1: 0.9417 - recall_1: 0.9417 - f1: 0.9409\n",
+ "Epoch 00196: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1366 - accuracy: 0.9417 - precision_1: 0.9417 - recall_1: 0.9417 - f1: 0.9409 - val_loss: 0.2583 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 197/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1366 - accuracy: 0.9479 - precision_1: 0.9479 - recall_1: 0.9479 - f1: 0.9478\n",
+ "Epoch 00197: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.1366 - accuracy: 0.9479 - precision_1: 0.9479 - recall_1: 0.9479 - f1: 0.9478 - val_loss: 0.2816 - val_accuracy: 0.9014 - val_precision_1: 0.9014 - val_recall_1: 0.9014 - val_f1: 0.9043\n",
+ "Epoch 198/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1375 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9409\n",
+ "Epoch 00198: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.1375 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9409 - val_loss: 0.3320 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 199/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1503 - accuracy: 0.9403 - precision_1: 0.9403 - recall_1: 0.9403 - f1: 0.9412\n",
+ "Epoch 00199: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 481ms/step - loss: 0.1503 - accuracy: 0.9403 - precision_1: 0.9403 - recall_1: 0.9403 - f1: 0.9412 - val_loss: 0.2676 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 200/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1367 - accuracy: 0.9441 - precision_1: 0.9441 - recall_1: 0.9441 - f1: 0.9432\n",
+ "Epoch 00200: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I1orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.1367 - accuracy: 0.9441 - precision_1: 0.9441 - recall_1: 0.9441 - f1: 0.9432 - val_loss: 0.2609 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/preloaded/madeModels/OGRUN_I2/assets\n",
+ "Epoch 1/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.8141 - accuracy: 0.6522 - precision_2: 0.6522 - recall_2: 0.6522 - f1: 0.6541\n",
+ "Epoch 00001: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.8141 - accuracy: 0.6522 - precision_2: 0.6522 - recall_2: 0.6522 - f1: 0.6541 - val_loss: 0.7746 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 2/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.5484 - accuracy: 0.7505 - precision_2: 0.7505 - recall_2: 0.7505 - f1: 0.7518\n",
+ "Epoch 00002: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.5484 - accuracy: 0.7505 - precision_2: 0.7505 - recall_2: 0.7505 - f1: 0.7518 - val_loss: 1.0067 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 3/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4555 - accuracy: 0.7826 - precision_2: 0.7826 - recall_2: 0.7826 - f1: 0.7825\n",
+ "Epoch 00003: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 466ms/step - loss: 0.4555 - accuracy: 0.7826 - precision_2: 0.7826 - recall_2: 0.7826 - f1: 0.7825 - val_loss: 1.2343 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 4/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4343 - accuracy: 0.7985 - precision_2: 0.7985 - recall_2: 0.7985 - f1: 0.7973\n",
+ "Epoch 00004: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.4343 - accuracy: 0.7985 - precision_2: 0.7985 - recall_2: 0.7985 - f1: 0.7973 - val_loss: 1.3371 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 5/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4146 - accuracy: 0.8054 - precision_2: 0.8054 - recall_2: 0.8054 - f1: 0.8067\n",
+ "Epoch 00005: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.4146 - accuracy: 0.8054 - precision_2: 0.8054 - recall_2: 0.8054 - f1: 0.8067 - val_loss: 1.5184 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 6/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3918 - accuracy: 0.8185 - precision_2: 0.8185 - recall_2: 0.8185 - f1: 0.8170\n",
+ "Epoch 00006: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.3918 - accuracy: 0.8185 - precision_2: 0.8185 - recall_2: 0.8185 - f1: 0.8170 - val_loss: 1.5229 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 7/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3767 - accuracy: 0.8210 - precision_2: 0.8210 - recall_2: 0.8210 - f1: 0.8210\n",
+ "Epoch 00007: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.3767 - accuracy: 0.8210 - precision_2: 0.8210 - recall_2: 0.8210 - f1: 0.8210 - val_loss: 1.2193 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 8/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3846 - accuracy: 0.8185 - precision_2: 0.8185 - recall_2: 0.8185 - f1: 0.8187\n",
+ "Epoch 00008: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 454ms/step - loss: 0.3846 - accuracy: 0.8185 - precision_2: 0.8185 - recall_2: 0.8185 - f1: 0.8187 - val_loss: 1.2713 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6318\n",
+ "Epoch 9/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3714 - accuracy: 0.8340 - precision_2: 0.8340 - recall_2: 0.8340 - f1: 0.8340\n",
+ "Epoch 00009: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.3714 - accuracy: 0.8340 - precision_2: 0.8340 - recall_2: 0.8340 - f1: 0.8340 - val_loss: 1.1264 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 10/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8240 - precision_2: 0.8240 - recall_2: 0.8240 - f1: 0.8233\n",
+ "Epoch 00010: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.3670 - accuracy: 0.8240 - precision_2: 0.8240 - recall_2: 0.8240 - f1: 0.8233 - val_loss: 0.5141 - val_accuracy: 0.7274 - val_precision_2: 0.7274 - val_recall_2: 0.7274 - val_f1: 0.7207\n",
+ "Epoch 11/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3480 - accuracy: 0.8427 - precision_2: 0.8427 - recall_2: 0.8427 - f1: 0.8442\n",
+ "Epoch 00011: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.3480 - accuracy: 0.8427 - precision_2: 0.8427 - recall_2: 0.8427 - f1: 0.8442 - val_loss: 0.4325 - val_accuracy: 0.7928 - val_precision_2: 0.7928 - val_recall_2: 0.7928 - val_f1: 0.7988\n",
+ "Epoch 12/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3518 - accuracy: 0.8392 - precision_2: 0.8392 - recall_2: 0.8392 - f1: 0.8374\n",
+ "Epoch 00012: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.3518 - accuracy: 0.8392 - precision_2: 0.8392 - recall_2: 0.8392 - f1: 0.8374 - val_loss: 0.4205 - val_accuracy: 0.7887 - val_precision_2: 0.7887 - val_recall_2: 0.7887 - val_f1: 0.7949\n",
+ "Epoch 13/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3586 - accuracy: 0.8354 - precision_2: 0.8354 - recall_2: 0.8354 - f1: 0.8319\n",
+ "Epoch 00013: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 480ms/step - loss: 0.3586 - accuracy: 0.8354 - precision_2: 0.8354 - recall_2: 0.8354 - f1: 0.8319 - val_loss: 0.3535 - val_accuracy: 0.8431 - val_precision_2: 0.8431 - val_recall_2: 0.8431 - val_f1: 0.8330\n",
+ "Epoch 14/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3459 - accuracy: 0.8482 - precision_2: 0.8482 - recall_2: 0.8482 - f1: 0.8497\n",
+ "Epoch 00014: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 448ms/step - loss: 0.3459 - accuracy: 0.8482 - precision_2: 0.8482 - recall_2: 0.8482 - f1: 0.8497 - val_loss: 0.4184 - val_accuracy: 0.8048 - val_precision_2: 0.8048 - val_recall_2: 0.8048 - val_f1: 0.7959\n",
+ "Epoch 15/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3453 - accuracy: 0.8413 - precision_2: 0.8413 - recall_2: 0.8413 - f1: 0.8403\n",
+ "Epoch 00015: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.3453 - accuracy: 0.8413 - precision_2: 0.8413 - recall_2: 0.8413 - f1: 0.8403 - val_loss: 0.3458 - val_accuracy: 0.8531 - val_precision_2: 0.8531 - val_recall_2: 0.8531 - val_f1: 0.8574\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 16/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3293 - accuracy: 0.8565 - precision_2: 0.8565 - recall_2: 0.8565 - f1: 0.8578\n",
+ "Epoch 00016: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.3293 - accuracy: 0.8565 - precision_2: 0.8565 - recall_2: 0.8565 - f1: 0.8578 - val_loss: 0.4759 - val_accuracy: 0.7847 - val_precision_2: 0.7847 - val_recall_2: 0.7847 - val_f1: 0.7910\n",
+ "Epoch 17/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3135 - accuracy: 0.8609 - precision_2: 0.8609 - recall_2: 0.8609 - f1: 0.8622\n",
+ "Epoch 00017: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.3135 - accuracy: 0.8609 - precision_2: 0.8609 - recall_2: 0.8609 - f1: 0.8622 - val_loss: 0.3455 - val_accuracy: 0.8410 - val_precision_2: 0.8410 - val_recall_2: 0.8410 - val_f1: 0.8311\n",
+ "Epoch 18/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3131 - accuracy: 0.8620 - precision_2: 0.8620 - recall_2: 0.8620 - f1: 0.8624\n",
+ "Epoch 00018: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.3131 - accuracy: 0.8620 - precision_2: 0.8620 - recall_2: 0.8620 - f1: 0.8624 - val_loss: 0.7714 - val_accuracy: 0.7173 - val_precision_2: 0.7173 - val_recall_2: 0.7173 - val_f1: 0.7109\n",
+ "Epoch 19/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3158 - accuracy: 0.8616 - precision_2: 0.8616 - recall_2: 0.8616 - f1: 0.8612\n",
+ "Epoch 00019: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 24s 512ms/step - loss: 0.3158 - accuracy: 0.8616 - precision_2: 0.8616 - recall_2: 0.8616 - f1: 0.8612 - val_loss: 0.7318 - val_accuracy: 0.6710 - val_precision_2: 0.6710 - val_recall_2: 0.6710 - val_f1: 0.6807\n",
+ "Epoch 20/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3084 - accuracy: 0.8709 - precision_2: 0.8709 - recall_2: 0.8709 - f1: 0.8686\n",
+ "Epoch 00020: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.3084 - accuracy: 0.8709 - precision_2: 0.8709 - recall_2: 0.8709 - f1: 0.8686 - val_loss: 0.7282 - val_accuracy: 0.6851 - val_precision_2: 0.6851 - val_recall_2: 0.6851 - val_f1: 0.6943\n",
+ "Epoch 21/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3007 - accuracy: 0.8689 - precision_2: 0.8689 - recall_2: 0.8689 - f1: 0.8692\n",
+ "Epoch 00021: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.3007 - accuracy: 0.8689 - precision_2: 0.8689 - recall_2: 0.8689 - f1: 0.8692 - val_loss: 0.4523 - val_accuracy: 0.7857 - val_precision_2: 0.7857 - val_recall_2: 0.7857 - val_f1: 0.7920\n",
+ "Epoch 22/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3155 - accuracy: 0.8599 - precision_2: 0.8599 - recall_2: 0.8599 - f1: 0.8604\n",
+ "Epoch 00022: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 403ms/step - loss: 0.3155 - accuracy: 0.8599 - precision_2: 0.8599 - recall_2: 0.8599 - f1: 0.8604 - val_loss: 0.4477 - val_accuracy: 0.8038 - val_precision_2: 0.8038 - val_recall_2: 0.8038 - val_f1: 0.7949\n",
+ "Epoch 23/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3068 - accuracy: 0.8671 - precision_2: 0.8671 - recall_2: 0.8671 - f1: 0.8658\n",
+ "Epoch 00023: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.3068 - accuracy: 0.8671 - precision_2: 0.8671 - recall_2: 0.8671 - f1: 0.8658 - val_loss: 0.5329 - val_accuracy: 0.7505 - val_precision_2: 0.7505 - val_recall_2: 0.7505 - val_f1: 0.7432\n",
+ "Epoch 24/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3161 - accuracy: 0.8592 - precision_2: 0.8592 - recall_2: 0.8592 - f1: 0.8588\n",
+ "Epoch 00024: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 493ms/step - loss: 0.3161 - accuracy: 0.8592 - precision_2: 0.8592 - recall_2: 0.8592 - f1: 0.8588 - val_loss: 0.6787 - val_accuracy: 0.7596 - val_precision_2: 0.7596 - val_recall_2: 0.7596 - val_f1: 0.7666\n",
+ "Epoch 25/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2946 - accuracy: 0.8720 - precision_2: 0.8720 - recall_2: 0.8720 - f1: 0.8722\n",
+ "Epoch 00025: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2946 - accuracy: 0.8720 - precision_2: 0.8720 - recall_2: 0.8720 - f1: 0.8722 - val_loss: 0.3346 - val_accuracy: 0.8400 - val_precision_2: 0.8400 - val_recall_2: 0.8400 - val_f1: 0.8301\n",
+ "Epoch 26/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2998 - accuracy: 0.8665 - precision_2: 0.8665 - recall_2: 0.8665 - f1: 0.8677\n",
+ "Epoch 00026: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.2998 - accuracy: 0.8665 - precision_2: 0.8665 - recall_2: 0.8665 - f1: 0.8677 - val_loss: 0.6488 - val_accuracy: 0.7394 - val_precision_2: 0.7394 - val_recall_2: 0.7394 - val_f1: 0.7471\n",
+ "Epoch 27/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2894 - accuracy: 0.8782 - precision_2: 0.8782 - recall_2: 0.8782 - f1: 0.8784\n",
+ "Epoch 00027: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.2894 - accuracy: 0.8782 - precision_2: 0.8782 - recall_2: 0.8782 - f1: 0.8784 - val_loss: 0.3938 - val_accuracy: 0.8199 - val_precision_2: 0.8199 - val_recall_2: 0.8199 - val_f1: 0.8252\n",
+ "Epoch 28/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2797 - accuracy: 0.8844 - precision_2: 0.8844 - recall_2: 0.8844 - f1: 0.8819\n",
+ "Epoch 00028: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.2797 - accuracy: 0.8844 - precision_2: 0.8844 - recall_2: 0.8844 - f1: 0.8819 - val_loss: 0.5819 - val_accuracy: 0.7465 - val_precision_2: 0.7465 - val_recall_2: 0.7465 - val_f1: 0.7246\n",
+ "Epoch 29/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2804 - accuracy: 0.8768 - precision_2: 0.8768 - recall_2: 0.8768 - f1: 0.8770\n",
+ "Epoch 00029: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2804 - accuracy: 0.8768 - precision_2: 0.8768 - recall_2: 0.8768 - f1: 0.8770 - val_loss: 0.4353 - val_accuracy: 0.8109 - val_precision_2: 0.8109 - val_recall_2: 0.8109 - val_f1: 0.8164\n",
+ "Epoch 30/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3016 - accuracy: 0.8668 - precision_2: 0.8668 - recall_2: 0.8668 - f1: 0.8663\n",
+ "Epoch 00030: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.3016 - accuracy: 0.8668 - precision_2: 0.8668 - recall_2: 0.8668 - f1: 0.8663 - val_loss: 0.4543 - val_accuracy: 0.7907 - val_precision_2: 0.7907 - val_recall_2: 0.7907 - val_f1: 0.7969\n",
+ "Epoch 31/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2876 - accuracy: 0.8734 - precision_2: 0.8734 - recall_2: 0.8734 - f1: 0.8701\n",
+ "Epoch 00031: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2876 - accuracy: 0.8734 - precision_2: 0.8734 - recall_2: 0.8734 - f1: 0.8701 - val_loss: 0.2993 - val_accuracy: 0.8742 - val_precision_2: 0.8742 - val_recall_2: 0.8742 - val_f1: 0.8779\n",
+ "Epoch 32/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2814 - accuracy: 0.8754 - precision_2: 0.8754 - recall_2: 0.8754 - f1: 0.8765\n",
+ "Epoch 00032: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 468ms/step - loss: 0.2814 - accuracy: 0.8754 - precision_2: 0.8754 - recall_2: 0.8754 - f1: 0.8765 - val_loss: 0.3413 - val_accuracy: 0.8521 - val_precision_2: 0.8521 - val_recall_2: 0.8521 - val_f1: 0.8564\n",
+ "Epoch 33/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2814 - accuracy: 0.8785 - precision_2: 0.8785 - recall_2: 0.8785 - f1: 0.8761\n",
+ "Epoch 00033: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2814 - accuracy: 0.8785 - precision_2: 0.8785 - recall_2: 0.8785 - f1: 0.8761 - val_loss: 0.3993 - val_accuracy: 0.8099 - val_precision_2: 0.8099 - val_recall_2: 0.8099 - val_f1: 0.8154\n",
+ "Epoch 34/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2924 - accuracy: 0.8761 - precision_2: 0.8761 - recall_2: 0.8761 - f1: 0.8772\n",
+ "Epoch 00034: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 469ms/step - loss: 0.2924 - accuracy: 0.8761 - precision_2: 0.8761 - recall_2: 0.8761 - f1: 0.8772 - val_loss: 0.3113 - val_accuracy: 0.8612 - val_precision_2: 0.8612 - val_recall_2: 0.8612 - val_f1: 0.8652\n",
+ "Epoch 35/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2806 - accuracy: 0.8796 - precision_2: 0.8796 - recall_2: 0.8796 - f1: 0.8806\n",
+ "Epoch 00035: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2806 - accuracy: 0.8796 - precision_2: 0.8796 - recall_2: 0.8796 - f1: 0.8806 - val_loss: 0.2860 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8604\n",
+ "Epoch 36/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2789 - accuracy: 0.8765 - precision_2: 0.8765 - recall_2: 0.8765 - f1: 0.8741\n",
+ "Epoch 00036: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 459ms/step - loss: 0.2789 - accuracy: 0.8765 - precision_2: 0.8765 - recall_2: 0.8765 - f1: 0.8741 - val_loss: 0.4116 - val_accuracy: 0.8099 - val_precision_2: 0.8099 - val_recall_2: 0.8099 - val_f1: 0.8008\n",
+ "Epoch 37/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2693 - accuracy: 0.8816 - precision_2: 0.8816 - recall_2: 0.8816 - f1: 0.8809\n",
+ "Epoch 00037: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2693 - accuracy: 0.8816 - precision_2: 0.8816 - recall_2: 0.8816 - f1: 0.8809 - val_loss: 0.3106 - val_accuracy: 0.8632 - val_precision_2: 0.8632 - val_recall_2: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 38/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2748 - accuracy: 0.8816 - precision_2: 0.8816 - recall_2: 0.8816 - f1: 0.8800\n",
+ "Epoch 00038: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2748 - accuracy: 0.8816 - precision_2: 0.8816 - recall_2: 0.8816 - f1: 0.8800 - val_loss: 0.3285 - val_accuracy: 0.8592 - val_precision_2: 0.8592 - val_recall_2: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 39/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2675 - accuracy: 0.8820 - precision_2: 0.8820 - recall_2: 0.8820 - f1: 0.8804\n",
+ "Epoch 00039: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 458ms/step - loss: 0.2675 - accuracy: 0.8820 - precision_2: 0.8820 - recall_2: 0.8820 - f1: 0.8804 - val_loss: 0.3549 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 40/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2791 - accuracy: 0.8810 - precision_2: 0.8810 - recall_2: 0.8810 - f1: 0.8802\n",
+ "Epoch 00040: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2791 - accuracy: 0.8810 - precision_2: 0.8810 - recall_2: 0.8810 - f1: 0.8802 - val_loss: 0.9096 - val_accuracy: 0.7022 - val_precision_2: 0.7022 - val_recall_2: 0.7022 - val_f1: 0.7109\n",
+ "Epoch 41/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2591 - accuracy: 0.8913 - precision_2: 0.8913 - recall_2: 0.8913 - f1: 0.8913\n",
+ "Epoch 00041: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.2591 - accuracy: 0.8913 - precision_2: 0.8913 - recall_2: 0.8913 - f1: 0.8913 - val_loss: 0.4312 - val_accuracy: 0.8149 - val_precision_2: 0.8149 - val_recall_2: 0.8149 - val_f1: 0.8057\n",
+ "Epoch 42/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2820 - accuracy: 0.8754 - precision_2: 0.8754 - recall_2: 0.8754 - f1: 0.8765\n",
+ "Epoch 00042: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2820 - accuracy: 0.8754 - precision_2: 0.8754 - recall_2: 0.8754 - f1: 0.8765 - val_loss: 0.2785 - val_accuracy: 0.8873 - val_precision_2: 0.8873 - val_recall_2: 0.8873 - val_f1: 0.8906\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 43/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2724 - accuracy: 0.8813 - precision_2: 0.8813 - recall_2: 0.8813 - f1: 0.8814\n",
+ "Epoch 00043: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 468ms/step - loss: 0.2724 - accuracy: 0.8813 - precision_2: 0.8813 - recall_2: 0.8813 - f1: 0.8814 - val_loss: 0.3731 - val_accuracy: 0.8481 - val_precision_2: 0.8481 - val_recall_2: 0.8481 - val_f1: 0.8379\n",
+ "Epoch 44/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2587 - accuracy: 0.8951 - precision_2: 0.8951 - recall_2: 0.8951 - f1: 0.8941\n",
+ "Epoch 00044: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 455ms/step - loss: 0.2587 - accuracy: 0.8951 - precision_2: 0.8951 - recall_2: 0.8951 - f1: 0.8941 - val_loss: 0.2915 - val_accuracy: 0.8722 - val_precision_2: 0.8722 - val_recall_2: 0.8722 - val_f1: 0.8760\n",
+ "Epoch 45/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2704 - accuracy: 0.8827 - precision_2: 0.8827 - recall_2: 0.8827 - f1: 0.8828\n",
+ "Epoch 00045: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2704 - accuracy: 0.8827 - precision_2: 0.8827 - recall_2: 0.8827 - f1: 0.8828 - val_loss: 0.5224 - val_accuracy: 0.8078 - val_precision_2: 0.8078 - val_recall_2: 0.8078 - val_f1: 0.7988\n",
+ "Epoch 46/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2611 - accuracy: 0.8986 - precision_2: 0.8986 - recall_2: 0.8986 - f1: 0.8975\n",
+ "Epoch 00046: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2611 - accuracy: 0.8986 - precision_2: 0.8986 - recall_2: 0.8986 - f1: 0.8975 - val_loss: 0.3149 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 47/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2732 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896\n",
+ "Epoch 00047: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2732 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896 - val_loss: 0.3320 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 48/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2511 - accuracy: 0.8941 - precision_2: 0.8941 - recall_2: 0.8941 - f1: 0.8922\n",
+ "Epoch 00048: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2511 - accuracy: 0.8941 - precision_2: 0.8941 - recall_2: 0.8941 - f1: 0.8922 - val_loss: 0.5763 - val_accuracy: 0.7857 - val_precision_2: 0.7857 - val_recall_2: 0.7857 - val_f1: 0.7920\n",
+ "Epoch 49/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2648 - accuracy: 0.8875 - precision_2: 0.8875 - recall_2: 0.8875 - f1: 0.8875\n",
+ "Epoch 00049: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.2648 - accuracy: 0.8875 - precision_2: 0.8875 - recall_2: 0.8875 - f1: 0.8875 - val_loss: 0.3896 - val_accuracy: 0.8048 - val_precision_2: 0.8048 - val_recall_2: 0.8048 - val_f1: 0.8105\n",
+ "Epoch 50/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2522 - accuracy: 0.8879 - precision_2: 0.8879 - recall_2: 0.8879 - f1: 0.8861\n",
+ "Epoch 00050: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2522 - accuracy: 0.8879 - precision_2: 0.8879 - recall_2: 0.8879 - f1: 0.8861 - val_loss: 0.3046 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 51/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2656 - accuracy: 0.8865 - precision_2: 0.8865 - recall_2: 0.8865 - f1: 0.8874\n",
+ "Epoch 00051: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2656 - accuracy: 0.8865 - precision_2: 0.8865 - recall_2: 0.8865 - f1: 0.8874 - val_loss: 0.3488 - val_accuracy: 0.8461 - val_precision_2: 0.8461 - val_recall_2: 0.8461 - val_f1: 0.8213\n",
+ "Epoch 52/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2550 - accuracy: 0.8954 - precision_2: 0.8954 - recall_2: 0.8954 - f1: 0.8962\n",
+ "Epoch 00052: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2550 - accuracy: 0.8954 - precision_2: 0.8954 - recall_2: 0.8954 - f1: 0.8962 - val_loss: 0.3545 - val_accuracy: 0.8511 - val_precision_2: 0.8511 - val_recall_2: 0.8511 - val_f1: 0.8408\n",
+ "Epoch 53/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2494 - accuracy: 0.8920 - precision_2: 0.8920 - recall_2: 0.8920 - f1: 0.8928\n",
+ "Epoch 00053: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2494 - accuracy: 0.8920 - precision_2: 0.8920 - recall_2: 0.8920 - f1: 0.8928 - val_loss: 0.3990 - val_accuracy: 0.8431 - val_precision_2: 0.8431 - val_recall_2: 0.8431 - val_f1: 0.8477\n",
+ "Epoch 54/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2603 - accuracy: 0.8954 - precision_2: 0.8954 - recall_2: 0.8954 - f1: 0.8962\n",
+ "Epoch 00054: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.2603 - accuracy: 0.8954 - precision_2: 0.8954 - recall_2: 0.8954 - f1: 0.8962 - val_loss: 0.3053 - val_accuracy: 0.8793 - val_precision_2: 0.8793 - val_recall_2: 0.8793 - val_f1: 0.8828\n",
+ "Epoch 55/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2441 - accuracy: 0.8982 - precision_2: 0.8982 - recall_2: 0.8982 - f1: 0.8972\n",
+ "Epoch 00055: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.2441 - accuracy: 0.8982 - precision_2: 0.8982 - recall_2: 0.8982 - f1: 0.8972 - val_loss: 0.4165 - val_accuracy: 0.8179 - val_precision_2: 0.8179 - val_recall_2: 0.8179 - val_f1: 0.8086\n",
+ "Epoch 56/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2597 - accuracy: 0.8820 - precision_2: 0.8820 - recall_2: 0.8820 - f1: 0.8812\n",
+ "Epoch 00056: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2597 - accuracy: 0.8820 - precision_2: 0.8820 - recall_2: 0.8820 - f1: 0.8812 - val_loss: 0.3886 - val_accuracy: 0.8370 - val_precision_2: 0.8370 - val_recall_2: 0.8370 - val_f1: 0.8418\n",
+ "Epoch 57/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2490 - accuracy: 0.8951 - precision_2: 0.8951 - recall_2: 0.8951 - f1: 0.8959\n",
+ "Epoch 00057: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.2490 - accuracy: 0.8951 - precision_2: 0.8951 - recall_2: 0.8951 - f1: 0.8959 - val_loss: 0.3039 - val_accuracy: 0.8742 - val_precision_2: 0.8742 - val_recall_2: 0.8742 - val_f1: 0.8633\n",
+ "Epoch 58/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.2378 - accuracy: 0.9014 - precision_2: 0.9014 - recall_2: 0.9014 - f1: 0.9014\n",
+ "Epoch 00058: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2375 - accuracy: 0.9013 - precision_2: 0.9013 - recall_2: 0.9013 - f1: 0.9011 - val_loss: 0.3199 - val_accuracy: 0.8702 - val_precision_2: 0.8702 - val_recall_2: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 59/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2414 - accuracy: 0.9006 - precision_2: 0.9006 - recall_2: 0.9006 - f1: 0.9004\n",
+ "Epoch 00059: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.2414 - accuracy: 0.9006 - precision_2: 0.9006 - recall_2: 0.9006 - f1: 0.9004 - val_loss: 0.3013 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8604\n",
+ "Epoch 60/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2432 - accuracy: 0.8972 - precision_2: 0.8972 - recall_2: 0.8972 - f1: 0.8970\n",
+ "Epoch 00060: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 449ms/step - loss: 0.2432 - accuracy: 0.8972 - precision_2: 0.8972 - recall_2: 0.8972 - f1: 0.8970 - val_loss: 0.3274 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8643\n",
+ "Epoch 61/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2527 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8900\n",
+ "Epoch 00061: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2527 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8900 - val_loss: 0.5477 - val_accuracy: 0.7928 - val_precision_2: 0.7928 - val_recall_2: 0.7928 - val_f1: 0.7988\n",
+ "Epoch 62/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2476 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8909\n",
+ "Epoch 00062: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2476 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8909 - val_loss: 0.6197 - val_accuracy: 0.7545 - val_precision_2: 0.7545 - val_recall_2: 0.7545 - val_f1: 0.7324\n",
+ "Epoch 63/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2369 - accuracy: 0.9006 - precision_2: 0.9006 - recall_2: 0.9006 - f1: 0.9013\n",
+ "Epoch 00063: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2369 - accuracy: 0.9006 - precision_2: 0.9006 - recall_2: 0.9006 - f1: 0.9013 - val_loss: 0.4176 - val_accuracy: 0.8199 - val_precision_2: 0.8199 - val_recall_2: 0.8199 - val_f1: 0.8252\n",
+ "Epoch 64/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2359 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9027\n",
+ "Epoch 00064: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2359 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9027 - val_loss: 0.7906 - val_accuracy: 0.7646 - val_precision_2: 0.7646 - val_recall_2: 0.7646 - val_f1: 0.7715\n",
+ "Epoch 65/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2389 - accuracy: 0.9017 - precision_2: 0.9017 - recall_2: 0.9017 - f1: 0.9006\n",
+ "Epoch 00065: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.2389 - accuracy: 0.9017 - precision_2: 0.9017 - recall_2: 0.9017 - f1: 0.9006 - val_loss: 0.4017 - val_accuracy: 0.8400 - val_precision_2: 0.8400 - val_recall_2: 0.8400 - val_f1: 0.8447\n",
+ "Epoch 66/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2419 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8926\n",
+ "Epoch 00066: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2419 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8926 - val_loss: 0.2669 - val_accuracy: 0.8833 - val_precision_2: 0.8833 - val_recall_2: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 67/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2378 - accuracy: 0.9003 - precision_2: 0.9003 - recall_2: 0.9003 - f1: 0.8975\n",
+ "Epoch 00067: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2378 - accuracy: 0.9003 - precision_2: 0.9003 - recall_2: 0.9003 - f1: 0.8975 - val_loss: 0.4017 - val_accuracy: 0.8260 - val_precision_2: 0.8260 - val_recall_2: 0.8260 - val_f1: 0.8311\n",
+ "Epoch 68/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2438 - accuracy: 0.8941 - precision_2: 0.8941 - recall_2: 0.8941 - f1: 0.8940\n",
+ "Epoch 00068: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2438 - accuracy: 0.8941 - precision_2: 0.8941 - recall_2: 0.8941 - f1: 0.8940 - val_loss: 0.3479 - val_accuracy: 0.8592 - val_precision_2: 0.8592 - val_recall_2: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 69/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2282 - accuracy: 0.9061 - precision_2: 0.9061 - recall_2: 0.9061 - f1: 0.9059\n",
+ "Epoch 00069: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2282 - accuracy: 0.9061 - precision_2: 0.9061 - recall_2: 0.9061 - f1: 0.9059 - val_loss: 0.4424 - val_accuracy: 0.8169 - val_precision_2: 0.8169 - val_recall_2: 0.8169 - val_f1: 0.8223\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 70/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2341 - accuracy: 0.9034 - precision_2: 0.9034 - recall_2: 0.9034 - f1: 0.9023\n",
+ "Epoch 00070: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2341 - accuracy: 0.9034 - precision_2: 0.9034 - recall_2: 0.9034 - f1: 0.9023 - val_loss: 0.3534 - val_accuracy: 0.8592 - val_precision_2: 0.8592 - val_recall_2: 0.8592 - val_f1: 0.8486\n",
+ "Epoch 71/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2378 - accuracy: 0.9089 - precision_2: 0.9089 - recall_2: 0.9089 - f1: 0.9086\n",
+ "Epoch 00071: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.2378 - accuracy: 0.9089 - precision_2: 0.9089 - recall_2: 0.9089 - f1: 0.9086 - val_loss: 0.4413 - val_accuracy: 0.8139 - val_precision_2: 0.8139 - val_recall_2: 0.8139 - val_f1: 0.8193\n",
+ "Epoch 72/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2472 - accuracy: 0.8920 - precision_2: 0.8920 - recall_2: 0.8920 - f1: 0.8911\n",
+ "Epoch 00072: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2472 - accuracy: 0.8920 - precision_2: 0.8920 - recall_2: 0.8920 - f1: 0.8911 - val_loss: 0.3843 - val_accuracy: 0.8511 - val_precision_2: 0.8511 - val_recall_2: 0.8511 - val_f1: 0.8408\n",
+ "Epoch 73/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2405 - accuracy: 0.8996 - precision_2: 0.8996 - recall_2: 0.8996 - f1: 0.8986\n",
+ "Epoch 00073: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2405 - accuracy: 0.8996 - precision_2: 0.8996 - recall_2: 0.8996 - f1: 0.8986 - val_loss: 0.2703 - val_accuracy: 0.8793 - val_precision_2: 0.8793 - val_recall_2: 0.8793 - val_f1: 0.8682\n",
+ "Epoch 74/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2090 - accuracy: 0.9134 - precision_2: 0.9134 - recall_2: 0.9134 - f1: 0.9121\n",
+ "Epoch 00074: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.2090 - accuracy: 0.9134 - precision_2: 0.9134 - recall_2: 0.9134 - f1: 0.9121 - val_loss: 0.3427 - val_accuracy: 0.8592 - val_precision_2: 0.8592 - val_recall_2: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 75/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2228 - accuracy: 0.9110 - precision_2: 0.9110 - recall_2: 0.9110 - f1: 0.9124\n",
+ "Epoch 00075: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2228 - accuracy: 0.9110 - precision_2: 0.9110 - recall_2: 0.9110 - f1: 0.9124 - val_loss: 0.4487 - val_accuracy: 0.8199 - val_precision_2: 0.8199 - val_recall_2: 0.8199 - val_f1: 0.8105\n",
+ "Epoch 76/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2241 - accuracy: 0.9086 - precision_2: 0.9086 - recall_2: 0.9086 - f1: 0.9100\n",
+ "Epoch 00076: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 27s 582ms/step - loss: 0.2241 - accuracy: 0.9086 - precision_2: 0.9086 - recall_2: 0.9086 - f1: 0.9100 - val_loss: 0.4481 - val_accuracy: 0.8169 - val_precision_2: 0.8169 - val_recall_2: 0.8169 - val_f1: 0.8223\n",
+ "Epoch 77/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2404 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9045\n",
+ "Epoch 00077: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.2404 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9045 - val_loss: 0.3399 - val_accuracy: 0.8692 - val_precision_2: 0.8692 - val_recall_2: 0.8692 - val_f1: 0.8730\n",
+ "Epoch 78/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2127 - accuracy: 0.9182 - precision_2: 0.9182 - recall_2: 0.9182 - f1: 0.9178\n",
+ "Epoch 00078: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2127 - accuracy: 0.9182 - precision_2: 0.9182 - recall_2: 0.9182 - f1: 0.9178 - val_loss: 0.2972 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 79/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2277 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9045\n",
+ "Epoch 00079: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.2277 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9045 - val_loss: 0.2949 - val_accuracy: 0.8753 - val_precision_2: 0.8753 - val_recall_2: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 80/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2155 - accuracy: 0.9061 - precision_2: 0.9061 - recall_2: 0.9061 - f1: 0.9050\n",
+ "Epoch 00080: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2155 - accuracy: 0.9061 - precision_2: 0.9061 - recall_2: 0.9061 - f1: 0.9050 - val_loss: 0.3301 - val_accuracy: 0.8692 - val_precision_2: 0.8692 - val_recall_2: 0.8692 - val_f1: 0.8730\n",
+ "Epoch 81/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2392 - accuracy: 0.9013 - precision_2: 0.9013 - recall_2: 0.9013 - f1: 0.9020\n",
+ "Epoch 00081: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2392 - accuracy: 0.9013 - precision_2: 0.9013 - recall_2: 0.9013 - f1: 0.9020 - val_loss: 0.5486 - val_accuracy: 0.8008 - val_precision_2: 0.8008 - val_recall_2: 0.8008 - val_f1: 0.7920\n",
+ "Epoch 82/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2203 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9067\n",
+ "Epoch 00082: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2203 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9067 - val_loss: 0.2993 - val_accuracy: 0.8763 - val_precision_2: 0.8763 - val_recall_2: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 83/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2045 - accuracy: 0.9148 - precision_2: 0.9148 - recall_2: 0.9148 - f1: 0.9161\n",
+ "Epoch 00083: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.2045 - accuracy: 0.9148 - precision_2: 0.9148 - recall_2: 0.9148 - f1: 0.9161 - val_loss: 0.4184 - val_accuracy: 0.8068 - val_precision_2: 0.8068 - val_recall_2: 0.8068 - val_f1: 0.8125\n",
+ "Epoch 84/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2231 - accuracy: 0.9110 - precision_2: 0.9110 - recall_2: 0.9110 - f1: 0.9106\n",
+ "Epoch 00084: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.2231 - accuracy: 0.9110 - precision_2: 0.9110 - recall_2: 0.9110 - f1: 0.9106 - val_loss: 0.2785 - val_accuracy: 0.8843 - val_precision_2: 0.8843 - val_recall_2: 0.8843 - val_f1: 0.8877\n",
+ "Epoch 85/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2054 - accuracy: 0.9148 - precision_2: 0.9148 - recall_2: 0.9148 - f1: 0.9152\n",
+ "Epoch 00085: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.2054 - accuracy: 0.9148 - precision_2: 0.9148 - recall_2: 0.9148 - f1: 0.9152 - val_loss: 0.2826 - val_accuracy: 0.8863 - val_precision_2: 0.8863 - val_recall_2: 0.8863 - val_f1: 0.8896\n",
+ "Epoch 86/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2132 - accuracy: 0.9106 - precision_2: 0.9106 - recall_2: 0.9106 - f1: 0.9112\n",
+ "Epoch 00086: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2132 - accuracy: 0.9106 - precision_2: 0.9106 - recall_2: 0.9106 - f1: 0.9112 - val_loss: 0.5459 - val_accuracy: 0.8219 - val_precision_2: 0.8219 - val_recall_2: 0.8219 - val_f1: 0.8271\n",
+ "Epoch 87/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2203 - accuracy: 0.9113 - precision_2: 0.9113 - recall_2: 0.9113 - f1: 0.9118\n",
+ "Epoch 00087: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.2203 - accuracy: 0.9113 - precision_2: 0.9113 - recall_2: 0.9113 - f1: 0.9118 - val_loss: 0.5444 - val_accuracy: 0.7968 - val_precision_2: 0.7968 - val_recall_2: 0.7968 - val_f1: 0.7881\n",
+ "Epoch 88/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2247 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9036\n",
+ "Epoch 00088: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 27s 588ms/step - loss: 0.2247 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9036 - val_loss: 0.2858 - val_accuracy: 0.8823 - val_precision_2: 0.8823 - val_recall_2: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 89/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2188 - accuracy: 0.9106 - precision_2: 0.9106 - recall_2: 0.9106 - f1: 0.9112\n",
+ "Epoch 00089: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2188 - accuracy: 0.9106 - precision_2: 0.9106 - recall_2: 0.9106 - f1: 0.9112 - val_loss: 0.3813 - val_accuracy: 0.8461 - val_precision_2: 0.8461 - val_recall_2: 0.8461 - val_f1: 0.8506\n",
+ "Epoch 90/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2227 - accuracy: 0.9058 - precision_2: 0.9058 - recall_2: 0.9058 - f1: 0.9055\n",
+ "Epoch 00090: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2227 - accuracy: 0.9058 - precision_2: 0.9058 - recall_2: 0.9058 - f1: 0.9055 - val_loss: 0.4304 - val_accuracy: 0.8390 - val_precision_2: 0.8390 - val_recall_2: 0.8390 - val_f1: 0.8291\n",
+ "Epoch 91/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2070 - accuracy: 0.9141 - precision_2: 0.9141 - recall_2: 0.9141 - f1: 0.9146\n",
+ "Epoch 00091: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.2070 - accuracy: 0.9141 - precision_2: 0.9141 - recall_2: 0.9141 - f1: 0.9146 - val_loss: 0.2852 - val_accuracy: 0.8823 - val_precision_2: 0.8823 - val_recall_2: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 92/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2057 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9118\n",
+ "Epoch 00092: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2057 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9118 - val_loss: 0.4953 - val_accuracy: 0.8209 - val_precision_2: 0.8209 - val_recall_2: 0.8209 - val_f1: 0.8262\n",
+ "Epoch 93/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2167 - accuracy: 0.9072 - precision_2: 0.9072 - recall_2: 0.9072 - f1: 0.9052\n",
+ "Epoch 00093: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 482ms/step - loss: 0.2167 - accuracy: 0.9072 - precision_2: 0.9072 - recall_2: 0.9072 - f1: 0.9052 - val_loss: 0.3353 - val_accuracy: 0.8813 - val_precision_2: 0.8813 - val_recall_2: 0.8813 - val_f1: 0.8701\n",
+ "Epoch 94/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2167 - accuracy: 0.9072 - precision_2: 0.9072 - recall_2: 0.9072 - f1: 0.9078\n",
+ "Epoch 00094: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2167 - accuracy: 0.9072 - precision_2: 0.9072 - recall_2: 0.9072 - f1: 0.9078 - val_loss: 0.5299 - val_accuracy: 0.8089 - val_precision_2: 0.8089 - val_recall_2: 0.8089 - val_f1: 0.8145\n",
+ "Epoch 95/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1957 - accuracy: 0.9199 - precision_2: 0.9199 - recall_2: 0.9199 - f1: 0.9186\n",
+ "Epoch 00095: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1957 - accuracy: 0.9199 - precision_2: 0.9199 - recall_2: 0.9199 - f1: 0.9186 - val_loss: 0.3866 - val_accuracy: 0.8421 - val_precision_2: 0.8421 - val_recall_2: 0.8421 - val_f1: 0.8467\n",
+ "Epoch 96/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2069 - accuracy: 0.9120 - precision_2: 0.9120 - recall_2: 0.9120 - f1: 0.9108\n",
+ "Epoch 00096: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2069 - accuracy: 0.9120 - precision_2: 0.9120 - recall_2: 0.9120 - f1: 0.9108 - val_loss: 0.2783 - val_accuracy: 0.8773 - val_precision_2: 0.8773 - val_recall_2: 0.8773 - val_f1: 0.8809\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 97/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2115 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9144\n",
+ "Epoch 00097: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2115 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9144 - val_loss: 0.7907 - val_accuracy: 0.7193 - val_precision_2: 0.7193 - val_recall_2: 0.7193 - val_f1: 0.7129\n",
+ "Epoch 98/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9127\n",
+ "Epoch 00098: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2136 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9127 - val_loss: 0.3360 - val_accuracy: 0.8722 - val_precision_2: 0.8722 - val_recall_2: 0.8722 - val_f1: 0.8613\n",
+ "Epoch 99/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2077 - accuracy: 0.9082 - precision_2: 0.9082 - recall_2: 0.9082 - f1: 0.9088\n",
+ "Epoch 00099: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 460ms/step - loss: 0.2077 - accuracy: 0.9082 - precision_2: 0.9082 - recall_2: 0.9082 - f1: 0.9088 - val_loss: 0.6549 - val_accuracy: 0.7978 - val_precision_2: 0.7978 - val_recall_2: 0.7978 - val_f1: 0.7891\n",
+ "Epoch 100/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2159 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9050\n",
+ "Epoch 00100: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.2159 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9050 - val_loss: 0.2979 - val_accuracy: 0.8753 - val_precision_2: 0.8753 - val_recall_2: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 101/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2046 - accuracy: 0.9127 - precision_2: 0.9127 - recall_2: 0.9127 - f1: 0.9115\n",
+ "Epoch 00101: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2046 - accuracy: 0.9127 - precision_2: 0.9127 - recall_2: 0.9127 - f1: 0.9115 - val_loss: 0.3770 - val_accuracy: 0.8541 - val_precision_2: 0.8541 - val_recall_2: 0.8541 - val_f1: 0.8437\n",
+ "Epoch 102/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2246 - accuracy: 0.9096 - precision_2: 0.9096 - recall_2: 0.9096 - f1: 0.9058\n",
+ "Epoch 00102: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2246 - accuracy: 0.9096 - precision_2: 0.9096 - recall_2: 0.9096 - f1: 0.9058 - val_loss: 0.3286 - val_accuracy: 0.8672 - val_precision_2: 0.8672 - val_recall_2: 0.8672 - val_f1: 0.8711\n",
+ "Epoch 103/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2060 - accuracy: 0.9144 - precision_2: 0.9144 - recall_2: 0.9144 - f1: 0.9149\n",
+ "Epoch 00103: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2060 - accuracy: 0.9144 - precision_2: 0.9144 - recall_2: 0.9144 - f1: 0.9149 - val_loss: 0.4221 - val_accuracy: 0.8541 - val_precision_2: 0.8541 - val_recall_2: 0.8541 - val_f1: 0.8437\n",
+ "Epoch 104/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2103 - accuracy: 0.9117 - precision_2: 0.9117 - recall_2: 0.9117 - f1: 0.9113\n",
+ "Epoch 00104: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2103 - accuracy: 0.9117 - precision_2: 0.9117 - recall_2: 0.9117 - f1: 0.9113 - val_loss: 0.2744 - val_accuracy: 0.8994 - val_precision_2: 0.8994 - val_recall_2: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 105/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1924 - accuracy: 0.9206 - precision_2: 0.9206 - recall_2: 0.9206 - f1: 0.9201\n",
+ "Epoch 00105: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.1924 - accuracy: 0.9206 - precision_2: 0.9206 - recall_2: 0.9206 - f1: 0.9201 - val_loss: 0.5658 - val_accuracy: 0.8350 - val_precision_2: 0.8350 - val_recall_2: 0.8350 - val_f1: 0.8398\n",
+ "Epoch 106/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2031 - accuracy: 0.9134 - precision_2: 0.9134 - recall_2: 0.9134 - f1: 0.9139\n",
+ "Epoch 00106: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2031 - accuracy: 0.9134 - precision_2: 0.9134 - recall_2: 0.9134 - f1: 0.9139 - val_loss: 0.3107 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 107/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1962 - accuracy: 0.9151 - precision_2: 0.9151 - recall_2: 0.9151 - f1: 0.9147\n",
+ "Epoch 00107: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.1962 - accuracy: 0.9151 - precision_2: 0.9151 - recall_2: 0.9151 - f1: 0.9147 - val_loss: 0.2658 - val_accuracy: 0.8964 - val_precision_2: 0.8964 - val_recall_2: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 108/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2035 - accuracy: 0.9155 - precision_2: 0.9155 - recall_2: 0.9155 - f1: 0.9133\n",
+ "Epoch 00108: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.2035 - accuracy: 0.9155 - precision_2: 0.9155 - recall_2: 0.9155 - f1: 0.9133 - val_loss: 0.2677 - val_accuracy: 0.8893 - val_precision_2: 0.8893 - val_recall_2: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 109/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2009 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9172\n",
+ "Epoch 00109: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2009 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9172 - val_loss: 0.3199 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 110/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.9158 - precision_2: 0.9158 - recall_2: 0.9158 - f1: 0.9136\n",
+ "Epoch 00110: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 462ms/step - loss: 0.2030 - accuracy: 0.9158 - precision_2: 0.9158 - recall_2: 0.9158 - f1: 0.9136 - val_loss: 0.4047 - val_accuracy: 0.8561 - val_precision_2: 0.8561 - val_recall_2: 0.8561 - val_f1: 0.8604\n",
+ "Epoch 111/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.9144 - precision_2: 0.9144 - recall_2: 0.9144 - f1: 0.9140\n",
+ "Epoch 00111: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.1983 - accuracy: 0.9144 - precision_2: 0.9144 - recall_2: 0.9144 - f1: 0.9140 - val_loss: 0.2880 - val_accuracy: 0.8763 - val_precision_2: 0.8763 - val_recall_2: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 112/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2060 - accuracy: 0.9124 - precision_2: 0.9124 - recall_2: 0.9124 - f1: 0.9111\n",
+ "Epoch 00112: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2060 - accuracy: 0.9124 - precision_2: 0.9124 - recall_2: 0.9124 - f1: 0.9111 - val_loss: 0.2879 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 113/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2095 - accuracy: 0.9106 - precision_2: 0.9106 - recall_2: 0.9106 - f1: 0.9094\n",
+ "Epoch 00113: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.2095 - accuracy: 0.9106 - precision_2: 0.9106 - recall_2: 0.9106 - f1: 0.9094 - val_loss: 0.4270 - val_accuracy: 0.8229 - val_precision_2: 0.8229 - val_recall_2: 0.8229 - val_f1: 0.8281\n",
+ "Epoch 114/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2137 - accuracy: 0.9155 - precision_2: 0.9155 - recall_2: 0.9155 - f1: 0.9150\n",
+ "Epoch 00114: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2137 - accuracy: 0.9155 - precision_2: 0.9155 - recall_2: 0.9155 - f1: 0.9150 - val_loss: 0.2558 - val_accuracy: 0.9024 - val_precision_2: 0.9024 - val_recall_2: 0.9024 - val_f1: 0.8906\n",
+ "Epoch 115/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1832 - accuracy: 0.9282 - precision_2: 0.9282 - recall_2: 0.9282 - f1: 0.9285\n",
+ "Epoch 00115: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.1832 - accuracy: 0.9282 - precision_2: 0.9282 - recall_2: 0.9282 - f1: 0.9285 - val_loss: 0.3715 - val_accuracy: 0.8521 - val_precision_2: 0.8521 - val_recall_2: 0.8521 - val_f1: 0.8564\n",
+ "Epoch 116/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1936 - accuracy: 0.9203 - precision_2: 0.9203 - recall_2: 0.9203 - f1: 0.9207\n",
+ "Epoch 00116: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 473ms/step - loss: 0.1936 - accuracy: 0.9203 - precision_2: 0.9203 - recall_2: 0.9203 - f1: 0.9207 - val_loss: 0.2924 - val_accuracy: 0.8773 - val_precision_2: 0.8773 - val_recall_2: 0.8773 - val_f1: 0.8809\n",
+ "Epoch 117/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1844 - accuracy: 0.9224 - precision_2: 0.9224 - recall_2: 0.9224 - f1: 0.9218\n",
+ "Epoch 00117: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1844 - accuracy: 0.9224 - precision_2: 0.9224 - recall_2: 0.9224 - f1: 0.9218 - val_loss: 0.2837 - val_accuracy: 0.8974 - val_precision_2: 0.8974 - val_recall_2: 0.8974 - val_f1: 0.8857\n",
+ "Epoch 118/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1862 - accuracy: 0.9217 - precision_2: 0.9217 - recall_2: 0.9217 - f1: 0.9212\n",
+ "Epoch 00118: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1862 - accuracy: 0.9217 - precision_2: 0.9217 - recall_2: 0.9217 - f1: 0.9212 - val_loss: 0.4298 - val_accuracy: 0.8511 - val_precision_2: 0.8511 - val_recall_2: 0.8511 - val_f1: 0.8555\n",
+ "Epoch 119/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1997 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9164\n",
+ "Epoch 00119: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1997 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9164 - val_loss: 0.3250 - val_accuracy: 0.8531 - val_precision_2: 0.8531 - val_recall_2: 0.8531 - val_f1: 0.8428\n",
+ "Epoch 120/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1961 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9196\n",
+ "Epoch 00120: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.1961 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9196 - val_loss: 0.6045 - val_accuracy: 0.7928 - val_precision_2: 0.7928 - val_recall_2: 0.7928 - val_f1: 0.7988\n",
+ "Epoch 121/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1913 - accuracy: 0.9227 - precision_2: 0.9227 - recall_2: 0.9227 - f1: 0.9230\n",
+ "Epoch 00121: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 28s 609ms/step - loss: 0.1913 - accuracy: 0.9227 - precision_2: 0.9227 - recall_2: 0.9227 - f1: 0.9230 - val_loss: 0.2452 - val_accuracy: 0.8994 - val_precision_2: 0.8994 - val_recall_2: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 122/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1823 - accuracy: 0.9265 - precision_2: 0.9265 - recall_2: 0.9265 - f1: 0.9259\n",
+ "Epoch 00122: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1823 - accuracy: 0.9265 - precision_2: 0.9265 - recall_2: 0.9265 - f1: 0.9259 - val_loss: 0.2750 - val_accuracy: 0.8893 - val_precision_2: 0.8893 - val_recall_2: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 123/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1755 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9295\n",
+ "Epoch 00123: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.1755 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9295 - val_loss: 0.2619 - val_accuracy: 0.9004 - val_precision_2: 0.9004 - val_recall_2: 0.9004 - val_f1: 0.9033\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 124/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1776 - accuracy: 0.9293 - precision_2: 0.9293 - recall_2: 0.9293 - f1: 0.9278\n",
+ "Epoch 00124: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1776 - accuracy: 0.9293 - precision_2: 0.9293 - recall_2: 0.9293 - f1: 0.9278 - val_loss: 0.4234 - val_accuracy: 0.8541 - val_precision_2: 0.8541 - val_recall_2: 0.8541 - val_f1: 0.8584\n",
+ "Epoch 125/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1850 - accuracy: 0.9251 - precision_2: 0.9251 - recall_2: 0.9251 - f1: 0.9263\n",
+ "Epoch 00125: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1850 - accuracy: 0.9251 - precision_2: 0.9251 - recall_2: 0.9251 - f1: 0.9263 - val_loss: 0.3985 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8496\n",
+ "Epoch 126/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1917 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9253\n",
+ "Epoch 00126: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1917 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9253 - val_loss: 0.2955 - val_accuracy: 0.8863 - val_precision_2: 0.8863 - val_recall_2: 0.8863 - val_f1: 0.8896\n",
+ "Epoch 127/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.9161 - precision_2: 0.9161 - recall_2: 0.9161 - f1: 0.9166\n",
+ "Epoch 00127: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 462ms/step - loss: 0.2052 - accuracy: 0.9161 - precision_2: 0.9161 - recall_2: 0.9161 - f1: 0.9166 - val_loss: 0.7648 - val_accuracy: 0.7425 - val_precision_2: 0.7425 - val_recall_2: 0.7425 - val_f1: 0.7354\n",
+ "Epoch 128/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1733 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9315\n",
+ "Epoch 00128: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1733 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9315 - val_loss: 0.2465 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 129/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1843 - accuracy: 0.9262 - precision_2: 0.9262 - recall_2: 0.9262 - f1: 0.9273\n",
+ "Epoch 00129: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 474ms/step - loss: 0.1843 - accuracy: 0.9262 - precision_2: 0.9262 - recall_2: 0.9262 - f1: 0.9273 - val_loss: 0.2855 - val_accuracy: 0.9044 - val_precision_2: 0.9044 - val_recall_2: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 130/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1776 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9261\n",
+ "Epoch 00130: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1776 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9261 - val_loss: 0.4033 - val_accuracy: 0.8622 - val_precision_2: 0.8622 - val_recall_2: 0.8622 - val_f1: 0.8662\n",
+ "Epoch 131/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1816 - accuracy: 0.9275 - precision_2: 0.9275 - recall_2: 0.9275 - f1: 0.9269\n",
+ "Epoch 00131: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 27s 587ms/step - loss: 0.1816 - accuracy: 0.9275 - precision_2: 0.9275 - recall_2: 0.9275 - f1: 0.9269 - val_loss: 0.5864 - val_accuracy: 0.8038 - val_precision_2: 0.8038 - val_recall_2: 0.8038 - val_f1: 0.8096\n",
+ "Epoch 132/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1906 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9179\n",
+ "Epoch 00132: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 28s 606ms/step - loss: 0.1906 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9179 - val_loss: 0.3849 - val_accuracy: 0.8551 - val_precision_2: 0.8551 - val_recall_2: 0.8551 - val_f1: 0.8301\n",
+ "Epoch 133/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2096 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9092\n",
+ "Epoch 00133: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 466ms/step - loss: 0.2096 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9092 - val_loss: 0.4237 - val_accuracy: 0.8471 - val_precision_2: 0.8471 - val_recall_2: 0.8471 - val_f1: 0.8516\n",
+ "Epoch 134/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.1785 - accuracy: 0.9250 - precision_2: 0.9250 - recall_2: 0.9250 - f1: 0.9250\n",
+ "Epoch 00134: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1784 - accuracy: 0.9248 - precision_2: 0.9248 - recall_2: 0.9248 - f1: 0.9242 - val_loss: 0.3228 - val_accuracy: 0.8793 - val_precision_2: 0.8793 - val_recall_2: 0.8793 - val_f1: 0.8682\n",
+ "Epoch 135/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1919 - accuracy: 0.9234 - precision_2: 0.9234 - recall_2: 0.9234 - f1: 0.9246\n",
+ "Epoch 00135: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1919 - accuracy: 0.9234 - precision_2: 0.9234 - recall_2: 0.9234 - f1: 0.9246 - val_loss: 0.2818 - val_accuracy: 0.8954 - val_precision_2: 0.8954 - val_recall_2: 0.8954 - val_f1: 0.8984\n",
+ "Epoch 136/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1731 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9273\n",
+ "Epoch 00136: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 457ms/step - loss: 0.1731 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9273 - val_loss: 0.8052 - val_accuracy: 0.7726 - val_precision_2: 0.7726 - val_recall_2: 0.7726 - val_f1: 0.7793\n",
+ "Epoch 137/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1791 - accuracy: 0.9244 - precision_2: 0.9244 - recall_2: 0.9244 - f1: 0.9221\n",
+ "Epoch 00137: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.1791 - accuracy: 0.9244 - precision_2: 0.9244 - recall_2: 0.9244 - f1: 0.9221 - val_loss: 0.2622 - val_accuracy: 0.9135 - val_precision_2: 0.9135 - val_recall_2: 0.9135 - val_f1: 0.9014\n",
+ "Epoch 138/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1973 - accuracy: 0.9196 - precision_2: 0.9196 - recall_2: 0.9196 - f1: 0.9183\n",
+ "Epoch 00138: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1973 - accuracy: 0.9196 - precision_2: 0.9196 - recall_2: 0.9196 - f1: 0.9183 - val_loss: 0.2593 - val_accuracy: 0.8934 - val_precision_2: 0.8934 - val_recall_2: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 139/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1689 - accuracy: 0.9317 - precision_2: 0.9317 - recall_2: 0.9317 - f1: 0.9327\n",
+ "Epoch 00139: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 470ms/step - loss: 0.1689 - accuracy: 0.9317 - precision_2: 0.9317 - recall_2: 0.9317 - f1: 0.9327 - val_loss: 0.2656 - val_accuracy: 0.9085 - val_precision_2: 0.9085 - val_recall_2: 0.9085 - val_f1: 0.9111\n",
+ "Epoch 140/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1777 - accuracy: 0.9282 - precision_2: 0.9282 - recall_2: 0.9282 - f1: 0.9285\n",
+ "Epoch 00140: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1777 - accuracy: 0.9282 - precision_2: 0.9282 - recall_2: 0.9282 - f1: 0.9285 - val_loss: 0.6223 - val_accuracy: 0.8028 - val_precision_2: 0.8028 - val_recall_2: 0.8028 - val_f1: 0.7939\n",
+ "Epoch 141/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1675 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9272\n",
+ "Epoch 00141: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1675 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9272 - val_loss: 0.3977 - val_accuracy: 0.8682 - val_precision_2: 0.8682 - val_recall_2: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 142/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1558 - accuracy: 0.9417 - precision_2: 0.9417 - recall_2: 0.9417 - f1: 0.9426\n",
+ "Epoch 00142: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 424ms/step - loss: 0.1558 - accuracy: 0.9417 - precision_2: 0.9417 - recall_2: 0.9417 - f1: 0.9426 - val_loss: 0.2671 - val_accuracy: 0.8863 - val_precision_2: 0.8863 - val_recall_2: 0.8863 - val_f1: 0.8896\n",
+ "Epoch 143/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1742 - accuracy: 0.9296 - precision_2: 0.9296 - recall_2: 0.9296 - f1: 0.9298\n",
+ "Epoch 00143: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1742 - accuracy: 0.9296 - precision_2: 0.9296 - recall_2: 0.9296 - f1: 0.9298 - val_loss: 0.2351 - val_accuracy: 0.9135 - val_precision_2: 0.9135 - val_recall_2: 0.9135 - val_f1: 0.9160\n",
+ "Epoch 144/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1759 - accuracy: 0.9272 - precision_2: 0.9272 - recall_2: 0.9272 - f1: 0.9249\n",
+ "Epoch 00144: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 454ms/step - loss: 0.1759 - accuracy: 0.9272 - precision_2: 0.9272 - recall_2: 0.9272 - f1: 0.9249 - val_loss: 0.3530 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8604\n",
+ "Epoch 145/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1797 - accuracy: 0.9275 - precision_2: 0.9275 - recall_2: 0.9275 - f1: 0.9269\n",
+ "Epoch 00145: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.1797 - accuracy: 0.9275 - precision_2: 0.9275 - recall_2: 0.9275 - f1: 0.9269 - val_loss: 0.3564 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8643\n",
+ "Epoch 146/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1854 - accuracy: 0.9196 - precision_2: 0.9196 - recall_2: 0.9196 - f1: 0.9209\n",
+ "Epoch 00146: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.1854 - accuracy: 0.9196 - precision_2: 0.9196 - recall_2: 0.9196 - f1: 0.9209 - val_loss: 0.2474 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 147/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1728 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9281\n",
+ "Epoch 00147: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.1728 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9281 - val_loss: 0.2327 - val_accuracy: 0.9054 - val_precision_2: 0.9054 - val_recall_2: 0.9054 - val_f1: 0.8936\n",
+ "Epoch 148/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1739 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9315\n",
+ "Epoch 00148: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 25s 540ms/step - loss: 0.1739 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9315 - val_loss: 0.3198 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8604\n",
+ "Epoch 149/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1846 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9253\n",
+ "Epoch 00149: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1846 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9253 - val_loss: 0.4104 - val_accuracy: 0.8441 - val_precision_2: 0.8441 - val_recall_2: 0.8441 - val_f1: 0.8486\n",
+ "Epoch 150/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1740 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9295\n",
+ "Epoch 00150: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 484ms/step - loss: 0.1740 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9295 - val_loss: 0.2581 - val_accuracy: 0.9024 - val_precision_2: 0.9024 - val_recall_2: 0.9024 - val_f1: 0.8906\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 151/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1657 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9315\n",
+ "Epoch 00151: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1657 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9315 - val_loss: 0.2649 - val_accuracy: 0.8964 - val_precision_2: 0.8964 - val_recall_2: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 152/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1723 - accuracy: 0.9262 - precision_2: 0.9262 - recall_2: 0.9262 - f1: 0.9273\n",
+ "Epoch 00152: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1723 - accuracy: 0.9262 - precision_2: 0.9262 - recall_2: 0.9262 - f1: 0.9273 - val_loss: 0.2666 - val_accuracy: 0.8964 - val_precision_2: 0.8964 - val_recall_2: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 153/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1695 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9356\n",
+ "Epoch 00153: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 439ms/step - loss: 0.1695 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9356 - val_loss: 0.2568 - val_accuracy: 0.9004 - val_precision_2: 0.9004 - val_recall_2: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 154/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1777 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9261\n",
+ "Epoch 00154: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1777 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9261 - val_loss: 0.2595 - val_accuracy: 0.8934 - val_precision_2: 0.8934 - val_recall_2: 0.8934 - val_f1: 0.8818\n",
+ "Epoch 155/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1804 - accuracy: 0.9293 - precision_2: 0.9293 - recall_2: 0.9293 - f1: 0.9304\n",
+ "Epoch 00155: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1804 - accuracy: 0.9293 - precision_2: 0.9293 - recall_2: 0.9293 - f1: 0.9304 - val_loss: 0.3570 - val_accuracy: 0.8642 - val_precision_2: 0.8642 - val_recall_2: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 156/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1651 - accuracy: 0.9386 - precision_2: 0.9386 - recall_2: 0.9386 - f1: 0.9387\n",
+ "Epoch 00156: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 467ms/step - loss: 0.1651 - accuracy: 0.9386 - precision_2: 0.9386 - recall_2: 0.9386 - f1: 0.9387 - val_loss: 0.3881 - val_accuracy: 0.8561 - val_precision_2: 0.8561 - val_recall_2: 0.8561 - val_f1: 0.8604\n",
+ "Epoch 157/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1719 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9244\n",
+ "Epoch 00157: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.1719 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9244 - val_loss: 0.2590 - val_accuracy: 0.8984 - val_precision_2: 0.8984 - val_recall_2: 0.8984 - val_f1: 0.9014\n",
+ "Epoch 158/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1693 - accuracy: 0.9282 - precision_2: 0.9282 - recall_2: 0.9282 - f1: 0.9276\n",
+ "Epoch 00158: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.1693 - accuracy: 0.9282 - precision_2: 0.9282 - recall_2: 0.9282 - f1: 0.9276 - val_loss: 0.2323 - val_accuracy: 0.9014 - val_precision_2: 0.9014 - val_recall_2: 0.9014 - val_f1: 0.9043\n",
+ "Epoch 159/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1748 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332\n",
+ "Epoch 00159: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1748 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332 - val_loss: 0.2546 - val_accuracy: 0.8944 - val_precision_2: 0.8944 - val_recall_2: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 160/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1728 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332\n",
+ "Epoch 00160: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 446ms/step - loss: 0.1728 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332 - val_loss: 0.4796 - val_accuracy: 0.8461 - val_precision_2: 0.8461 - val_recall_2: 0.8461 - val_f1: 0.8359\n",
+ "Epoch 161/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1712 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9312\n",
+ "Epoch 00161: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.1712 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9312 - val_loss: 0.2751 - val_accuracy: 0.8944 - val_precision_2: 0.8944 - val_recall_2: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 162/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1526 - accuracy: 0.9417 - precision_2: 0.9417 - recall_2: 0.9417 - f1: 0.9426\n",
+ "Epoch 00162: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 468ms/step - loss: 0.1526 - accuracy: 0.9417 - precision_2: 0.9417 - recall_2: 0.9417 - f1: 0.9426 - val_loss: 0.2736 - val_accuracy: 0.8863 - val_precision_2: 0.8863 - val_recall_2: 0.8863 - val_f1: 0.8896\n",
+ "Epoch 163/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1653 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332\n",
+ "Epoch 00163: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.1653 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332 - val_loss: 0.2269 - val_accuracy: 0.9145 - val_precision_2: 0.9145 - val_recall_2: 0.9145 - val_f1: 0.9170\n",
+ "Epoch 164/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1585 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9365\n",
+ "Epoch 00164: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.1585 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9365 - val_loss: 0.2172 - val_accuracy: 0.9205 - val_precision_2: 0.9205 - val_recall_2: 0.9205 - val_f1: 0.9229\n",
+ "Epoch 165/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9347\n",
+ "Epoch 00165: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1630 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9347 - val_loss: 0.3311 - val_accuracy: 0.8732 - val_precision_2: 0.8732 - val_recall_2: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 166/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1610 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9303\n",
+ "Epoch 00166: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1610 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9303 - val_loss: 0.7710 - val_accuracy: 0.7907 - val_precision_2: 0.7907 - val_recall_2: 0.7907 - val_f1: 0.7969\n",
+ "Epoch 167/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1838 - accuracy: 0.9220 - precision_2: 0.9220 - recall_2: 0.9220 - f1: 0.9189\n",
+ "Epoch 00167: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1838 - accuracy: 0.9220 - precision_2: 0.9220 - recall_2: 0.9220 - f1: 0.9189 - val_loss: 0.2204 - val_accuracy: 0.9095 - val_precision_2: 0.9095 - val_recall_2: 0.9095 - val_f1: 0.9121\n",
+ "Epoch 168/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1628 - accuracy: 0.9369 - precision_2: 0.9369 - recall_2: 0.9369 - f1: 0.9344\n",
+ "Epoch 00168: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 472ms/step - loss: 0.1628 - accuracy: 0.9369 - precision_2: 0.9369 - recall_2: 0.9369 - f1: 0.9344 - val_loss: 0.2376 - val_accuracy: 0.9085 - val_precision_2: 0.9085 - val_recall_2: 0.9085 - val_f1: 0.9111\n",
+ "Epoch 169/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1556 - accuracy: 0.9365 - precision_2: 0.9365 - recall_2: 0.9365 - f1: 0.9358\n",
+ "Epoch 00169: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1556 - accuracy: 0.9365 - precision_2: 0.9365 - recall_2: 0.9365 - f1: 0.9358 - val_loss: 0.2589 - val_accuracy: 0.9115 - val_precision_2: 0.9115 - val_recall_2: 0.9115 - val_f1: 0.9141\n",
+ "Epoch 170/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1629 - accuracy: 0.9400 - precision_2: 0.9400 - recall_2: 0.9400 - f1: 0.9400\n",
+ "Epoch 00170: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1629 - accuracy: 0.9400 - precision_2: 0.9400 - recall_2: 0.9400 - f1: 0.9400 - val_loss: 0.2950 - val_accuracy: 0.8863 - val_precision_2: 0.8863 - val_recall_2: 0.8863 - val_f1: 0.8896\n",
+ "Epoch 171/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1787 - accuracy: 0.9289 - precision_2: 0.9289 - recall_2: 0.9289 - f1: 0.9248\n",
+ "Epoch 00171: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1787 - accuracy: 0.9289 - precision_2: 0.9289 - recall_2: 0.9289 - f1: 0.9248 - val_loss: 0.2236 - val_accuracy: 0.9145 - val_precision_2: 0.9145 - val_recall_2: 0.9145 - val_f1: 0.9170\n",
+ "Epoch 172/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9351 - precision_2: 0.9351 - recall_2: 0.9351 - f1: 0.9344\n",
+ "Epoch 00172: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 443ms/step - loss: 0.1562 - accuracy: 0.9351 - precision_2: 0.9351 - recall_2: 0.9351 - f1: 0.9344 - val_loss: 0.3758 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8525\n",
+ "Epoch 173/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1595 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9351\n",
+ "Epoch 00173: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 460ms/step - loss: 0.1595 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9351 - val_loss: 0.3461 - val_accuracy: 0.8934 - val_precision_2: 0.8934 - val_recall_2: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 174/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1465 - accuracy: 0.9395 - precision_2: 0.9395 - recall_2: 0.9395 - f1: 0.9395\n",
+ "Epoch 00174: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.1465 - accuracy: 0.9395 - precision_2: 0.9395 - recall_2: 0.9395 - f1: 0.9395 - val_loss: 0.2178 - val_accuracy: 0.9135 - val_precision_2: 0.9135 - val_recall_2: 0.9135 - val_f1: 0.9014\n",
+ "Epoch 175/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1640 - accuracy: 0.9379 - precision_2: 0.9379 - recall_2: 0.9379 - f1: 0.9380\n",
+ "Epoch 00175: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.1640 - accuracy: 0.9379 - precision_2: 0.9379 - recall_2: 0.9379 - f1: 0.9380 - val_loss: 0.2039 - val_accuracy: 0.9205 - val_precision_2: 0.9205 - val_recall_2: 0.9205 - val_f1: 0.9229\n",
+ "Epoch 176/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1799 - accuracy: 0.9217 - precision_2: 0.9217 - recall_2: 0.9217 - f1: 0.9203\n",
+ "Epoch 00176: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1799 - accuracy: 0.9217 - precision_2: 0.9217 - recall_2: 0.9217 - f1: 0.9203 - val_loss: 0.2288 - val_accuracy: 0.9074 - val_precision_2: 0.9074 - val_recall_2: 0.9074 - val_f1: 0.8955\n",
+ "Epoch 177/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1521 - accuracy: 0.9406 - precision_2: 0.9406 - recall_2: 0.9406 - f1: 0.9398\n",
+ "Epoch 00177: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1521 - accuracy: 0.9406 - precision_2: 0.9406 - recall_2: 0.9406 - f1: 0.9398 - val_loss: 0.2492 - val_accuracy: 0.9115 - val_precision_2: 0.9115 - val_recall_2: 0.9115 - val_f1: 0.9141\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 178/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1445 - accuracy: 0.9448 - precision_2: 0.9448 - recall_2: 0.9448 - f1: 0.9448\n",
+ "Epoch 00178: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 495ms/step - loss: 0.1445 - accuracy: 0.9448 - precision_2: 0.9448 - recall_2: 0.9448 - f1: 0.9448 - val_loss: 0.3406 - val_accuracy: 0.8924 - val_precision_2: 0.8924 - val_recall_2: 0.8924 - val_f1: 0.8809\n",
+ "Epoch 179/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1593 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9324\n",
+ "Epoch 00179: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 464ms/step - loss: 0.1593 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9324 - val_loss: 0.2983 - val_accuracy: 0.8994 - val_precision_2: 0.8994 - val_recall_2: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 180/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1570 - accuracy: 0.9369 - precision_2: 0.9369 - recall_2: 0.9369 - f1: 0.9361\n",
+ "Epoch 00180: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.1570 - accuracy: 0.9369 - precision_2: 0.9369 - recall_2: 0.9369 - f1: 0.9361 - val_loss: 0.2272 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 181/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9375 - precision_2: 0.9375 - recall_2: 0.9375 - f1: 0.9359\n",
+ "Epoch 00181: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 445ms/step - loss: 0.1630 - accuracy: 0.9375 - precision_2: 0.9375 - recall_2: 0.9375 - f1: 0.9359 - val_loss: 0.2194 - val_accuracy: 0.9256 - val_precision_2: 0.9256 - val_recall_2: 0.9256 - val_f1: 0.9277\n",
+ "Epoch 182/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1725 - accuracy: 0.9300 - precision_2: 0.9300 - recall_2: 0.9300 - f1: 0.9276\n",
+ "Epoch 00182: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.1725 - accuracy: 0.9300 - precision_2: 0.9300 - recall_2: 0.9300 - f1: 0.9276 - val_loss: 0.2454 - val_accuracy: 0.9225 - val_precision_2: 0.9225 - val_recall_2: 0.9225 - val_f1: 0.9102\n",
+ "Epoch 183/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1472 - accuracy: 0.9393 - precision_2: 0.9393 - recall_2: 0.9393 - f1: 0.9385\n",
+ "Epoch 00183: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.1472 - accuracy: 0.9393 - precision_2: 0.9393 - recall_2: 0.9393 - f1: 0.9385 - val_loss: 0.2120 - val_accuracy: 0.9145 - val_precision_2: 0.9145 - val_recall_2: 0.9145 - val_f1: 0.9170\n",
+ "Epoch 184/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1587 - accuracy: 0.9365 - precision_2: 0.9365 - recall_2: 0.9365 - f1: 0.9366\n",
+ "Epoch 00184: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1587 - accuracy: 0.9365 - precision_2: 0.9365 - recall_2: 0.9365 - f1: 0.9366 - val_loss: 0.3151 - val_accuracy: 0.8853 - val_precision_2: 0.8853 - val_recall_2: 0.8853 - val_f1: 0.8740\n",
+ "Epoch 185/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1501 - accuracy: 0.9403 - precision_2: 0.9403 - recall_2: 0.9403 - f1: 0.9404\n",
+ "Epoch 00185: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 479ms/step - loss: 0.1501 - accuracy: 0.9403 - precision_2: 0.9403 - recall_2: 0.9403 - f1: 0.9404 - val_loss: 0.2309 - val_accuracy: 0.9034 - val_precision_2: 0.9034 - val_recall_2: 0.9034 - val_f1: 0.9062\n",
+ "Epoch 186/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1494 - accuracy: 0.9424 - precision_2: 0.9424 - recall_2: 0.9424 - f1: 0.9407\n",
+ "Epoch 00186: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1494 - accuracy: 0.9424 - precision_2: 0.9424 - recall_2: 0.9424 - f1: 0.9407 - val_loss: 0.3604 - val_accuracy: 0.8883 - val_precision_2: 0.8883 - val_recall_2: 0.8883 - val_f1: 0.8623\n",
+ "Epoch 187/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1586 - accuracy: 0.9351 - precision_2: 0.9351 - recall_2: 0.9351 - f1: 0.9353\n",
+ "Epoch 00187: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1586 - accuracy: 0.9351 - precision_2: 0.9351 - recall_2: 0.9351 - f1: 0.9353 - val_loss: 0.2565 - val_accuracy: 0.9044 - val_precision_2: 0.9044 - val_recall_2: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 188/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1371 - accuracy: 0.9403 - precision_2: 0.9403 - recall_2: 0.9403 - f1: 0.9404\n",
+ "Epoch 00188: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.1371 - accuracy: 0.9403 - precision_2: 0.9403 - recall_2: 0.9403 - f1: 0.9404 - val_loss: 0.3093 - val_accuracy: 0.8974 - val_precision_2: 0.8974 - val_recall_2: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 189/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1488 - accuracy: 0.9406 - precision_2: 0.9406 - recall_2: 0.9406 - f1: 0.9398\n",
+ "Epoch 00189: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1488 - accuracy: 0.9406 - precision_2: 0.9406 - recall_2: 0.9406 - f1: 0.9398 - val_loss: 0.2287 - val_accuracy: 0.9105 - val_precision_2: 0.9105 - val_recall_2: 0.9105 - val_f1: 0.8984\n",
+ "Epoch 190/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9343\n",
+ "Epoch 00190: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1562 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9343 - val_loss: 0.2323 - val_accuracy: 0.9165 - val_precision_2: 0.9165 - val_recall_2: 0.9165 - val_f1: 0.9189\n",
+ "Epoch 191/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1591 - accuracy: 0.9375 - precision_2: 0.9375 - recall_2: 0.9375 - f1: 0.9368\n",
+ "Epoch 00191: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 25s 550ms/step - loss: 0.1591 - accuracy: 0.9375 - precision_2: 0.9375 - recall_2: 0.9375 - f1: 0.9368 - val_loss: 0.2971 - val_accuracy: 0.8893 - val_precision_2: 0.8893 - val_recall_2: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 192/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1474 - accuracy: 0.9410 - precision_2: 0.9410 - recall_2: 0.9410 - f1: 0.9410\n",
+ "Epoch 00192: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1474 - accuracy: 0.9410 - precision_2: 0.9410 - recall_2: 0.9410 - f1: 0.9410 - val_loss: 0.4500 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 193/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1330 - accuracy: 0.9479 - precision_2: 0.9479 - recall_2: 0.9479 - f1: 0.9470\n",
+ "Epoch 00193: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1330 - accuracy: 0.9479 - precision_2: 0.9479 - recall_2: 0.9479 - f1: 0.9470 - val_loss: 0.2238 - val_accuracy: 0.9185 - val_precision_2: 0.9185 - val_recall_2: 0.9185 - val_f1: 0.9209\n",
+ "Epoch 194/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1374 - accuracy: 0.9362 - precision_2: 0.9362 - recall_2: 0.9362 - f1: 0.9354\n",
+ "Epoch 00194: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.1374 - accuracy: 0.9362 - precision_2: 0.9362 - recall_2: 0.9362 - f1: 0.9354 - val_loss: 0.2436 - val_accuracy: 0.9235 - val_precision_2: 0.9235 - val_recall_2: 0.9235 - val_f1: 0.9258\n",
+ "Epoch 195/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1347 - accuracy: 0.9441 - precision_2: 0.9441 - recall_2: 0.9441 - f1: 0.9415\n",
+ "Epoch 00195: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.1347 - accuracy: 0.9441 - precision_2: 0.9441 - recall_2: 0.9441 - f1: 0.9415 - val_loss: 0.2488 - val_accuracy: 0.9115 - val_precision_2: 0.9115 - val_recall_2: 0.9115 - val_f1: 0.8994\n",
+ "Epoch 196/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1469 - accuracy: 0.9455 - precision_2: 0.9455 - recall_2: 0.9455 - f1: 0.9446\n",
+ "Epoch 00196: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1469 - accuracy: 0.9455 - precision_2: 0.9455 - recall_2: 0.9455 - f1: 0.9446 - val_loss: 0.2758 - val_accuracy: 0.8974 - val_precision_2: 0.8974 - val_recall_2: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 197/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1314 - accuracy: 0.9462 - precision_2: 0.9462 - recall_2: 0.9462 - f1: 0.9461\n",
+ "Epoch 00197: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 24s 525ms/step - loss: 0.1314 - accuracy: 0.9462 - precision_2: 0.9462 - recall_2: 0.9462 - f1: 0.9461 - val_loss: 0.2730 - val_accuracy: 0.8893 - val_precision_2: 0.8893 - val_recall_2: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 198/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1444 - accuracy: 0.9431 - precision_2: 0.9431 - recall_2: 0.9431 - f1: 0.9431\n",
+ "Epoch 00198: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.1444 - accuracy: 0.9431 - precision_2: 0.9431 - recall_2: 0.9431 - f1: 0.9431 - val_loss: 0.2191 - val_accuracy: 0.9165 - val_precision_2: 0.9165 - val_recall_2: 0.9165 - val_f1: 0.9189\n",
+ "Epoch 199/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1520 - accuracy: 0.9400 - precision_2: 0.9400 - recall_2: 0.9400 - f1: 0.9400\n",
+ "Epoch 00199: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.1520 - accuracy: 0.9400 - precision_2: 0.9400 - recall_2: 0.9400 - f1: 0.9400 - val_loss: 0.2470 - val_accuracy: 0.9054 - val_precision_2: 0.9054 - val_recall_2: 0.9054 - val_f1: 0.9082\n",
+ "Epoch 200/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1399 - accuracy: 0.9493 - precision_2: 0.9493 - recall_2: 0.9493 - f1: 0.9483\n",
+ "Epoch 00200: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I2orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1399 - accuracy: 0.9493 - precision_2: 0.9493 - recall_2: 0.9493 - f1: 0.9483 - val_loss: 0.2267 - val_accuracy: 0.9085 - val_precision_2: 0.9085 - val_recall_2: 0.9085 - val_f1: 0.9111\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/preloaded/madeModels/OGRUN_I3/assets\n",
+ "Epoch 1/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.9874 - accuracy: 0.6087 - precision_3: 0.6087 - recall_3: 0.6087 - f1: 0.6105\n",
+ "Epoch 00001: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 465ms/step - loss: 0.9874 - accuracy: 0.6087 - precision_3: 0.6087 - recall_3: 0.6087 - f1: 0.6105 - val_loss: 0.6464 - val_accuracy: 0.6509 - val_precision_3: 0.6509 - val_recall_3: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 2/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.6168 - accuracy: 0.7029 - precision_3: 0.7029 - recall_3: 0.7029 - f1: 0.7015\n",
+ "Epoch 00002: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.6168 - accuracy: 0.7029 - precision_3: 0.7029 - recall_3: 0.7029 - f1: 0.7015 - val_loss: 0.6689 - val_accuracy: 0.6509 - val_precision_3: 0.6509 - val_recall_3: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 3/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.5122 - accuracy: 0.7543 - precision_3: 0.7543 - recall_3: 0.7543 - f1: 0.7538\n",
+ "Epoch 00003: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.5122 - accuracy: 0.7543 - precision_3: 0.7543 - recall_3: 0.7543 - f1: 0.7538 - val_loss: 0.7468 - val_accuracy: 0.6509 - val_precision_3: 0.6509 - val_recall_3: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 4/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4533 - accuracy: 0.7864 - precision_3: 0.7864 - recall_3: 0.7864 - f1: 0.7880\n",
+ "Epoch 00004: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.4533 - accuracy: 0.7864 - precision_3: 0.7864 - recall_3: 0.7864 - f1: 0.7880 - val_loss: 0.7865 - val_accuracy: 0.6509 - val_precision_3: 0.6509 - val_recall_3: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 5/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4408 - accuracy: 0.7905 - precision_3: 0.7905 - recall_3: 0.7905 - f1: 0.7895\n",
+ "Epoch 00005: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.4408 - accuracy: 0.7905 - precision_3: 0.7905 - recall_3: 0.7905 - f1: 0.7895 - val_loss: 0.8070 - val_accuracy: 0.6509 - val_precision_3: 0.6509 - val_recall_3: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 6/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4310 - accuracy: 0.7916 - precision_3: 0.7916 - recall_3: 0.7916 - f1: 0.7922\n",
+ "Epoch 00006: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 434ms/step - loss: 0.4310 - accuracy: 0.7916 - precision_3: 0.7916 - recall_3: 0.7916 - f1: 0.7922 - val_loss: 0.6521 - val_accuracy: 0.6509 - val_precision_3: 0.6509 - val_recall_3: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 7/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4041 - accuracy: 0.8050 - precision_3: 0.8050 - recall_3: 0.8050 - f1: 0.8046\n",
+ "Epoch 00007: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 405ms/step - loss: 0.4041 - accuracy: 0.8050 - precision_3: 0.8050 - recall_3: 0.8050 - f1: 0.8046 - val_loss: 0.6540 - val_accuracy: 0.6559 - val_precision_3: 0.6559 - val_recall_3: 0.6559 - val_f1: 0.6514\n",
+ "Epoch 8/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4003 - accuracy: 0.8088 - precision_3: 0.8088 - recall_3: 0.8088 - f1: 0.8057\n",
+ "Epoch 00008: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 454ms/step - loss: 0.4003 - accuracy: 0.8088 - precision_3: 0.8088 - recall_3: 0.8088 - f1: 0.8057 - val_loss: 0.5331 - val_accuracy: 0.6761 - val_precision_3: 0.6761 - val_recall_3: 0.6761 - val_f1: 0.6855\n",
+ "Epoch 9/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3883 - accuracy: 0.8161 - precision_3: 0.8161 - recall_3: 0.8161 - f1: 0.8163\n",
+ "Epoch 00009: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.3883 - accuracy: 0.8161 - precision_3: 0.8161 - recall_3: 0.8161 - f1: 0.8163 - val_loss: 0.4997 - val_accuracy: 0.7254 - val_precision_3: 0.7254 - val_recall_3: 0.7254 - val_f1: 0.7187\n",
+ "Epoch 10/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3884 - accuracy: 0.8247 - precision_3: 0.8247 - recall_3: 0.8247 - f1: 0.8257\n",
+ "Epoch 00010: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.3884 - accuracy: 0.8247 - precision_3: 0.8247 - recall_3: 0.8247 - f1: 0.8257 - val_loss: 0.5636 - val_accuracy: 0.7093 - val_precision_3: 0.7093 - val_recall_3: 0.7093 - val_f1: 0.7031\n",
+ "Epoch 11/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3774 - accuracy: 0.8257 - precision_3: 0.8257 - recall_3: 0.8257 - f1: 0.8224\n",
+ "Epoch 00011: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.3774 - accuracy: 0.8257 - precision_3: 0.8257 - recall_3: 0.8257 - f1: 0.8224 - val_loss: 0.4440 - val_accuracy: 0.7686 - val_precision_3: 0.7686 - val_recall_3: 0.7686 - val_f1: 0.7607\n",
+ "Epoch 12/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3803 - accuracy: 0.8268 - precision_3: 0.8268 - recall_3: 0.8268 - f1: 0.8260\n",
+ "Epoch 00012: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.3803 - accuracy: 0.8268 - precision_3: 0.8268 - recall_3: 0.8268 - f1: 0.8260 - val_loss: 0.4339 - val_accuracy: 0.7706 - val_precision_3: 0.7706 - val_recall_3: 0.7706 - val_f1: 0.7627\n",
+ "Epoch 13/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3699 - accuracy: 0.8282 - precision_3: 0.8282 - recall_3: 0.8282 - f1: 0.8265\n",
+ "Epoch 00013: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.3699 - accuracy: 0.8282 - precision_3: 0.8282 - recall_3: 0.8282 - f1: 0.8265 - val_loss: 1.1334 - val_accuracy: 0.6982 - val_precision_3: 0.6982 - val_recall_3: 0.6982 - val_f1: 0.6924\n",
+ "Epoch 14/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3706 - accuracy: 0.8385 - precision_3: 0.8385 - recall_3: 0.8385 - f1: 0.8376\n",
+ "Epoch 00014: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.3706 - accuracy: 0.8385 - precision_3: 0.8385 - recall_3: 0.8385 - f1: 0.8376 - val_loss: 0.4553 - val_accuracy: 0.7837 - val_precision_3: 0.7837 - val_recall_3: 0.7837 - val_f1: 0.7754\n",
+ "Epoch 15/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3651 - accuracy: 0.8295 - precision_3: 0.8295 - recall_3: 0.8295 - f1: 0.8287\n",
+ "Epoch 00015: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 470ms/step - loss: 0.3651 - accuracy: 0.8295 - precision_3: 0.8295 - recall_3: 0.8295 - f1: 0.8287 - val_loss: 0.3615 - val_accuracy: 0.8300 - val_precision_3: 0.8300 - val_recall_3: 0.8300 - val_f1: 0.8203\n",
+ "Epoch 16/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3571 - accuracy: 0.8337 - precision_3: 0.8337 - recall_3: 0.8337 - f1: 0.8345\n",
+ "Epoch 00016: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.3571 - accuracy: 0.8337 - precision_3: 0.8337 - recall_3: 0.8337 - f1: 0.8345 - val_loss: 0.6051 - val_accuracy: 0.7495 - val_precision_3: 0.7495 - val_recall_3: 0.7495 - val_f1: 0.7568\n",
+ "Epoch 17/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3498 - accuracy: 0.8402 - precision_3: 0.8402 - recall_3: 0.8402 - f1: 0.8384\n",
+ "Epoch 00017: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.3498 - accuracy: 0.8402 - precision_3: 0.8402 - recall_3: 0.8402 - f1: 0.8384 - val_loss: 0.6266 - val_accuracy: 0.7525 - val_precision_3: 0.7525 - val_recall_3: 0.7525 - val_f1: 0.7598\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 18/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3451 - accuracy: 0.8413 - precision_3: 0.8413 - recall_3: 0.8413 - f1: 0.8411\n",
+ "Epoch 00018: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.3451 - accuracy: 0.8413 - precision_3: 0.8413 - recall_3: 0.8413 - f1: 0.8411 - val_loss: 0.4329 - val_accuracy: 0.7726 - val_precision_3: 0.7726 - val_recall_3: 0.7726 - val_f1: 0.7793\n",
+ "Epoch 19/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3428 - accuracy: 0.8520 - precision_3: 0.8520 - recall_3: 0.8520 - f1: 0.8525\n",
+ "Epoch 00019: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.3428 - accuracy: 0.8520 - precision_3: 0.8520 - recall_3: 0.8520 - f1: 0.8525 - val_loss: 0.8087 - val_accuracy: 0.7384 - val_precision_3: 0.7384 - val_recall_3: 0.7384 - val_f1: 0.7461\n",
+ "Epoch 20/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3405 - accuracy: 0.8478 - precision_3: 0.8478 - recall_3: 0.8478 - f1: 0.8485\n",
+ "Epoch 00020: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.3405 - accuracy: 0.8478 - precision_3: 0.8478 - recall_3: 0.8478 - f1: 0.8485 - val_loss: 0.3700 - val_accuracy: 0.8320 - val_precision_3: 0.8320 - val_recall_3: 0.8320 - val_f1: 0.8223\n",
+ "Epoch 21/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3319 - accuracy: 0.8561 - precision_3: 0.8561 - recall_3: 0.8561 - f1: 0.8575\n",
+ "Epoch 00021: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 474ms/step - loss: 0.3319 - accuracy: 0.8561 - precision_3: 0.8561 - recall_3: 0.8561 - f1: 0.8575 - val_loss: 0.5281 - val_accuracy: 0.7746 - val_precision_3: 0.7746 - val_recall_3: 0.7746 - val_f1: 0.7666\n",
+ "Epoch 22/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3395 - accuracy: 0.8461 - precision_3: 0.8461 - recall_3: 0.8461 - f1: 0.8459\n",
+ "Epoch 00022: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 27s 577ms/step - loss: 0.3395 - accuracy: 0.8461 - precision_3: 0.8461 - recall_3: 0.8461 - f1: 0.8459 - val_loss: 0.3581 - val_accuracy: 0.8239 - val_precision_3: 0.8239 - val_recall_3: 0.8239 - val_f1: 0.8291\n",
+ "Epoch 23/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3313 - accuracy: 0.8533 - precision_3: 0.8533 - recall_3: 0.8533 - f1: 0.8522\n",
+ "Epoch 00023: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 471ms/step - loss: 0.3313 - accuracy: 0.8533 - precision_3: 0.8533 - recall_3: 0.8533 - f1: 0.8522 - val_loss: 0.6135 - val_accuracy: 0.7626 - val_precision_3: 0.7626 - val_recall_3: 0.7626 - val_f1: 0.7549\n",
+ "Epoch 24/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3305 - accuracy: 0.8485 - precision_3: 0.8485 - recall_3: 0.8485 - f1: 0.8465\n",
+ "Epoch 00024: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 445ms/step - loss: 0.3305 - accuracy: 0.8485 - precision_3: 0.8485 - recall_3: 0.8485 - f1: 0.8465 - val_loss: 0.3882 - val_accuracy: 0.8179 - val_precision_3: 0.8179 - val_recall_3: 0.8179 - val_f1: 0.8232\n",
+ "Epoch 25/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3256 - accuracy: 0.8578 - precision_3: 0.8578 - recall_3: 0.8578 - f1: 0.8575\n",
+ "Epoch 00025: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.3256 - accuracy: 0.8578 - precision_3: 0.8578 - recall_3: 0.8578 - f1: 0.8575 - val_loss: 0.3626 - val_accuracy: 0.8340 - val_precision_3: 0.8340 - val_recall_3: 0.8340 - val_f1: 0.8389\n",
+ "Epoch 26/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3149 - accuracy: 0.8644 - precision_3: 0.8644 - recall_3: 0.8644 - f1: 0.8613\n",
+ "Epoch 00026: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.3149 - accuracy: 0.8644 - precision_3: 0.8644 - recall_3: 0.8644 - f1: 0.8613 - val_loss: 0.4386 - val_accuracy: 0.7817 - val_precision_3: 0.7817 - val_recall_3: 0.7817 - val_f1: 0.7881\n",
+ "Epoch 27/200\n",
+ " 7/46 [===>..........................] - ETA: 8s - loss: 0.2999 - accuracy: 0.8795 - precision_3: 0.8795 - recall_3: 0.8795 - f1: 0.8795"
+ ]
+ }
+ ],
+ "source": [
+ "for number in np.arange(1,5):\n",
+ " name = f\"OGRUN_I{number}\"\n",
+ "\n",
+ " aug = ImageDataGenerator(\n",
+ " rotation_range=30,\n",
+ " zoom_range=0.15,\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2,\n",
+ " shear_range=0.15,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")\n",
+ "\n",
+ " aug_val = ImageDataGenerator()\n",
+ "\n",
+ " # train_generator = aug.flow(Xtrain, Ytrain, batch_size=BATCH_SIZE)\n",
+ " # validation_generator = aug_val.flow(Xvalidation, Yvalidation)\n",
+ " model = FireDetectionNet.build(width=128, height=128, depth=3)\n",
+ " model.save(f\"/userdata/kerasData/preloaded/madeModels/{name}\")\n",
+ " model = keras.models.load_model(f\"/userdata/kerasData/preloaded/madeModels/{name}\", compile=False)\n",
+ " model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1])\n",
+ "\n",
+ " mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/pyimagesearch/output/{name}orgPYimageSearch.model', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ " early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=150)\n",
+ " H = model.fit(\n",
+ " aug.flow(Xtrain, Ytrain, batch_size=BATCH_SIZE),\n",
+ " validation_data=aug.flow(Xvalidation, Yvalidation),\n",
+ " steps_per_epoch=Xtrain.shape[0] // BATCH_SIZE,\n",
+ " epochs=200,\n",
+ " # class_weight=classWeight,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " curr = \"H128_OHE_128_v1\"\n",
+ " output=f\"/userdata/kerasData/output/recreate/{curr}{name}\"\n",
+ " pd.DataFrame.from_dict(H.history).to_csv(output, index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/.ipynb_checkpoints/historyEvaluater-checkpoint.ipynb b/scripts/.ipynb_checkpoints/historyEvaluater-checkpoint.ipynb
new file mode 100644
index 0000000..65e481a
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/historyEvaluater-checkpoint.ipynb
@@ -0,0 +1,256 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy import stats\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for name in addresses:\n",
+ " results.append(pd.read_csv(name))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results=[]\n",
+ "for name in np.arange(1,4):\n",
+ " results.append(pd.read_csv(f\"/userdata/kerasData/output/recreate/transfer/HPWRENGroundUp_1024_SPLIT1_v1_e{name}.csv\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "for index, H in enumerate(results):\n",
+ " H = H[0:92]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"loss\"])\n",
+ " plt.plot(H[\"accuracy\"])\n",
+ " plt.plot(H[\"val_loss\"])\n",
+ " plt.plot(H[\"val_accuracy\"])\n",
+ " plt.title(f'{number} model loss functions')\n",
+ " plt.legend(['train loss', \"accuracy\", 'validation loss', 'validation accuracy'], loc='upper left')\n",
+ " plt.ylabel('loss')\n",
+ " plt.xlabel('epoch')\n",
+ "# plt.ylim(0,1)\n",
+ " plt.show()\n",
+ "\n",
+ "for index, H in enumerate(results):\n",
+ " H = H[0:100]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"f1\"])\n",
+ " plt.plot(H[\"val_f1\"])\n",
+ " plt.title(f'{number} model f1 functions')\n",
+ " plt.legend(['train f1', 'validation f1',], loc='upper left')\n",
+ " plt.ylabel('Percentage')\n",
+ " plt.xlabel('epoch')\n",
+ " plt.ylim(0,1)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "51"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "H.shape[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#create 2 dataframes with random integers. I don't have data to simulate your case.\n",
+ "numCols = H.shape[0]\n",
+ "df1 = pd.DataFrame(H.accuracy).transpose()\n",
+ "\n",
+ "#apply the Kolmogorov-Smirnov Test\n",
+ "p_value = 0.05\n",
+ "p_values = []\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndexError",
+ "evalue": "single positional indexer is out-of-bounds",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-50-153be853155f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumCols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mks_2samp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mp_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#create the box plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1760\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1761\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1763\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1764\u001b[0m \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 2065\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2067\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2068\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2069\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Too many indexers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 704\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 705\u001b[0m raise ValueError(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1992\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1993\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1994\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1995\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1996\u001b[0m \u001b[0;31m# a tuple should already have been caught by this point\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 2061\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2062\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2063\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2064\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2065\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
+ ]
+ }
+ ],
+ "source": [
+ "for col in range(numCols):\n",
+ " test = stats.ks_2samp(df1.iloc[col,], df1.iloc[col,])\n",
+ " p_values.append(test[1])\n",
+ "\n",
+ "#create the box plot\n",
+ "\n",
+ "plt.boxplot(p_values)\n",
+ "plt.title('Boxplot of p-values')\n",
+ "plt.ylabel(\"p_values\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for index, H in enumerate(results):\n",
+ " print(max(H.accuracy))\n",
+ " plt.boxplot(H.accuracy[60:])\n",
+ " plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/.ipynb_checkpoints/testing-checkpoint.ipynb b/scripts/.ipynb_checkpoints/testing-checkpoint.ipynb
new file mode 100644
index 0000000..860c591
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/testing-checkpoint.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[name: \"/device:CPU:0\"\n",
+ "device_type: \"CPU\"\n",
+ "memory_limit: 268435456\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 12397120884647711703\n",
+ ", name: \"/device:XLA_CPU:0\"\n",
+ "device_type: \"XLA_CPU\"\n",
+ "memory_limit: 17179869184\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 7450379519501849834\n",
+ "physical_device_desc: \"device: XLA_CPU device\"\n",
+ ", name: \"/device:XLA_GPU:0\"\n",
+ "device_type: \"XLA_GPU\"\n",
+ "memory_limit: 17179869184\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 9162883243846547682\n",
+ "physical_device_desc: \"device: XLA_GPU device\"\n",
+ ", name: \"/device:GPU:0\"\n",
+ "device_type: \"GPU\"\n",
+ "memory_limit: 23695670656\n",
+ "locality {\n",
+ " bus_id: 2\n",
+ " numa_node: 1\n",
+ " links {\n",
+ " }\n",
+ "}\n",
+ "incarnation: 5784131758698526549\n",
+ "physical_device_desc: \"device: 0, name: TITAN RTX, pci bus id: 0000:86:00.0, compute capability: 7.5\"\n",
+ "]\n",
+ "Found 648 images belonging to 2 classes.\n",
+ "Found 243 images belonging to 2 classes.\n",
+ "Found 239 images belonging to 2 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import BatchNormalization\n",
+ "from tensorflow.keras.layers import SeparableConv2D\n",
+ "from tensorflow.keras.layers import MaxPooling2D\n",
+ "from tensorflow.keras.layers import Activation\n",
+ "from tensorflow.keras.layers import Flatten\n",
+ "from tensorflow.keras.layers import Dropout\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\") \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import tensorflow as tf\n",
+ "# from imutils import paths\n",
+ "\n",
+ "# import the necessary packages\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import pandas as pd\n",
+ "import keras\n",
+ "import tempfile\n",
+ "from tensorflow.keras.callbacks import LambdaCallback\n",
+ "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
+ "# physical_devices = tf.config.experimental.list_physical_device \n",
+ "\n",
+ "tf.config.experimental.set_memory_growth(physical_devices[0], True) \n",
+ "assert tf.config.experimental.get_memory_growth(physical_devices[0]) \n",
+ "import keras\n",
+ "from keras import backend as K\n",
+ "# K.tensorflow_backend._get_available_gpus()\n",
+ "from tensorflow.python.client import device_lib\n",
+ "print(device_lib.list_local_devices())\n",
+ "\n",
+ "dataDirectoryTrain = \"/userdata/kerasData/preloaded/flowDirectory2/train/\"\n",
+ "dataDirectoryValidation = \"/userdata/kerasData/preloaded/flowDirectory2/validation/\"\n",
+ "dataDirectoryTest = \"/userdata/kerasData/preloaded/flowDirectory2/test/\"\n",
+ "\n",
+ "TRAIN_SPLIT = 0.75\n",
+ "TEST_SPLIT = 0.25\n",
+ "INIT_LR = 1e-2\n",
+ "BATCH_SIZE = 8\n",
+ "NUM_EPOCHS = 50\n",
+ "image_size = 2048,1536\n",
+ "class_mode = \"categorical\"\n",
+ "\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,\n",
+ " zoom_range=0.15,\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2,\n",
+ " shear_range=0.15,\n",
+ " validation_split=0,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")\n",
+ "\n",
+ "image_generatorCLASSIC = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,\n",
+ " zoom_range=0,\n",
+ " width_shift_range=0,\n",
+ " height_shift_range=0,\n",
+ " shear_range=0,\n",
+ " validation_split=0,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")\n",
+ "\n",
+ "trainingGeneratorHPWREN = image_generator.flow_from_directory(\n",
+ " dataDirectoryTrain,\n",
+ " target_size=image_size,\n",
+ " seed=42,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " class_mode=class_mode,\n",
+ " subset=\"training\")\n",
+ "\n",
+ "validationGeneratorHPWREN = image_generator.flow_from_directory(\n",
+ " dataDirectoryValidation,\n",
+ " target_size=image_size,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " seed=42,\n",
+ " class_mode=class_mode,\n",
+ " subset = \"training\")\n",
+ "\n",
+ "testGeneratorHPWREN = image_generatorCLASSIC.flow_from_directory(\n",
+ " dataDirectoryTest,\n",
+ " target_size=image_size,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " seed=42,\n",
+ " class_mode=class_mode,\n",
+ " subset = \"training\")\n",
+ "\n",
+ "def f1(y_true, y_pred):\n",
+ " \n",
+ " def recall(y_true, y_pred):\n",
+ " \"\"\"Recall metric.\n",
+ "\n",
+ " Only computes a batch-wise average of recall.\n",
+ "\n",
+ " Computes the recall, a metric for multi-label classification of\n",
+ " how many relevant items are selected.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ " def precision(y_true, y_pred):\n",
+ " \"\"\"Precision metric.\n",
+ "\n",
+ " Only computes a batch-wise average of precision.\n",
+ "\n",
+ " Computes the precision, a metric for multi-label classification of\n",
+ " how many selected items are relevant.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ " precision = precision(y_true, y_pred)\n",
+ " recall = recall(y_true, y_pred)\n",
+ " return 2*((precision*recall)/(precision+recall+K.epsilon()))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class FireDetectionNet:\n",
+ " @staticmethod\n",
+ " def build(width, height, depth):\n",
+ " # initialize the model along with the input shape to be\n",
+ " # \"channels last\" and the channels dimension itself\n",
+ " model = Sequential()\n",
+ " inputShape = (height, width, depth)\n",
+ " chanDim = -1\n",
+ " \n",
+ " model.add(SeparableConv2D(16, (7, 7), padding=\"same\",\n",
+ " input_shape=inputShape))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(32, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(Flatten())\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # second set of FC => RELU layers\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # softmax classifier\n",
+ " model.add(Dense(2))\n",
+ " model.add(Activation(\"softmax\"))\n",
+ "\n",
+ " # return the constructed network architecture\n",
+ " return model\n",
+ "\n",
+ "# name = \"HPWRENGroundUp_2048_V1_BATCH\"\n",
+ "# opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ "# decay=INIT_LR / NUM_EPOCHS)\n",
+ "# groundUpModel = FireDetectionNet.build(width=2048, height=1536, depth=3)\n",
+ "# groundUpModel.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ "# metrics=[\"accuracy\", tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1])\n",
+ "# mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/pyimagesearch/output/experimental/{name}HPWREN.model', \n",
+ "# monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "# early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_1\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d_4 (Separabl (None, 2048, 1536, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation_7 (Activation) (None, 2048, 1536, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_6 (Batch (None, 2048, 1536, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_3 (MaxPooling2 (None, 1024, 768, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_5 (Separabl (None, 1024, 768, 32) 688 \n",
+ "_________________________________________________________________\n",
+ "activation_8 (Activation) (None, 1024, 768, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_7 (Batch (None, 1024, 768, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 512, 384, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_6 (Separabl (None, 512, 384, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_9 (Activation) (None, 512, 384, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_8 (Batch (None, 512, 384, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_7 (Separabl (None, 512, 384, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_10 (Activation) (None, 512, 384, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_9 (Batch (None, 512, 384, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_5 (MaxPooling2 (None, 256, 192, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 3145728) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 128) 402653312 \n",
+ "_________________________________________________________________\n",
+ "activation_11 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_10 (Batc (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_2 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 128) 16512 \n",
+ "_________________________________________________________________\n",
+ "activation_12 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_11 (Batc (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_3 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 2) 258 \n",
+ "_________________________________________________________________\n",
+ "activation_13 (Activation) (None, 2) 0 \n",
+ "=================================================================\n",
+ "Total params: 402,679,845\n",
+ "Trainable params: 402,678,981\n",
+ "Non-trainable params: 864\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "testModel = FireDetectionNet.build(1536,2048,3)\n",
+ "testModel.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e8Modeladdress = \"/userdata/kerasData/pyimagesearch/output/experimental/HPWRENGroundUp_e8_2048_SPLIT1_v1HPWREN.model\"\n",
+ "e7Modeladdress = \"/userdata/kerasData/pyimagesearch/output/experimental/HPWRENGroundUp_e8_2048_SPLIT1_v2_e7HPWREN.model\"\n",
+ "e6Modeladdress = \"/userdata/kerasData/pyimagesearch/output/experimental/HPWRENGroundUp_2048_SPLIT1_v3_e6HPWREN.model\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model = FireDetectionNet.build(width=2048, height=1536, depth=3)\n",
+ "# model.load_weights(e8Modeladdress)\n",
+ "attempt = tf.saved_model.load(e8Modeladdress)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e8model = tf.keras.models.load_model(e8Modeladdress,custom_objects={\"f1\":f1})\n",
+ "e7model = tf.keras.models.load_model(e7Modeladdress,custom_objects={\"f1\":f1})\n",
+ "e6model = tf.keras.models.load_model(e6Modeladdress,custom_objects={\"f1\":f1})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "30/30 [==============================] - 338s 11s/step - loss: 0.8079 - accuracy: 0.4351 - precision: 0.4351 - recall: 0.4351 - f1: 0.4351\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[0.8078833818435669,\n",
+ " 0.4351464509963989,\n",
+ " 0.4351464509963989,\n",
+ " 0.4351464509963989,\n",
+ " 0.43511903285980225]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "e8model.evaluate(testGeneratorHPWREN)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "30/30 [==============================] - 239s 8s/step - loss: 0.8104 - accuracy: 0.5397 - precision: 0.5397 - recall: 0.5397 - f1: 0.5393\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[0.8103770613670349,\n",
+ " 0.5397489666938782,\n",
+ " 0.5397489666938782,\n",
+ " 0.5397489666938782,\n",
+ " 0.5392856597900391]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "e7model.evaluate(testGeneratorHPWREN)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "30/30 [==============================] - 242s 8s/step - loss: 0.8597 - accuracy: 0.4728 - precision: 0.4728 - recall: 0.4728 - f1: 0.4720\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[0.8597329258918762,\n",
+ " 0.47280335426330566,\n",
+ " 0.47280335426330566,\n",
+ " 0.47280335426330566,\n",
+ " 0.47202378511428833]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "e6model.evaluate(testGeneratorHPWREN)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Loss')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "lrs = LRfINDER.lrs[10:-1]\n",
+ "losses = LRfINDER.losses[10:-1]\n",
+ "plt.plot(lrs, losses)\n",
+ "plt.xscale(\"log\")\n",
+ "plt.xlabel(\"Learning Rate (Log Scale)\")\n",
+ "plt.ylabel(\"Loss\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,\n",
+ " zoom_range=0.15,\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2,\n",
+ " shear_range=0.15,\n",
+ " validation_split=0,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "testImage = image_generator.flow_from_(\"/userdata/kerasData/preloaded/flowDirectory2/test/fire/1512676696_+02100.jpg\")))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1536, 2048, 3)"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "testImage.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "in user code:\n\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function *\n outputs = self.distribute_strategy.run(\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n return self._call_for_each_replica(fn, args, kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n return fn(*args, **kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step **\n return self(x, training=False)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__\n self.name)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility\n str(x.shape.as_list()))\n\n ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [32, 2048, 3]\n",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-48-d3b35444c1d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me6model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestImage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 86\u001b[0m raise ValueError('{} is not supported in multi-worker mode.'.format(\n\u001b[1;32m 87\u001b[0m method.__name__))\n\u001b[0;32m---> 88\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m return tf_decorator.make_decorator(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1267\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_predict_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1268\u001b[0;31m \u001b[0mtmp_batch_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1269\u001b[0m \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 628\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 629\u001b[0m \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m 504\u001b[0m self._concrete_stateful_fn = (\n\u001b[1;32m 505\u001b[0m self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access\n\u001b[0;32m--> 506\u001b[0;31m *args, **kwds))\n\u001b[0m\u001b[1;32m 507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2444\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2445\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2446\u001b[0;31m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2447\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 2775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2776\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2777\u001b[0;31m \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2778\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2779\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 2665\u001b[0m \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2666\u001b[0m \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2667\u001b[0;31m capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m 2668\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2669\u001b[0m \u001b[0;31m# Tell the ConcreteFunction to clean up its graph once it goes out of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 983\u001b[0m \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 442\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 969\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 970\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: in user code:\n\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function *\n outputs = self.distribute_strategy.run(\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n return self._call_for_each_replica(fn, args, kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n return fn(*args, **kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step **\n return self(x, training=False)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__\n self.name)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility\n str(x.shape.as_list()))\n\n ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [32, 2048, 3]\n"
+ ]
+ }
+ ],
+ "source": [
+ "e6model.predict(testImage)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/ImageLoader.ipynb b/scripts/ImageLoader.ipynb
new file mode 100644
index 0000000..de1e9f3
--- /dev/null
+++ b/scripts/ImageLoader.ipynb
@@ -0,0 +1,18555 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\")\n",
+ " \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import tensorflow as tf\n",
+ "from os import listdir\n",
+ "from os.path import isdir, join, isfile\n",
+ "from numpy import asarray\n",
+ "from numpy import save\n",
+ "import itertools\n",
+ "import shutil \n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mygrouper(n, iterable):\n",
+ " args = [iter(iterable)] * n\n",
+ " return ([e for e in t if e != None] for t in itertools.zip_longest(*args))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mypath = \"/userdata/kerasData/preloaded/subsets/set2\"\n",
+ "savepath = \"/userdata/kerasData/flowDirectory\"\n",
+ "onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = list(mygrouper(10, onlyfiles))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dimentionsEE={}\n",
+ "\n",
+ "for fire in onlyfiles:\n",
+ " rhoice = random.choice(os.listdir(mypath + \"/\"+ fire))\n",
+ " cur = Image.open(mypath+\"/\"+fire+\"/\"+rhoice)\n",
+ " dimentionsEE[fire] = cur.size\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 1 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "firstTrigger = True \n",
+ "count = 0\n",
+ "fireCount = 0\n",
+ "test_label=[]\n",
+ "train_label=[]\n",
+ "validation_label=[]\n",
+ "\n",
+ "onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# \"201710 26-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ "train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ "train, validation = train_test_split(train, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ "print(len(train), len(test), len(validation))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pixelSize = {}\n",
+ "leftout=[]\n",
+ "\n",
+ "def load_dataset(datasetPath, outputPath):\n",
+ " \n",
+ " firstTrigger = True \n",
+ " count = 0\n",
+ " fireCount = 0\n",
+ " test_label=[]\n",
+ " train_label=[]\n",
+ " validation_label=[]\n",
+ " \n",
+ " mypath = datasetPath\n",
+ " onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# \"20171026-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ " train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " train, validation = train_test_split(train, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " print(len(train), len(test), len(validation))\n",
+ "\n",
+ " for fire in test:\n",
+ " if not os.path.exists(f\"{outputPath}/test\"):\n",
+ " os.makedirs(f\"{outputPath}/test\")\n",
+ " os.makedirs(f\"{outputPath}/test/fire\")\n",
+ " os.makedirs(f'{outputPath}/test/nonfire')\n",
+ " fireCount +=1\n",
+ " print(f'{fire} - test fire number {fireCount}')\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"/test/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"/test/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " count +=1\n",
+ " print(count)\n",
+ " if \"+\" in element:\n",
+ " test_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " test_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in train:\n",
+ " if not os.path.exists(f\"{outputPath}/train\"):\n",
+ " os.makedirs(f\"{outputPath}/train\")\n",
+ " os.makedirs(f\"{outputPath}/train/fire\")\n",
+ " os.makedirs(f'{outputPath}/train/nonfire')\n",
+ " print(f\"{fire} - train-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"/train/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"/train/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " print(count)\n",
+ " count += 1\n",
+ "\n",
+ " if \"+\" in element:\n",
+ " train_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " train_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in validation:\n",
+ " print(f\"{fire} - validation-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " # pixelSize.setdefault(fire, set([]))\n",
+ " if not os.path.exists(f\"{outputPath}/validation\"):\n",
+ " os.makedirs(f\"{outputPath}/validation\")\n",
+ " os.makedirs(f\"{outputPath}/validation/fire\")\n",
+ " os.makedirs(f'{outputPath}/validation/nonfire')\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " print(count)\n",
+ " count += 1\n",
+ " dst1 = outputPath+\"/validation/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"/validation/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " if \"+\" in element:\n",
+ " validation_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " validation_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 1 1\n",
+ "20170609_FIRE_sm-n-mobo-c - test fire number 1\n",
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+ ]
+ }
+ ],
+ "source": [
+ "load_dataset(\"/userdata/kerasData/preloaded/subsets/set2\", \"/userdata/kerasData/preloaded/flowDirectory4\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pixelSize = {}\n",
+ "leftout=[]\n",
+ "\n",
+ "def load_dataset(datasetPath, outputPath):\n",
+ " \n",
+ " firstTrigger = True \n",
+ " count = 0\n",
+ " fireCount = 0\n",
+ " test_label=[]\n",
+ " train_label=[]\n",
+ " validation_label=[]\n",
+ " \n",
+ " mypath = datasetPath\n",
+ " onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# \"20171026-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ " train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " train, validation = train_test_split(train, test_size = 0.2, train_size = 0.80, shuffle=True, random_state = 2100)\n",
+ " print(len(train), len(test), len(validation))\n",
+ "\n",
+ " for fire in test:\n",
+ " if not os.path.exists(\"/userdata/kerasData/preloaded/flowDirectory/test\"):\n",
+ "# os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test\")\n",
+ " os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test/fire\")\n",
+ " os.makedirs('/userdata/kerasData/preloaded/flowDirectory/test/nonfire')\n",
+ " fireCount +=1\n",
+ " print(f'{fire} - test fire number {fireCount}')\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"test/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"test/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " count +=1\n",
+ " print(count)\n",
+ " if \"+\" in element:\n",
+ " test_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " test_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in train:\n",
+ " if not os.path.exists(\"/userdata/kerasData/preloaded/flowDirectory/train\"):\n",
+ " # os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test\")\n",
+ " os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/train/fire\")\n",
+ " os.makedirs('/userdata/kerasData/preloaded/flowDirectory/train/nonfire')\n",
+ " print(f\"{fire} - train-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " dst1 = outputPath+\"train/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"train/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " print(count)\n",
+ " count += 1\n",
+ "\n",
+ " if \"+\" in element:\n",
+ " train_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " train_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " a = fireCount\n",
+ " \n",
+ " for fire in validation:\n",
+ " print(f\"{fire} - validation-fire number {fireCount - a +1}\")\n",
+ " fireCount+=1\n",
+ " # pixelSize.setdefault(fire, set([]))\n",
+ " if not os.path.exists(\"/userdata/kerasData/preloaded/flowDirectory/validation\"):\n",
+ " # os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/test\")\n",
+ " os.makedirs(\"/userdata/kerasData/preloaded/flowDirectory/validation/fire\")\n",
+ " os.makedirs('/userdata/kerasData/preloaded/flowDirectory/validation/nonfire')\n",
+ " for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ " print(count)\n",
+ " count += 1\n",
+ " dst1 = outputPath+\"validation/\"+\"fire/\"\n",
+ " dst2 = outputPath+\"validation/\"+\"nonfire/\"\n",
+ " src = datasetPath + \"/\" + fire + \"/\" + element\n",
+ " if \"+\" in element:\n",
+ " validation_label.append(1)\n",
+ " shutil.copy(src, dst1)\n",
+ " else:\n",
+ " validation_label.append(0)\n",
+ " shutil.copy(src, dst2)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "138 44 35\n",
+ "20191001_FIRE_smer-tcs9-mobo-c - test fire number 1\n",
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+ ]
+ }
+ ],
+ "source": [
+ "mypath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "savepath = \"/userdata/kerasData/preloaded/flowDirectory/\"\n",
+ "# endPath = \"/userdata/kerasData/imageData/\"\n",
+ "\n",
+ "# Xtrain, Xtest, Xvalidation, Ytrain, Ytest, Yvalidation, pixels, count, classWeight = load_dataset(mypath, savepath)\n",
+ "load_dataset(mypath, savepath)\n",
+ "# Xvalidation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test = pixels\n",
+ "\n",
+ "for key, value in test.items():\n",
+ " print(f\"{key} : {value.pop()}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Code for Numpy Arrays"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NON_FIRE_PATH = \"/userdata/kerasData/Robbery_Accident_Fire_Database2/Robbery\"\n",
+ "# FIRE_PATH = \"/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire\"\n",
+ "# CLASSES = [\"Non-Fire\", \"Fire\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TRAIN_SPLIT = 0.75\n",
+ "# TEST_SPLIT = 0.25\n",
+ "# INIT_LR = 1e-2\n",
+ "# BATCH_SIZE = 64\n",
+ "# NUM_EPOCHS = 50\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# MODEL_PATH = os.path.sep.join([\"~/output\", \"pyimage_fire_detection.model\"])\n",
+ "# TRAINING_PLOT_PATH = os.path.sep.join([\"~/output\", \"training_plot.png\"])\n",
+ "# SAMPLE_SIZE = 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pixelSize = {}\n",
+ "# leftout=[]\n",
+ "\n",
+ "# def load_dataset(datasetPath):\n",
+ "# testX = []\n",
+ "# trainX = []\n",
+ "# validationX = [] \n",
+ " \n",
+ "# test_label = []\n",
+ "# train_label = []\n",
+ "# validation_label = []\n",
+ " \n",
+ "# firstTrigger = True\n",
+ " \n",
+ "# count = 0\n",
+ "# fireCount = 0\n",
+ "\n",
+ "# mypath = datasetPath\n",
+ "# onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# # onlyfiles = [\"20190716-Meadowfire-hp-n-mobo-c\", \"20180706-West-lp-n-mobo-c\", \"20171207-FIRE-bh-w-mobo-c\", \n",
+ "# # \"20171026-FIRE-rm-n-mobo-c\", \"20170807-FIRE-bh-n-mobo-c\", \"20170722-FIRE-bm-n-mobo-c\", \"20170708-Whittier-syp-n-mobo-m\", \"20170520-FIRE-pi-w-mobo-c\"]\n",
+ "\n",
+ "# train, test = train_test_split(onlyfiles, test_size = 0.25, train_size = 0.75, shuffle=True, random_state = 2100)\n",
+ "# train, validation = train_test_split(train, test_size = 0.25, train_size = 0.75, shuffle=True, random_state = 2100)\n",
+ "# print(len(train), len(test), len(validation))\n",
+ "\n",
+ "# for index,testsplit in enumerate(list(mygrouper(10, test))):\n",
+ "# testX = []\n",
+ "# for fire in testsplit:\n",
+ "# fireCount +=1\n",
+ "# print(f'{fire} - fire number {fireCount}')\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# count +=1\n",
+ "# print(count)\n",
+ "# if \"+\" in element:\n",
+ "# test_label.append(1)\n",
+ "# else:\n",
+ "# test_label.append(0)\n",
+ "\n",
+ "# fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# pixelSize[fire].add(fire_im.size)\n",
+ "# try:\n",
+ "# fire_im = fire_im.resize((2048,1536))\n",
+ "# except Error:\n",
+ "# print(fire)\n",
+ "# leftout.append(fire)\n",
+ "# break\n",
+ "\n",
+ "# inArrayim = np.asarray(fire_im) \n",
+ "# inArrayim = inArrayim/255\n",
+ "\n",
+ "# # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # image = cv2.resize(image, (128,128))\n",
+ "# testX.append(inArrayim)\n",
+ "# name = f\"testX_{index}.npy\"\n",
+ "# save(name, testX)\n",
+ " \n",
+ "# a = fireCount\n",
+ " \n",
+ "# # for fire in train:\n",
+ "# # print(f\"{fire} - train-fire number {fireCount - a +1}\")\n",
+ "# # fireCount+=1\n",
+ "# # pixelSize.setdefault(fire, set([]))\n",
+ "# # for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# # print(count)\n",
+ "# # count += 1\n",
+ "\n",
+ "# # if \"+\" in element:\n",
+ "# # train_label.append(1)\n",
+ "# # else:\n",
+ "# # train_label.append(0)\n",
+ " \n",
+ "# # fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# # pixelSize[fire].add(fire_im.size)\n",
+ "# # fire_im = fire_im.resize((2048,1536))\n",
+ "# # inArrayim = np.asarray(fire_im)\n",
+ "# # inArrayim = inArrayim/255\n",
+ "# # # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # # image = cv2.resize(image, (128,128))\n",
+ "# # trainX.append(inArrayim) \n",
+ " \n",
+ "# # # a = fireCount \n",
+ "\n",
+ "# # a = 0\n",
+ "# # for fire in validation:\n",
+ "# # print(f\"{fire} - validation-fire number {fireCount - a +1}\")\n",
+ "# # fireCount+=1\n",
+ "# # pixelSize.setdefault(fire, set([]))\n",
+ "# # for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# # print(count)\n",
+ "# # count += 1\n",
+ "\n",
+ "# # if \"+\" in element:\n",
+ "# # validation_label.append(1)\n",
+ "# # else:\n",
+ "# # validation_label.append(0)\n",
+ " \n",
+ "# # fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# # pixelSize[fire].add(fire_im.size)\n",
+ "# # fire_im = fire_im.resize((2048,1536))\n",
+ "# # inArrayim = np.asarray(fire_im)\n",
+ "# # inArrayim = inArrayim/255\n",
+ "\n",
+ "# # # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # # image = cv2.resize(image, (128,128))\n",
+ "# # # print(validationX)\n",
+ "# # validationX.append(inArrayim) \n",
+ " \n",
+ "# # print(fireCount)\n",
+ " \n",
+ "\n",
+ "# # save(\"trainX.npy\", trainX)\n",
+ "# # save(\"testX.npy\", testX)\n",
+ "# # save(\"validationX.npy\", validationX)\n",
+ " \n",
+ "# trainY = tf.keras.utils.to_categorical(np.array(train_label), num_classes=2)\n",
+ "# testY = tf.keras.utils.to_categorical(np.array(test_label), num_classes=2)\n",
+ "# validationY = tf.keras.utils.to_categorical(np.array(validation_label), num_classes = 2)\n",
+ " \n",
+ "# save(\"trainY.npy\", trainY)\n",
+ "# save(\"testY.npy\", testY)\n",
+ "# save(\"validationY.npy\", validationY)\n",
+ " \n",
+ "# # labels = np.append(trainY, testY, validationY)\n",
+ "# labels = np.vstack((trainY, testY))\n",
+ "# labels = np.vstack((labels, validationY))\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ "# save(\"classWeight.npy\", classWeight)\n",
+ "\n",
+ "# # return np.array(trainX, dtype=\"float32\"), np.array(testX, dtype=\"float32\"), np.array(validationX, dtype=\"float32\"), trainY, testY, validationY, pixelSize, count, classWeight\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.8, shuffle=True, random_state=200)\n",
+ "\n",
+ "# count = 0\n",
+ "# countTest = 0\n",
+ "\n",
+ "# for fire in train:\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ train):\n",
+ "# count +=1\n",
+ "# if \"+\" in element:\n",
+ "# label = 1\n",
+ "# label = tf.keras.utils.to_categorical(label, num_classes=2)\n",
+ "# width, height = Image.open(datasetPath + \"/\"+ train+ \"/\" +element).size\n",
+ "# print(width*height)\n",
+ "# print(datasetPath + \"/\"+ element + \"/\" + element)\n",
+ "\n",
+ "# for fire in test:\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# countTest +=1\n",
+ "# if \"+\" in element:\n",
+ "# label = 1\n",
+ "# label = tf.keras.utils.to_categorical(label, num_classes=2)\n",
+ "# width, height = Image.open(datasetPath + \"/\"+ element).size\n",
+ "# print(datasetPath + \"/\"+ element + \"/\" + element)\n",
+ "# print(count, countTest)\n",
+ "\n",
+ "# image = cv2.resize(image, (128,128))\n",
+ "# trainX.insert(image)\n",
+ "# to_categorical(labels)\n",
+ "\n",
+ "\n",
+ "# def load_dataset(datasetPath):\n",
+ "# # grab the paths to all images in our dataset directory, then\n",
+ "# # initialize our lists of images\n",
+ "# imagePaths = os.listdir(datasetPath)\n",
+ "# trainXList = []\n",
+ "# testXList = []\n",
+ "# testX = np.array([])\n",
+ "# trainY = np.array([])\n",
+ "# trainY = np.array([])\n",
+ "# testY = np.array([])\n",
+ "\n",
+ "# testI = 0 \n",
+ " \n",
+ "# # loop over the image paths\n",
+ "# for directories in imagePaths:\n",
+ "# tempF= []\n",
+ "# tempNF = []\n",
+ " \n",
+ "# for element in os.listdir(datasetPath + \"/\"+ directories):\n",
+ "# if re.search(\".jpg\", element):\n",
+ "# image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# image = cv2.resize(image, (128,128))\n",
+ "# if \"+\" in element:\n",
+ "# tempF.append(image)\n",
+ "# else:\n",
+ "# tempNF.append(image)\n",
+ " \n",
+ "# tempF = np.array(tempF, dtype=\"float32\")\n",
+ "# tempNF = np.array(tempNF, dtype=\"float32\")\n",
+ " \n",
+ "# fireLabels = np.ones((tempF.shape[0],))\n",
+ "# nonFireLabels = np.zeros((tempNF.shape[0],))\n",
+ "# data = np.vstack([tempF, tempNF])\n",
+ "# labels = np.hstack([fireLabels, nonFireLabels])\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ " \n",
+ "# #print(labels)\n",
+ " \n",
+ "# data /= 255\n",
+ "\n",
+ "# (t_trainX, t_testX, t_trainY, t_testY) = train_test_split(data, labels,\n",
+ "# test_size=0.2, random_state=42)\n",
+ " \n",
+ "# trainXList.append(t_trainX)\n",
+ "# testXList.append(t_testX)\n",
+ "# print(t_trainY.shape, trainY.shape)\n",
+ " \n",
+ "# if trainY.size == 0:\n",
+ "# trainY = t_trainY\n",
+ "# testY = t_testY\n",
+ "# else:\n",
+ "# trainY = np.append(trainY, t_trainY, axis = 0)\n",
+ "# testY = np.append(testY, t_testY, axis = 0)\n",
+ "\n",
+ " \n",
+ "# trainX = np.vstack(trainXList)\n",
+ "# testX = np.vstack(testXList)\n",
+ "# trainY = np.hstack(trainYList)\n",
+ "# testY = np.hstack(testYList)\n",
+ " \n",
+ "# labels = np.append(trainY, testY)\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ " \n",
+ "# print(trainX.shape, testX.shape, trainY.shape, testY.shape)\n",
+ " \n",
+ "# return trainX, testX, trainY, testY, classWeight\n",
+ " \n",
+ "# # load the image and resize it to be a fixed 128x128 pixels,\n",
+ "# # ignoring aspect ratio\n",
+ "# # image = cv2.imread(imagePath)\n",
+ "# # image = cv2.resize(image, (128, 128))\n",
+ " \n",
+ "# # add the image to the data lists\n",
+ "# # data.append(image)\n",
+ "\n",
+ "# # return the data list as a NumPy array\n",
+ "# # return np.array(data, dtype=\"float32\")\n",
+ "\n",
+ "# labels = np.append(trainY, testY)\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ "# classWeight\n",
+ "\n",
+ "# from numpy import asarray\n",
+ "# from numpy import save\n",
+ "# from numpy import load\n",
+ "\n",
+ "# try:\n",
+ "# fireData = load(\"firedata1.npy\")\n",
+ "# nonFireData = load(\"nonfiredata1.npy\")\n",
+ "# except IOError:\n",
+ "# print(\"Loading...\")\n",
+ "# fireData = load_dataset(FIRE_PATH)\n",
+ "# nonFireData = load_dataset(NON_FIRE_PATH)\n",
+ "# save(\"firedata1.npy\", fireData)\n",
+ "# save(\"nonfiredata1.npy\", nonFireData)\n",
+ "\n",
+ "# fireLabels = np.ones((fireData.shape[0],))\n",
+ "# nonFireLabels = np.zeros((nonFireData.shape[0],))\n",
+ "\n",
+ "# data = np.vstack([fireData, nonFireData])\n",
+ "# labels = np.hstack([fireLabels, nonFireLabels])\n",
+ "# data /= 255\n",
+ "# data.shape\n",
+ "\n",
+ "# labels = to_categorical(labels, num_classes=2)\n",
+ "# classTotals = labels.sum(axis=0)\n",
+ "# classWeight = classTotals.max() / classTotals\n",
+ "\n",
+ "# im = Image.open(\"/userdata/kerasData/images/hpwren.ucsd.edu/HWB/HPWREN-FIgLib/20180614-Hope-wc-e-mobo-c/1529002400_+01440.jpg\")\n",
+ "# a = np.asarray(im)\n",
+ "# a = a/255\n",
+ "# # cv2.imread(\"/userdata/kerasData/images/hpwren.ucsd.edu/HWB/HPWREN-FIgLib/20180614-Hope-wc-e-mobo-c/1529002400_+01440.jpg\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# testX = []\n",
+ "# trainX = []\n",
+ "# validationX = []\n",
+ "# pixelSize = {}\n",
+ "# datasetPath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "# savepath = \"/userdata/kerasData/preloaded\"\n",
+ "\n",
+ "# firstTrigger = True\n",
+ "\n",
+ "# count = 0\n",
+ "# fireCount = 0\n",
+ "# test_label = []\n",
+ "# train_label = []\n",
+ "# validation_label = []\n",
+ "# finfin = np.array([])\n",
+ "\n",
+ "# for index,test in enumerate(onlyfiles):\n",
+ "# for fire in test:\n",
+ "# testX= []\n",
+ "# fireCount +=1\n",
+ "# print(f'{fire} - fire number {fireCount}')\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# for element in os.listdir(datasetPath + \"/\"+ fire):\n",
+ "# count +=1\n",
+ "# print(count)\n",
+ "# if \"+\" in element:\n",
+ "# test_label.append(1)\n",
+ "# else:\n",
+ "# test_label.append(0)\n",
+ "# fire_im = Image.open(datasetPath + \"/\" + fire + \"/\" + element)\n",
+ "# pixelSize[fire].add(fire_im.size)\n",
+ "# try:\n",
+ "# fire_im = fire_im.resize((2048,1536))\n",
+ "# except Error:\n",
+ "# print(fire)\n",
+ "# leftout.append(fire)\n",
+ "# break\n",
+ "\n",
+ "# inArrayim = np.asarray(fire_im) \n",
+ "# inArrayim = inArrayim/255\n",
+ "\n",
+ "# # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # image = cv2.resize(image, (128,128))\n",
+ "# testX.append(inArrayim)\n",
+ "# name = f\"\n",
+ "# print(\"DONE\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/OGimplementation.ipynb b/scripts/OGimplementation.ipynb
new file mode 100644
index 0000000..23006e3
--- /dev/null
+++ b/scripts/OGimplementation.ipynb
@@ -0,0 +1,7374 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import BatchNormalization\n",
+ "from tensorflow.keras.layers import SeparableConv2D\n",
+ "from tensorflow.keras.layers import MaxPooling2D\n",
+ "from tensorflow.keras.layers import Activation\n",
+ "from tensorflow.keras.layers import Flatten\n",
+ "from tensorflow.keras.layers import Dropout\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\") \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\")\n",
+ " \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import tensorflow as tf\n",
+ "from os import listdir\n",
+ "from os.path import isdir, join, isfile\n",
+ "from numpy import asarray\n",
+ "from numpy import save\n",
+ "import itertools\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\")\n",
+ " \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import pandas as pd\n",
+ "import keras"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def is_jpg(filename):\n",
+ " try:\n",
+ " i=Image.open(filename)\n",
+ " return i.format =='JPEG'\n",
+ " except IOError:\n",
+ " return False\n",
+ " \n",
+ "def loadDataARRAY(savePath):\n",
+ " pixelSize = {}\n",
+ "\n",
+ " testX = []\n",
+ " trainX = []\n",
+ " validationX = []\n",
+ "\n",
+ " test_label = []\n",
+ " train_label = []\n",
+ " validation_label = []\n",
+ "\n",
+ " nonfirecount = 0\n",
+ " fireCount = 0 \n",
+ " TRAIN_PATH = \"/userdata/kerasData/preloaded/recreate_2/train\"\n",
+ "# TEST_PATH = \"/userdata/kerasData/preloaded/recreate_2/test\"\n",
+ " VALIDATION_PATH = \"/userdata/kerasData/preloaded/recreate_2/validation\"\n",
+ "\n",
+ " train_fire = os.listdir(f\"{TRAIN_PATH}/fire\")\n",
+ " train_nonfire = os.listdir(f\"{TRAIN_PATH}/nonfire\")\n",
+ "\n",
+ "# test_fire = os.listdir(f\"{TEST_PATH}/fire\")\n",
+ "# test_nonfire = os.listdir(f\"{TEST_PATH}/nonfire\")\n",
+ "\n",
+ " validation_fire = os.listdir(f\"{VALIDATION_PATH}/fire\")\n",
+ " validation_nonfire = os.listdir(f\"{VALIDATION_PATH}/nonfire\")\n",
+ "\n",
+ "# print(f\"train: {len(train_fire)} FIRES {len(train_nonfire)} NONFIRES, test: {len(test_fire)} FIRES {len(test_nonfire)} NONFIRES, validation: {len(validation_fire)} FIRES {len(validation_nonfire)} NONFIRES\")\n",
+ " i = 0\n",
+ "\n",
+ "\n",
+ " for path in train_fire:\n",
+ " fire = f\"{TRAIN_PATH}/fire/{path}\"\n",
+ " if is_jpg(fire):\n",
+ " fireCount +=1\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ " fire_im = Image.open(fire)\n",
+ " pixelSize[fire].add(fire_im.size)\n",
+ " fire_im = fire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(fire_im) \n",
+ " inArrayim = inArrayim/255\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} TRAIN\")\n",
+ " trainX.append(inArrayim)\n",
+ " train_label.append(1)\n",
+ "\n",
+ " for path in train_nonfire:\n",
+ " nonfire = f\"{TRAIN_PATH}/nonfire/{path}\"\n",
+ " if is_jpg(nonfire):\n",
+ " nonfirecount +=1\n",
+ " pixelSize.setdefault(nonfire, set([]))\n",
+ " nonfire_im = Image.open(nonfire)\n",
+ " pixelSize[nonfire].add(nonfire_im.size)\n",
+ "\n",
+ " nonfire_im = nonfire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(nonfire_im) \n",
+ " inArrayim = inArrayim/255\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} TRAIN\")\n",
+ " trainX.append(inArrayim)\n",
+ " train_label.append(0)\n",
+ "\n",
+ "# for path in test_fire:\n",
+ "# fire = f\"{TEST_PATH}/fire/{path}\"\n",
+ "# if is_jpg(fire):\n",
+ "# fireCount+=1\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# fire_im = Image.open(fire)\n",
+ "# pixelSize[fire].add(fire_im.size)\n",
+ "# fire_im = fire_im.resize((128,128))\n",
+ "# inArrayim = np.asarray(fire_im)\n",
+ "# inArrayim = inArrayim/255\n",
+ "# # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# # image = cv2.resize(image, (128,128))\n",
+ "# shape = inArrayim.shape\n",
+ "# if(shape == (128, 128, 3)):\n",
+ "# print(f\"{path} TEST\")\n",
+ "# testX.append(inArrayim)\n",
+ "# test_label.append(1)\n",
+ "\n",
+ "# for path in test_nonfire:\n",
+ "# nonfire = f\"{TEST_PATH}/nonfire/{path}\"\n",
+ "# if is_jpg(nonfire):\n",
+ "# nonfirecount +=1\n",
+ "# pixelSize.setdefault(nonfire, set([]))\n",
+ "# nonfire_im = Image.open(nonfire)\n",
+ "# pixelSize[nonfire].add(nonfire_im.size)\n",
+ "# nonfire_im = nonfire_im.resize((128,128))\n",
+ "# inArrayim = np.asarray(nonfire_im) \n",
+ "# inArrayim = inArrayim/255\n",
+ "# shape = inArrayim.shape\n",
+ "# if(shape == (128, 128, 3)):\n",
+ "# print(f\"{path} TEST\")\n",
+ "# testX.append(inArrayim)\n",
+ "# test_label.append(0)\n",
+ "\n",
+ " for path in validation_fire:\n",
+ " fire = f\"{VALIDATION_PATH}/fire/{path}\"\n",
+ " if is_jpg(fire):\n",
+ " print(f\"{path} VALIDATION\")\n",
+ " fireCount+=1\n",
+ " pixelSize.setdefault(fire, set([]))\n",
+ "\n",
+ " fire_im = Image.open(fire)\n",
+ " pixelSize[fire].add(fire_im.size)\n",
+ " fire_im = fire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(fire_im)\n",
+ " inArrayim = inArrayim/255\n",
+ " # image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ " # image = cv2.resize(image, (128,128))\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} VALIDATION\")\n",
+ " validationX.append(inArrayim)\n",
+ " validation_label.append(1)\n",
+ "\n",
+ " for path in validation_nonfire:\n",
+ " nonfire =f\"{VALIDATION_PATH}/nonfire/{path}\"\n",
+ " if is_jpg(nonfire):\n",
+ " nonfirecount +=1\n",
+ " pixelSize.setdefault(nonfire, set([]))\n",
+ " nonfire_im = Image.open(nonfire)\n",
+ " pixelSize[nonfire].add(nonfire_im.size)\n",
+ " nonfire_im = nonfire_im.resize((128,128))\n",
+ " inArrayim = np.asarray(nonfire_im) \n",
+ " inArrayim = inArrayim/255\n",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " print(f\"{path} VALIDATION\")\n",
+ " validationX.append(inArrayim)\n",
+ " validation_label.append(0)\n",
+ "\n",
+ " save(f\"{savePath}rere_trainX.npy\", trainX)\n",
+ "# save(f\"{savePath}testX.npy\", testX)\n",
+ " save(f\"{savePath}rere_validationX.npy\", validationX)\n",
+ " \n",
+ " trainY = np.array(train_label)\n",
+ " testY = np.array(test_label)\n",
+ " validationY = np.array(validation_label)\n",
+ " \n",
+ " save(f\"{savePath}rere_trainY.npy\", trainY)\n",
+ "# save(f\"{savePath}testY.npy\", testY)\n",
+ " save(f\"{savePath}rere_validationY.npy\", validationY)\n",
+ " \n",
+ " obj = [fireCount, nonfirecount]\n",
+ " labels = np.append(trainY, testY)\n",
+ " labels = np.append(labels, validationY)\n",
+ " labels = to_categorical(labels, num_classes=2)\n",
+ " classTotals = labels.sum(axis=0)\n",
+ " print(classTotals, obj)\n",
+ " \n",
+ " classWeight = classTotals.max() / classTotals\n",
+ " save(f\"{savePath}rere_classWeight.npy\", classWeight)\n",
+ " return trainX, testX, validationX, trainY, testY, validationY"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "opencountry_land352.jpg VALIDATION\n",
+ "street_par185.jpg VALIDATION\n",
+ "street_par143.jpg VALIDATION\n",
+ "street_street94.jpg VALIDATION\n",
+ "mountain_land142.jpg VALIDATION\n",
+ "mountain_land26.jpg VALIDATION\n",
+ "tallbuilding_art1481.jpg VALIDATION\n",
+ "forest_nat203.jpg VALIDATION\n",
+ "coast_n291057.jpg VALIDATION\n",
+ "insidecity_urb991.jpg VALIDATION\n",
+ "opencountry_land616.jpg VALIDATION\n",
+ "insidecity_a129053.jpg VALIDATION\n",
+ "mountain_sharp59.jpg VALIDATION\n",
+ "forest_nat719.jpg VALIDATION\n",
+ "insidecity_par104.jpg VALIDATION\n",
+ "tallbuilding_urban828.jpg VALIDATION\n",
+ "insidecity_a804060.jpg VALIDATION\n",
+ "forest_urb767.jpg VALIDATION\n",
+ "highway_art576.jpg VALIDATION\n",
+ "coast_bea14.jpg VALIDATION\n",
+ "mountain_nat686.jpg VALIDATION\n",
+ "forest_nat312.jpg VALIDATION\n",
+ "opencountry_nat965.jpg VALIDATION\n",
+ "opencountry_land271.jpg VALIDATION\n",
+ "highway_bost172.jpg VALIDATION\n",
+ "insidecity_boston149.jpg VALIDATION\n",
+ "street_par52.jpg VALIDATION\n",
+ "insidecity_urb612.jpg VALIDATION\n",
+ "street_par22.jpg VALIDATION\n",
+ "opencountry_moun17.jpg VALIDATION\n",
+ "forest_text41.jpg VALIDATION\n",
+ "mountain_land15.jpg VALIDATION\n",
+ "mountain_n603032.jpg VALIDATION\n",
+ "highway_gre657.jpg VALIDATION\n",
+ "tallbuilding_urban1110.jpg VALIDATION\n",
+ "forest_natu428.jpg VALIDATION\n",
+ "tallbuilding_a244081.jpg VALIDATION\n",
+ "forest_land159.jpg VALIDATION\n",
+ "mountain_nat88.jpg VALIDATION\n",
+ "insidecity_urb362.jpg VALIDATION\n",
+ "opencountry_nat873.jpg VALIDATION\n",
+ "forest_nat619.jpg VALIDATION\n",
+ "mountain_land13.jpg VALIDATION\n",
+ "insidecity_bost141.jpg VALIDATION\n",
+ "tallbuilding_a803053.jpg VALIDATION\n",
+ "coast_nat755.jpg VALIDATION\n",
+ "tallbuilding_a805085.jpg VALIDATION\n",
+ "coast_nat908.jpg VALIDATION\n",
+ "coast_n736062.jpg VALIDATION\n",
+ "insidecity_urb881.jpg VALIDATION\n",
+ "opencountry_natu55.jpg VALIDATION\n",
+ "opencountry_land416.jpg VALIDATION\n",
+ "insidecity_art1597.jpg VALIDATION\n",
+ "mountain_land778.jpg VALIDATION\n",
+ "opencountry_open42.jpg VALIDATION\n",
+ "coast_n739046.jpg VALIDATION\n",
+ "forest_for132.jpg VALIDATION\n",
+ "mountain_land644.jpg VALIDATION\n",
+ "tallbuilding_urban744.jpg VALIDATION\n",
+ "insidecity_art770.jpg VALIDATION\n",
+ "mountain_n44004.jpg VALIDATION\n",
+ "opencountry_fie14.jpg VALIDATION\n",
+ "opencountry_natu535.jpg VALIDATION\n",
+ "mountain_nat1207.jpg VALIDATION\n",
+ "highway_art489.jpg VALIDATION\n",
+ "mountain_nat485.jpg VALIDATION\n",
+ "forest_for116.jpg VALIDATION\n",
+ "forest_land64.jpg VALIDATION\n",
+ "opencountry_nat733.jpg VALIDATION\n",
+ "coast_n347041.jpg VALIDATION\n",
+ "opencountry_natu984.jpg VALIDATION\n",
+ "tallbuilding_city7.jpg VALIDATION\n",
+ "opencountry_natu734.jpg VALIDATION\n",
+ "mountain_n213095.jpg VALIDATION\n",
+ "forest_nat211.jpg VALIDATION\n",
+ "coast_natu531.jpg VALIDATION\n",
+ "forest_for42.jpg VALIDATION\n",
+ "opencountry_land911.jpg VALIDATION\n",
+ "forest_for96.jpg VALIDATION\n",
+ "street_par14.jpg VALIDATION\n",
+ "opencountry_natu519.jpg VALIDATION\n",
+ "mountain_n255068.jpg VALIDATION\n",
+ "street_par5.jpg VALIDATION\n",
+ "forest_nat324.jpg VALIDATION\n",
+ "tallbuilding_art554.jpg VALIDATION\n",
+ "coast_nat167.jpg VALIDATION\n",
+ "coast_bea39.jpg VALIDATION\n",
+ "tallbuilding_urban983.jpg VALIDATION\n",
+ "opencountry_land663.jpg VALIDATION\n",
+ "forest_text48.jpg VALIDATION\n",
+ "coast_bea38.jpg VALIDATION\n",
+ "mountain_land210.jpg VALIDATION\n",
+ "highway_bost149.jpg VALIDATION\n",
+ "coast_arnat59.jpg VALIDATION\n",
+ "mountain_n266018.jpg VALIDATION\n",
+ "coast_cdmc845.jpg VALIDATION\n",
+ "highway_bost336.jpg VALIDATION\n",
+ "mountain_n371063.jpg VALIDATION\n",
+ "tallbuilding_art360.jpg VALIDATION\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mountain_nat93.jpg VALIDATION\n",
+ "forest_land760.jpg VALIDATION\n",
+ "coast_nat109.jpg VALIDATION\n",
+ "coast_n672069.jpg VALIDATION\n",
+ "mountain_nat117.jpg VALIDATION\n",
+ "highway_gre504.jpg VALIDATION\n",
+ "opencountry_natu380.jpg VALIDATION\n",
+ "mountain_nat79.jpg VALIDATION\n",
+ "mountain_sharp13.jpg VALIDATION\n",
+ "mountain_land131.jpg VALIDATION\n",
+ "opencountry_land606.jpg VALIDATION\n",
+ "highway_bost182.jpg VALIDATION\n",
+ "insidecity_art676.jpg VALIDATION\n",
+ "coast_land100.jpg VALIDATION\n",
+ "highway_gre144.jpg VALIDATION\n",
+ "coast_cdmc942.jpg VALIDATION\n",
+ "street_urb274.jpg VALIDATION\n",
+ "street_art379.jpg VALIDATION\n",
+ "tallbuilding_urb492.jpg VALIDATION\n",
+ "street_par64.jpg VALIDATION\n",
+ "forest_for85.jpg VALIDATION\n",
+ "mountain_land130.jpg VALIDATION\n",
+ "street_bost76.jpg VALIDATION\n",
+ "tallbuilding_archi603.jpg VALIDATION\n",
+ "highway_bost167.jpg VALIDATION\n",
+ "forest_nat263.jpg VALIDATION\n",
+ "tallbuilding_a438038.jpg VALIDATION\n",
+ "street_boston351.jpg VALIDATION\n",
+ "insidecity_gre19.jpg VALIDATION\n",
+ "opencountry_land348.jpg VALIDATION\n",
+ "coast_osun56.jpg VALIDATION\n",
+ "opencountry_land525.jpg VALIDATION\n",
+ "tallbuilding_sky37.jpg VALIDATION\n",
+ "insidecity_art829.jpg VALIDATION\n",
+ "insidecity_bost27.jpg VALIDATION\n",
+ "coast_n424079.jpg VALIDATION\n",
+ "street_gre130.jpg VALIDATION\n",
+ "highway_gre414.jpg VALIDATION\n",
+ "highway_bost154.jpg VALIDATION\n",
+ "highway_urb754.jpg VALIDATION\n",
+ "insidecity_urb584.jpg VALIDATION\n",
+ "tallbuilding_art181.jpg VALIDATION\n",
+ "street_par125.jpg VALIDATION\n",
+ "opencountry_nat511.jpg VALIDATION\n",
+ "forest_bost190.jpg VALIDATION\n",
+ "forest_natu707.jpg VALIDATION\n",
+ "tallbuilding_urban982.jpg VALIDATION\n",
+ "mountain_n18009.jpg VALIDATION\n",
+ "mountain_n737041.jpg VALIDATION\n",
+ "insidecity_boston305.jpg VALIDATION\n",
+ "mountain_sharp74.jpg VALIDATION\n",
+ "insidecity_a462055.jpg VALIDATION\n",
+ "highway_bost158.jpg VALIDATION\n",
+ "opencountry_n251011.jpg VALIDATION\n",
+ "insidecity_urb844.jpg VALIDATION\n",
+ "opencountry_urb969.jpg VALIDATION\n",
+ "mountain_nat78.jpg VALIDATION\n",
+ "opencountry_land290.jpg VALIDATION\n",
+ "insidecity_art641.jpg VALIDATION\n",
+ "mountain_nat1003.jpg VALIDATION\n",
+ "opencountry_natu138.jpg VALIDATION\n",
+ "highway_art885.jpg VALIDATION\n",
+ "forest_for84.jpg VALIDATION\n",
+ "mountain_land223.jpg VALIDATION\n",
+ "coast_nat1091.jpg VALIDATION\n",
+ "highway_gre50.jpg VALIDATION\n",
+ "mountain_land161.jpg VALIDATION\n",
+ "tallbuilding_art219.jpg VALIDATION\n",
+ "mountain_n480098.jpg VALIDATION\n",
+ "coast_nat910.jpg VALIDATION\n",
+ "mountain_nat801.jpg VALIDATION\n",
+ "highway_gre470.jpg VALIDATION\n",
+ "highway_gre473.jpg VALIDATION\n",
+ "tallbuilding_city50.jpg VALIDATION\n",
+ "street_gre11.jpg VALIDATION\n",
+ "highway_gre125.jpg VALIDATION\n",
+ "coast_natu815.jpg VALIDATION\n",
+ "forest_natu169.jpg VALIDATION\n",
+ "highway_gre474.jpg VALIDATION\n",
+ "[2688. 1268.] [1281, 2688]\n"
+ ]
+ }
+ ],
+ "source": [
+ "trainX, testX, validationX, trainY, testY, validationY = loadDataARRAY(\"/userdata/kerasData/preloaded/recreate/loaded_arrays/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0., 1.],\n",
+ " [0., 1.],\n",
+ " [0., 1.],\n",
+ " ...,\n",
+ " [1., 0.],\n",
+ " [1., 0.],\n",
+ " [1., 0.]], dtype=float32)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def loadData(pathToFiles):\n",
+ " Xtrain = np.load(f\"{pathToFiles}trainX.npy\")\n",
+ "# Xtest = np.load(f\"{pathToFiles}testX.npy\")\n",
+ " XValidation = np.load(f\"{pathToFiles}validationX.npy\")\n",
+ " \n",
+ " Ytrain = np.load(f\"{pathToFiles}trainY.npy\")\n",
+ "# Ytest = np.load(f\"{pathToFiles}testY.npy\")\n",
+ " YValidation = np.load(f\"{pathToFiles}validationY.npy\")\n",
+ " \n",
+ " classWeight = np.load(f\"{pathToFiles}classWeight.npy\")\n",
+ " return Xtrain, XValidation, Ytrain, YValidation, classWeight\n",
+ "\n",
+ "mypath = \"/userdata/kerasData/preloaded/loaded_arrays/recreate_3/\"\n",
+ "Xtrain, Xvalidation, Ytrain, Yvalidation, classWeight = loadData(mypath)\n",
+ "\n",
+ "Ytrain = to_categorical(Ytrain, num_classes=2)\n",
+ "Yvalidation =to_categorical(Yvalidation, num_classes=2)\n",
+ "\n",
+ "Yvalidation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(994, 2)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Yvalidation.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class FireDetectionNet:\n",
+ " @staticmethod\n",
+ " def build(width, height, depth):\n",
+ " # initialize the model along with the input shape to be\n",
+ " # \"channels last\" and the channels dimension itself\n",
+ " model = Sequential()\n",
+ " inputShape = (height, width, depth)\n",
+ " chanDim = -1\n",
+ " \n",
+ " model.add(SeparableConv2D(16, (7, 7), padding=\"same\",\n",
+ " input_shape=inputShape))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(32, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(Flatten())\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # second set of FC => RELU layers\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # softmax classifier\n",
+ " model.add(Dense(2))\n",
+ " model.add(Activation(\"softmax\"))\n",
+ "\n",
+ " # return the constructed network architecture\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from keras import backend as K\n",
+ "\n",
+ "\n",
+ "def f1(y_true, y_pred):\n",
+ " \n",
+ " def recall(y_true, y_pred):\n",
+ " \"\"\"Recall metric.\n",
+ "\n",
+ " Only computes a batch-wise average of recall.\n",
+ "\n",
+ " Computes the recall, a metric for multi-label classification of\n",
+ " how many relevant items are selected.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ " def precision(y_true, y_pred):\n",
+ " \"\"\"Precision metric.\n",
+ "\n",
+ " Only computes a batch-wise average of precision.\n",
+ "\n",
+ " Computes the precision, a metric for multi-label classification of\n",
+ " how many selected items are relevant.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ " precision = precision(y_true, y_pred)\n",
+ " recall = recall(y_true, y_pred)\n",
+ " return 2*((precision*recall)/(precision+recall+K.epsilon()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define the initial learning rate, batch size, and number of epochs\n",
+ "INIT_LR = 1e-2\n",
+ "BATCH_SIZE = 64\n",
+ "NUM_EPOCHS = 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<tensorflow.python.keras.losses.BinaryCrossentropy at 0x7fb9be28c908>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bincross = tf.keras.losses.BinaryCrossentropy(\n",
+ " from_logits=False, label_smoothing=0,\n",
+ " name='binary_crossentropy'\n",
+ ")\n",
+ "\n",
+ "bincross"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/preloaded/madeModels/OGRUN_I3/assets\n",
+ "Epoch 1/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.7833 - accuracy: 0.6366 - precision_1: 0.6366 - recall_1: 0.6366 - f1: 0.6397\n",
+ "Epoch 00001: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.7833 - accuracy: 0.6366 - precision_1: 0.6366 - recall_1: 0.6366 - f1: 0.6397 - val_loss: 1.0734 - val_accuracy: 0.4306 - val_precision_1: 0.4306 - val_recall_1: 0.4306 - val_f1: 0.4180\n",
+ "Epoch 2/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.5048 - accuracy: 0.7533 - precision_1: 0.7533 - recall_1: 0.7533 - f1: 0.7537\n",
+ "Epoch 00002: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.5048 - accuracy: 0.7533 - precision_1: 0.7533 - recall_1: 0.7533 - f1: 0.7537 - val_loss: 0.6034 - val_accuracy: 0.6781 - val_precision_1: 0.6781 - val_recall_1: 0.6781 - val_f1: 0.6875\n",
+ "Epoch 3/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4731 - accuracy: 0.7578 - precision_1: 0.7578 - recall_1: 0.7578 - f1: 0.7572\n",
+ "Epoch 00003: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 404ms/step - loss: 0.4731 - accuracy: 0.7578 - precision_1: 0.7578 - recall_1: 0.7578 - f1: 0.7572 - val_loss: 0.5742 - val_accuracy: 0.6670 - val_precision_1: 0.6670 - val_recall_1: 0.6670 - val_f1: 0.6621\n",
+ "Epoch 4/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4533 - accuracy: 0.7830 - precision_1: 0.7830 - recall_1: 0.7830 - f1: 0.7837\n",
+ "Epoch 00004: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.4533 - accuracy: 0.7830 - precision_1: 0.7830 - recall_1: 0.7830 - f1: 0.7837 - val_loss: 0.6743 - val_accuracy: 0.6519 - val_precision_1: 0.6519 - val_recall_1: 0.6519 - val_f1: 0.6621\n",
+ "Epoch 5/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4573 - accuracy: 0.7785 - precision_1: 0.7785 - recall_1: 0.7785 - f1: 0.7767\n",
+ "Epoch 00005: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.4573 - accuracy: 0.7785 - precision_1: 0.7785 - recall_1: 0.7785 - f1: 0.7767 - val_loss: 0.5713 - val_accuracy: 0.6761 - val_precision_1: 0.6761 - val_recall_1: 0.6761 - val_f1: 0.6709\n",
+ "Epoch 6/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4220 - accuracy: 0.7974 - precision_1: 0.7974 - recall_1: 0.7974 - f1: 0.7971\n",
+ "Epoch 00006: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.4220 - accuracy: 0.7974 - precision_1: 0.7974 - recall_1: 0.7974 - f1: 0.7971 - val_loss: 0.7218 - val_accuracy: 0.6559 - val_precision_1: 0.6559 - val_recall_1: 0.6559 - val_f1: 0.6660\n",
+ "Epoch 7/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3924 - accuracy: 0.8192 - precision_1: 0.8192 - recall_1: 0.8192 - f1: 0.8211\n",
+ "Epoch 00007: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.3924 - accuracy: 0.8192 - precision_1: 0.8192 - recall_1: 0.8192 - f1: 0.8211 - val_loss: 0.5270 - val_accuracy: 0.7294 - val_precision_1: 0.7294 - val_recall_1: 0.7294 - val_f1: 0.7227\n",
+ "Epoch 8/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3848 - accuracy: 0.8195 - precision_1: 0.8195 - recall_1: 0.8195 - f1: 0.8206\n",
+ "Epoch 00008: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 18s 402ms/step - loss: 0.3848 - accuracy: 0.8195 - precision_1: 0.8195 - recall_1: 0.8195 - f1: 0.8206 - val_loss: 0.6152 - val_accuracy: 0.7304 - val_precision_1: 0.7304 - val_recall_1: 0.7304 - val_f1: 0.7236\n",
+ "Epoch 9/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3775 - accuracy: 0.8257 - precision_1: 0.8257 - recall_1: 0.8257 - f1: 0.8267\n",
+ "Epoch 00009: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.3775 - accuracy: 0.8257 - precision_1: 0.8257 - recall_1: 0.8257 - f1: 0.8267 - val_loss: 0.4070 - val_accuracy: 0.8038 - val_precision_1: 0.8038 - val_recall_1: 0.8038 - val_f1: 0.8096\n",
+ "Epoch 10/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3776 - accuracy: 0.8202 - precision_1: 0.8202 - recall_1: 0.8202 - f1: 0.8178\n",
+ "Epoch 00010: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.3776 - accuracy: 0.8202 - precision_1: 0.8202 - recall_1: 0.8202 - f1: 0.8178 - val_loss: 0.6204 - val_accuracy: 0.7555 - val_precision_1: 0.7555 - val_recall_1: 0.7555 - val_f1: 0.7627\n",
+ "Epoch 11/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3597 - accuracy: 0.8292 - precision_1: 0.8292 - recall_1: 0.8292 - f1: 0.8284\n",
+ "Epoch 00011: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.3597 - accuracy: 0.8292 - precision_1: 0.8292 - recall_1: 0.8292 - f1: 0.8284 - val_loss: 0.4649 - val_accuracy: 0.7847 - val_precision_1: 0.7847 - val_recall_1: 0.7847 - val_f1: 0.7910\n",
+ "Epoch 12/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3575 - accuracy: 0.8437 - precision_1: 0.8437 - recall_1: 0.8437 - f1: 0.8427\n",
+ "Epoch 00012: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.3575 - accuracy: 0.8437 - precision_1: 0.8437 - recall_1: 0.8437 - f1: 0.8427 - val_loss: 1.3099 - val_accuracy: 0.7072 - val_precision_1: 0.7072 - val_recall_1: 0.7072 - val_f1: 0.7158\n",
+ "Epoch 13/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3423 - accuracy: 0.8464 - precision_1: 0.8464 - recall_1: 0.8464 - f1: 0.8454\n",
+ "Epoch 00013: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 18s 399ms/step - loss: 0.3423 - accuracy: 0.8464 - precision_1: 0.8464 - recall_1: 0.8464 - f1: 0.8454 - val_loss: 0.3993 - val_accuracy: 0.8219 - val_precision_1: 0.8219 - val_recall_1: 0.8219 - val_f1: 0.8271\n",
+ "Epoch 14/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3440 - accuracy: 0.8458 - precision_1: 0.8458 - recall_1: 0.8458 - f1: 0.8456\n",
+ "Epoch 00014: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 443ms/step - loss: 0.3440 - accuracy: 0.8458 - precision_1: 0.8458 - recall_1: 0.8458 - f1: 0.8456 - val_loss: 0.5951 - val_accuracy: 0.7907 - val_precision_1: 0.7907 - val_recall_1: 0.7907 - val_f1: 0.7969\n",
+ "Epoch 15/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3306 - accuracy: 0.8533 - precision_1: 0.8533 - recall_1: 0.8533 - f1: 0.8539\n",
+ "Epoch 00015: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.3306 - accuracy: 0.8533 - precision_1: 0.8533 - recall_1: 0.8533 - f1: 0.8539 - val_loss: 0.3406 - val_accuracy: 0.8551 - val_precision_1: 0.8551 - val_recall_1: 0.8551 - val_f1: 0.8447\n",
+ "Epoch 16/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3249 - accuracy: 0.8616 - precision_1: 0.8616 - recall_1: 0.8616 - f1: 0.8629\n",
+ "Epoch 00016: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.3249 - accuracy: 0.8616 - precision_1: 0.8616 - recall_1: 0.8616 - f1: 0.8629 - val_loss: 0.3552 - val_accuracy: 0.8451 - val_precision_1: 0.8451 - val_recall_1: 0.8451 - val_f1: 0.8496\n",
+ "Epoch 17/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.3285 - accuracy: 0.8524 - precision_1: 0.8524 - recall_1: 0.8524 - f1: 0.8524\n",
+ "Epoch 00017: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.3298 - accuracy: 0.8520 - precision_1: 0.8520 - recall_1: 0.8520 - f1: 0.8508 - val_loss: 0.4336 - val_accuracy: 0.8189 - val_precision_1: 0.8189 - val_recall_1: 0.8189 - val_f1: 0.8242\n",
+ "Epoch 18/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3262 - accuracy: 0.8578 - precision_1: 0.8578 - recall_1: 0.8578 - f1: 0.8575\n",
+ "Epoch 00018: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.3262 - accuracy: 0.8578 - precision_1: 0.8578 - recall_1: 0.8578 - f1: 0.8575 - val_loss: 0.4108 - val_accuracy: 0.8280 - val_precision_1: 0.8280 - val_recall_1: 0.8280 - val_f1: 0.8330\n",
+ "Epoch 19/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3166 - accuracy: 0.8620 - precision_1: 0.8620 - recall_1: 0.8620 - f1: 0.8633\n",
+ "Epoch 00019: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.3166 - accuracy: 0.8620 - precision_1: 0.8620 - recall_1: 0.8620 - f1: 0.8633 - val_loss: 0.8205 - val_accuracy: 0.7455 - val_precision_1: 0.7455 - val_recall_1: 0.7455 - val_f1: 0.7529\n",
+ "Epoch 20/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3148 - accuracy: 0.8599 - precision_1: 0.8599 - recall_1: 0.8599 - f1: 0.8578\n",
+ "Epoch 00020: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.3148 - accuracy: 0.8599 - precision_1: 0.8599 - recall_1: 0.8599 - f1: 0.8578 - val_loss: 0.3517 - val_accuracy: 0.8451 - val_precision_1: 0.8451 - val_recall_1: 0.8451 - val_f1: 0.8496\n",
+ "Epoch 21/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3186 - accuracy: 0.8675 - precision_1: 0.8675 - recall_1: 0.8675 - f1: 0.8675\n",
+ "Epoch 00021: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.3186 - accuracy: 0.8675 - precision_1: 0.8675 - recall_1: 0.8675 - f1: 0.8675 - val_loss: 0.3300 - val_accuracy: 0.8461 - val_precision_1: 0.8461 - val_recall_1: 0.8461 - val_f1: 0.8506\n",
+ "Epoch 22/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3086 - accuracy: 0.8647 - precision_1: 0.8647 - recall_1: 0.8647 - f1: 0.8651\n",
+ "Epoch 00022: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 405ms/step - loss: 0.3086 - accuracy: 0.8647 - precision_1: 0.8647 - recall_1: 0.8647 - f1: 0.8651 - val_loss: 1.3610 - val_accuracy: 0.7032 - val_precision_1: 0.7032 - val_recall_1: 0.7032 - val_f1: 0.7119\n",
+ "Epoch 23/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3182 - accuracy: 0.8623 - precision_1: 0.8623 - recall_1: 0.8623 - f1: 0.8636\n",
+ "Epoch 00023: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.3182 - accuracy: 0.8623 - precision_1: 0.8623 - recall_1: 0.8623 - f1: 0.8636 - val_loss: 0.4473 - val_accuracy: 0.8149 - val_precision_1: 0.8149 - val_recall_1: 0.8149 - val_f1: 0.8057\n",
+ "Epoch 24/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2980 - accuracy: 0.8741 - precision_1: 0.8741 - recall_1: 0.8741 - f1: 0.8725\n",
+ "Epoch 00024: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 18s 401ms/step - loss: 0.2980 - accuracy: 0.8741 - precision_1: 0.8741 - recall_1: 0.8741 - f1: 0.8725 - val_loss: 0.3432 - val_accuracy: 0.8491 - val_precision_1: 0.8491 - val_recall_1: 0.8491 - val_f1: 0.8535\n",
+ "Epoch 25/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2953 - accuracy: 0.8723 - precision_1: 0.8723 - recall_1: 0.8723 - f1: 0.8717\n",
+ "Epoch 00025: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 469ms/step - loss: 0.2953 - accuracy: 0.8723 - precision_1: 0.8723 - recall_1: 0.8723 - f1: 0.8717 - val_loss: 0.5701 - val_accuracy: 0.7837 - val_precision_1: 0.7837 - val_recall_1: 0.7837 - val_f1: 0.7754\n",
+ "Epoch 26/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2997 - accuracy: 0.8716 - precision_1: 0.8716 - recall_1: 0.8716 - f1: 0.8702\n",
+ "Epoch 00026: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.2997 - accuracy: 0.8716 - precision_1: 0.8716 - recall_1: 0.8716 - f1: 0.8702 - val_loss: 0.3122 - val_accuracy: 0.8622 - val_precision_1: 0.8622 - val_recall_1: 0.8622 - val_f1: 0.8662\n",
+ "Epoch 27/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3034 - accuracy: 0.8689 - precision_1: 0.8689 - recall_1: 0.8689 - f1: 0.8692\n",
+ "Epoch 00027: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 405ms/step - loss: 0.3034 - accuracy: 0.8689 - precision_1: 0.8689 - recall_1: 0.8689 - f1: 0.8692 - val_loss: 0.5612 - val_accuracy: 0.7736 - val_precision_1: 0.7736 - val_recall_1: 0.7736 - val_f1: 0.7803\n",
+ "Epoch 28/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2930 - accuracy: 0.8730 - precision_1: 0.8730 - recall_1: 0.8730 - f1: 0.8715\n",
+ "Epoch 00028: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2930 - accuracy: 0.8730 - precision_1: 0.8730 - recall_1: 0.8730 - f1: 0.8715 - val_loss: 0.3758 - val_accuracy: 0.8229 - val_precision_1: 0.8229 - val_recall_1: 0.8229 - val_f1: 0.8135\n",
+ "Epoch 29/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3153 - accuracy: 0.8607 - precision_1: 0.8607 - recall_1: 0.8607 - f1: 0.8607\n",
+ "Epoch 00029: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.3153 - accuracy: 0.8607 - precision_1: 0.8607 - recall_1: 0.8607 - f1: 0.8607 - val_loss: 0.3641 - val_accuracy: 0.8310 - val_precision_1: 0.8310 - val_recall_1: 0.8310 - val_f1: 0.8359\n",
+ "Epoch 30/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3026 - accuracy: 0.8647 - precision_1: 0.8647 - recall_1: 0.8647 - f1: 0.8625\n",
+ "Epoch 00030: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.3026 - accuracy: 0.8647 - precision_1: 0.8647 - recall_1: 0.8647 - f1: 0.8625 - val_loss: 0.3746 - val_accuracy: 0.8260 - val_precision_1: 0.8260 - val_recall_1: 0.8260 - val_f1: 0.8164\n",
+ "Epoch 31/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2887 - accuracy: 0.8730 - precision_1: 0.8730 - recall_1: 0.8730 - f1: 0.8715\n",
+ "Epoch 00031: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2887 - accuracy: 0.8730 - precision_1: 0.8730 - recall_1: 0.8730 - f1: 0.8715 - val_loss: 0.3128 - val_accuracy: 0.8642 - val_precision_1: 0.8642 - val_recall_1: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 32/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2808 - accuracy: 0.8823 - precision_1: 0.8823 - recall_1: 0.8823 - f1: 0.8807\n",
+ "Epoch 00032: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 25s 550ms/step - loss: 0.2808 - accuracy: 0.8823 - precision_1: 0.8823 - recall_1: 0.8823 - f1: 0.8807 - val_loss: 0.3309 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 33/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3009 - accuracy: 0.8675 - precision_1: 0.8675 - recall_1: 0.8675 - f1: 0.8661\n",
+ "Epoch 00033: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.3009 - accuracy: 0.8675 - precision_1: 0.8675 - recall_1: 0.8675 - f1: 0.8661 - val_loss: 0.3655 - val_accuracy: 0.8249 - val_precision_1: 0.8249 - val_recall_1: 0.8249 - val_f1: 0.8301\n",
+ "Epoch 34/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2692 - accuracy: 0.8892 - precision_1: 0.8892 - recall_1: 0.8892 - f1: 0.8884\n",
+ "Epoch 00034: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.2692 - accuracy: 0.8892 - precision_1: 0.8892 - recall_1: 0.8892 - f1: 0.8884 - val_loss: 0.2956 - val_accuracy: 0.8672 - val_precision_1: 0.8672 - val_recall_1: 0.8672 - val_f1: 0.8711\n",
+ "Epoch 35/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2771 - accuracy: 0.8830 - precision_1: 0.8830 - recall_1: 0.8830 - f1: 0.8840\n",
+ "Epoch 00035: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2771 - accuracy: 0.8830 - precision_1: 0.8830 - recall_1: 0.8830 - f1: 0.8840 - val_loss: 0.6475 - val_accuracy: 0.7646 - val_precision_1: 0.7646 - val_recall_1: 0.7646 - val_f1: 0.7568\n",
+ "Epoch 36/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2731 - accuracy: 0.8844 - precision_1: 0.8844 - recall_1: 0.8844 - f1: 0.8845\n",
+ "Epoch 00036: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.2731 - accuracy: 0.8844 - precision_1: 0.8844 - recall_1: 0.8844 - f1: 0.8845 - val_loss: 0.3063 - val_accuracy: 0.8753 - val_precision_1: 0.8753 - val_recall_1: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 37/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2754 - accuracy: 0.8810 - precision_1: 0.8810 - recall_1: 0.8810 - f1: 0.8819\n",
+ "Epoch 00037: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.2754 - accuracy: 0.8810 - precision_1: 0.8810 - recall_1: 0.8810 - f1: 0.8819 - val_loss: 0.3319 - val_accuracy: 0.8421 - val_precision_1: 0.8421 - val_recall_1: 0.8421 - val_f1: 0.8467\n",
+ "Epoch 38/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.8813 - precision_1: 0.8813 - recall_1: 0.8813 - f1: 0.8823\n",
+ "Epoch 00038: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.2744 - accuracy: 0.8813 - precision_1: 0.8813 - recall_1: 0.8813 - f1: 0.8823 - val_loss: 0.3686 - val_accuracy: 0.8209 - val_precision_1: 0.8209 - val_recall_1: 0.8209 - val_f1: 0.8115\n",
+ "Epoch 39/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2774 - accuracy: 0.8741 - precision_1: 0.8741 - recall_1: 0.8741 - f1: 0.8734\n",
+ "Epoch 00039: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 18s 401ms/step - loss: 0.2774 - accuracy: 0.8741 - precision_1: 0.8741 - recall_1: 0.8741 - f1: 0.8734 - val_loss: 0.2861 - val_accuracy: 0.8672 - val_precision_1: 0.8672 - val_recall_1: 0.8672 - val_f1: 0.8711\n",
+ "Epoch 40/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2773 - accuracy: 0.8858 - precision_1: 0.8858 - recall_1: 0.8858 - f1: 0.8858\n",
+ "Epoch 00040: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2773 - accuracy: 0.8858 - precision_1: 0.8858 - recall_1: 0.8858 - f1: 0.8858 - val_loss: 0.5208 - val_accuracy: 0.7958 - val_precision_1: 0.7958 - val_recall_1: 0.7958 - val_f1: 0.7871\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 41/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2809 - accuracy: 0.8806 - precision_1: 0.8806 - recall_1: 0.8806 - f1: 0.8799\n",
+ "Epoch 00041: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.2809 - accuracy: 0.8806 - precision_1: 0.8806 - recall_1: 0.8806 - f1: 0.8799 - val_loss: 0.2981 - val_accuracy: 0.8682 - val_precision_1: 0.8682 - val_recall_1: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 42/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2600 - accuracy: 0.8910 - precision_1: 0.8910 - recall_1: 0.8910 - f1: 0.8910\n",
+ "Epoch 00042: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2600 - accuracy: 0.8910 - precision_1: 0.8910 - recall_1: 0.8910 - f1: 0.8910 - val_loss: 0.3046 - val_accuracy: 0.8753 - val_precision_1: 0.8753 - val_recall_1: 0.8753 - val_f1: 0.8643\n",
+ "Epoch 43/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2875 - accuracy: 0.8751 - precision_1: 0.8751 - recall_1: 0.8751 - f1: 0.8744\n",
+ "Epoch 00043: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.2875 - accuracy: 0.8751 - precision_1: 0.8751 - recall_1: 0.8751 - f1: 0.8744 - val_loss: 0.3117 - val_accuracy: 0.8732 - val_precision_1: 0.8732 - val_recall_1: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 44/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2741 - accuracy: 0.8889 - precision_1: 0.8889 - recall_1: 0.8889 - f1: 0.8863\n",
+ "Epoch 00044: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2741 - accuracy: 0.8889 - precision_1: 0.8889 - recall_1: 0.8889 - f1: 0.8863 - val_loss: 0.3472 - val_accuracy: 0.8330 - val_precision_1: 0.8330 - val_recall_1: 0.8330 - val_f1: 0.8379\n",
+ "Epoch 45/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2641 - accuracy: 0.8868 - precision_1: 0.8868 - recall_1: 0.8868 - f1: 0.8869\n",
+ "Epoch 00045: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2641 - accuracy: 0.8868 - precision_1: 0.8868 - recall_1: 0.8868 - f1: 0.8869 - val_loss: 0.2992 - val_accuracy: 0.8753 - val_precision_1: 0.8753 - val_recall_1: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 46/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.2663 - accuracy: 0.8865 - precision_1: 0.8865 - recall_1: 0.8865 - f1: 0.8865\n",
+ "Epoch 00046: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.2666 - accuracy: 0.8865 - precision_1: 0.8865 - recall_1: 0.8865 - f1: 0.8865 - val_loss: 0.7938 - val_accuracy: 0.7736 - val_precision_1: 0.7736 - val_recall_1: 0.7736 - val_f1: 0.7803\n",
+ "Epoch 47/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2523 - accuracy: 0.8892 - precision_1: 0.8892 - recall_1: 0.8892 - f1: 0.8875\n",
+ "Epoch 00047: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.2523 - accuracy: 0.8892 - precision_1: 0.8892 - recall_1: 0.8892 - f1: 0.8875 - val_loss: 0.2979 - val_accuracy: 0.8672 - val_precision_1: 0.8672 - val_recall_1: 0.8672 - val_f1: 0.8711\n",
+ "Epoch 48/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2660 - accuracy: 0.8837 - precision_1: 0.8837 - recall_1: 0.8837 - f1: 0.8838\n",
+ "Epoch 00048: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 434ms/step - loss: 0.2660 - accuracy: 0.8837 - precision_1: 0.8837 - recall_1: 0.8837 - f1: 0.8838 - val_loss: 0.3736 - val_accuracy: 0.8451 - val_precision_1: 0.8451 - val_recall_1: 0.8451 - val_f1: 0.8496\n",
+ "Epoch 49/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2760 - accuracy: 0.8847 - precision_1: 0.8847 - recall_1: 0.8847 - f1: 0.8831\n",
+ "Epoch 00049: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.2760 - accuracy: 0.8847 - precision_1: 0.8847 - recall_1: 0.8847 - f1: 0.8831 - val_loss: 0.2789 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8740\n",
+ "Epoch 50/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2667 - accuracy: 0.8875 - precision_1: 0.8875 - recall_1: 0.8875 - f1: 0.8867\n",
+ "Epoch 00050: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2667 - accuracy: 0.8875 - precision_1: 0.8875 - recall_1: 0.8875 - f1: 0.8867 - val_loss: 0.4567 - val_accuracy: 0.7948 - val_precision_1: 0.7948 - val_recall_1: 0.7948 - val_f1: 0.8008\n",
+ "Epoch 51/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.8810 - precision_1: 0.8810 - recall_1: 0.8810 - f1: 0.8802\n",
+ "Epoch 00051: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 446ms/step - loss: 0.2677 - accuracy: 0.8810 - precision_1: 0.8810 - recall_1: 0.8810 - f1: 0.8802 - val_loss: 0.3038 - val_accuracy: 0.8652 - val_precision_1: 0.8652 - val_recall_1: 0.8652 - val_f1: 0.8691\n",
+ "Epoch 52/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2603 - accuracy: 0.8906 - precision_1: 0.8906 - recall_1: 0.8906 - f1: 0.8906\n",
+ "Epoch 00052: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2603 - accuracy: 0.8906 - precision_1: 0.8906 - recall_1: 0.8906 - f1: 0.8906 - val_loss: 0.3098 - val_accuracy: 0.8712 - val_precision_1: 0.8712 - val_recall_1: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 53/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2549 - accuracy: 0.8930 - precision_1: 0.8930 - recall_1: 0.8930 - f1: 0.8904\n",
+ "Epoch 00053: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.2549 - accuracy: 0.8930 - precision_1: 0.8930 - recall_1: 0.8930 - f1: 0.8904 - val_loss: 0.2627 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 54/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2645 - accuracy: 0.8847 - precision_1: 0.8847 - recall_1: 0.8847 - f1: 0.8839\n",
+ "Epoch 00054: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 464ms/step - loss: 0.2645 - accuracy: 0.8847 - precision_1: 0.8847 - recall_1: 0.8847 - f1: 0.8839 - val_loss: 0.3375 - val_accuracy: 0.8380 - val_precision_1: 0.8380 - val_recall_1: 0.8380 - val_f1: 0.8428\n",
+ "Epoch 55/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2634 - accuracy: 0.8851 - precision_1: 0.8851 - recall_1: 0.8851 - f1: 0.8843\n",
+ "Epoch 00055: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2634 - accuracy: 0.8851 - precision_1: 0.8851 - recall_1: 0.8851 - f1: 0.8843 - val_loss: 0.3113 - val_accuracy: 0.8581 - val_precision_1: 0.8581 - val_recall_1: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 56/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2582 - accuracy: 0.8903 - precision_1: 0.8903 - recall_1: 0.8903 - f1: 0.8885\n",
+ "Epoch 00056: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 406ms/step - loss: 0.2582 - accuracy: 0.8903 - precision_1: 0.8903 - recall_1: 0.8903 - f1: 0.8885 - val_loss: 0.4269 - val_accuracy: 0.8089 - val_precision_1: 0.8089 - val_recall_1: 0.8089 - val_f1: 0.8145\n",
+ "Epoch 57/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2624 - accuracy: 0.8872 - precision_1: 0.8872 - recall_1: 0.8872 - f1: 0.8872\n",
+ "Epoch 00057: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.2624 - accuracy: 0.8872 - precision_1: 0.8872 - recall_1: 0.8872 - f1: 0.8872 - val_loss: 0.2763 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 58/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2693 - accuracy: 0.8882 - precision_1: 0.8882 - recall_1: 0.8882 - f1: 0.8882\n",
+ "Epoch 00058: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 405ms/step - loss: 0.2693 - accuracy: 0.8882 - precision_1: 0.8882 - recall_1: 0.8882 - f1: 0.8882 - val_loss: 0.2711 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 59/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2473 - accuracy: 0.8958 - precision_1: 0.8958 - recall_1: 0.8958 - f1: 0.8957\n",
+ "Epoch 00059: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2473 - accuracy: 0.8958 - precision_1: 0.8958 - recall_1: 0.8958 - f1: 0.8957 - val_loss: 0.3315 - val_accuracy: 0.8571 - val_precision_1: 0.8571 - val_recall_1: 0.8571 - val_f1: 0.8613\n",
+ "Epoch 60/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2542 - accuracy: 0.8961 - precision_1: 0.8961 - recall_1: 0.8961 - f1: 0.8960\n",
+ "Epoch 00060: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 473ms/step - loss: 0.2542 - accuracy: 0.8961 - precision_1: 0.8961 - recall_1: 0.8961 - f1: 0.8960 - val_loss: 0.2946 - val_accuracy: 0.8712 - val_precision_1: 0.8712 - val_recall_1: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 61/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2404 - accuracy: 0.8986 - precision_1: 0.8986 - recall_1: 0.8986 - f1: 0.8993\n",
+ "Epoch 00061: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2404 - accuracy: 0.8986 - precision_1: 0.8986 - recall_1: 0.8986 - f1: 0.8993 - val_loss: 0.2854 - val_accuracy: 0.8783 - val_precision_1: 0.8783 - val_recall_1: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 62/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2383 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8986\n",
+ "Epoch 00062: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.2383 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8986 - val_loss: 0.4450 - val_accuracy: 0.8058 - val_precision_1: 0.8058 - val_recall_1: 0.8058 - val_f1: 0.8115\n",
+ "Epoch 63/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2556 - accuracy: 0.8889 - precision_1: 0.8889 - recall_1: 0.8889 - f1: 0.8889\n",
+ "Epoch 00063: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.2556 - accuracy: 0.8889 - precision_1: 0.8889 - recall_1: 0.8889 - f1: 0.8889 - val_loss: 0.4650 - val_accuracy: 0.8109 - val_precision_1: 0.8109 - val_recall_1: 0.8109 - val_f1: 0.8164\n",
+ "Epoch 64/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2532 - accuracy: 0.8934 - precision_1: 0.8934 - recall_1: 0.8934 - f1: 0.8942\n",
+ "Epoch 00064: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2532 - accuracy: 0.8934 - precision_1: 0.8934 - recall_1: 0.8934 - f1: 0.8942 - val_loss: 0.2933 - val_accuracy: 0.8712 - val_precision_1: 0.8712 - val_recall_1: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 65/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2461 - accuracy: 0.8951 - precision_1: 0.8951 - recall_1: 0.8951 - f1: 0.8941\n",
+ "Epoch 00065: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 443ms/step - loss: 0.2461 - accuracy: 0.8951 - precision_1: 0.8951 - recall_1: 0.8951 - f1: 0.8941 - val_loss: 0.3643 - val_accuracy: 0.8400 - val_precision_1: 0.8400 - val_recall_1: 0.8400 - val_f1: 0.8301\n",
+ "Epoch 66/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.2621 - accuracy: 0.8948 - precision_1: 0.8948 - recall_1: 0.8948 - f1: 0.8948\n",
+ "Epoch 00066: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2622 - accuracy: 0.8948 - precision_1: 0.8948 - recall_1: 0.8948 - f1: 0.8947 - val_loss: 0.2748 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 67/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2452 - accuracy: 0.9003 - precision_1: 0.9003 - recall_1: 0.9003 - f1: 0.9018\n",
+ "Epoch 00067: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 505ms/step - loss: 0.2452 - accuracy: 0.9003 - precision_1: 0.9003 - recall_1: 0.9003 - f1: 0.9018 - val_loss: 0.3384 - val_accuracy: 0.8501 - val_precision_1: 0.8501 - val_recall_1: 0.8501 - val_f1: 0.8398\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 68/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2397 - accuracy: 0.9013 - precision_1: 0.9013 - recall_1: 0.9013 - f1: 0.9002\n",
+ "Epoch 00068: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2397 - accuracy: 0.9013 - precision_1: 0.9013 - recall_1: 0.9013 - f1: 0.9002 - val_loss: 0.2789 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8955\n",
+ "Epoch 69/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2361 - accuracy: 0.8989 - precision_1: 0.8989 - recall_1: 0.8989 - f1: 0.8987\n",
+ "Epoch 00069: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 407ms/step - loss: 0.2361 - accuracy: 0.8989 - precision_1: 0.8989 - recall_1: 0.8989 - f1: 0.8987 - val_loss: 0.3477 - val_accuracy: 0.8602 - val_precision_1: 0.8602 - val_recall_1: 0.8602 - val_f1: 0.8643\n",
+ "Epoch 70/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2422 - accuracy: 0.9003 - precision_1: 0.9003 - recall_1: 0.9003 - f1: 0.9018\n",
+ "Epoch 00070: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.2422 - accuracy: 0.9003 - precision_1: 0.9003 - recall_1: 0.9003 - f1: 0.9018 - val_loss: 0.3250 - val_accuracy: 0.8581 - val_precision_1: 0.8581 - val_recall_1: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 71/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2374 - accuracy: 0.9075 - precision_1: 0.9075 - recall_1: 0.9075 - f1: 0.9072\n",
+ "Epoch 00071: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.2374 - accuracy: 0.9075 - precision_1: 0.9075 - recall_1: 0.9075 - f1: 0.9072 - val_loss: 0.3354 - val_accuracy: 0.8581 - val_precision_1: 0.8581 - val_recall_1: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 72/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.8961 - precision_1: 0.8961 - recall_1: 0.8961 - f1: 0.8960\n",
+ "Epoch 00072: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2465 - accuracy: 0.8961 - precision_1: 0.8961 - recall_1: 0.8961 - f1: 0.8960 - val_loss: 0.3459 - val_accuracy: 0.8561 - val_precision_1: 0.8561 - val_recall_1: 0.8561 - val_f1: 0.8604\n",
+ "Epoch 73/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2190 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9084\n",
+ "Epoch 00073: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.2190 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9084 - val_loss: 0.2670 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 74/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2327 - accuracy: 0.9041 - precision_1: 0.9041 - recall_1: 0.9041 - f1: 0.9047\n",
+ "Epoch 00074: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2327 - accuracy: 0.9041 - precision_1: 0.9041 - recall_1: 0.9041 - f1: 0.9047 - val_loss: 0.2982 - val_accuracy: 0.8551 - val_precision_1: 0.8551 - val_recall_1: 0.8551 - val_f1: 0.8594\n",
+ "Epoch 75/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2346 - accuracy: 0.9034 - precision_1: 0.9034 - recall_1: 0.9034 - f1: 0.9040\n",
+ "Epoch 00075: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2346 - accuracy: 0.9034 - precision_1: 0.9034 - recall_1: 0.9034 - f1: 0.9040 - val_loss: 0.3443 - val_accuracy: 0.8541 - val_precision_1: 0.8541 - val_recall_1: 0.8541 - val_f1: 0.8584\n",
+ "Epoch 76/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2380 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8994\n",
+ "Epoch 00076: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2380 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8994 - val_loss: 0.3196 - val_accuracy: 0.8712 - val_precision_1: 0.8712 - val_recall_1: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 77/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.2287 - accuracy: 0.9101 - precision_1: 0.9101 - recall_1: 0.9101 - f1: 0.9101\n",
+ "Epoch 00077: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 436ms/step - loss: 0.2284 - accuracy: 0.9103 - precision_1: 0.9103 - recall_1: 0.9103 - f1: 0.9108 - val_loss: 0.2583 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 78/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2306 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.9003\n",
+ "Epoch 00078: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2306 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.9003 - val_loss: 0.2756 - val_accuracy: 0.8873 - val_precision_1: 0.8873 - val_recall_1: 0.8873 - val_f1: 0.8906\n",
+ "Epoch 79/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2229 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9089\n",
+ "Epoch 00079: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2229 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9089 - val_loss: 0.2671 - val_accuracy: 0.8873 - val_precision_1: 0.8873 - val_recall_1: 0.8873 - val_f1: 0.8906\n",
+ "Epoch 80/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2237 - accuracy: 0.9065 - precision_1: 0.9065 - recall_1: 0.9065 - f1: 0.9071\n",
+ "Epoch 00080: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.2237 - accuracy: 0.9065 - precision_1: 0.9065 - recall_1: 0.9065 - f1: 0.9071 - val_loss: 0.2927 - val_accuracy: 0.8742 - val_precision_1: 0.8742 - val_recall_1: 0.8742 - val_f1: 0.8779\n",
+ "Epoch 81/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2233 - accuracy: 0.9072 - precision_1: 0.9072 - recall_1: 0.9072 - f1: 0.9078\n",
+ "Epoch 00081: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2233 - accuracy: 0.9072 - precision_1: 0.9072 - recall_1: 0.9072 - f1: 0.9078 - val_loss: 0.4043 - val_accuracy: 0.8370 - val_precision_1: 0.8370 - val_recall_1: 0.8370 - val_f1: 0.8418\n",
+ "Epoch 82/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2351 - accuracy: 0.8989 - precision_1: 0.8989 - recall_1: 0.8989 - f1: 0.8996\n",
+ "Epoch 00082: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2351 - accuracy: 0.8989 - precision_1: 0.8989 - recall_1: 0.8989 - f1: 0.8996 - val_loss: 0.3169 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 83/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2158 - accuracy: 0.9113 - precision_1: 0.9113 - recall_1: 0.9113 - f1: 0.9118\n",
+ "Epoch 00083: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.2158 - accuracy: 0.9113 - precision_1: 0.9113 - recall_1: 0.9113 - f1: 0.9118 - val_loss: 0.2723 - val_accuracy: 0.8924 - val_precision_1: 0.8924 - val_recall_1: 0.8924 - val_f1: 0.8809\n",
+ "Epoch 84/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2084 - accuracy: 0.9124 - precision_1: 0.9124 - recall_1: 0.9124 - f1: 0.9124\n",
+ "Epoch 00084: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.2084 - accuracy: 0.9124 - precision_1: 0.9124 - recall_1: 0.9124 - f1: 0.9124 - val_loss: 0.2672 - val_accuracy: 0.8954 - val_precision_1: 0.8954 - val_recall_1: 0.8954 - val_f1: 0.8984\n",
+ "Epoch 85/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2169 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9093\n",
+ "Epoch 00085: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2169 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9093 - val_loss: 0.2627 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8789\n",
+ "Epoch 86/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2168 - accuracy: 0.9124 - precision_1: 0.9124 - recall_1: 0.9124 - f1: 0.9120\n",
+ "Epoch 00086: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2168 - accuracy: 0.9124 - precision_1: 0.9124 - recall_1: 0.9124 - f1: 0.9120 - val_loss: 0.5894 - val_accuracy: 0.8038 - val_precision_1: 0.8038 - val_recall_1: 0.8038 - val_f1: 0.8096\n",
+ "Epoch 87/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2308 - accuracy: 0.9034 - precision_1: 0.9034 - recall_1: 0.9034 - f1: 0.9040\n",
+ "Epoch 00087: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.2308 - accuracy: 0.9034 - precision_1: 0.9034 - recall_1: 0.9034 - f1: 0.9040 - val_loss: 0.2528 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 88/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2281 - accuracy: 0.9006 - precision_1: 0.9006 - recall_1: 0.9006 - f1: 0.9004\n",
+ "Epoch 00088: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2281 - accuracy: 0.9006 - precision_1: 0.9006 - recall_1: 0.9006 - f1: 0.9004 - val_loss: 0.2860 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 89/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2225 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8994\n",
+ "Epoch 00089: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 448ms/step - loss: 0.2225 - accuracy: 0.8996 - precision_1: 0.8996 - recall_1: 0.8996 - f1: 0.8994 - val_loss: 0.2709 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 90/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2293 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9081\n",
+ "Epoch 00090: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.2293 - accuracy: 0.9092 - precision_1: 0.9092 - recall_1: 0.9092 - f1: 0.9081 - val_loss: 0.3670 - val_accuracy: 0.8481 - val_precision_1: 0.8481 - val_recall_1: 0.8481 - val_f1: 0.8379\n",
+ "Epoch 91/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2154 - accuracy: 0.9158 - precision_1: 0.9158 - recall_1: 0.9158 - f1: 0.9145\n",
+ "Epoch 00091: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.2154 - accuracy: 0.9158 - precision_1: 0.9158 - recall_1: 0.9158 - f1: 0.9145 - val_loss: 0.3658 - val_accuracy: 0.8642 - val_precision_1: 0.8642 - val_recall_1: 0.8642 - val_f1: 0.8535\n",
+ "Epoch 92/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2239 - accuracy: 0.9006 - precision_1: 0.9006 - recall_1: 0.9006 - f1: 0.9022\n",
+ "Epoch 00092: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2239 - accuracy: 0.9006 - precision_1: 0.9006 - recall_1: 0.9006 - f1: 0.9022 - val_loss: 0.4757 - val_accuracy: 0.8109 - val_precision_1: 0.8109 - val_recall_1: 0.8109 - val_f1: 0.8018\n",
+ "Epoch 93/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2185 - accuracy: 0.9068 - precision_1: 0.9068 - recall_1: 0.9068 - f1: 0.9057\n",
+ "Epoch 00093: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 405ms/step - loss: 0.2185 - accuracy: 0.9068 - precision_1: 0.9068 - recall_1: 0.9068 - f1: 0.9057 - val_loss: 0.4906 - val_accuracy: 0.8089 - val_precision_1: 0.8089 - val_recall_1: 0.8089 - val_f1: 0.8145\n",
+ "Epoch 94/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2376 - accuracy: 0.9044 - precision_1: 0.9044 - recall_1: 0.9044 - f1: 0.9042\n",
+ "Epoch 00094: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.2376 - accuracy: 0.9044 - precision_1: 0.9044 - recall_1: 0.9044 - f1: 0.9042 - val_loss: 0.2991 - val_accuracy: 0.8642 - val_precision_1: 0.8642 - val_recall_1: 0.8642 - val_f1: 0.8682\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 95/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2100 - accuracy: 0.9144 - precision_1: 0.9144 - recall_1: 0.9144 - f1: 0.9132\n",
+ "Epoch 00095: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.2100 - accuracy: 0.9144 - precision_1: 0.9144 - recall_1: 0.9144 - f1: 0.9132 - val_loss: 0.2588 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8818\n",
+ "Epoch 96/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1977 - accuracy: 0.9151 - precision_1: 0.9151 - recall_1: 0.9151 - f1: 0.9138\n",
+ "Epoch 00096: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.1977 - accuracy: 0.9151 - precision_1: 0.9151 - recall_1: 0.9151 - f1: 0.9138 - val_loss: 0.2782 - val_accuracy: 0.8823 - val_precision_1: 0.8823 - val_recall_1: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 97/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2010 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188\n",
+ "Epoch 00097: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2010 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188 - val_loss: 0.4301 - val_accuracy: 0.8209 - val_precision_1: 0.8209 - val_recall_1: 0.8209 - val_f1: 0.8262\n",
+ "Epoch 98/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2175 - accuracy: 0.9048 - precision_1: 0.9048 - recall_1: 0.9048 - f1: 0.9045\n",
+ "Epoch 00098: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.2175 - accuracy: 0.9048 - precision_1: 0.9048 - recall_1: 0.9048 - f1: 0.9045 - val_loss: 0.3389 - val_accuracy: 0.8652 - val_precision_1: 0.8652 - val_recall_1: 0.8652 - val_f1: 0.8691\n",
+ "Epoch 99/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2238 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9058\n",
+ "Epoch 00099: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2238 - accuracy: 0.9079 - precision_1: 0.9079 - recall_1: 0.9079 - f1: 0.9058 - val_loss: 0.4248 - val_accuracy: 0.8290 - val_precision_1: 0.8290 - val_recall_1: 0.8290 - val_f1: 0.8340\n",
+ "Epoch 100/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2111 - accuracy: 0.9155 - precision_1: 0.9155 - recall_1: 0.9155 - f1: 0.9124\n",
+ "Epoch 00100: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2111 - accuracy: 0.9155 - precision_1: 0.9155 - recall_1: 0.9155 - f1: 0.9124 - val_loss: 0.2921 - val_accuracy: 0.8742 - val_precision_1: 0.8742 - val_recall_1: 0.8742 - val_f1: 0.8779\n",
+ "Epoch 101/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9167\n",
+ "Epoch 00101: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2030 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9167 - val_loss: 0.2464 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 102/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1972 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9176\n",
+ "Epoch 00102: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.1972 - accuracy: 0.9172 - precision_1: 0.9172 - recall_1: 0.9172 - f1: 0.9176 - val_loss: 0.3119 - val_accuracy: 0.8763 - val_precision_1: 0.8763 - val_recall_1: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 103/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2102 - accuracy: 0.9158 - precision_1: 0.9158 - recall_1: 0.9158 - f1: 0.9158\n",
+ "Epoch 00103: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.2102 - accuracy: 0.9158 - precision_1: 0.9158 - recall_1: 0.9158 - f1: 0.9158 - val_loss: 0.2995 - val_accuracy: 0.8783 - val_precision_1: 0.8783 - val_recall_1: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 104/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2084 - accuracy: 0.9124 - precision_1: 0.9124 - recall_1: 0.9124 - f1: 0.9120\n",
+ "Epoch 00104: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2084 - accuracy: 0.9124 - precision_1: 0.9124 - recall_1: 0.9124 - f1: 0.9120 - val_loss: 0.2530 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 105/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2023 - accuracy: 0.9175 - precision_1: 0.9175 - recall_1: 0.9175 - f1: 0.9171\n",
+ "Epoch 00105: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2023 - accuracy: 0.9175 - precision_1: 0.9175 - recall_1: 0.9175 - f1: 0.9171 - val_loss: 0.2891 - val_accuracy: 0.8843 - val_precision_1: 0.8843 - val_recall_1: 0.8843 - val_f1: 0.8730\n",
+ "Epoch 106/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1879 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9246\n",
+ "Epoch 00106: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1879 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9246 - val_loss: 0.2879 - val_accuracy: 0.8682 - val_precision_1: 0.8682 - val_recall_1: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 107/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2091 - accuracy: 0.9168 - precision_1: 0.9168 - recall_1: 0.9168 - f1: 0.9155\n",
+ "Epoch 00107: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 449ms/step - loss: 0.2091 - accuracy: 0.9168 - precision_1: 0.9168 - recall_1: 0.9168 - f1: 0.9155 - val_loss: 0.2989 - val_accuracy: 0.8632 - val_precision_1: 0.8632 - val_recall_1: 0.8632 - val_f1: 0.8379\n",
+ "Epoch 108/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2002 - accuracy: 0.9144 - precision_1: 0.9144 - recall_1: 0.9144 - f1: 0.9158\n",
+ "Epoch 00108: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2002 - accuracy: 0.9144 - precision_1: 0.9144 - recall_1: 0.9144 - f1: 0.9158 - val_loss: 0.2954 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 109/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2112 - accuracy: 0.9127 - precision_1: 0.9127 - recall_1: 0.9127 - f1: 0.9141\n",
+ "Epoch 00109: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.2112 - accuracy: 0.9127 - precision_1: 0.9127 - recall_1: 0.9127 - f1: 0.9141 - val_loss: 0.2625 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 110/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2024 - accuracy: 0.9144 - precision_1: 0.9144 - recall_1: 0.9144 - f1: 0.9149\n",
+ "Epoch 00110: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.2024 - accuracy: 0.9144 - precision_1: 0.9144 - recall_1: 0.9144 - f1: 0.9149 - val_loss: 0.3078 - val_accuracy: 0.8813 - val_precision_1: 0.8813 - val_recall_1: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 111/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1949 - accuracy: 0.9231 - precision_1: 0.9231 - recall_1: 0.9231 - f1: 0.9234\n",
+ "Epoch 00111: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 434ms/step - loss: 0.1949 - accuracy: 0.9231 - precision_1: 0.9231 - recall_1: 0.9231 - f1: 0.9234 - val_loss: 0.2419 - val_accuracy: 0.9054 - val_precision_1: 0.9054 - val_recall_1: 0.9054 - val_f1: 0.9082\n",
+ "Epoch 112/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1934 - accuracy: 0.9196 - precision_1: 0.9196 - recall_1: 0.9196 - f1: 0.9200\n",
+ "Epoch 00112: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1934 - accuracy: 0.9196 - precision_1: 0.9196 - recall_1: 0.9196 - f1: 0.9200 - val_loss: 0.2734 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8721\n",
+ "Epoch 113/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2021 - accuracy: 0.9148 - precision_1: 0.9148 - recall_1: 0.9148 - f1: 0.9161\n",
+ "Epoch 00113: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.2021 - accuracy: 0.9148 - precision_1: 0.9148 - recall_1: 0.9148 - f1: 0.9161 - val_loss: 0.2926 - val_accuracy: 0.8843 - val_precision_1: 0.8843 - val_recall_1: 0.8843 - val_f1: 0.8730\n",
+ "Epoch 114/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1924 - accuracy: 0.9210 - precision_1: 0.9210 - recall_1: 0.9210 - f1: 0.9205\n",
+ "Epoch 00114: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1924 - accuracy: 0.9210 - precision_1: 0.9210 - recall_1: 0.9210 - f1: 0.9205 - val_loss: 0.2743 - val_accuracy: 0.8913 - val_precision_1: 0.8913 - val_recall_1: 0.8913 - val_f1: 0.8945\n",
+ "Epoch 115/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2049 - accuracy: 0.9120 - precision_1: 0.9120 - recall_1: 0.9120 - f1: 0.9125\n",
+ "Epoch 00115: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.2049 - accuracy: 0.9120 - precision_1: 0.9120 - recall_1: 0.9120 - f1: 0.9125 - val_loss: 0.2500 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 116/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1918 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9222\n",
+ "Epoch 00116: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.1918 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9222 - val_loss: 0.2436 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8916\n",
+ "Epoch 117/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2112 - accuracy: 0.9137 - precision_1: 0.9137 - recall_1: 0.9137 - f1: 0.9142\n",
+ "Epoch 00117: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 444ms/step - loss: 0.2112 - accuracy: 0.9137 - precision_1: 0.9137 - recall_1: 0.9137 - f1: 0.9142 - val_loss: 0.3092 - val_accuracy: 0.8732 - val_precision_1: 0.8732 - val_recall_1: 0.8732 - val_f1: 0.8623\n",
+ "Epoch 118/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1784 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9305\n",
+ "Epoch 00118: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1784 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9305 - val_loss: 0.2867 - val_accuracy: 0.8823 - val_precision_1: 0.8823 - val_recall_1: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 119/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2077 - accuracy: 0.9137 - precision_1: 0.9137 - recall_1: 0.9137 - f1: 0.9125\n",
+ "Epoch 00119: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 457ms/step - loss: 0.2077 - accuracy: 0.9137 - precision_1: 0.9137 - recall_1: 0.9137 - f1: 0.9125 - val_loss: 0.2845 - val_accuracy: 0.8803 - val_precision_1: 0.8803 - val_recall_1: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 120/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2029 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9195\n",
+ "Epoch 00120: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.2029 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9195 - val_loss: 0.2854 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8809\n",
+ "Epoch 121/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1899 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188\n",
+ "Epoch 00121: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.1899 - accuracy: 0.9193 - precision_1: 0.9193 - recall_1: 0.9193 - f1: 0.9188 - val_loss: 0.2585 - val_accuracy: 0.8763 - val_precision_1: 0.8763 - val_recall_1: 0.8763 - val_f1: 0.8799\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 122/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1842 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9247\n",
+ "Epoch 00122: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1842 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9247 - val_loss: 0.2676 - val_accuracy: 0.8863 - val_precision_1: 0.8863 - val_recall_1: 0.8863 - val_f1: 0.8750\n",
+ "Epoch 123/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1940 - accuracy: 0.9231 - precision_1: 0.9231 - recall_1: 0.9231 - f1: 0.9225\n",
+ "Epoch 00123: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1940 - accuracy: 0.9231 - precision_1: 0.9231 - recall_1: 0.9231 - f1: 0.9225 - val_loss: 0.3083 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8662\n",
+ "Epoch 124/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.1978 - accuracy: 0.9201 - precision_1: 0.9201 - recall_1: 0.9201 - f1: 0.9201\n",
+ "Epoch 00124: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.1979 - accuracy: 0.9199 - precision_1: 0.9199 - recall_1: 0.9199 - f1: 0.9195 - val_loss: 0.2453 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 125/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1828 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9187\n",
+ "Epoch 00125: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1828 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9187 - val_loss: 0.2531 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8828\n",
+ "Epoch 126/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1868 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9264\n",
+ "Epoch 00126: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 445ms/step - loss: 0.1868 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9264 - val_loss: 0.3139 - val_accuracy: 0.8682 - val_precision_1: 0.8682 - val_recall_1: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 127/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1822 - accuracy: 0.9272 - precision_1: 0.9272 - recall_1: 0.9272 - f1: 0.9257\n",
+ "Epoch 00127: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1822 - accuracy: 0.9272 - precision_1: 0.9272 - recall_1: 0.9272 - f1: 0.9257 - val_loss: 0.2555 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 128/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1836 - accuracy: 0.9265 - precision_1: 0.9265 - recall_1: 0.9265 - f1: 0.9276\n",
+ "Epoch 00128: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.1836 - accuracy: 0.9265 - precision_1: 0.9265 - recall_1: 0.9265 - f1: 0.9276 - val_loss: 0.4275 - val_accuracy: 0.8350 - val_precision_1: 0.8350 - val_recall_1: 0.8350 - val_f1: 0.8398\n",
+ "Epoch 129/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2020 - accuracy: 0.9148 - precision_1: 0.9148 - recall_1: 0.9148 - f1: 0.9126\n",
+ "Epoch 00129: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 434ms/step - loss: 0.2020 - accuracy: 0.9148 - precision_1: 0.9148 - recall_1: 0.9148 - f1: 0.9126 - val_loss: 0.3254 - val_accuracy: 0.8652 - val_precision_1: 0.8652 - val_recall_1: 0.8652 - val_f1: 0.8691\n",
+ "Epoch 130/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1926 - accuracy: 0.9213 - precision_1: 0.9213 - recall_1: 0.9213 - f1: 0.9217\n",
+ "Epoch 00130: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 488ms/step - loss: 0.1926 - accuracy: 0.9213 - precision_1: 0.9213 - recall_1: 0.9213 - f1: 0.9217 - val_loss: 0.2778 - val_accuracy: 0.8823 - val_precision_1: 0.8823 - val_recall_1: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 131/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1779 - accuracy: 0.9289 - precision_1: 0.9289 - recall_1: 0.9289 - f1: 0.9292\n",
+ "Epoch 00131: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.1779 - accuracy: 0.9289 - precision_1: 0.9289 - recall_1: 0.9289 - f1: 0.9292 - val_loss: 0.2691 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 132/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1817 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9246\n",
+ "Epoch 00132: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1817 - accuracy: 0.9251 - precision_1: 0.9251 - recall_1: 0.9251 - f1: 0.9246 - val_loss: 0.2655 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 133/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1740 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9314\n",
+ "Epoch 00133: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 450ms/step - loss: 0.1740 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9314 - val_loss: 0.3297 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8594\n",
+ "Epoch 134/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1879 - accuracy: 0.9217 - precision_1: 0.9217 - recall_1: 0.9217 - f1: 0.9220\n",
+ "Epoch 00134: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1879 - accuracy: 0.9217 - precision_1: 0.9217 - recall_1: 0.9217 - f1: 0.9220 - val_loss: 0.2318 - val_accuracy: 0.9044 - val_precision_1: 0.9044 - val_recall_1: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 135/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1998 - accuracy: 0.9203 - precision_1: 0.9203 - recall_1: 0.9203 - f1: 0.9207\n",
+ "Epoch 00135: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 27s 577ms/step - loss: 0.1998 - accuracy: 0.9203 - precision_1: 0.9203 - recall_1: 0.9203 - f1: 0.9207 - val_loss: 0.2546 - val_accuracy: 0.9054 - val_precision_1: 0.9054 - val_recall_1: 0.9054 - val_f1: 0.9082\n",
+ "Epoch 136/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1909 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9204\n",
+ "Epoch 00136: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.1909 - accuracy: 0.9227 - precision_1: 0.9227 - recall_1: 0.9227 - f1: 0.9204 - val_loss: 0.3398 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8809\n",
+ "Epoch 137/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1746 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9255\n",
+ "Epoch 00137: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1746 - accuracy: 0.9296 - precision_1: 0.9296 - recall_1: 0.9296 - f1: 0.9255 - val_loss: 0.2589 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 138/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1766 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9264\n",
+ "Epoch 00138: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1766 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9264 - val_loss: 0.2921 - val_accuracy: 0.8702 - val_precision_1: 0.8702 - val_recall_1: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 139/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1968 - accuracy: 0.9182 - precision_1: 0.9182 - recall_1: 0.9182 - f1: 0.9169\n",
+ "Epoch 00139: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.1968 - accuracy: 0.9182 - precision_1: 0.9182 - recall_1: 0.9182 - f1: 0.9169 - val_loss: 0.2477 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 140/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1845 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9242\n",
+ "Epoch 00140: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1845 - accuracy: 0.9248 - precision_1: 0.9248 - recall_1: 0.9248 - f1: 0.9242 - val_loss: 0.2609 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 141/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1862 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9271\n",
+ "Epoch 00141: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1862 - accuracy: 0.9286 - precision_1: 0.9286 - recall_1: 0.9286 - f1: 0.9271 - val_loss: 0.2619 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 142/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1924 - accuracy: 0.9192 - precision_1: 0.9192 - recall_1: 0.9192 - f1: 0.9192\n",
+ "Epoch 00142: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.1924 - accuracy: 0.9192 - precision_1: 0.9192 - recall_1: 0.9192 - f1: 0.9192 - val_loss: 0.3116 - val_accuracy: 0.8793 - val_precision_1: 0.8793 - val_recall_1: 0.8793 - val_f1: 0.8682\n",
+ "Epoch 143/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1616 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9332\n",
+ "Epoch 00143: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 449ms/step - loss: 0.1616 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9332 - val_loss: 0.2405 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 144/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1778 - accuracy: 0.9265 - precision_1: 0.9265 - recall_1: 0.9265 - f1: 0.9268\n",
+ "Epoch 00144: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1778 - accuracy: 0.9265 - precision_1: 0.9265 - recall_1: 0.9265 - f1: 0.9268 - val_loss: 0.2365 - val_accuracy: 0.9054 - val_precision_1: 0.9054 - val_recall_1: 0.9054 - val_f1: 0.8936\n",
+ "Epoch 145/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1718 - accuracy: 0.9320 - precision_1: 0.9320 - recall_1: 0.9320 - f1: 0.9322\n",
+ "Epoch 00145: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 458ms/step - loss: 0.1718 - accuracy: 0.9320 - precision_1: 0.9320 - recall_1: 0.9320 - f1: 0.9322 - val_loss: 0.2311 - val_accuracy: 0.9054 - val_precision_1: 0.9054 - val_recall_1: 0.9054 - val_f1: 0.9082\n",
+ "Epoch 146/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1814 - accuracy: 0.9268 - precision_1: 0.9268 - recall_1: 0.9268 - f1: 0.9245\n",
+ "Epoch 00146: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 465ms/step - loss: 0.1814 - accuracy: 0.9268 - precision_1: 0.9268 - recall_1: 0.9268 - f1: 0.9245 - val_loss: 0.3009 - val_accuracy: 0.8682 - val_precision_1: 0.8682 - val_recall_1: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 147/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1940 - accuracy: 0.9206 - precision_1: 0.9206 - recall_1: 0.9206 - f1: 0.9167\n",
+ "Epoch 00147: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1940 - accuracy: 0.9206 - precision_1: 0.9206 - recall_1: 0.9206 - f1: 0.9167 - val_loss: 0.2430 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 148/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1789 - accuracy: 0.9231 - precision_1: 0.9231 - recall_1: 0.9231 - f1: 0.9216\n",
+ "Epoch 00148: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1789 - accuracy: 0.9231 - precision_1: 0.9231 - recall_1: 0.9231 - f1: 0.9216 - val_loss: 0.2650 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 149/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1579 - accuracy: 0.9431 - precision_1: 0.9431 - recall_1: 0.9431 - f1: 0.9431\n",
+ "Epoch 00149: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.1579 - accuracy: 0.9431 - precision_1: 0.9431 - recall_1: 0.9431 - f1: 0.9431 - val_loss: 0.2994 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8809\n",
+ "Epoch 150/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1628 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380\n",
+ "Epoch 00150: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 469ms/step - loss: 0.1628 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380 - val_loss: 0.2590 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8848\n",
+ "Epoch 151/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1660 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9306\n",
+ "Epoch 00151: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 437ms/step - loss: 0.1660 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9306 - val_loss: 0.2419 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 152/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1575 - accuracy: 0.9355 - precision_1: 0.9355 - recall_1: 0.9355 - f1: 0.9347\n",
+ "Epoch 00152: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1575 - accuracy: 0.9355 - precision_1: 0.9355 - recall_1: 0.9355 - f1: 0.9347 - val_loss: 0.2406 - val_accuracy: 0.9064 - val_precision_1: 0.9064 - val_recall_1: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 153/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1990 - accuracy: 0.9186 - precision_1: 0.9186 - recall_1: 0.9186 - f1: 0.9164\n",
+ "Epoch 00153: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 482ms/step - loss: 0.1990 - accuracy: 0.9186 - precision_1: 0.9186 - recall_1: 0.9186 - f1: 0.9164 - val_loss: 0.3020 - val_accuracy: 0.8793 - val_precision_1: 0.8793 - val_recall_1: 0.8793 - val_f1: 0.8828\n",
+ "Epoch 154/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1691 - accuracy: 0.9300 - precision_1: 0.9300 - recall_1: 0.9300 - f1: 0.9293\n",
+ "Epoch 00154: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 445ms/step - loss: 0.1691 - accuracy: 0.9300 - precision_1: 0.9300 - recall_1: 0.9300 - f1: 0.9293 - val_loss: 0.2341 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 155/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1592 - accuracy: 0.9334 - precision_1: 0.9334 - recall_1: 0.9334 - f1: 0.9344\n",
+ "Epoch 00155: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.1592 - accuracy: 0.9334 - precision_1: 0.9334 - recall_1: 0.9334 - f1: 0.9344 - val_loss: 0.2572 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 156/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1575 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9331\n",
+ "Epoch 00156: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1575 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9331 - val_loss: 0.4251 - val_accuracy: 0.8592 - val_precision_1: 0.8592 - val_recall_1: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 157/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1509 - accuracy: 0.9417 - precision_1: 0.9417 - recall_1: 0.9417 - f1: 0.9426\n",
+ "Epoch 00157: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.1509 - accuracy: 0.9417 - precision_1: 0.9417 - recall_1: 0.9417 - f1: 0.9426 - val_loss: 0.2245 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 158/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.9382 - precision_1: 0.9382 - recall_1: 0.9382 - f1: 0.9383\n",
+ "Epoch 00158: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.1639 - accuracy: 0.9382 - precision_1: 0.9382 - recall_1: 0.9382 - f1: 0.9383 - val_loss: 0.2377 - val_accuracy: 0.9064 - val_precision_1: 0.9064 - val_recall_1: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 159/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1653 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9296\n",
+ "Epoch 00159: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1653 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9296 - val_loss: 0.2652 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8926\n",
+ "Epoch 160/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9273\n",
+ "Epoch 00160: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 29s 625ms/step - loss: 0.1848 - accuracy: 0.9262 - precision_1: 0.9262 - recall_1: 0.9262 - f1: 0.9273 - val_loss: 0.2352 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 161/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1680 - accuracy: 0.9327 - precision_1: 0.9327 - recall_1: 0.9327 - f1: 0.9329\n",
+ "Epoch 00161: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 457ms/step - loss: 0.1680 - accuracy: 0.9327 - precision_1: 0.9327 - recall_1: 0.9327 - f1: 0.9329 - val_loss: 0.2456 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.8906\n",
+ "Epoch 162/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1596 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9346\n",
+ "Epoch 00162: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1596 - accuracy: 0.9362 - precision_1: 0.9362 - recall_1: 0.9362 - f1: 0.9346 - val_loss: 0.3755 - val_accuracy: 0.8763 - val_precision_1: 0.8763 - val_recall_1: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 163/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1775 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9305\n",
+ "Epoch 00163: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.1775 - accuracy: 0.9303 - precision_1: 0.9303 - recall_1: 0.9303 - f1: 0.9305 - val_loss: 0.2483 - val_accuracy: 0.9054 - val_precision_1: 0.9054 - val_recall_1: 0.9054 - val_f1: 0.9082\n",
+ "Epoch 164/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1721 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9315\n",
+ "Epoch 00164: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1721 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9315 - val_loss: 0.2247 - val_accuracy: 0.9105 - val_precision_1: 0.9105 - val_recall_1: 0.9105 - val_f1: 0.8984\n",
+ "Epoch 165/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1605 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9323\n",
+ "Epoch 00165: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.1605 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9323 - val_loss: 0.3118 - val_accuracy: 0.8742 - val_precision_1: 0.8742 - val_recall_1: 0.8742 - val_f1: 0.8779\n",
+ "Epoch 166/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1758 - accuracy: 0.9289 - precision_1: 0.9289 - recall_1: 0.9289 - f1: 0.9283\n",
+ "Epoch 00166: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 462ms/step - loss: 0.1758 - accuracy: 0.9289 - precision_1: 0.9289 - recall_1: 0.9289 - f1: 0.9283 - val_loss: 0.2397 - val_accuracy: 0.9064 - val_precision_1: 0.9064 - val_recall_1: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 167/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1583 - accuracy: 0.9341 - precision_1: 0.9341 - recall_1: 0.9341 - f1: 0.9334\n",
+ "Epoch 00167: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1583 - accuracy: 0.9341 - precision_1: 0.9341 - recall_1: 0.9341 - f1: 0.9334 - val_loss: 0.2414 - val_accuracy: 0.8974 - val_precision_1: 0.8974 - val_recall_1: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 168/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1599 - accuracy: 0.9355 - precision_1: 0.9355 - recall_1: 0.9355 - f1: 0.9339\n",
+ "Epoch 00168: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 467ms/step - loss: 0.1599 - accuracy: 0.9355 - precision_1: 0.9355 - recall_1: 0.9355 - f1: 0.9339 - val_loss: 0.3091 - val_accuracy: 0.8873 - val_precision_1: 0.8873 - val_recall_1: 0.8873 - val_f1: 0.8906\n",
+ "Epoch 169/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1608 - accuracy: 0.9344 - precision_1: 0.9344 - recall_1: 0.9344 - f1: 0.9346\n",
+ "Epoch 00169: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.1608 - accuracy: 0.9344 - precision_1: 0.9344 - recall_1: 0.9344 - f1: 0.9346 - val_loss: 0.2402 - val_accuracy: 0.9125 - val_precision_1: 0.9125 - val_recall_1: 0.9125 - val_f1: 0.9150\n",
+ "Epoch 170/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1671 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9307\n",
+ "Epoch 00170: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 484ms/step - loss: 0.1671 - accuracy: 0.9313 - precision_1: 0.9313 - recall_1: 0.9313 - f1: 0.9307 - val_loss: 0.2693 - val_accuracy: 0.8883 - val_precision_1: 0.8883 - val_recall_1: 0.8883 - val_f1: 0.8770\n",
+ "Epoch 171/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1632 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9332\n",
+ "Epoch 00171: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 448ms/step - loss: 0.1632 - accuracy: 0.9331 - precision_1: 0.9331 - recall_1: 0.9331 - f1: 0.9332 - val_loss: 0.2600 - val_accuracy: 0.8833 - val_precision_1: 0.8833 - val_recall_1: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 172/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1751 - accuracy: 0.9275 - precision_1: 0.9275 - recall_1: 0.9275 - f1: 0.9278\n",
+ "Epoch 00172: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 473ms/step - loss: 0.1751 - accuracy: 0.9275 - precision_1: 0.9275 - recall_1: 0.9275 - f1: 0.9278 - val_loss: 0.2226 - val_accuracy: 0.9266 - val_precision_1: 0.9266 - val_recall_1: 0.9266 - val_f1: 0.9287\n",
+ "Epoch 173/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1730 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9323\n",
+ "Epoch 00173: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1730 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9323 - val_loss: 0.3622 - val_accuracy: 0.8853 - val_precision_1: 0.8853 - val_recall_1: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 174/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1635 - accuracy: 0.9327 - precision_1: 0.9327 - recall_1: 0.9327 - f1: 0.9329\n",
+ "Epoch 00174: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.1635 - accuracy: 0.9327 - precision_1: 0.9327 - recall_1: 0.9327 - f1: 0.9329 - val_loss: 0.3370 - val_accuracy: 0.8753 - val_precision_1: 0.8753 - val_recall_1: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 175/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1640 - accuracy: 0.9351 - precision_1: 0.9351 - recall_1: 0.9351 - f1: 0.9344\n",
+ "Epoch 00175: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.1640 - accuracy: 0.9351 - precision_1: 0.9351 - recall_1: 0.9351 - f1: 0.9344 - val_loss: 0.2630 - val_accuracy: 0.8863 - val_precision_1: 0.8863 - val_recall_1: 0.8863 - val_f1: 0.8896\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 176/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1435 - accuracy: 0.9458 - precision_1: 0.9458 - recall_1: 0.9458 - f1: 0.9467\n",
+ "Epoch 00176: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1435 - accuracy: 0.9458 - precision_1: 0.9458 - recall_1: 0.9458 - f1: 0.9467 - val_loss: 0.2261 - val_accuracy: 0.9155 - val_precision_1: 0.9155 - val_recall_1: 0.9155 - val_f1: 0.9180\n",
+ "Epoch 177/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1713 - accuracy: 0.9289 - precision_1: 0.9289 - recall_1: 0.9289 - f1: 0.9283\n",
+ "Epoch 00177: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 471ms/step - loss: 0.1713 - accuracy: 0.9289 - precision_1: 0.9289 - recall_1: 0.9289 - f1: 0.9283 - val_loss: 0.4658 - val_accuracy: 0.8451 - val_precision_1: 0.8451 - val_recall_1: 0.8451 - val_f1: 0.8350\n",
+ "Epoch 178/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.9403 - precision_1: 0.9403 - recall_1: 0.9403 - f1: 0.9412\n",
+ "Epoch 00178: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 449ms/step - loss: 0.1401 - accuracy: 0.9403 - precision_1: 0.9403 - recall_1: 0.9403 - f1: 0.9412 - val_loss: 0.2825 - val_accuracy: 0.8954 - val_precision_1: 0.8954 - val_recall_1: 0.8954 - val_f1: 0.8984\n",
+ "Epoch 179/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1606 - accuracy: 0.9351 - precision_1: 0.9351 - recall_1: 0.9351 - f1: 0.9353\n",
+ "Epoch 00179: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.1606 - accuracy: 0.9351 - precision_1: 0.9351 - recall_1: 0.9351 - f1: 0.9353 - val_loss: 0.2644 - val_accuracy: 0.9004 - val_precision_1: 0.9004 - val_recall_1: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 180/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1598 - accuracy: 0.9382 - precision_1: 0.9382 - recall_1: 0.9382 - f1: 0.9392\n",
+ "Epoch 00180: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1598 - accuracy: 0.9382 - precision_1: 0.9382 - recall_1: 0.9382 - f1: 0.9392 - val_loss: 0.3497 - val_accuracy: 0.8642 - val_precision_1: 0.8642 - val_recall_1: 0.8642 - val_f1: 0.8535\n",
+ "Epoch 181/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1571 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9358\n",
+ "Epoch 00181: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 493ms/step - loss: 0.1571 - accuracy: 0.9348 - precision_1: 0.9348 - recall_1: 0.9348 - f1: 0.9358 - val_loss: 0.2660 - val_accuracy: 0.8964 - val_precision_1: 0.8964 - val_recall_1: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 182/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1632 - accuracy: 0.9351 - precision_1: 0.9351 - recall_1: 0.9351 - f1: 0.9344\n",
+ "Epoch 00182: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 30s 645ms/step - loss: 0.1632 - accuracy: 0.9351 - precision_1: 0.9351 - recall_1: 0.9351 - f1: 0.9344 - val_loss: 0.2879 - val_accuracy: 0.8903 - val_precision_1: 0.8903 - val_recall_1: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 183/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1606 - accuracy: 0.9306 - precision_1: 0.9306 - recall_1: 0.9306 - f1: 0.9300\n",
+ "Epoch 00183: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.1606 - accuracy: 0.9306 - precision_1: 0.9306 - recall_1: 0.9306 - f1: 0.9300 - val_loss: 0.2951 - val_accuracy: 0.8934 - val_precision_1: 0.8934 - val_recall_1: 0.8934 - val_f1: 0.8965\n",
+ "Epoch 184/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1539 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380\n",
+ "Epoch 00184: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 479ms/step - loss: 0.1539 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380 - val_loss: 0.2512 - val_accuracy: 0.9085 - val_precision_1: 0.9085 - val_recall_1: 0.9085 - val_f1: 0.9111\n",
+ "Epoch 185/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1502 - accuracy: 0.9431 - precision_1: 0.9431 - recall_1: 0.9431 - f1: 0.9431\n",
+ "Epoch 00185: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.1502 - accuracy: 0.9431 - precision_1: 0.9431 - recall_1: 0.9431 - f1: 0.9431 - val_loss: 0.2308 - val_accuracy: 0.9095 - val_precision_1: 0.9095 - val_recall_1: 0.9095 - val_f1: 0.9121\n",
+ "Epoch 186/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1522 - accuracy: 0.9389 - precision_1: 0.9389 - recall_1: 0.9389 - f1: 0.9390\n",
+ "Epoch 00186: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 508ms/step - loss: 0.1522 - accuracy: 0.9389 - precision_1: 0.9389 - recall_1: 0.9389 - f1: 0.9390 - val_loss: 0.2527 - val_accuracy: 0.9125 - val_precision_1: 0.9125 - val_recall_1: 0.9125 - val_f1: 0.9150\n",
+ "Epoch 187/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1455 - accuracy: 0.9403 - precision_1: 0.9403 - recall_1: 0.9403 - f1: 0.9412\n",
+ "Epoch 00187: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 439ms/step - loss: 0.1455 - accuracy: 0.9403 - precision_1: 0.9403 - recall_1: 0.9403 - f1: 0.9412 - val_loss: 0.2807 - val_accuracy: 0.8913 - val_precision_1: 0.8913 - val_recall_1: 0.8913 - val_f1: 0.8945\n",
+ "Epoch 188/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1470 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9400\n",
+ "Epoch 00188: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 500ms/step - loss: 0.1470 - accuracy: 0.9400 - precision_1: 0.9400 - recall_1: 0.9400 - f1: 0.9400 - val_loss: 0.2345 - val_accuracy: 0.9115 - val_precision_1: 0.9115 - val_recall_1: 0.9115 - val_f1: 0.8848\n",
+ "Epoch 189/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1511 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380\n",
+ "Epoch 00189: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 487ms/step - loss: 0.1511 - accuracy: 0.9379 - precision_1: 0.9379 - recall_1: 0.9379 - f1: 0.9380 - val_loss: 0.2763 - val_accuracy: 0.8944 - val_precision_1: 0.8944 - val_recall_1: 0.8944 - val_f1: 0.8828\n",
+ "Epoch 190/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1556 - accuracy: 0.9386 - precision_1: 0.9386 - recall_1: 0.9386 - f1: 0.9387\n",
+ "Epoch 00190: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.1556 - accuracy: 0.9386 - precision_1: 0.9386 - recall_1: 0.9386 - f1: 0.9387 - val_loss: 0.2918 - val_accuracy: 0.8823 - val_precision_1: 0.8823 - val_recall_1: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 191/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1541 - accuracy: 0.9393 - precision_1: 0.9393 - recall_1: 0.9393 - f1: 0.9393\n",
+ "Epoch 00191: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 25s 542ms/step - loss: 0.1541 - accuracy: 0.9393 - precision_1: 0.9393 - recall_1: 0.9393 - f1: 0.9393 - val_loss: 0.2546 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 192/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1460 - accuracy: 0.9417 - precision_1: 0.9417 - recall_1: 0.9417 - f1: 0.9417\n",
+ "Epoch 00192: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 459ms/step - loss: 0.1460 - accuracy: 0.9417 - precision_1: 0.9417 - recall_1: 0.9417 - f1: 0.9417 - val_loss: 0.2531 - val_accuracy: 0.9024 - val_precision_1: 0.9024 - val_recall_1: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 193/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1466 - accuracy: 0.9393 - precision_1: 0.9393 - recall_1: 0.9393 - f1: 0.9402\n",
+ "Epoch 00193: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 479ms/step - loss: 0.1466 - accuracy: 0.9393 - precision_1: 0.9393 - recall_1: 0.9393 - f1: 0.9402 - val_loss: 0.2510 - val_accuracy: 0.9074 - val_precision_1: 0.9074 - val_recall_1: 0.9074 - val_f1: 0.9102\n",
+ "Epoch 194/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1486 - accuracy: 0.9444 - precision_1: 0.9444 - recall_1: 0.9444 - f1: 0.9444\n",
+ "Epoch 00194: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 493ms/step - loss: 0.1486 - accuracy: 0.9444 - precision_1: 0.9444 - recall_1: 0.9444 - f1: 0.9444 - val_loss: 0.2302 - val_accuracy: 0.9105 - val_precision_1: 0.9105 - val_recall_1: 0.9105 - val_f1: 0.8984\n",
+ "Epoch 195/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1386 - accuracy: 0.9482 - precision_1: 0.9482 - recall_1: 0.9482 - f1: 0.9473\n",
+ "Epoch 00195: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.1386 - accuracy: 0.9482 - precision_1: 0.9482 - recall_1: 0.9482 - f1: 0.9473 - val_loss: 0.2475 - val_accuracy: 0.9185 - val_precision_1: 0.9185 - val_recall_1: 0.9185 - val_f1: 0.9209\n",
+ "Epoch 196/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1513 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9375\n",
+ "Epoch 00196: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1513 - accuracy: 0.9365 - precision_1: 0.9365 - recall_1: 0.9365 - f1: 0.9375 - val_loss: 0.2067 - val_accuracy: 0.9215 - val_precision_1: 0.9215 - val_recall_1: 0.9215 - val_f1: 0.9238\n",
+ "Epoch 197/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1409 - accuracy: 0.9438 - precision_1: 0.9438 - recall_1: 0.9438 - f1: 0.9420\n",
+ "Epoch 00197: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 432ms/step - loss: 0.1409 - accuracy: 0.9438 - precision_1: 0.9438 - recall_1: 0.9438 - f1: 0.9420 - val_loss: 0.2479 - val_accuracy: 0.9074 - val_precision_1: 0.9074 - val_recall_1: 0.9074 - val_f1: 0.9102\n",
+ "Epoch 198/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1350 - accuracy: 0.9479 - precision_1: 0.9479 - recall_1: 0.9479 - f1: 0.9470\n",
+ "Epoch 00198: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 484ms/step - loss: 0.1350 - accuracy: 0.9479 - precision_1: 0.9479 - recall_1: 0.9479 - f1: 0.9470 - val_loss: 0.2987 - val_accuracy: 0.8893 - val_precision_1: 0.8893 - val_recall_1: 0.8893 - val_f1: 0.8779\n",
+ "Epoch 199/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1500 - accuracy: 0.9358 - precision_1: 0.9358 - recall_1: 0.9358 - f1: 0.9351\n",
+ "Epoch 00199: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 490ms/step - loss: 0.1500 - accuracy: 0.9358 - precision_1: 0.9358 - recall_1: 0.9358 - f1: 0.9351 - val_loss: 0.2642 - val_accuracy: 0.9044 - val_precision_1: 0.9044 - val_recall_1: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 200/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1371 - accuracy: 0.9465 - precision_1: 0.9465 - recall_1: 0.9465 - f1: 0.9447\n",
+ "Epoch 00200: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I3orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 479ms/step - loss: 0.1371 - accuracy: 0.9465 - precision_1: 0.9465 - recall_1: 0.9465 - f1: 0.9447 - val_loss: 0.3324 - val_accuracy: 0.8773 - val_precision_1: 0.8773 - val_recall_1: 0.8773 - val_f1: 0.8809\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/preloaded/madeModels/OGRUN_I4/assets\n",
+ "Epoch 1/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.7488 - accuracy: 0.6712 - precision_2: 0.6712 - recall_2: 0.6712 - f1: 0.6720\n",
+ "Epoch 00001: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 451ms/step - loss: 0.7488 - accuracy: 0.6712 - precision_2: 0.6712 - recall_2: 0.6712 - f1: 0.6720 - val_loss: 0.6600 - val_accuracy: 0.6177 - val_precision_2: 0.6177 - val_recall_2: 0.6177 - val_f1: 0.6289\n",
+ "Epoch 2/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.5131 - accuracy: 0.7553 - precision_2: 0.7553 - recall_2: 0.7553 - f1: 0.7566\n",
+ "Epoch 00002: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "46/46 [==============================] - 23s 502ms/step - loss: 0.5131 - accuracy: 0.7553 - precision_2: 0.7553 - recall_2: 0.7553 - f1: 0.7566 - val_loss: 0.6194 - val_accuracy: 0.6549 - val_precision_2: 0.6549 - val_recall_2: 0.6549 - val_f1: 0.6504\n",
+ "Epoch 3/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4686 - accuracy: 0.7812 - precision_2: 0.7812 - recall_2: 0.7812 - f1: 0.7786\n",
+ "Epoch 00003: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.4686 - accuracy: 0.7812 - precision_2: 0.7812 - recall_2: 0.7812 - f1: 0.7786 - val_loss: 0.6644 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6465\n",
+ "Epoch 4/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4340 - accuracy: 0.7881 - precision_2: 0.7881 - recall_2: 0.7881 - f1: 0.7871\n",
+ "Epoch 00004: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 449ms/step - loss: 0.4340 - accuracy: 0.7881 - precision_2: 0.7881 - recall_2: 0.7881 - f1: 0.7871 - val_loss: 0.7921 - val_accuracy: 0.6579 - val_precision_2: 0.6579 - val_recall_2: 0.6579 - val_f1: 0.6533\n",
+ "Epoch 5/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.4015 - accuracy: 0.8144 - precision_2: 0.8144 - recall_2: 0.8144 - f1: 0.8155\n",
+ "Epoch 00005: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 24s 526ms/step - loss: 0.4015 - accuracy: 0.8144 - precision_2: 0.8144 - recall_2: 0.8144 - f1: 0.8155 - val_loss: 0.8937 - val_accuracy: 0.6509 - val_precision_2: 0.6509 - val_recall_2: 0.6509 - val_f1: 0.6611\n",
+ "Epoch 6/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3847 - accuracy: 0.8244 - precision_2: 0.8244 - recall_2: 0.8244 - f1: 0.8245\n",
+ "Epoch 00006: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.3847 - accuracy: 0.8244 - precision_2: 0.8244 - recall_2: 0.8244 - f1: 0.8245 - val_loss: 0.7659 - val_accuracy: 0.6590 - val_precision_2: 0.6590 - val_recall_2: 0.6590 - val_f1: 0.6543\n",
+ "Epoch 7/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3710 - accuracy: 0.8337 - precision_2: 0.8337 - recall_2: 0.8337 - f1: 0.8328\n",
+ "Epoch 00007: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.3710 - accuracy: 0.8337 - precision_2: 0.8337 - recall_2: 0.8337 - f1: 0.8328 - val_loss: 0.6205 - val_accuracy: 0.7123 - val_precision_2: 0.7123 - val_recall_2: 0.7123 - val_f1: 0.7061\n",
+ "Epoch 8/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3410 - accuracy: 0.8482 - precision_2: 0.8482 - recall_2: 0.8482 - f1: 0.8488\n",
+ "Epoch 00008: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.3410 - accuracy: 0.8482 - precision_2: 0.8482 - recall_2: 0.8482 - f1: 0.8488 - val_loss: 0.7290 - val_accuracy: 0.7294 - val_precision_2: 0.7294 - val_recall_2: 0.7294 - val_f1: 0.7227\n",
+ "Epoch 9/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3675 - accuracy: 0.8382 - precision_2: 0.8382 - recall_2: 0.8382 - f1: 0.8372\n",
+ "Epoch 00009: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.3675 - accuracy: 0.8382 - precision_2: 0.8382 - recall_2: 0.8382 - f1: 0.8372 - val_loss: 0.5839 - val_accuracy: 0.7716 - val_precision_2: 0.7716 - val_recall_2: 0.7716 - val_f1: 0.7637\n",
+ "Epoch 10/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3601 - accuracy: 0.8323 - precision_2: 0.8323 - recall_2: 0.8323 - f1: 0.8280\n",
+ "Epoch 00010: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 24s 512ms/step - loss: 0.3601 - accuracy: 0.8323 - precision_2: 0.8323 - recall_2: 0.8323 - f1: 0.8280 - val_loss: 0.5633 - val_accuracy: 0.7535 - val_precision_2: 0.7535 - val_recall_2: 0.7535 - val_f1: 0.7607\n",
+ "Epoch 11/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3520 - accuracy: 0.8416 - precision_2: 0.8416 - recall_2: 0.8416 - f1: 0.8424\n",
+ "Epoch 00011: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 472ms/step - loss: 0.3520 - accuracy: 0.8416 - precision_2: 0.8416 - recall_2: 0.8416 - f1: 0.8424 - val_loss: 0.3982 - val_accuracy: 0.8219 - val_precision_2: 0.8219 - val_recall_2: 0.8219 - val_f1: 0.8271\n",
+ "Epoch 12/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3309 - accuracy: 0.8527 - precision_2: 0.8527 - recall_2: 0.8527 - f1: 0.8515\n",
+ "Epoch 00012: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.3309 - accuracy: 0.8527 - precision_2: 0.8527 - recall_2: 0.8527 - f1: 0.8515 - val_loss: 0.5274 - val_accuracy: 0.7847 - val_precision_2: 0.7847 - val_recall_2: 0.7847 - val_f1: 0.7764\n",
+ "Epoch 13/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3205 - accuracy: 0.8568 - precision_2: 0.8568 - recall_2: 0.8568 - f1: 0.8564\n",
+ "Epoch 00013: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 455ms/step - loss: 0.3205 - accuracy: 0.8568 - precision_2: 0.8568 - recall_2: 0.8568 - f1: 0.8564 - val_loss: 0.4399 - val_accuracy: 0.8139 - val_precision_2: 0.8139 - val_recall_2: 0.8139 - val_f1: 0.8047\n",
+ "Epoch 14/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3234 - accuracy: 0.8571 - precision_2: 0.8571 - recall_2: 0.8571 - f1: 0.8559\n",
+ "Epoch 00014: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.3234 - accuracy: 0.8571 - precision_2: 0.8571 - recall_2: 0.8571 - f1: 0.8559 - val_loss: 0.3913 - val_accuracy: 0.8179 - val_precision_2: 0.8179 - val_recall_2: 0.8179 - val_f1: 0.8232\n",
+ "Epoch 15/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3277 - accuracy: 0.8568 - precision_2: 0.8568 - recall_2: 0.8568 - f1: 0.8573\n",
+ "Epoch 00015: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 488ms/step - loss: 0.3277 - accuracy: 0.8568 - precision_2: 0.8568 - recall_2: 0.8568 - f1: 0.8573 - val_loss: 0.4639 - val_accuracy: 0.7918 - val_precision_2: 0.7918 - val_recall_2: 0.7918 - val_f1: 0.7979\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 16/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3134 - accuracy: 0.8630 - precision_2: 0.8630 - recall_2: 0.8630 - f1: 0.8599\n",
+ "Epoch 00016: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 476ms/step - loss: 0.3134 - accuracy: 0.8630 - precision_2: 0.8630 - recall_2: 0.8630 - f1: 0.8599 - val_loss: 0.3423 - val_accuracy: 0.8431 - val_precision_2: 0.8431 - val_recall_2: 0.8431 - val_f1: 0.8477\n",
+ "Epoch 17/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3239 - accuracy: 0.8565 - precision_2: 0.8565 - recall_2: 0.8565 - f1: 0.8544\n",
+ "Epoch 00017: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 435ms/step - loss: 0.3239 - accuracy: 0.8565 - precision_2: 0.8565 - recall_2: 0.8565 - f1: 0.8544 - val_loss: 0.3503 - val_accuracy: 0.8501 - val_precision_2: 0.8501 - val_recall_2: 0.8501 - val_f1: 0.8545\n",
+ "Epoch 18/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3143 - accuracy: 0.8585 - precision_2: 0.8585 - recall_2: 0.8585 - f1: 0.8599\n",
+ "Epoch 00018: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.3143 - accuracy: 0.8585 - precision_2: 0.8585 - recall_2: 0.8585 - f1: 0.8599 - val_loss: 1.1189 - val_accuracy: 0.7505 - val_precision_2: 0.7505 - val_recall_2: 0.7505 - val_f1: 0.7578\n",
+ "Epoch 19/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.2978 - accuracy: 0.8687 - precision_2: 0.8687 - recall_2: 0.8687 - f1: 0.8687\n",
+ "Epoch 00019: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 471ms/step - loss: 0.2977 - accuracy: 0.8685 - precision_2: 0.8685 - recall_2: 0.8685 - f1: 0.8680 - val_loss: 0.3444 - val_accuracy: 0.8501 - val_precision_2: 0.8501 - val_recall_2: 0.8501 - val_f1: 0.8545\n",
+ "Epoch 20/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3226 - accuracy: 0.8547 - precision_2: 0.8547 - recall_2: 0.8547 - f1: 0.8561\n",
+ "Epoch 00020: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.3226 - accuracy: 0.8547 - precision_2: 0.8547 - recall_2: 0.8547 - f1: 0.8561 - val_loss: 0.3867 - val_accuracy: 0.8078 - val_precision_2: 0.8078 - val_recall_2: 0.8078 - val_f1: 0.8135\n",
+ "Epoch 21/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3019 - accuracy: 0.8706 - precision_2: 0.8706 - recall_2: 0.8706 - f1: 0.8709\n",
+ "Epoch 00021: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.3019 - accuracy: 0.8706 - precision_2: 0.8706 - recall_2: 0.8706 - f1: 0.8709 - val_loss: 0.5000 - val_accuracy: 0.7918 - val_precision_2: 0.7918 - val_recall_2: 0.7918 - val_f1: 0.7832\n",
+ "Epoch 22/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2996 - accuracy: 0.8754 - precision_2: 0.8754 - recall_2: 0.8754 - f1: 0.8756\n",
+ "Epoch 00022: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 481ms/step - loss: 0.2996 - accuracy: 0.8754 - precision_2: 0.8754 - recall_2: 0.8754 - f1: 0.8756 - val_loss: 0.3461 - val_accuracy: 0.8410 - val_precision_2: 0.8410 - val_recall_2: 0.8410 - val_f1: 0.8457\n",
+ "Epoch 23/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2939 - accuracy: 0.8784 - precision_2: 0.8784 - recall_2: 0.8784 - f1: 0.8784\n",
+ "Epoch 00023: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2939 - accuracy: 0.8784 - precision_2: 0.8784 - recall_2: 0.8784 - f1: 0.8784 - val_loss: 0.6942 - val_accuracy: 0.7676 - val_precision_2: 0.7676 - val_recall_2: 0.7676 - val_f1: 0.7598\n",
+ "Epoch 24/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3025 - accuracy: 0.8713 - precision_2: 0.8713 - recall_2: 0.8713 - f1: 0.8716\n",
+ "Epoch 00024: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 459ms/step - loss: 0.3025 - accuracy: 0.8713 - precision_2: 0.8713 - recall_2: 0.8713 - f1: 0.8716 - val_loss: 0.3433 - val_accuracy: 0.8531 - val_precision_2: 0.8531 - val_recall_2: 0.8531 - val_f1: 0.8574\n",
+ "Epoch 25/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2941 - accuracy: 0.8730 - precision_2: 0.8730 - recall_2: 0.8730 - f1: 0.8715\n",
+ "Epoch 00025: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 428ms/step - loss: 0.2941 - accuracy: 0.8730 - precision_2: 0.8730 - recall_2: 0.8730 - f1: 0.8715 - val_loss: 0.3400 - val_accuracy: 0.8421 - val_precision_2: 0.8421 - val_recall_2: 0.8421 - val_f1: 0.8467\n",
+ "Epoch 26/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3027 - accuracy: 0.8713 - precision_2: 0.8713 - recall_2: 0.8713 - f1: 0.8707\n",
+ "Epoch 00026: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 24s 514ms/step - loss: 0.3027 - accuracy: 0.8713 - precision_2: 0.8713 - recall_2: 0.8713 - f1: 0.8707 - val_loss: 0.5812 - val_accuracy: 0.7746 - val_precision_2: 0.7746 - val_recall_2: 0.7746 - val_f1: 0.7812\n",
+ "Epoch 27/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3090 - accuracy: 0.8720 - precision_2: 0.8720 - recall_2: 0.8720 - f1: 0.8731\n",
+ "Epoch 00027: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.3090 - accuracy: 0.8720 - precision_2: 0.8720 - recall_2: 0.8720 - f1: 0.8731 - val_loss: 0.2946 - val_accuracy: 0.8672 - val_precision_2: 0.8672 - val_recall_2: 0.8672 - val_f1: 0.8711\n",
+ "Epoch 28/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3090 - accuracy: 0.8651 - precision_2: 0.8651 - recall_2: 0.8651 - f1: 0.8628\n",
+ "Epoch 00028: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.3090 - accuracy: 0.8651 - precision_2: 0.8651 - recall_2: 0.8651 - f1: 0.8628 - val_loss: 0.4050 - val_accuracy: 0.8099 - val_precision_2: 0.8099 - val_recall_2: 0.8099 - val_f1: 0.8008\n",
+ "Epoch 29/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.3036 - accuracy: 0.8647 - precision_2: 0.8647 - recall_2: 0.8647 - f1: 0.8642\n",
+ "Epoch 00029: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 459ms/step - loss: 0.3036 - accuracy: 0.8647 - precision_2: 0.8647 - recall_2: 0.8647 - f1: 0.8642 - val_loss: 0.3239 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 30/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2984 - accuracy: 0.8737 - precision_2: 0.8737 - recall_2: 0.8737 - f1: 0.8748\n",
+ "Epoch 00030: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.2984 - accuracy: 0.8737 - precision_2: 0.8737 - recall_2: 0.8737 - f1: 0.8748 - val_loss: 0.3068 - val_accuracy: 0.8592 - val_precision_2: 0.8592 - val_recall_2: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 31/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8768 - precision_2: 0.8768 - recall_2: 0.8768 - f1: 0.8779\n",
+ "Epoch 00031: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2872 - accuracy: 0.8768 - precision_2: 0.8768 - recall_2: 0.8768 - f1: 0.8779 - val_loss: 0.2952 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8457\n",
+ "Epoch 32/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2869 - accuracy: 0.8813 - precision_2: 0.8813 - recall_2: 0.8813 - f1: 0.8823\n",
+ "Epoch 00032: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.2869 - accuracy: 0.8813 - precision_2: 0.8813 - recall_2: 0.8813 - f1: 0.8823 - val_loss: 0.3222 - val_accuracy: 0.8511 - val_precision_2: 0.8511 - val_recall_2: 0.8511 - val_f1: 0.8408\n",
+ "Epoch 33/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2853 - accuracy: 0.8751 - precision_2: 0.8751 - recall_2: 0.8751 - f1: 0.8753\n",
+ "Epoch 00033: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 481ms/step - loss: 0.2853 - accuracy: 0.8751 - precision_2: 0.8751 - recall_2: 0.8751 - f1: 0.8753 - val_loss: 0.3602 - val_accuracy: 0.8360 - val_precision_2: 0.8360 - val_recall_2: 0.8360 - val_f1: 0.8408\n",
+ "Epoch 34/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2664 - accuracy: 0.8837 - precision_2: 0.8837 - recall_2: 0.8837 - f1: 0.8847\n",
+ "Epoch 00034: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.2664 - accuracy: 0.8837 - precision_2: 0.8837 - recall_2: 0.8837 - f1: 0.8847 - val_loss: 0.4969 - val_accuracy: 0.8018 - val_precision_2: 0.8018 - val_recall_2: 0.8018 - val_f1: 0.8076\n",
+ "Epoch 35/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2804 - accuracy: 0.8820 - precision_2: 0.8820 - recall_2: 0.8820 - f1: 0.8830\n",
+ "Epoch 00035: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2804 - accuracy: 0.8820 - precision_2: 0.8820 - recall_2: 0.8820 - f1: 0.8830 - val_loss: 0.3923 - val_accuracy: 0.8360 - val_precision_2: 0.8360 - val_recall_2: 0.8360 - val_f1: 0.8408\n",
+ "Epoch 36/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2686 - accuracy: 0.8837 - precision_2: 0.8837 - recall_2: 0.8837 - f1: 0.8821\n",
+ "Epoch 00036: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.2686 - accuracy: 0.8837 - precision_2: 0.8837 - recall_2: 0.8837 - f1: 0.8821 - val_loss: 0.4274 - val_accuracy: 0.8129 - val_precision_2: 0.8129 - val_recall_2: 0.8129 - val_f1: 0.8184\n",
+ "Epoch 37/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8772 - precision_2: 0.8772 - recall_2: 0.8772 - f1: 0.8765\n",
+ "Epoch 00037: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2872 - accuracy: 0.8772 - precision_2: 0.8772 - recall_2: 0.8772 - f1: 0.8765 - val_loss: 0.5236 - val_accuracy: 0.7877 - val_precision_2: 0.7877 - val_recall_2: 0.7877 - val_f1: 0.7939\n",
+ "Epoch 38/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2739 - accuracy: 0.8906 - precision_2: 0.8906 - recall_2: 0.8906 - f1: 0.8923\n",
+ "Epoch 00038: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 434ms/step - loss: 0.2739 - accuracy: 0.8906 - precision_2: 0.8906 - recall_2: 0.8906 - f1: 0.8923 - val_loss: 0.2907 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 39/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2755 - accuracy: 0.8858 - precision_2: 0.8858 - recall_2: 0.8858 - f1: 0.8858\n",
+ "Epoch 00039: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 25s 534ms/step - loss: 0.2755 - accuracy: 0.8858 - precision_2: 0.8858 - recall_2: 0.8858 - f1: 0.8858 - val_loss: 0.2860 - val_accuracy: 0.8793 - val_precision_2: 0.8793 - val_recall_2: 0.8793 - val_f1: 0.8828\n",
+ "Epoch 40/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2759 - accuracy: 0.8772 - precision_2: 0.8772 - recall_2: 0.8772 - f1: 0.8773\n",
+ "Epoch 00040: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2759 - accuracy: 0.8772 - precision_2: 0.8772 - recall_2: 0.8772 - f1: 0.8773 - val_loss: 0.3970 - val_accuracy: 0.8089 - val_precision_2: 0.8089 - val_recall_2: 0.8089 - val_f1: 0.8145\n",
+ "Epoch 41/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2564 - accuracy: 0.8916 - precision_2: 0.8916 - recall_2: 0.8916 - f1: 0.8925\n",
+ "Epoch 00041: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.2564 - accuracy: 0.8916 - precision_2: 0.8916 - recall_2: 0.8916 - f1: 0.8925 - val_loss: 0.3641 - val_accuracy: 0.8471 - val_precision_2: 0.8471 - val_recall_2: 0.8471 - val_f1: 0.8369\n",
+ "Epoch 42/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2734 - accuracy: 0.8861 - precision_2: 0.8861 - recall_2: 0.8861 - f1: 0.8844\n",
+ "Epoch 00042: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2734 - accuracy: 0.8861 - precision_2: 0.8861 - recall_2: 0.8861 - f1: 0.8844 - val_loss: 0.4061 - val_accuracy: 0.8260 - val_precision_2: 0.8260 - val_recall_2: 0.8260 - val_f1: 0.8311\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 43/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2601 - accuracy: 0.8889 - precision_2: 0.8889 - recall_2: 0.8889 - f1: 0.8863\n",
+ "Epoch 00043: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2601 - accuracy: 0.8889 - precision_2: 0.8889 - recall_2: 0.8889 - f1: 0.8863 - val_loss: 0.3072 - val_accuracy: 0.8682 - val_precision_2: 0.8682 - val_recall_2: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 44/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2723 - accuracy: 0.8854 - precision_2: 0.8854 - recall_2: 0.8854 - f1: 0.8855\n",
+ "Epoch 00044: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 489ms/step - loss: 0.2723 - accuracy: 0.8854 - precision_2: 0.8854 - recall_2: 0.8854 - f1: 0.8855 - val_loss: 0.3871 - val_accuracy: 0.8441 - val_precision_2: 0.8441 - val_recall_2: 0.8441 - val_f1: 0.8486\n",
+ "Epoch 45/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2572 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896\n",
+ "Epoch 00045: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.2572 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896 - val_loss: 0.3004 - val_accuracy: 0.8732 - val_precision_2: 0.8732 - val_recall_2: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 46/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2576 - accuracy: 0.8858 - precision_2: 0.8858 - recall_2: 0.8858 - f1: 0.8867\n",
+ "Epoch 00046: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 442ms/step - loss: 0.2576 - accuracy: 0.8858 - precision_2: 0.8858 - recall_2: 0.8858 - f1: 0.8867 - val_loss: 0.3709 - val_accuracy: 0.8481 - val_precision_2: 0.8481 - val_recall_2: 0.8481 - val_f1: 0.8379\n",
+ "Epoch 47/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2676 - accuracy: 0.8858 - precision_2: 0.8858 - recall_2: 0.8858 - f1: 0.8841\n",
+ "Epoch 00047: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.2676 - accuracy: 0.8858 - precision_2: 0.8858 - recall_2: 0.8858 - f1: 0.8841 - val_loss: 0.3197 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8623\n",
+ "Epoch 48/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2519 - accuracy: 0.9023 - precision_2: 0.9023 - recall_2: 0.9023 - f1: 0.9030\n",
+ "Epoch 00048: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.2519 - accuracy: 0.9023 - precision_2: 0.9023 - recall_2: 0.9023 - f1: 0.9030 - val_loss: 0.3028 - val_accuracy: 0.8581 - val_precision_2: 0.8581 - val_recall_2: 0.8581 - val_f1: 0.8477\n",
+ "Epoch 49/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2605 - accuracy: 0.8910 - precision_2: 0.8910 - recall_2: 0.8910 - f1: 0.8918\n",
+ "Epoch 00049: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2605 - accuracy: 0.8910 - precision_2: 0.8910 - recall_2: 0.8910 - f1: 0.8918 - val_loss: 0.3539 - val_accuracy: 0.8632 - val_precision_2: 0.8632 - val_recall_2: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 50/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2556 - accuracy: 0.8916 - precision_2: 0.8916 - recall_2: 0.8916 - f1: 0.8916\n",
+ "Epoch 00050: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.2556 - accuracy: 0.8916 - precision_2: 0.8916 - recall_2: 0.8916 - f1: 0.8916 - val_loss: 0.4881 - val_accuracy: 0.8058 - val_precision_2: 0.8058 - val_recall_2: 0.8058 - val_f1: 0.8115\n",
+ "Epoch 51/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2546 - accuracy: 0.8958 - precision_2: 0.8958 - recall_2: 0.8958 - f1: 0.8948\n",
+ "Epoch 00051: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.2546 - accuracy: 0.8958 - precision_2: 0.8958 - recall_2: 0.8958 - f1: 0.8948 - val_loss: 0.3214 - val_accuracy: 0.8642 - val_precision_2: 0.8642 - val_recall_2: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 52/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2566 - accuracy: 0.8882 - precision_2: 0.8882 - recall_2: 0.8882 - f1: 0.8873\n",
+ "Epoch 00052: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2566 - accuracy: 0.8882 - precision_2: 0.8882 - recall_2: 0.8882 - f1: 0.8873 - val_loss: 0.4145 - val_accuracy: 0.8270 - val_precision_2: 0.8270 - val_recall_2: 0.8270 - val_f1: 0.8320\n",
+ "Epoch 53/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.8906 - precision_2: 0.8906 - recall_2: 0.8906 - f1: 0.8915\n",
+ "Epoch 00053: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2502 - accuracy: 0.8906 - precision_2: 0.8906 - recall_2: 0.8906 - f1: 0.8915 - val_loss: 0.2865 - val_accuracy: 0.8722 - val_precision_2: 0.8722 - val_recall_2: 0.8722 - val_f1: 0.8760\n",
+ "Epoch 54/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2580 - accuracy: 0.8923 - precision_2: 0.8923 - recall_2: 0.8923 - f1: 0.8914\n",
+ "Epoch 00054: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 446ms/step - loss: 0.2580 - accuracy: 0.8923 - precision_2: 0.8923 - recall_2: 0.8923 - f1: 0.8914 - val_loss: 0.3822 - val_accuracy: 0.8290 - val_precision_2: 0.8290 - val_recall_2: 0.8290 - val_f1: 0.8340\n",
+ "Epoch 55/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2673 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896\n",
+ "Epoch 00055: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 466ms/step - loss: 0.2673 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896 - val_loss: 0.5701 - val_accuracy: 0.7797 - val_precision_2: 0.7797 - val_recall_2: 0.7797 - val_f1: 0.7861\n",
+ "Epoch 56/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2535 - accuracy: 0.8892 - precision_2: 0.8892 - recall_2: 0.8892 - f1: 0.8901\n",
+ "Epoch 00056: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 486ms/step - loss: 0.2535 - accuracy: 0.8892 - precision_2: 0.8892 - recall_2: 0.8892 - f1: 0.8901 - val_loss: 0.3205 - val_accuracy: 0.8511 - val_precision_2: 0.8511 - val_recall_2: 0.8511 - val_f1: 0.8408\n",
+ "Epoch 57/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2434 - accuracy: 0.8913 - precision_2: 0.8913 - recall_2: 0.8913 - f1: 0.8930\n",
+ "Epoch 00057: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 27s 584ms/step - loss: 0.2434 - accuracy: 0.8913 - precision_2: 0.8913 - recall_2: 0.8913 - f1: 0.8930 - val_loss: 0.2919 - val_accuracy: 0.8793 - val_precision_2: 0.8793 - val_recall_2: 0.8793 - val_f1: 0.8828\n",
+ "Epoch 58/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2518 - accuracy: 0.8913 - precision_2: 0.8913 - recall_2: 0.8913 - f1: 0.8904\n",
+ "Epoch 00058: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.2518 - accuracy: 0.8913 - precision_2: 0.8913 - recall_2: 0.8913 - f1: 0.8904 - val_loss: 0.2791 - val_accuracy: 0.8833 - val_precision_2: 0.8833 - val_recall_2: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 59/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2492 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896\n",
+ "Epoch 00059: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 453ms/step - loss: 0.2492 - accuracy: 0.8896 - precision_2: 0.8896 - recall_2: 0.8896 - f1: 0.8896 - val_loss: 0.3352 - val_accuracy: 0.8592 - val_precision_2: 0.8592 - val_recall_2: 0.8592 - val_f1: 0.8633\n",
+ "Epoch 60/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2436 - accuracy: 0.8948 - precision_2: 0.8948 - recall_2: 0.8948 - f1: 0.8947\n",
+ "Epoch 00060: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2436 - accuracy: 0.8948 - precision_2: 0.8948 - recall_2: 0.8948 - f1: 0.8947 - val_loss: 0.2811 - val_accuracy: 0.8732 - val_precision_2: 0.8732 - val_recall_2: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 61/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2471 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8926\n",
+ "Epoch 00061: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 438ms/step - loss: 0.2471 - accuracy: 0.8927 - precision_2: 0.8927 - recall_2: 0.8927 - f1: 0.8926 - val_loss: 0.3052 - val_accuracy: 0.8682 - val_precision_2: 0.8682 - val_recall_2: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 62/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2497 - accuracy: 0.8920 - precision_2: 0.8920 - recall_2: 0.8920 - f1: 0.8911\n",
+ "Epoch 00062: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 482ms/step - loss: 0.2497 - accuracy: 0.8920 - precision_2: 0.8920 - recall_2: 0.8920 - f1: 0.8911 - val_loss: 0.3893 - val_accuracy: 0.8290 - val_precision_2: 0.8290 - val_recall_2: 0.8290 - val_f1: 0.8193\n",
+ "Epoch 63/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2679 - accuracy: 0.8834 - precision_2: 0.8834 - recall_2: 0.8834 - f1: 0.8817\n",
+ "Epoch 00063: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 30s 657ms/step - loss: 0.2679 - accuracy: 0.8834 - precision_2: 0.8834 - recall_2: 0.8834 - f1: 0.8817 - val_loss: 0.3346 - val_accuracy: 0.8642 - val_precision_2: 0.8642 - val_recall_2: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 64/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2431 - accuracy: 0.8941 - precision_2: 0.8941 - recall_2: 0.8941 - f1: 0.8940\n",
+ "Epoch 00064: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 460ms/step - loss: 0.2431 - accuracy: 0.8941 - precision_2: 0.8941 - recall_2: 0.8941 - f1: 0.8940 - val_loss: 0.2964 - val_accuracy: 0.8571 - val_precision_2: 0.8571 - val_recall_2: 0.8571 - val_f1: 0.8613\n",
+ "Epoch 65/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.8937 - precision_2: 0.8937 - recall_2: 0.8937 - f1: 0.8936\n",
+ "Epoch 00065: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2483 - accuracy: 0.8937 - precision_2: 0.8937 - recall_2: 0.8937 - f1: 0.8936 - val_loss: 0.3909 - val_accuracy: 0.8501 - val_precision_2: 0.8501 - val_recall_2: 0.8501 - val_f1: 0.8545\n",
+ "Epoch 66/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2350 - accuracy: 0.8996 - precision_2: 0.8996 - recall_2: 0.8996 - f1: 0.8977\n",
+ "Epoch 00066: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 441ms/step - loss: 0.2350 - accuracy: 0.8996 - precision_2: 0.8996 - recall_2: 0.8996 - f1: 0.8977 - val_loss: 0.3423 - val_accuracy: 0.8410 - val_precision_2: 0.8410 - val_recall_2: 0.8410 - val_f1: 0.8457\n",
+ "Epoch 67/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2290 - accuracy: 0.8982 - precision_2: 0.8982 - recall_2: 0.8982 - f1: 0.8989\n",
+ "Epoch 00067: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 488ms/step - loss: 0.2290 - accuracy: 0.8982 - precision_2: 0.8982 - recall_2: 0.8982 - f1: 0.8989 - val_loss: 0.3336 - val_accuracy: 0.8632 - val_precision_2: 0.8632 - val_recall_2: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 68/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2352 - accuracy: 0.8958 - precision_2: 0.8958 - recall_2: 0.8958 - f1: 0.8957\n",
+ "Epoch 00068: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 424ms/step - loss: 0.2352 - accuracy: 0.8958 - precision_2: 0.8958 - recall_2: 0.8958 - f1: 0.8957 - val_loss: 0.3225 - val_accuracy: 0.8441 - val_precision_2: 0.8441 - val_recall_2: 0.8441 - val_f1: 0.8486\n",
+ "Epoch 69/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2398 - accuracy: 0.9065 - precision_2: 0.9065 - recall_2: 0.9065 - f1: 0.9053\n",
+ "Epoch 00069: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2398 - accuracy: 0.9065 - precision_2: 0.9065 - recall_2: 0.9065 - f1: 0.9053 - val_loss: 0.4994 - val_accuracy: 0.8089 - val_precision_2: 0.8089 - val_recall_2: 0.8089 - val_f1: 0.7998\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 70/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2497 - accuracy: 0.8930 - precision_2: 0.8930 - recall_2: 0.8930 - f1: 0.8921\n",
+ "Epoch 00070: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 510ms/step - loss: 0.2497 - accuracy: 0.8930 - precision_2: 0.8930 - recall_2: 0.8930 - f1: 0.8921 - val_loss: 0.3379 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8496\n",
+ "Epoch 71/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2276 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9027\n",
+ "Epoch 00071: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2276 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9027 - val_loss: 0.4453 - val_accuracy: 0.8260 - val_precision_2: 0.8260 - val_recall_2: 0.8260 - val_f1: 0.8311\n",
+ "Epoch 72/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2302 - accuracy: 0.9023 - precision_2: 0.9023 - recall_2: 0.9023 - f1: 0.9030\n",
+ "Epoch 00072: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2302 - accuracy: 0.9023 - precision_2: 0.9023 - recall_2: 0.9023 - f1: 0.9030 - val_loss: 0.2689 - val_accuracy: 0.8853 - val_precision_2: 0.8853 - val_recall_2: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 73/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2275 - accuracy: 0.9068 - precision_2: 0.9068 - recall_2: 0.9068 - f1: 0.9074\n",
+ "Epoch 00073: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.2275 - accuracy: 0.9068 - precision_2: 0.9068 - recall_2: 0.9068 - f1: 0.9074 - val_loss: 0.2944 - val_accuracy: 0.8742 - val_precision_2: 0.8742 - val_recall_2: 0.8742 - val_f1: 0.8779\n",
+ "Epoch 74/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2340 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9009\n",
+ "Epoch 00074: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.2340 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9009 - val_loss: 0.3168 - val_accuracy: 0.8813 - val_precision_2: 0.8813 - val_recall_2: 0.8813 - val_f1: 0.8848\n",
+ "Epoch 75/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2199 - accuracy: 0.9051 - precision_2: 0.9051 - recall_2: 0.9051 - f1: 0.9066\n",
+ "Epoch 00075: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 469ms/step - loss: 0.2199 - accuracy: 0.9051 - precision_2: 0.9051 - recall_2: 0.9051 - f1: 0.9066 - val_loss: 0.3050 - val_accuracy: 0.8803 - val_precision_2: 0.8803 - val_recall_2: 0.8803 - val_f1: 0.8691\n",
+ "Epoch 76/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2178 - accuracy: 0.9075 - precision_2: 0.9075 - recall_2: 0.9075 - f1: 0.9081\n",
+ "Epoch 00076: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.2178 - accuracy: 0.9075 - precision_2: 0.9075 - recall_2: 0.9075 - f1: 0.9081 - val_loss: 0.3244 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 77/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.9086 - precision_2: 0.9086 - recall_2: 0.9086 - f1: 0.9065\n",
+ "Epoch 00077: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 431ms/step - loss: 0.2287 - accuracy: 0.9086 - precision_2: 0.9086 - recall_2: 0.9086 - f1: 0.9065 - val_loss: 0.3108 - val_accuracy: 0.8763 - val_precision_2: 0.8763 - val_recall_2: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 78/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2385 - accuracy: 0.8999 - precision_2: 0.8999 - recall_2: 0.8999 - f1: 0.9006\n",
+ "Epoch 00078: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2385 - accuracy: 0.8999 - precision_2: 0.8999 - recall_2: 0.8999 - f1: 0.9006 - val_loss: 0.3033 - val_accuracy: 0.8702 - val_precision_2: 0.8702 - val_recall_2: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 79/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2421 - accuracy: 0.9017 - precision_2: 0.9017 - recall_2: 0.9017 - f1: 0.9015\n",
+ "Epoch 00079: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2421 - accuracy: 0.9017 - precision_2: 0.9017 - recall_2: 0.9017 - f1: 0.9015 - val_loss: 0.2862 - val_accuracy: 0.8753 - val_precision_2: 0.8753 - val_recall_2: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 80/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9058 - precision_2: 0.9058 - recall_2: 0.9058 - f1: 0.9064\n",
+ "Epoch 00080: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 464ms/step - loss: 0.2235 - accuracy: 0.9058 - precision_2: 0.9058 - recall_2: 0.9058 - f1: 0.9064 - val_loss: 0.3309 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8496\n",
+ "Epoch 81/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2171 - accuracy: 0.9124 - precision_2: 0.9124 - recall_2: 0.9124 - f1: 0.9137\n",
+ "Epoch 00081: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.2171 - accuracy: 0.9124 - precision_2: 0.9124 - recall_2: 0.9124 - f1: 0.9137 - val_loss: 0.2847 - val_accuracy: 0.8833 - val_precision_2: 0.8833 - val_recall_2: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 82/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2160 - accuracy: 0.9110 - precision_2: 0.9110 - recall_2: 0.9110 - f1: 0.9089\n",
+ "Epoch 00082: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 433ms/step - loss: 0.2160 - accuracy: 0.9110 - precision_2: 0.9110 - recall_2: 0.9110 - f1: 0.9089 - val_loss: 0.2860 - val_accuracy: 0.8924 - val_precision_2: 0.8924 - val_recall_2: 0.8924 - val_f1: 0.8955\n",
+ "Epoch 83/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2263 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9027\n",
+ "Epoch 00083: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2263 - accuracy: 0.9020 - precision_2: 0.9020 - recall_2: 0.9020 - f1: 0.9027 - val_loss: 0.5372 - val_accuracy: 0.7998 - val_precision_2: 0.7998 - val_recall_2: 0.7998 - val_f1: 0.8057\n",
+ "Epoch 84/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2290 - accuracy: 0.8986 - precision_2: 0.8986 - recall_2: 0.8986 - f1: 0.8984\n",
+ "Epoch 00084: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2290 - accuracy: 0.8986 - precision_2: 0.8986 - recall_2: 0.8986 - f1: 0.8984 - val_loss: 0.2785 - val_accuracy: 0.8753 - val_precision_2: 0.8753 - val_recall_2: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 85/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2280 - accuracy: 0.9061 - precision_2: 0.9061 - recall_2: 0.9061 - f1: 0.9059\n",
+ "Epoch 00085: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.2280 - accuracy: 0.9061 - precision_2: 0.9061 - recall_2: 0.9061 - f1: 0.9059 - val_loss: 0.8025 - val_accuracy: 0.7525 - val_precision_2: 0.7525 - val_recall_2: 0.7525 - val_f1: 0.7305\n",
+ "Epoch 86/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2210 - accuracy: 0.9089 - precision_2: 0.9089 - recall_2: 0.9089 - f1: 0.9077\n",
+ "Epoch 00086: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.2210 - accuracy: 0.9089 - precision_2: 0.9089 - recall_2: 0.9089 - f1: 0.9077 - val_loss: 0.4455 - val_accuracy: 0.8350 - val_precision_2: 0.8350 - val_recall_2: 0.8350 - val_f1: 0.8252\n",
+ "Epoch 87/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2325 - accuracy: 0.9023 - precision_2: 0.9023 - recall_2: 0.9023 - f1: 0.9013\n",
+ "Epoch 00087: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2325 - accuracy: 0.9023 - precision_2: 0.9023 - recall_2: 0.9023 - f1: 0.9013 - val_loss: 0.2727 - val_accuracy: 0.8873 - val_precision_2: 0.8873 - val_recall_2: 0.8873 - val_f1: 0.8906\n",
+ "Epoch 88/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2242 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9084\n",
+ "Epoch 00088: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2242 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9084 - val_loss: 0.3036 - val_accuracy: 0.8823 - val_precision_2: 0.8823 - val_recall_2: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 89/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2272 - accuracy: 0.9017 - precision_2: 0.9017 - recall_2: 0.9017 - f1: 0.9015\n",
+ "Epoch 00089: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 410ms/step - loss: 0.2272 - accuracy: 0.9017 - precision_2: 0.9017 - recall_2: 0.9017 - f1: 0.9015 - val_loss: 0.3043 - val_accuracy: 0.8692 - val_precision_2: 0.8692 - val_recall_2: 0.8692 - val_f1: 0.8730\n",
+ "Epoch 90/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2047 - accuracy: 0.9117 - precision_2: 0.9117 - recall_2: 0.9117 - f1: 0.9122\n",
+ "Epoch 00090: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.2047 - accuracy: 0.9117 - precision_2: 0.9117 - recall_2: 0.9117 - f1: 0.9122 - val_loss: 0.2954 - val_accuracy: 0.8803 - val_precision_2: 0.8803 - val_recall_2: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 91/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2224 - accuracy: 0.9096 - precision_2: 0.9096 - recall_2: 0.9096 - f1: 0.9110\n",
+ "Epoch 00091: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.2224 - accuracy: 0.9096 - precision_2: 0.9096 - recall_2: 0.9096 - f1: 0.9110 - val_loss: 0.4420 - val_accuracy: 0.8360 - val_precision_2: 0.8360 - val_recall_2: 0.8360 - val_f1: 0.8262\n",
+ "Epoch 92/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2122 - accuracy: 0.9165 - precision_2: 0.9165 - recall_2: 0.9165 - f1: 0.9152\n",
+ "Epoch 00092: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.2122 - accuracy: 0.9165 - precision_2: 0.9165 - recall_2: 0.9165 - f1: 0.9152 - val_loss: 0.2855 - val_accuracy: 0.8873 - val_precision_2: 0.8873 - val_recall_2: 0.8873 - val_f1: 0.8906\n",
+ "Epoch 93/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2100 - accuracy: 0.9137 - precision_2: 0.9137 - recall_2: 0.9137 - f1: 0.9133\n",
+ "Epoch 00093: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.2100 - accuracy: 0.9137 - precision_2: 0.9137 - recall_2: 0.9137 - f1: 0.9133 - val_loss: 0.3879 - val_accuracy: 0.8370 - val_precision_2: 0.8370 - val_recall_2: 0.8370 - val_f1: 0.8418\n",
+ "Epoch 94/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2156 - accuracy: 0.9120 - precision_2: 0.9120 - recall_2: 0.9120 - f1: 0.9116\n",
+ "Epoch 00094: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.2156 - accuracy: 0.9120 - precision_2: 0.9120 - recall_2: 0.9120 - f1: 0.9116 - val_loss: 0.2945 - val_accuracy: 0.8803 - val_precision_2: 0.8803 - val_recall_2: 0.8803 - val_f1: 0.8691\n",
+ "Epoch 95/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2281 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9019\n",
+ "Epoch 00095: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.2281 - accuracy: 0.9048 - precision_2: 0.9048 - recall_2: 0.9048 - f1: 0.9019 - val_loss: 0.5036 - val_accuracy: 0.8300 - val_precision_2: 0.8300 - val_recall_2: 0.8300 - val_f1: 0.8350\n",
+ "Epoch 96/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2172 - accuracy: 0.9051 - precision_2: 0.9051 - recall_2: 0.9051 - f1: 0.9049\n",
+ "Epoch 00096: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 475ms/step - loss: 0.2172 - accuracy: 0.9051 - precision_2: 0.9051 - recall_2: 0.9051 - f1: 0.9049 - val_loss: 0.2835 - val_accuracy: 0.8803 - val_precision_2: 0.8803 - val_recall_2: 0.8803 - val_f1: 0.8838\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 97/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2208 - accuracy: 0.9072 - precision_2: 0.9072 - recall_2: 0.9072 - f1: 0.9043\n",
+ "Epoch 00097: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.2208 - accuracy: 0.9072 - precision_2: 0.9072 - recall_2: 0.9072 - f1: 0.9043 - val_loss: 0.3093 - val_accuracy: 0.8632 - val_precision_2: 0.8632 - val_recall_2: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 98/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2074 - accuracy: 0.9193 - precision_2: 0.9193 - recall_2: 0.9193 - f1: 0.9196\n",
+ "Epoch 00098: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 419ms/step - loss: 0.2074 - accuracy: 0.9193 - precision_2: 0.9193 - recall_2: 0.9193 - f1: 0.9196 - val_loss: 0.2768 - val_accuracy: 0.8913 - val_precision_2: 0.8913 - val_recall_2: 0.8913 - val_f1: 0.8945\n",
+ "Epoch 99/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2009 - accuracy: 0.9141 - precision_2: 0.9141 - recall_2: 0.9141 - f1: 0.9137\n",
+ "Epoch 00099: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.2009 - accuracy: 0.9141 - precision_2: 0.9141 - recall_2: 0.9141 - f1: 0.9137 - val_loss: 0.3039 - val_accuracy: 0.8763 - val_precision_2: 0.8763 - val_recall_2: 0.8763 - val_f1: 0.8799\n",
+ "Epoch 100/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2027 - accuracy: 0.9175 - precision_2: 0.9175 - recall_2: 0.9175 - f1: 0.9179\n",
+ "Epoch 00100: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2027 - accuracy: 0.9175 - precision_2: 0.9175 - recall_2: 0.9175 - f1: 0.9179 - val_loss: 0.3567 - val_accuracy: 0.8501 - val_precision_2: 0.8501 - val_recall_2: 0.8501 - val_f1: 0.8545\n",
+ "Epoch 101/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2045 - accuracy: 0.9158 - precision_2: 0.9158 - recall_2: 0.9158 - f1: 0.9145\n",
+ "Epoch 00101: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2045 - accuracy: 0.9158 - precision_2: 0.9158 - recall_2: 0.9158 - f1: 0.9145 - val_loss: 0.2885 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8818\n",
+ "Epoch 102/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.9107 - precision_2: 0.9107 - recall_2: 0.9107 - f1: 0.9107\n",
+ "Epoch 00102: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 454ms/step - loss: 0.2106 - accuracy: 0.9107 - precision_2: 0.9107 - recall_2: 0.9107 - f1: 0.9107 - val_loss: 0.4048 - val_accuracy: 0.8461 - val_precision_2: 0.8461 - val_recall_2: 0.8461 - val_f1: 0.8359\n",
+ "Epoch 103/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1988 - accuracy: 0.9175 - precision_2: 0.9175 - recall_2: 0.9175 - f1: 0.9162\n",
+ "Epoch 00103: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 424ms/step - loss: 0.1988 - accuracy: 0.9175 - precision_2: 0.9175 - recall_2: 0.9175 - f1: 0.9162 - val_loss: 0.3660 - val_accuracy: 0.8561 - val_precision_2: 0.8561 - val_recall_2: 0.8561 - val_f1: 0.8604\n",
+ "Epoch 104/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2074 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9076\n",
+ "Epoch 00104: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.2074 - accuracy: 0.9079 - precision_2: 0.9079 - recall_2: 0.9079 - f1: 0.9076 - val_loss: 0.3248 - val_accuracy: 0.8632 - val_precision_2: 0.8632 - val_recall_2: 0.8632 - val_f1: 0.8672\n",
+ "Epoch 105/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2174 - accuracy: 0.9096 - precision_2: 0.9096 - recall_2: 0.9096 - f1: 0.9101\n",
+ "Epoch 00105: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.2174 - accuracy: 0.9096 - precision_2: 0.9096 - recall_2: 0.9096 - f1: 0.9101 - val_loss: 0.3719 - val_accuracy: 0.8521 - val_precision_2: 0.8521 - val_recall_2: 0.8521 - val_f1: 0.8564\n",
+ "Epoch 106/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2058 - accuracy: 0.9151 - precision_2: 0.9151 - recall_2: 0.9151 - f1: 0.9147\n",
+ "Epoch 00106: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 408ms/step - loss: 0.2058 - accuracy: 0.9151 - precision_2: 0.9151 - recall_2: 0.9151 - f1: 0.9147 - val_loss: 0.3532 - val_accuracy: 0.8622 - val_precision_2: 0.8622 - val_recall_2: 0.8622 - val_f1: 0.8662\n",
+ "Epoch 107/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2148 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9101\n",
+ "Epoch 00107: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 449ms/step - loss: 0.2148 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9101 - val_loss: 0.5382 - val_accuracy: 0.7968 - val_precision_2: 0.7968 - val_recall_2: 0.7968 - val_f1: 0.8027\n",
+ "Epoch 108/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2002 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9190\n",
+ "Epoch 00108: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2002 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9190 - val_loss: 0.3268 - val_accuracy: 0.8642 - val_precision_2: 0.8642 - val_recall_2: 0.8642 - val_f1: 0.8682\n",
+ "Epoch 109/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2033 - accuracy: 0.9175 - precision_2: 0.9175 - recall_2: 0.9175 - f1: 0.9162\n",
+ "Epoch 00109: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.2033 - accuracy: 0.9175 - precision_2: 0.9175 - recall_2: 0.9175 - f1: 0.9162 - val_loss: 0.2780 - val_accuracy: 0.8803 - val_precision_2: 0.8803 - val_recall_2: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 110/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2151 - accuracy: 0.9137 - precision_2: 0.9137 - recall_2: 0.9137 - f1: 0.9142\n",
+ "Epoch 00110: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2151 - accuracy: 0.9137 - precision_2: 0.9137 - recall_2: 0.9137 - f1: 0.9142 - val_loss: 0.2644 - val_accuracy: 0.8903 - val_precision_2: 0.8903 - val_recall_2: 0.8903 - val_f1: 0.8936\n",
+ "Epoch 111/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.9220 - precision_2: 0.9220 - recall_2: 0.9220 - f1: 0.9189\n",
+ "Epoch 00111: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1983 - accuracy: 0.9220 - precision_2: 0.9220 - recall_2: 0.9220 - f1: 0.9189 - val_loss: 0.3489 - val_accuracy: 0.8571 - val_precision_2: 0.8571 - val_recall_2: 0.8571 - val_f1: 0.8613\n",
+ "Epoch 112/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2058 - accuracy: 0.9182 - precision_2: 0.9182 - recall_2: 0.9182 - f1: 0.9160\n",
+ "Epoch 00112: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 461ms/step - loss: 0.2058 - accuracy: 0.9182 - precision_2: 0.9182 - recall_2: 0.9182 - f1: 0.9160 - val_loss: 0.2897 - val_accuracy: 0.8732 - val_precision_2: 0.8732 - val_recall_2: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 113/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1984 - accuracy: 0.9209 - precision_2: 0.9209 - recall_2: 0.9209 - f1: 0.9209\n",
+ "Epoch 00113: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 456ms/step - loss: 0.1984 - accuracy: 0.9209 - precision_2: 0.9209 - recall_2: 0.9209 - f1: 0.9209 - val_loss: 0.2788 - val_accuracy: 0.8803 - val_precision_2: 0.8803 - val_recall_2: 0.8803 - val_f1: 0.8838\n",
+ "Epoch 114/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1869 - accuracy: 0.9217 - precision_2: 0.9217 - recall_2: 0.9217 - f1: 0.9220\n",
+ "Epoch 00114: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 476ms/step - loss: 0.1869 - accuracy: 0.9217 - precision_2: 0.9217 - recall_2: 0.9217 - f1: 0.9220 - val_loss: 0.2704 - val_accuracy: 0.9034 - val_precision_2: 0.9034 - val_recall_2: 0.9034 - val_f1: 0.9062\n",
+ "Epoch 115/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2086 - accuracy: 0.9158 - precision_2: 0.9158 - recall_2: 0.9158 - f1: 0.9171\n",
+ "Epoch 00115: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.2086 - accuracy: 0.9158 - precision_2: 0.9158 - recall_2: 0.9158 - f1: 0.9171 - val_loss: 0.2618 - val_accuracy: 0.8964 - val_precision_2: 0.8964 - val_recall_2: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 116/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1955 - accuracy: 0.9213 - precision_2: 0.9213 - recall_2: 0.9213 - f1: 0.9208\n",
+ "Epoch 00116: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 425ms/step - loss: 0.1955 - accuracy: 0.9213 - precision_2: 0.9213 - recall_2: 0.9213 - f1: 0.9208 - val_loss: 0.2543 - val_accuracy: 0.8853 - val_precision_2: 0.8853 - val_recall_2: 0.8853 - val_f1: 0.8887\n",
+ "Epoch 117/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1910 - accuracy: 0.9168 - precision_2: 0.9168 - recall_2: 0.9168 - f1: 0.9155\n",
+ "Epoch 00117: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1910 - accuracy: 0.9168 - precision_2: 0.9168 - recall_2: 0.9168 - f1: 0.9155 - val_loss: 0.3211 - val_accuracy: 0.8712 - val_precision_2: 0.8712 - val_recall_2: 0.8712 - val_f1: 0.8750\n",
+ "Epoch 118/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1829 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9252\n",
+ "Epoch 00118: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 481ms/step - loss: 0.1829 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9252 - val_loss: 0.4696 - val_accuracy: 0.8300 - val_precision_2: 0.8300 - val_recall_2: 0.8300 - val_f1: 0.8350\n",
+ "Epoch 119/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1973 - accuracy: 0.9220 - precision_2: 0.9220 - recall_2: 0.9220 - f1: 0.9232\n",
+ "Epoch 00119: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 430ms/step - loss: 0.1973 - accuracy: 0.9220 - precision_2: 0.9220 - recall_2: 0.9220 - f1: 0.9232 - val_loss: 0.3056 - val_accuracy: 0.8702 - val_precision_2: 0.8702 - val_recall_2: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 120/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1817 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9261\n",
+ "Epoch 00120: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.1817 - accuracy: 0.9258 - precision_2: 0.9258 - recall_2: 0.9258 - f1: 0.9261 - val_loss: 0.2788 - val_accuracy: 0.8773 - val_precision_2: 0.8773 - val_recall_2: 0.8773 - val_f1: 0.8809\n",
+ "Epoch 121/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2028 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9109\n",
+ "Epoch 00121: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.2028 - accuracy: 0.9130 - precision_2: 0.9130 - recall_2: 0.9130 - f1: 0.9109 - val_loss: 0.3939 - val_accuracy: 0.8400 - val_precision_2: 0.8400 - val_recall_2: 0.8400 - val_f1: 0.8447\n",
+ "Epoch 122/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.2173 - accuracy: 0.9082 - precision_2: 0.9082 - recall_2: 0.9082 - f1: 0.9070\n",
+ "Epoch 00122: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.2173 - accuracy: 0.9082 - precision_2: 0.9082 - recall_2: 0.9082 - f1: 0.9070 - val_loss: 0.2650 - val_accuracy: 0.8944 - val_precision_2: 0.8944 - val_recall_2: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 123/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1997 - accuracy: 0.9137 - precision_2: 0.9137 - recall_2: 0.9137 - f1: 0.9125\n",
+ "Epoch 00123: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 443ms/step - loss: 0.1997 - accuracy: 0.9137 - precision_2: 0.9137 - recall_2: 0.9137 - f1: 0.9125 - val_loss: 0.3993 - val_accuracy: 0.8511 - val_precision_2: 0.8511 - val_recall_2: 0.8511 - val_f1: 0.8555\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 124/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1875 - accuracy: 0.9199 - precision_2: 0.9199 - recall_2: 0.9199 - f1: 0.9195\n",
+ "Epoch 00124: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 454ms/step - loss: 0.1875 - accuracy: 0.9199 - precision_2: 0.9199 - recall_2: 0.9199 - f1: 0.9195 - val_loss: 0.3769 - val_accuracy: 0.8541 - val_precision_2: 0.8541 - val_recall_2: 0.8541 - val_f1: 0.8437\n",
+ "Epoch 125/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1839 - accuracy: 0.9199 - precision_2: 0.9199 - recall_2: 0.9199 - f1: 0.9195\n",
+ "Epoch 00125: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1839 - accuracy: 0.9199 - precision_2: 0.9199 - recall_2: 0.9199 - f1: 0.9195 - val_loss: 0.3733 - val_accuracy: 0.8612 - val_precision_2: 0.8612 - val_recall_2: 0.8612 - val_f1: 0.8506\n",
+ "Epoch 126/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1952 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9213\n",
+ "Epoch 00126: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1952 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9213 - val_loss: 0.3895 - val_accuracy: 0.8451 - val_precision_2: 0.8451 - val_recall_2: 0.8451 - val_f1: 0.8496\n",
+ "Epoch 127/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1967 - accuracy: 0.9231 - precision_2: 0.9231 - recall_2: 0.9231 - f1: 0.9216\n",
+ "Epoch 00127: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.1967 - accuracy: 0.9231 - precision_2: 0.9231 - recall_2: 0.9231 - f1: 0.9216 - val_loss: 0.2873 - val_accuracy: 0.8753 - val_precision_2: 0.8753 - val_recall_2: 0.8753 - val_f1: 0.8789\n",
+ "Epoch 128/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1873 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9235\n",
+ "Epoch 00128: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1873 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9235 - val_loss: 0.2701 - val_accuracy: 0.8984 - val_precision_2: 0.8984 - val_recall_2: 0.8984 - val_f1: 0.9014\n",
+ "Epoch 129/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1836 - accuracy: 0.9317 - precision_2: 0.9317 - recall_2: 0.9317 - f1: 0.9327\n",
+ "Epoch 00129: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 448ms/step - loss: 0.1836 - accuracy: 0.9317 - precision_2: 0.9317 - recall_2: 0.9317 - f1: 0.9327 - val_loss: 0.2606 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 130/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9253\n",
+ "Epoch 00130: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1969 - accuracy: 0.9241 - precision_2: 0.9241 - recall_2: 0.9241 - f1: 0.9253 - val_loss: 0.2714 - val_accuracy: 0.8924 - val_precision_2: 0.8924 - val_recall_2: 0.8924 - val_f1: 0.8809\n",
+ "Epoch 131/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.1904 - accuracy: 0.9205 - precision_2: 0.9205 - recall_2: 0.9205 - f1: 0.9205\n",
+ "Epoch 00131: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1899 - accuracy: 0.9206 - precision_2: 0.9206 - recall_2: 0.9206 - f1: 0.9210 - val_loss: 0.2792 - val_accuracy: 0.8732 - val_precision_2: 0.8732 - val_recall_2: 0.8732 - val_f1: 0.8623\n",
+ "Epoch 132/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.9244 - precision_2: 0.9244 - recall_2: 0.9244 - f1: 0.9239\n",
+ "Epoch 00132: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1848 - accuracy: 0.9244 - precision_2: 0.9244 - recall_2: 0.9244 - f1: 0.9239 - val_loss: 0.3582 - val_accuracy: 0.8732 - val_precision_2: 0.8732 - val_recall_2: 0.8732 - val_f1: 0.8770\n",
+ "Epoch 133/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1740 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9298\n",
+ "Epoch 00133: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 409ms/step - loss: 0.1740 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9298 - val_loss: 0.3018 - val_accuracy: 0.8793 - val_precision_2: 0.8793 - val_recall_2: 0.8793 - val_f1: 0.8828\n",
+ "Epoch 134/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1749 - accuracy: 0.9300 - precision_2: 0.9300 - recall_2: 0.9300 - f1: 0.9284\n",
+ "Epoch 00134: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1749 - accuracy: 0.9300 - precision_2: 0.9300 - recall_2: 0.9300 - f1: 0.9284 - val_loss: 0.2869 - val_accuracy: 0.8823 - val_precision_2: 0.8823 - val_recall_2: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 135/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1910 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9172\n",
+ "Epoch 00135: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 464ms/step - loss: 0.1910 - accuracy: 0.9186 - precision_2: 0.9186 - recall_2: 0.9186 - f1: 0.9172 - val_loss: 0.2703 - val_accuracy: 0.8944 - val_precision_2: 0.8944 - val_recall_2: 0.8944 - val_f1: 0.8975\n",
+ "Epoch 136/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1868 - accuracy: 0.9248 - precision_2: 0.9248 - recall_2: 0.9248 - f1: 0.9242\n",
+ "Epoch 00136: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 434ms/step - loss: 0.1868 - accuracy: 0.9248 - precision_2: 0.9248 - recall_2: 0.9248 - f1: 0.9242 - val_loss: 0.3343 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8672\n",
+ "Epoch 137/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1749 - accuracy: 0.9317 - precision_2: 0.9317 - recall_2: 0.9317 - f1: 0.9301\n",
+ "Epoch 00137: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.1749 - accuracy: 0.9317 - precision_2: 0.9317 - recall_2: 0.9317 - f1: 0.9301 - val_loss: 0.3805 - val_accuracy: 0.8491 - val_precision_2: 0.8491 - val_recall_2: 0.8491 - val_f1: 0.8535\n",
+ "Epoch 138/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1789 - accuracy: 0.9262 - precision_2: 0.9262 - recall_2: 0.9262 - f1: 0.9256\n",
+ "Epoch 00138: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1789 - accuracy: 0.9262 - precision_2: 0.9262 - recall_2: 0.9262 - f1: 0.9256 - val_loss: 0.2481 - val_accuracy: 0.8994 - val_precision_2: 0.8994 - val_recall_2: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 139/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1865 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9222\n",
+ "Epoch 00139: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 26s 574ms/step - loss: 0.1865 - accuracy: 0.9210 - precision_2: 0.9210 - recall_2: 0.9210 - f1: 0.9222 - val_loss: 0.3032 - val_accuracy: 0.8833 - val_precision_2: 0.8833 - val_recall_2: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 140/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1717 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9273\n",
+ "Epoch 00140: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 459ms/step - loss: 0.1717 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9273 - val_loss: 0.3296 - val_accuracy: 0.8763 - val_precision_2: 0.8763 - val_recall_2: 0.8763 - val_f1: 0.8652\n",
+ "Epoch 141/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1803 - accuracy: 0.9251 - precision_2: 0.9251 - recall_2: 0.9251 - f1: 0.9246\n",
+ "Epoch 00141: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1803 - accuracy: 0.9251 - precision_2: 0.9251 - recall_2: 0.9251 - f1: 0.9246 - val_loss: 0.5597 - val_accuracy: 0.8199 - val_precision_2: 0.8199 - val_recall_2: 0.8199 - val_f1: 0.8252\n",
+ "Epoch 142/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1900 - accuracy: 0.9196 - precision_2: 0.9196 - recall_2: 0.9196 - f1: 0.9165\n",
+ "Epoch 00142: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.1900 - accuracy: 0.9196 - precision_2: 0.9196 - recall_2: 0.9196 - f1: 0.9165 - val_loss: 0.2458 - val_accuracy: 0.9024 - val_precision_2: 0.9024 - val_recall_2: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 143/200\n",
+ "45/46 [============================>.] - ETA: 0s - loss: 0.1765 - accuracy: 0.9288 - precision_2: 0.9288 - recall_2: 0.9288 - f1: 0.9288\n",
+ "Epoch 00143: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1803 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9255 - val_loss: 0.2409 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 144/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1826 - accuracy: 0.9237 - precision_2: 0.9237 - recall_2: 0.9237 - f1: 0.9241\n",
+ "Epoch 00144: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1826 - accuracy: 0.9237 - precision_2: 0.9237 - recall_2: 0.9237 - f1: 0.9241 - val_loss: 0.2748 - val_accuracy: 0.9004 - val_precision_2: 0.9004 - val_recall_2: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 145/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1788 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9281\n",
+ "Epoch 00145: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1788 - accuracy: 0.9279 - precision_2: 0.9279 - recall_2: 0.9279 - f1: 0.9281 - val_loss: 0.2542 - val_accuracy: 0.8974 - val_precision_2: 0.8974 - val_recall_2: 0.8974 - val_f1: 0.9004\n",
+ "Epoch 146/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1758 - accuracy: 0.9237 - precision_2: 0.9237 - recall_2: 0.9237 - f1: 0.9249\n",
+ "Epoch 00146: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.1758 - accuracy: 0.9237 - precision_2: 0.9237 - recall_2: 0.9237 - f1: 0.9249 - val_loss: 0.2312 - val_accuracy: 0.9145 - val_precision_2: 0.9145 - val_recall_2: 0.9145 - val_f1: 0.9170\n",
+ "Epoch 147/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1751 - accuracy: 0.9289 - precision_2: 0.9289 - recall_2: 0.9289 - f1: 0.9283\n",
+ "Epoch 00147: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 414ms/step - loss: 0.1751 - accuracy: 0.9289 - precision_2: 0.9289 - recall_2: 0.9289 - f1: 0.9283 - val_loss: 0.2768 - val_accuracy: 0.8823 - val_precision_2: 0.8823 - val_recall_2: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 148/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1701 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332\n",
+ "Epoch 00148: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1701 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9332 - val_loss: 0.2874 - val_accuracy: 0.9024 - val_precision_2: 0.9024 - val_recall_2: 0.9024 - val_f1: 0.9053\n",
+ "Epoch 149/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1852 - accuracy: 0.9293 - precision_2: 0.9293 - recall_2: 0.9293 - f1: 0.9293\n",
+ "Epoch 00149: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1852 - accuracy: 0.9293 - precision_2: 0.9293 - recall_2: 0.9293 - f1: 0.9293 - val_loss: 0.3041 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8643\n",
+ "Epoch 150/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1675 - accuracy: 0.9334 - precision_2: 0.9334 - recall_2: 0.9334 - f1: 0.9344\n",
+ "Epoch 00150: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 412ms/step - loss: 0.1675 - accuracy: 0.9334 - precision_2: 0.9334 - recall_2: 0.9334 - f1: 0.9344 - val_loss: 0.3007 - val_accuracy: 0.8753 - val_precision_2: 0.8753 - val_recall_2: 0.8753 - val_f1: 0.8789\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 151/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1565 - accuracy: 0.9351 - precision_2: 0.9351 - recall_2: 0.9351 - f1: 0.9335\n",
+ "Epoch 00151: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 447ms/step - loss: 0.1565 - accuracy: 0.9351 - precision_2: 0.9351 - recall_2: 0.9351 - f1: 0.9335 - val_loss: 0.3828 - val_accuracy: 0.8682 - val_precision_2: 0.8682 - val_recall_2: 0.8682 - val_f1: 0.8721\n",
+ "Epoch 152/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1658 - accuracy: 0.9320 - precision_2: 0.9320 - recall_2: 0.9320 - f1: 0.9313\n",
+ "Epoch 00152: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 413ms/step - loss: 0.1658 - accuracy: 0.9320 - precision_2: 0.9320 - recall_2: 0.9320 - f1: 0.9313 - val_loss: 0.2401 - val_accuracy: 0.9054 - val_precision_2: 0.9054 - val_recall_2: 0.9054 - val_f1: 0.8936\n",
+ "Epoch 153/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1727 - accuracy: 0.9300 - precision_2: 0.9300 - recall_2: 0.9300 - f1: 0.9293\n",
+ "Epoch 00153: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1727 - accuracy: 0.9300 - precision_2: 0.9300 - recall_2: 0.9300 - f1: 0.9293 - val_loss: 0.2587 - val_accuracy: 0.8893 - val_precision_2: 0.8893 - val_recall_2: 0.8893 - val_f1: 0.8779\n",
+ "Epoch 154/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1693 - accuracy: 0.9265 - precision_2: 0.9265 - recall_2: 0.9265 - f1: 0.9259\n",
+ "Epoch 00154: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1693 - accuracy: 0.9265 - precision_2: 0.9265 - recall_2: 0.9265 - f1: 0.9259 - val_loss: 0.3002 - val_accuracy: 0.8783 - val_precision_2: 0.8783 - val_recall_2: 0.8783 - val_f1: 0.8672\n",
+ "Epoch 155/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1713 - accuracy: 0.9289 - precision_2: 0.9289 - recall_2: 0.9289 - f1: 0.9292\n",
+ "Epoch 00155: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1713 - accuracy: 0.9289 - precision_2: 0.9289 - recall_2: 0.9289 - f1: 0.9292 - val_loss: 0.2608 - val_accuracy: 0.9054 - val_precision_2: 0.9054 - val_recall_2: 0.9054 - val_f1: 0.9082\n",
+ "Epoch 156/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1746 - accuracy: 0.9265 - precision_2: 0.9265 - recall_2: 0.9265 - f1: 0.9268\n",
+ "Epoch 00156: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 411ms/step - loss: 0.1746 - accuracy: 0.9265 - precision_2: 0.9265 - recall_2: 0.9265 - f1: 0.9268 - val_loss: 0.3994 - val_accuracy: 0.8602 - val_precision_2: 0.8602 - val_recall_2: 0.8602 - val_f1: 0.8643\n",
+ "Epoch 157/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1735 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9289\n",
+ "Epoch 00157: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 458ms/step - loss: 0.1735 - accuracy: 0.9313 - precision_2: 0.9313 - recall_2: 0.9313 - f1: 0.9289 - val_loss: 0.3214 - val_accuracy: 0.8702 - val_precision_2: 0.8702 - val_recall_2: 0.8702 - val_f1: 0.8740\n",
+ "Epoch 158/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1709 - accuracy: 0.9306 - precision_2: 0.9306 - recall_2: 0.9306 - f1: 0.9317\n",
+ "Epoch 00158: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 457ms/step - loss: 0.1709 - accuracy: 0.9306 - precision_2: 0.9306 - recall_2: 0.9306 - f1: 0.9317 - val_loss: 0.2580 - val_accuracy: 0.9004 - val_precision_2: 0.9004 - val_recall_2: 0.9004 - val_f1: 0.9033\n",
+ "Epoch 159/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1678 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9351\n",
+ "Epoch 00159: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 429ms/step - loss: 0.1678 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9351 - val_loss: 0.2665 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 160/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1664 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9312\n",
+ "Epoch 00160: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1664 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9312 - val_loss: 0.2760 - val_accuracy: 0.8994 - val_precision_2: 0.8994 - val_recall_2: 0.8994 - val_f1: 0.9023\n",
+ "Epoch 161/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1736 - accuracy: 0.9324 - precision_2: 0.9324 - recall_2: 0.9324 - f1: 0.9334\n",
+ "Epoch 00161: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1736 - accuracy: 0.9324 - precision_2: 0.9324 - recall_2: 0.9324 - f1: 0.9334 - val_loss: 0.2300 - val_accuracy: 0.9135 - val_precision_2: 0.9135 - val_recall_2: 0.9135 - val_f1: 0.9160\n",
+ "Epoch 162/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1657 - accuracy: 0.9365 - precision_2: 0.9365 - recall_2: 0.9365 - f1: 0.9340\n",
+ "Epoch 00162: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 22s 483ms/step - loss: 0.1657 - accuracy: 0.9365 - precision_2: 0.9365 - recall_2: 0.9365 - f1: 0.9340 - val_loss: 0.3083 - val_accuracy: 0.8883 - val_precision_2: 0.8883 - val_recall_2: 0.8883 - val_f1: 0.8770\n",
+ "Epoch 163/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1713 - accuracy: 0.9275 - precision_2: 0.9275 - recall_2: 0.9275 - f1: 0.9278\n",
+ "Epoch 00163: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 424ms/step - loss: 0.1713 - accuracy: 0.9275 - precision_2: 0.9275 - recall_2: 0.9275 - f1: 0.9278 - val_loss: 0.4197 - val_accuracy: 0.8541 - val_precision_2: 0.8541 - val_recall_2: 0.8541 - val_f1: 0.8584\n",
+ "Epoch 164/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9382 - precision_2: 0.9382 - recall_2: 0.9382 - f1: 0.9366\n",
+ "Epoch 00164: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 426ms/step - loss: 0.1557 - accuracy: 0.9382 - precision_2: 0.9382 - recall_2: 0.9382 - f1: 0.9366 - val_loss: 0.2872 - val_accuracy: 0.8913 - val_precision_2: 0.8913 - val_recall_2: 0.8913 - val_f1: 0.8945\n",
+ "Epoch 165/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1603 - accuracy: 0.9348 - precision_2: 0.9348 - recall_2: 0.9348 - f1: 0.9349\n",
+ "Epoch 00165: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 421ms/step - loss: 0.1603 - accuracy: 0.9348 - precision_2: 0.9348 - recall_2: 0.9348 - f1: 0.9349 - val_loss: 0.2242 - val_accuracy: 0.9105 - val_precision_2: 0.9105 - val_recall_2: 0.9105 - val_f1: 0.9131\n",
+ "Epoch 166/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1497 - accuracy: 0.9358 - precision_2: 0.9358 - recall_2: 0.9358 - f1: 0.9351\n",
+ "Epoch 00166: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 417ms/step - loss: 0.1497 - accuracy: 0.9358 - precision_2: 0.9358 - recall_2: 0.9358 - f1: 0.9351 - val_loss: 0.2414 - val_accuracy: 0.9105 - val_precision_2: 0.9105 - val_recall_2: 0.9105 - val_f1: 0.9131\n",
+ "Epoch 167/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1534 - accuracy: 0.9369 - precision_2: 0.9369 - recall_2: 0.9369 - f1: 0.9361\n",
+ "Epoch 00167: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 416ms/step - loss: 0.1534 - accuracy: 0.9369 - precision_2: 0.9369 - recall_2: 0.9369 - f1: 0.9361 - val_loss: 0.2545 - val_accuracy: 0.9105 - val_precision_2: 0.9105 - val_recall_2: 0.9105 - val_f1: 0.8984\n",
+ "Epoch 168/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1705 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9324\n",
+ "Epoch 00168: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 23s 495ms/step - loss: 0.1705 - accuracy: 0.9331 - precision_2: 0.9331 - recall_2: 0.9331 - f1: 0.9324 - val_loss: 0.2655 - val_accuracy: 0.9074 - val_precision_2: 0.9074 - val_recall_2: 0.9074 - val_f1: 0.9102\n",
+ "Epoch 169/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1699 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9303\n",
+ "Epoch 00169: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 422ms/step - loss: 0.1699 - accuracy: 0.9310 - precision_2: 0.9310 - recall_2: 0.9310 - f1: 0.9303 - val_loss: 0.2506 - val_accuracy: 0.9095 - val_precision_2: 0.9095 - val_recall_2: 0.9095 - val_f1: 0.9121\n",
+ "Epoch 170/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1620 - accuracy: 0.9320 - precision_2: 0.9320 - recall_2: 0.9320 - f1: 0.9313\n",
+ "Epoch 00170: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1620 - accuracy: 0.9320 - precision_2: 0.9320 - recall_2: 0.9320 - f1: 0.9313 - val_loss: 0.3314 - val_accuracy: 0.8622 - val_precision_2: 0.8622 - val_recall_2: 0.8622 - val_f1: 0.8662\n",
+ "Epoch 171/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1541 - accuracy: 0.9393 - precision_2: 0.9393 - recall_2: 0.9393 - f1: 0.9393\n",
+ "Epoch 00171: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 440ms/step - loss: 0.1541 - accuracy: 0.9393 - precision_2: 0.9393 - recall_2: 0.9393 - f1: 0.9393 - val_loss: 0.3353 - val_accuracy: 0.8833 - val_precision_2: 0.8833 - val_recall_2: 0.8833 - val_f1: 0.8867\n",
+ "Epoch 172/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1453 - accuracy: 0.9417 - precision_2: 0.9417 - recall_2: 0.9417 - f1: 0.9426\n",
+ "Epoch 00172: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 452ms/step - loss: 0.1453 - accuracy: 0.9417 - precision_2: 0.9417 - recall_2: 0.9417 - f1: 0.9426 - val_loss: 0.3180 - val_accuracy: 0.8823 - val_precision_2: 0.8823 - val_recall_2: 0.8823 - val_f1: 0.8857\n",
+ "Epoch 173/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1533 - accuracy: 0.9382 - precision_2: 0.9382 - recall_2: 0.9382 - f1: 0.9383\n",
+ "Epoch 00173: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1533 - accuracy: 0.9382 - precision_2: 0.9382 - recall_2: 0.9382 - f1: 0.9383 - val_loss: 0.3529 - val_accuracy: 0.8843 - val_precision_2: 0.8843 - val_recall_2: 0.8843 - val_f1: 0.8877\n",
+ "Epoch 174/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1703 - accuracy: 0.9306 - precision_2: 0.9306 - recall_2: 0.9306 - f1: 0.9300\n",
+ "Epoch 00174: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 463ms/step - loss: 0.1703 - accuracy: 0.9306 - precision_2: 0.9306 - recall_2: 0.9306 - f1: 0.9300 - val_loss: 0.6099 - val_accuracy: 0.8129 - val_precision_2: 0.8129 - val_recall_2: 0.8129 - val_f1: 0.8184\n",
+ "Epoch 175/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1566 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9356\n",
+ "Epoch 00175: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 420ms/step - loss: 0.1566 - accuracy: 0.9355 - precision_2: 0.9355 - recall_2: 0.9355 - f1: 0.9356 - val_loss: 0.2422 - val_accuracy: 0.9064 - val_precision_2: 0.9064 - val_recall_2: 0.9064 - val_f1: 0.9092\n",
+ "Epoch 176/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1478 - accuracy: 0.9375 - precision_2: 0.9375 - recall_2: 0.9375 - f1: 0.9377\n",
+ "Epoch 00176: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1478 - accuracy: 0.9375 - precision_2: 0.9375 - recall_2: 0.9375 - f1: 0.9377 - val_loss: 0.2828 - val_accuracy: 0.8964 - val_precision_2: 0.8964 - val_recall_2: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 177/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1530 - accuracy: 0.9400 - precision_2: 0.9400 - recall_2: 0.9400 - f1: 0.9392\n",
+ "Epoch 00177: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 418ms/step - loss: 0.1530 - accuracy: 0.9400 - precision_2: 0.9400 - recall_2: 0.9400 - f1: 0.9392 - val_loss: 0.2781 - val_accuracy: 0.9074 - val_precision_2: 0.9074 - val_recall_2: 0.9074 - val_f1: 0.9102\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 178/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1569 - accuracy: 0.9396 - precision_2: 0.9396 - recall_2: 0.9396 - f1: 0.9388\n",
+ "Epoch 00178: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 20s 427ms/step - loss: 0.1569 - accuracy: 0.9396 - precision_2: 0.9396 - recall_2: 0.9396 - f1: 0.9388 - val_loss: 0.2396 - val_accuracy: 0.9044 - val_precision_2: 0.9044 - val_recall_2: 0.9044 - val_f1: 0.9072\n",
+ "Epoch 179/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1678 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9316\n",
+ "Epoch 00179: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 415ms/step - loss: 0.1678 - accuracy: 0.9341 - precision_2: 0.9341 - recall_2: 0.9341 - f1: 0.9316 - val_loss: 0.2871 - val_accuracy: 0.8964 - val_precision_2: 0.8964 - val_recall_2: 0.8964 - val_f1: 0.8994\n",
+ "Epoch 180/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1652 - accuracy: 0.9337 - precision_2: 0.9337 - recall_2: 0.9337 - f1: 0.9330\n",
+ "Epoch 00180: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 21s 465ms/step - loss: 0.1652 - accuracy: 0.9337 - precision_2: 0.9337 - recall_2: 0.9337 - f1: 0.9330 - val_loss: 0.2718 - val_accuracy: 0.8843 - val_precision_2: 0.8843 - val_recall_2: 0.8843 - val_f1: 0.8877\n",
+ "Epoch 181/200\n",
+ "46/46 [==============================] - ETA: 0s - loss: 0.1558 - accuracy: 0.9389 - precision_2: 0.9389 - recall_2: 0.9389 - f1: 0.9390\n",
+ "Epoch 00181: saving model to /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model\n",
+ "INFO:tensorflow:Assets written to: /userdata/kerasData/pyimagesearch/output/OGRUN_I4orgPYimageSearch.model/assets\n",
+ "46/46 [==============================] - 19s 423ms/step - loss: 0.1558 - accuracy: 0.9389 - precision_2: 0.9389 - recall_2: 0.9389 - f1: 0.9390 - val_loss: 0.3988 - val_accuracy: 0.8461 - val_precision_2: 0.8461 - val_recall_2: 0.8461 - val_f1: 0.8506\n",
+ "Epoch 182/200\n",
+ "27/46 [================>.............] - ETA: 4s - loss: 0.1636 - accuracy: 0.9352 - precision_2: 0.9352 - recall_2: 0.9352 - f1: 0.9369"
+ ]
+ }
+ ],
+ "source": [
+ "for number in [3,4,5]:\n",
+ " name = f\"OGRUN_I{number}\"\n",
+ "\n",
+ " aug = ImageDataGenerator(\n",
+ " rotation_range=30,\n",
+ " zoom_range=0.15,\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2,\n",
+ " shear_range=0.15,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")\n",
+ "\n",
+ " aug_val = ImageDataGenerator()\n",
+ " opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ " decay=INIT_LR / NUM_EPOCHS)\n",
+ " # train_generator = aug.flow(Xtrain, Ytrain, batch_size=BATCH_SIZE)\n",
+ " # validation_generator = aug_val.flow(Xvalidation, Yvalidation)\n",
+ " model = FireDetectionNet.build(width=128, height=128, depth=3)\n",
+ " model.save(f\"/userdata/kerasData/preloaded/madeModels/{name}\")\n",
+ " model = keras.models.load_model(f\"/userdata/kerasData/preloaded/madeModels/{name}\", compile=False)\n",
+ " model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1])\n",
+ "\n",
+ " mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/pyimagesearch/output/{name}orgPYimageSearch.model', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ " early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=150)\n",
+ " H = model.fit(\n",
+ " aug.flow(Xtrain, Ytrain, batch_size=BATCH_SIZE),\n",
+ " validation_data=aug.flow(Xvalidation, Yvalidation),\n",
+ " steps_per_epoch=Xtrain.shape[0] // BATCH_SIZE,\n",
+ " epochs=200,\n",
+ " # class_weight=classWeight,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " curr = \"H128_OHE_128_v1\"\n",
+ " output=f\"/userdata/kerasData/output/recreate/{curr}{name}\"\n",
+ " pd.DataFrame.from_dict(H.history).to_csv(output, index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['loss', 'accuracy', 'precision_1', 'recall_1', 'f1', 'val_loss',\n",
+ " 'val_accuracy', 'val_precision_1', 'val_recall_1', 'val_f1'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results[0].columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results=[]\n",
+ "for name in np.arange(7,9):\n",
+ " results.append(pd.read_csv(f\"/userdata/kerasData/output/recreate/HPWRENGroundUp_2048_SPLIT1_v3_e{name}.csv\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "for index, H in enumerate(results):\n",
+ " H = H[0:92]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"loss\"])\n",
+ " plt.plot(H[\"accuracy\"])\n",
+ " plt.plot(H[\"val_loss\"])\n",
+ " plt.plot(H[\"val_accuracy\"])\n",
+ " plt.title(f'{number} model loss functions')\n",
+ " plt.legend(['train loss', \"accuracy\", 'validation loss', 'validation accuracy'], loc='upper left')\n",
+ " plt.ylabel('loss')\n",
+ " plt.xlabel('epoch')\n",
+ "# plt.ylim(0,1)\n",
+ " plt.show()\n",
+ "\n",
+ "for index, H in enumerate(results):\n",
+ " H = H[0:100]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"f1\"])\n",
+ " plt.plot(H[\"val_f1\"])\n",
+ " plt.title(f'{number} model f1 functions')\n",
+ " plt.legend(['train f1', 'validation f1',], loc='upper left')\n",
+ " plt.ylabel('Percentage')\n",
+ " plt.xlabel('epoch')\n",
+ " plt.ylim(0,1)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results2=[]\n",
+ "for name in np.arange(1,4):\n",
+ " results2.append(pd.read_csv(f\"/userdata/kerasData/output/recreate/transfer/HPWRENGroundUp_1024_SPLIT1_v1_e{name}.csv\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "for index, H in enumerate(results2):\n",
+ " H = H[0:92]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"loss\"])\n",
+ " plt.plot(H[\"accuracy\"])\n",
+ " plt.plot(H[\"val_loss\"])\n",
+ " plt.plot(H[\"val_accuracy\"])\n",
+ " plt.title(f'model version {number} loss functions')\n",
+ " plt.legend(['train loss', \"accuracy\", 'validation loss', 'validation accuracy'], loc='upper left')\n",
+ " plt.ylabel('loss')\n",
+ " plt.xlabel('epoch')\n",
+ "# plt.ylim(0,1)\n",
+ " plt.show()\n",
+ "\n",
+ "for index, H in enumerate(results2):\n",
+ " H = H[0:100]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"f1\"])\n",
+ " plt.plot(H[\"val_f1\"])\n",
+ " plt.plot(H[\"val_precision\"])\n",
+ "\n",
+ " plt.title(f'model version {number} f1 functions')\n",
+ " plt.legend(['train f1', 'validation f1', \"val_precision\"], loc='upper left')\n",
+ " plt.ylabel('Percentage')\n",
+ " plt.xlabel('epoch')\n",
+ " plt.ylim(0,1)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'H2' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-10-1a9431199ea7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mH\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m103\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mH2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m103\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m#apply the Kolmogorov-Smirnov Test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'H2' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy import stats\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#create 2 dataframes with random integers. I don't have data to simulate your case.\n",
+ "\n",
+ "df1 = pd.DataFrame(H.accuracy, columns=range(1,103))\n",
+ "df2 = pd.DataFrame(H2.accuracy, columns=range(1,103))\n",
+ "\n",
+ "#apply the Kolmogorov-Smirnov Test\n",
+ "\n",
+ "p_value = 0.05\n",
+ "p_values = []\n",
+ "for col in range(103):\n",
+ " test = stats.ks_2samp(df1.iloc[col,], df2.iloc[col,])\n",
+ " p_values.append(test[1])\n",
+ "\n",
+ "#create the box plot\n",
+ "\n",
+ "plt.boxplot(p_values)\n",
+ "plt.title('Boxplot of p-values')\n",
+ "plt.ylabel(\"p_values\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.625\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.6625000238418579\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for index, H in enumerate(results):\n",
+ " print(max(H.accuracy))\n",
+ " plt.boxplot(H.accuracy[60:])\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/e5_2048Training.py b/scripts/e5_2048Training.py
new file mode 100644
index 0000000..6786332
--- /dev/null
+++ b/scripts/e5_2048Training.py
@@ -0,0 +1,304 @@
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import SeparableConv2D
+from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import Flatten
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import Dense
+
+
+import matplotlib
+matplotlib.use("Agg")
+# import the necessary packages
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+# from imutils import paths
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import cv2
+import os
+import sys
+import re
+from PIL import Image
+import matplotlib
+matplotlib.use("Agg")
+
+# import the necessary packages
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+
+# from imutils import paths
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import cv2
+import os
+import sys
+import re
+from PIL import Image
+import tensorflow as tf
+from os import listdir
+from os.path import isdir, join, isfile
+from numpy import asarray
+from numpy import save
+import itertools
+
+import matplotlib
+matplotlib.use("Agg")
+
+# import the necessary packages
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+# from imutils import paths
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import cv2
+import os
+import sys
+import re
+from PIL import Image
+import pandas as pd
+import keras
+import tempfile
+from tensorflow.keras.callbacks import LambdaCallback
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+# physical_devices = tf.config.experimental.list_physical_device
+
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+assert tf.config.experimental.get_memory_growth(physical_devices[0])
+import keras
+from keras import backend as K
+# K.tensorflow_backend._get_available_gpus()
+from tensorflow.python.client import device_lib
+
+print(device_lib.list_local_devices())
+
+from keras import backend as K
+
+def mcor(y_true, y_pred):
+ #matthews_correlation
+ y_pred_pos = K.round(K.clip(y_pred, 0, 1))
+ y_pred_neg = 1 - y_pred_pos
+
+
+ y_pos = K.round(K.clip(y_true, 0, 1))
+ y_neg = 1 - y_pos
+
+
+ tp = K.sum(y_pos * y_pred_pos)
+ tn = K.sum(y_neg * y_pred_neg)
+
+
+ fp = K.sum(y_neg * y_pred_pos)
+ fn = K.sum(y_pos * y_pred_neg)
+
+
+ numerator = (tp * tn - fp * fn)
+ denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+
+
+ return numerator / (denominator + K.epsilon())
+
+
+def precision(y_true, y_pred):
+ """Precision metric.
+
+ Only computes a batch-wise average of precision.
+
+ Computes the precision, a metric for multi-label classification of
+ how many selected items are relevant.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+ precision = true_positives / (predicted_positives + K.epsilon())
+ return precision
+
+def recall(y_true, y_pred):
+ """Recall metric.
+
+ Only computes a batch-wise average of recall.
+
+ Computes the recall, a metric for multi-label classification of
+ how many relevant items are selected.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+ recall = true_positives / (possible_positives + K.epsilon())
+ return recall
+
+
+def f1(y_true, y_pred):
+ def recall(y_true, y_pred):
+ """Recall metric.
+
+ Only computes a batch-wise average of recall.
+
+ Computes the recall, a metric for multi-label classification of
+ how many relevant items are selected.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+ recall = true_positives / (possible_positives + K.epsilon())
+ return recall
+
+ def precision(y_true, y_pred):
+ """Precision metric.
+
+ Only computes a batch-wise average of precision.
+
+ Computes the precision, a metric for multi-label classification of
+ how many selected items are relevant.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+ precision = true_positives / (predicted_positives + K.epsilon())
+ return precision
+ precision = precision(y_true, y_pred)
+ recall = recall(y_true, y_pred)
+ return 2*((precision*recall)/(precision+recall+K.epsilon()))
+
+TRAIN_SPLIT = 0.75
+TEST_SPLIT = 0.25
+INIT_LR = 1e-7
+BATCH_SIZE = 8
+NUM_EPOCHS = 100
+image_size = 1024,768
+class_mode = "binary"
+
+image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,
+ zoom_range=0.15,
+ width_shift_range=0.2,
+ height_shift_range=0.2,
+ shear_range=0.15,
+ validation_split=0,
+ horizontal_flip=True,
+ fill_mode="nearest")
+
+image_generatorCLASSIC = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,
+ zoom_range=0,
+ width_shift_range=0,
+ height_shift_range=0,
+ shear_range=0,
+ validation_split=0,
+ horizontal_flip=True,
+ fill_mode="nearest")
+
+
+
+dataDirectoryTrain = "/userdata/kerasData/preloaded/flowDirectory4/train/"
+dataDirectoryValidation = "/userdata/kerasData/preloaded/flowDirectory4/validation/"
+dataDirectoryTest = "/userdata/kerasData/preloaded/flowDirectory4/test/"
+
+
+trainingGeneratorHPWREN = image_generator.flow_from_directory(
+ dataDirectoryTrain,
+ target_size=image_size,
+ seed=42,
+ batch_size=BATCH_SIZE,
+ class_mode=class_mode,
+ subset="training")
+
+validationGeneratorHPWREN = image_generator.flow_from_directory(
+ dataDirectoryValidation,
+ target_size=image_size,
+ batch_size=BATCH_SIZE,
+ seed=42,
+ class_mode=class_mode,
+ subset = "training")
+
+testGeneratorHPWREN = image_generatorCLASSIC.flow_from_directory(
+ dataDirectoryTest,
+ target_size=image_size,
+ batch_size=BATCH_SIZE,
+ seed=42,
+ class_mode=class_mode,
+ subset = "training")
+
+
+class FireDetectionNet:
+ @staticmethod
+ def build(width, height, depth):
+ # initialize the model along with the input shape to be
+ # "channels last" and the channels dimension itself
+ model = Sequential()
+ inputShape = (height, width, depth)
+ chanDim = -1
+
+ model.add(SeparableConv2D(32, (7, 7), padding="same",
+ input_shape=inputShape))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+
+ model.add(SeparableConv2D(64, (5,5), padding="same"))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+
+ model.add(SeparableConv2D(64, (3, 3), padding="same"))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+
+ model.add(SeparableConv2D(128, (3, 3), padding="same"))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+
+
+
+ model.add(MaxPooling2D(pool_size=(5,5)))
+
+ model.add(Flatten())
+ model.add(Dense(64))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization())
+ model.add(Dropout(0.5))
+
+ # second set of FC => RELU layers
+ model.add(Dense(128))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization())
+ model.add(Dropout(0.5))
+
+ # softmax classifier
+ model.add(Dense(2))
+ model.add(Activation("softmax"))
+
+ # return the constructed network architecture
+ return model
+
+
+name = "HPWRENGroundUp_1024_SPLIT2_v1_e3"
+opt = SGD(lr=INIT_LR, momentum=0.9,
+ decay=INIT_LR / NUM_EPOCHS)
+groundUpModel = FireDetectionNet.build(width=1024, height=768, depth=3)
+groundUpModel.compile(loss="binary_crossentropy", optimizer=opt,
+metrics=["accuracy", precision, recall, f1])
+mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/pyimagesearch/output/experimental/{name}HPWREN.model', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)
+early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)
+
+history = groundUpModel.fit(
+ trainingGeneratorHPWREN,
+ validation_data=validationGeneratorHPWREN,
+ steps_per_epoch=len(trainingGeneratorHPWREN) // BATCH_SIZE,
+ validation_steps= len(validationGeneratorHPWREN) // BATCH_SIZE,
+ epochs=NUM_EPOCHS,
+ callbacks=[mc, early_stopping_callback],
+ verbose=1
+)
+
+history_df = pd.DataFrame(history.history)
+hist_csv_file=f"/userdata/kerasData/output/recreate/{name}"
+with open(hist_csv_file, mode='w') as f:
+ history_df.to_csv(f)
diff --git a/scripts/historyEvaluater.ipynb b/scripts/historyEvaluater.ipynb
new file mode 100644
index 0000000..65e481a
--- /dev/null
+++ b/scripts/historyEvaluater.ipynb
@@ -0,0 +1,256 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy import stats\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for name in addresses:\n",
+ " results.append(pd.read_csv(name))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results=[]\n",
+ "for name in np.arange(1,4):\n",
+ " results.append(pd.read_csv(f\"/userdata/kerasData/output/recreate/transfer/HPWRENGroundUp_1024_SPLIT1_v1_e{name}.csv\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "for index, H in enumerate(results):\n",
+ " H = H[0:92]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"loss\"])\n",
+ " plt.plot(H[\"accuracy\"])\n",
+ " plt.plot(H[\"val_loss\"])\n",
+ " plt.plot(H[\"val_accuracy\"])\n",
+ " plt.title(f'{number} model loss functions')\n",
+ " plt.legend(['train loss', \"accuracy\", 'validation loss', 'validation accuracy'], loc='upper left')\n",
+ " plt.ylabel('loss')\n",
+ " plt.xlabel('epoch')\n",
+ "# plt.ylim(0,1)\n",
+ " plt.show()\n",
+ "\n",
+ "for index, H in enumerate(results):\n",
+ " H = H[0:100]\n",
+ " number = index +1\n",
+ " plt.plot(H[\"f1\"])\n",
+ " plt.plot(H[\"val_f1\"])\n",
+ " plt.title(f'{number} model f1 functions')\n",
+ " plt.legend(['train f1', 'validation f1',], loc='upper left')\n",
+ " plt.ylabel('Percentage')\n",
+ " plt.xlabel('epoch')\n",
+ " plt.ylim(0,1)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "51"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "H.shape[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#create 2 dataframes with random integers. I don't have data to simulate your case.\n",
+ "numCols = H.shape[0]\n",
+ "df1 = pd.DataFrame(H.accuracy).transpose()\n",
+ "\n",
+ "#apply the Kolmogorov-Smirnov Test\n",
+ "p_value = 0.05\n",
+ "p_values = []\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndexError",
+ "evalue": "single positional indexer is out-of-bounds",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-50-153be853155f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumCols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mks_2samp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mp_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#create the box plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1760\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1761\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1763\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1764\u001b[0m \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 2065\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2067\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2068\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2069\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Too many indexers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 704\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 705\u001b[0m raise ValueError(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1992\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1993\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1994\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1995\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1996\u001b[0m \u001b[0;31m# a tuple should already have been caught by this point\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 2061\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2062\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2063\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2064\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2065\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
+ ]
+ }
+ ],
+ "source": [
+ "for col in range(numCols):\n",
+ " test = stats.ks_2samp(df1.iloc[col,], df1.iloc[col,])\n",
+ " p_values.append(test[1])\n",
+ "\n",
+ "#create the box plot\n",
+ "\n",
+ "plt.boxplot(p_values)\n",
+ "plt.title('Boxplot of p-values')\n",
+ "plt.ylabel(\"p_values\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for index, H in enumerate(results):\n",
+ " print(max(H.accuracy))\n",
+ " plt.boxplot(H.accuracy[60:])\n",
+ " plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/testing.ipynb b/scripts/testing.ipynb
new file mode 100644
index 0000000..d513919
--- /dev/null
+++ b/scripts/testing.ipynb
@@ -0,0 +1,814 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[name: \"/device:CPU:0\"\n",
+ "device_type: \"CPU\"\n",
+ "memory_limit: 268435456\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 319115137438604482\n",
+ ", name: \"/device:XLA_CPU:0\"\n",
+ "device_type: \"XLA_CPU\"\n",
+ "memory_limit: 17179869184\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 11759642501008274543\n",
+ "physical_device_desc: \"device: XLA_CPU device\"\n",
+ ", name: \"/device:XLA_GPU:0\"\n",
+ "device_type: \"XLA_GPU\"\n",
+ "memory_limit: 17179869184\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 7382767696932267757\n",
+ "physical_device_desc: \"device: XLA_GPU device\"\n",
+ ", name: \"/device:GPU:0\"\n",
+ "device_type: \"GPU\"\n",
+ "memory_limit: 11197360384\n",
+ "locality {\n",
+ " bus_id: 1\n",
+ " links {\n",
+ " }\n",
+ "}\n",
+ "incarnation: 541433991047198862\n",
+ "physical_device_desc: \"device: 0, name: Tesla K40c, pci bus id: 0000:05:00.0, compute capability: 3.5\"\n",
+ "]\n",
+ "Found 648 images belonging to 2 classes.\n",
+ "Found 243 images belonging to 2 classes.\n",
+ "Found 239 images belonging to 2 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import BatchNormalization\n",
+ "from tensorflow.keras.layers import SeparableConv2D\n",
+ "from tensorflow.keras.layers import MaxPooling2D\n",
+ "from tensorflow.keras.layers import Activation\n",
+ "from tensorflow.keras.layers import Flatten\n",
+ "from tensorflow.keras.layers import Dropout\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use(\"Agg\") \n",
+ "# import the necessary packages\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.optimizers import SGD\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report\n",
+ "# from imutils import paths\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import tensorflow as tf\n",
+ "# from imutils import paths\n",
+ "\n",
+ "# import the necessary packages\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "import cv2\n",
+ "import os\n",
+ "import sys\n",
+ "import re\n",
+ "from PIL import Image\n",
+ "import pandas as pd\n",
+ "import keras\n",
+ "import tempfile\n",
+ "from tensorflow.keras.callbacks import LambdaCallback\n",
+ "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
+ "# physical_devices = tf.config.experimental.list_physical_device \n",
+ "\n",
+ "tf.config.experimental.set_memory_growth(physical_devices[0], True) \n",
+ "assert tf.config.experimental.get_memory_growth(physical_devices[0]) \n",
+ "import keras\n",
+ "from keras import backend as K\n",
+ "# K.tensorflow_backend._get_available_gpus()\n",
+ "from tensorflow.python.client import device_lib\n",
+ "print(device_lib.list_local_devices())\n",
+ "\n",
+ "dataDirectoryTrain = \"/userdata/kerasData/preloaded/flowDirectory2/train/\"\n",
+ "dataDirectoryValidation = \"/userdata/kerasData/preloaded/flowDirectory2/validation/\"\n",
+ "dataDirectoryTest = \"/userdata/kerasData/preloaded/flowDirectory2/test/\"\n",
+ "\n",
+ "TRAIN_SPLIT = 0.75\n",
+ "TEST_SPLIT = 0.25\n",
+ "INIT_LR = 1e-2\n",
+ "BATCH_SIZE = 8\n",
+ "NUM_EPOCHS = 50\n",
+ "image_size = 2048,1536\n",
+ "class_mode = \"categorical\"\n",
+ "\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,\n",
+ " zoom_range=0.15,\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2,\n",
+ " shear_range=0.15,\n",
+ " validation_split=0,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")\n",
+ "\n",
+ "image_generatorCLASSIC = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,\n",
+ " zoom_range=0,\n",
+ " width_shift_range=0,\n",
+ " height_shift_range=0,\n",
+ " shear_range=0,\n",
+ " validation_split=0,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")\n",
+ "\n",
+ "trainingGeneratorHPWREN = image_generator.flow_from_directory(\n",
+ " dataDirectoryTrain,\n",
+ " target_size=image_size,\n",
+ " seed=42,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " class_mode=class_mode,\n",
+ " subset=\"training\")\n",
+ "\n",
+ "validationGeneratorHPWREN = image_generator.flow_from_directory(\n",
+ " dataDirectoryValidation,\n",
+ " target_size=image_size,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " seed=42,\n",
+ " class_mode=class_mode,\n",
+ " subset = \"training\")\n",
+ "\n",
+ "testGeneratorHPWREN = image_generatorCLASSIC.flow_from_directory(\n",
+ " dataDirectoryTest,\n",
+ " target_size=image_size,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " seed=42,\n",
+ " class_mode=class_mode,\n",
+ " subset = \"training\")\n",
+ "\n",
+ "\n",
+ "def precision(y_true, y_pred):\n",
+ " \"\"\"Precision metric.\n",
+ "\n",
+ " Only computes a batch-wise average of precision.\n",
+ "\n",
+ " Computes the precision, a metric for multi-label classification of\n",
+ " how many selected items are relevant.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ "\n",
+ "def recall(y_true, y_pred):\n",
+ " \"\"\"Recall metric.\n",
+ "\n",
+ " Only computes a batch-wise average of recall.\n",
+ "\n",
+ " Computes the recall, a metric for multi-label classification of\n",
+ " how many relevant items are selected.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ "\n",
+ "def f1(y_true, y_pred):\n",
+ " def recall(y_true, y_pred):\n",
+ " \"\"\"Recall metric.\n",
+ "\n",
+ " Only computes a batch-wise average of recall.\n",
+ "\n",
+ " Computes the recall, a metric for multi-label classification of\n",
+ " how many relevant items are selected.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ " def precision(y_true, y_pred):\n",
+ " \"\"\"Precision metric.\n",
+ "\n",
+ " Only computes a batch-wise average of precision.\n",
+ "\n",
+ " Computes the precision, a metric for multi-label classification of\n",
+ " how many selected items are relevant.\n",
+ " \"\"\"\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ " precision = precision(y_true, y_pred)\n",
+ " recall = recall(y_true, y_pred)\n",
+ " return 2*((precision*recall)/(precision+recall+K.epsilon()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class FireDetectionNet:\n",
+ " @staticmethod\n",
+ " def build(width, height, depth):\n",
+ " # initialize the model along with the input shape to be\n",
+ " # \"channels last\" and the channels dimension itself\n",
+ " model = Sequential()\n",
+ " inputShape = (height, width, depth)\n",
+ " chanDim = -1\n",
+ " \n",
+ " model.add(SeparableConv2D(16, (7, 7), padding=\"same\",\n",
+ " input_shape=inputShape))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(32, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(SeparableConv2D(64, (3, 3), padding=\"same\"))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization(axis=chanDim))\n",
+ " model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ " \n",
+ " model.add(Flatten())\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # second set of FC => RELU layers\n",
+ " model.add(Dense(128))\n",
+ " model.add(Activation(\"relu\"))\n",
+ " model.add(BatchNormalization())\n",
+ " model.add(Dropout(0.5))\n",
+ "\n",
+ " # softmax classifier\n",
+ " model.add(Dense(2))\n",
+ " model.add(Activation(\"softmax\"))\n",
+ "\n",
+ " # return the constructed network architecture\n",
+ " return model\n",
+ "\n",
+ "# name = \"HPWRENGroundUp_2048_V1_BATCH\"\n",
+ "# opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ "# decay=INIT_LR / NUM_EPOCHS)\n",
+ "# groundUpModel = FireDetectionNet.build(width=2048, height=1536, depth=3)\n",
+ "# groundUpModel.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ "# metrics=[\"accuracy\", tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1])\n",
+ "# mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/pyimagesearch/output/experimental/{name}HPWREN.model', \n",
+ "# monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "# early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e8Modeladdress = \"/userdata/kerasData/pyimagesearch/output/experimental/HPWRENGroundUp_2048_SPLIT1_v3_e6HPWREN.model\"\n",
+ "e7Modeladdress = \"/userdata/kerasData/pyimagesearch/output/experimental/HPWRENGroundUp_2048_SPLIT1_v3_e6HPWREN.model\"\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "testModel = FireDetectionNet.build(1536,2048,3)\n",
+ "testModel.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model = FireDetectionNet.build(width=2048, height=1536, depth=3)\n",
+ "# model.load_weights(e8Modeladdress)\n",
+ "attempt = tf.saved_model.load(e8Modeladdress)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e8model = tf.keras.models.load_model(e8Modeladdress,custom_objects={\"f1\":f1, \"precision\":precision, \"recall\":recall})\n",
+ "e7model = tf.keras.models.load_model(e7Modeladdress,custom_objects={\"f1\":f1, \"precision\":precision, \"recall\":recall})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d (SeparableC (None, 1536, 2048, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation (Activation) (None, 1536, 2048, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization (BatchNo (None, 1536, 2048, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d (MaxPooling2D) (None, 768, 1024, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_1 (Separabl (None, 768, 1024, 32) 944 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 768, 1024, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 768, 1024, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 384, 512, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_2 (Separabl (None, 384, 512, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_3 (Separabl (None, 384, 512, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 76, 102, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten (Flatten) (None, 496128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense (Dense) (None, 64) 31752256 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "dropout (Dropout) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 128) 8320 \n",
+ "_________________________________________________________________\n",
+ "activation_5 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_5 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 2) 258 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 2) 0 \n",
+ "=================================================================\n",
+ "Total params: 31,770,597\n",
+ "Trainable params: 31,769,861\n",
+ "Non-trainable params: 736\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "e7model.\n",
+ "e7model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d (SeparableC (None, 1536, 2048, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation (Activation) (None, 1536, 2048, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization (BatchNo (None, 1536, 2048, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d (MaxPooling2D) (None, 768, 1024, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_1 (Separabl (None, 768, 1024, 32) 944 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 768, 1024, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 768, 1024, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 384, 512, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_2 (Separabl (None, 384, 512, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_3 (Separabl (None, 384, 512, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 76, 102, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten (Flatten) (None, 496128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense (Dense) (None, 64) 31752256 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "dropout (Dropout) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 128) 8320 \n",
+ "_________________________________________________________________\n",
+ "activation_5 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_5 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 2) 258 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 2) 0 \n",
+ "=================================================================\n",
+ "Total params: 31,770,597\n",
+ "Trainable params: 31,769,861\n",
+ "Non-trainable params: 736\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "e8model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d (SeparableC (None, 768, 1024, 32) 275 \n",
+ "_________________________________________________________________\n",
+ "activation (Activation) (None, 768, 1024, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization (BatchNo (None, 768, 1024, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d (MaxPooling2D) (None, 384, 512, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_1 (Separabl (None, 384, 512, 64) 2912 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 192, 256, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_2 (Separabl (None, 192, 256, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 192, 256, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 192, 256, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_3 (Separabl (None, 192, 256, 128) 8896 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 192, 256, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 192, 256, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_4 (Separabl (None, 192, 256, 256) 34176 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 192, 256, 256) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 192, 256, 256) 1024 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 38, 51, 256) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten (Flatten) (None, 496128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense (Dense) (None, 64) 31752256 \n",
+ "_________________________________________________________________\n",
+ "activation_5 (Activation) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_5 (Batch (None, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "dropout (Dropout) (None, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 128) 8320 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_6 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 2) 258 \n",
+ "_________________________________________________________________\n",
+ "activation_7 (Activation) (None, 2) 0 \n",
+ "=================================================================\n",
+ "Total params: 31,814,773\n",
+ "Trainable params: 31,813,301\n",
+ "Non-trainable params: 1,472\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "e1048model = tf.keras.models.load_model(\"/userdata/kerasData/pyimagesearch/output/experimental/HPWRENGroundUp_1024_SPLIT1_v1_e3HPWREN.model\",custom_objects={\"f1\":f1, \"precision\":precision, \"recall\":recall})\n",
+ "e1048model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e8model.evaluate(testGeneratorHPWREN)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e7model.evaluate(testGeneratorHPWREN)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "30/30 [==============================] - 242s 8s/step - loss: 0.8597 - accuracy: 0.4728 - precision: 0.4728 - recall: 0.4728 - f1: 0.4720\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[0.8597329258918762,\n",
+ " 0.47280335426330566,\n",
+ " 0.47280335426330566,\n",
+ " 0.47280335426330566,\n",
+ " 0.47202378511428833]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "e6model.evaluate(testGeneratorHPWREN)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Loss')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "lrs = LRfINDER.lrs[10:-1]\n",
+ "losses = LRfINDER.losses[10:-1]\n",
+ "plt.plot(lrs, losses)\n",
+ "plt.xscale(\"log\")\n",
+ "plt.xlabel(\"Learning Rate (Log Scale)\")\n",
+ "plt.ylabel(\"Loss\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,\n",
+ " zoom_range=0.15,\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2,\n",
+ " shear_range=0.15,\n",
+ " validation_split=0,\n",
+ " horizontal_flip=True,\n",
+ " fill_mode=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "testImage = image_generator.flow_from_(\"/userdata/kerasData/preloaded/flowDirectory2/test/fire/1512676696_+02100.jpg\")))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1536, 2048, 3)"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "testImage.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "in user code:\n\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function *\n outputs = self.distribute_strategy.run(\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n return self._call_for_each_replica(fn, args, kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n return fn(*args, **kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step **\n return self(x, training=False)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__\n self.name)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility\n str(x.shape.as_list()))\n\n ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [32, 2048, 3]\n",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-48-d3b35444c1d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me6model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestImage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 86\u001b[0m raise ValueError('{} is not supported in multi-worker mode.'.format(\n\u001b[1;32m 87\u001b[0m method.__name__))\n\u001b[0;32m---> 88\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m return tf_decorator.make_decorator(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1267\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_predict_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1268\u001b[0;31m \u001b[0mtmp_batch_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1269\u001b[0m \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 628\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 629\u001b[0m \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m 504\u001b[0m self._concrete_stateful_fn = (\n\u001b[1;32m 505\u001b[0m self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access\n\u001b[0;32m--> 506\u001b[0;31m *args, **kwds))\n\u001b[0m\u001b[1;32m 507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2444\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2445\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2446\u001b[0;31m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2447\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 2775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2776\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2777\u001b[0;31m \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2778\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2779\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 2665\u001b[0m \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2666\u001b[0m \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2667\u001b[0;31m capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m 2668\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2669\u001b[0m \u001b[0;31m# Tell the ConcreteFunction to clean up its graph once it goes out of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 983\u001b[0m \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 442\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 969\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 970\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: in user code:\n\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function *\n outputs = self.distribute_strategy.run(\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica\n return self._call_for_each_replica(fn, args, kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica\n return fn(*args, **kwargs)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1122 predict_step **\n return self(x, training=False)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:886 __call__\n self.name)\n /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility\n str(x.shape.as_list()))\n\n ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [32, 2048, 3]\n"
+ ]
+ }
+ ],
+ "source": [
+ "e6model.predict(testImage)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/trainingScript.py b/scripts/trainingScript.py
new file mode 100644
index 0000000..fb5f39a
--- /dev/null
+++ b/scripts/trainingScript.py
@@ -0,0 +1,259 @@
+import keras
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import SeparableConv2D
+from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import Flatten
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import Dense
+
+import matplotlib
+matplotlib.use("Agg")
+# import the necessary packages
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+# from imutils import paths
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import cv2
+import os
+import sys
+import re
+from PIL import Image
+import matplotlib
+matplotlib.use("Agg")
+
+import pandas as pd
+import tempfile
+from tensorflow.keras.callbacks import LambdaCallback
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+# physical_devices = tf.config.experimental.list_physical_device
+
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+assert tf.config.experimental.get_memory_growth(physical_devices[0])
+# K.tensorflow_backend._get_available_gpus()
+from tensorflow.python.client import device_lib
+print(device_lib.list_local_devices())
+
+from keras import backend as K
+
+def mcor(y_true, y_pred):
+ #matthews_correlation
+ y_pred_pos = K.round(K.clip(y_pred, 0, 1))
+ y_pred_neg = 1 - y_pred_pos
+
+ y_pos = K.round(K.clip(y_true, 0, 1))
+ y_neg = 1 - y_pos
+
+ tp = K.sum(y_pos * y_pred_pos)
+ tn = K.sum(y_neg * y_pred_neg)
+
+ fp = K.sum(y_neg * y_pred_pos)
+ fn = K.sum(y_pos * y_pred_neg)
+
+ numerator = (tp * tn - fp * fn)
+ denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+
+ return numerator / (denominator + K.epsilon())
+
+
+def precision(y_true, y_pred):
+ """Precision metric.
+
+ Only computes a batch-wise average of precision.
+
+ Computes the precision, a metric for multi-label classification of
+ how many selected items are relevant.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+ precision = true_positives / (predicted_positives + K.epsilon())
+ return precision
+
+def recall(y_true, y_pred):
+ """Recall metric.
+
+ Only computes a batch-wise average of recall.
+
+ Computes the recall, a metric for multi-label classification of
+ how many relevant items are selected.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+ recall = true_positives / (possible_positives + K.epsilon())
+ return recall
+
+
+def f1(y_true, y_pred):
+ def recall(y_true, y_pred):
+ """Recall metric.
+
+ Only computes a batch-wise average of recall.
+
+ Computes the recall, a metric for multi-label classification of
+ how many relevant items are selected.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+ recall = true_positives / (possible_positives + K.epsilon())
+ return recall
+
+ def precision(y_true, y_pred):
+ """Precision metric.
+
+ Only computes a batch-wise average of precision.
+
+ Computes the precision, a metric for multi-label classification of
+ how many selected items are relevant.
+ """
+ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+ predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+ precision = true_positives / (predicted_positives + K.epsilon())
+ return precision
+ precision = precision(y_true, y_pred)
+ recall = recall(y_true, y_pred)
+ return 2*((precision*recall)/(precision+recall+K.epsilon()))
+
+
+#Split portions between traning and test
+TRAIN_SPLIT = 0.75
+TEST_SPLIT = 0.25
+#Learning Rate
+INIT_LR = 1e-7
+BATCH_SIZE = 8
+NUM_EPOCHS = 100
+#Image sizes
+image_size = 1024,768
+#Important to corroborate with the entropy function
+class_mode = "binary"
+
+#image generators
+image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,
+ zoom_range=0.15,
+ width_shift_range=0.2,
+ height_shift_range=0.2,
+ shear_range=0.15,
+ validation_split=0,
+ horizontal_flip=True,
+ fill_mode="nearest")
+
+image_generatorCLASSIC = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=30,
+ zoom_range=0,
+ width_shift_range=0,
+ height_shift_range=0,
+ shear_range=0,
+ validation_split=0,
+ horizontal_flip=True,
+ fill_mode="nearest")
+
+#Location where the data is, in fire/nonfire sub-directories
+dataDirectoryTrain = "/userdata/kerasData/preloaded/flowDirectory4/train/"
+dataDirectoryValidation = "/userdata/kerasData/preloaded/flowDirectory4/validation/"
+dataDirectoryTest = "/userdata/kerasData/preloaded/flowDirectory4/test/"
+
+# Image Generators, check for the subset error
+trainingGeneratorHPWREN = image_generator.flow_from_directory(
+ dataDirectoryTrain,
+ target_size=image_size,
+ seed=42,
+ batch_size=BATCH_SIZE,
+ class_mode=class_mode)
+validationGeneratorHPWREN = image_generator.flow_from_directory(
+ dataDirectoryValidation,
+ target_size=image_size,
+ batch_size=BATCH_SIZE,
+ seed=42,
+ class_mode=class_mode)
+# subset = "training")
+
+testGeneratorHPWREN = image_generatorCLASSIC.flow_from_directory(
+ dataDirectoryTest,
+ target_size=image_size,
+ batch_size=BATCH_SIZE,
+ seed=42,
+ class_mode=class_mode)
+# subset = "training")
+
+# Define the model here
+class FireDetectionNet:
+ @staticmethod
+ def build(width, height, depth):
+ # initialize the model along with the input shape to be
+ # "channels last" and the channels dimension itself
+ model = Sequential()
+ inputShape = (height, width, depth)
+ chanDim = -1
+
+ model.add(SeparableConv2D(32, (7, 7), padding="same",
+ input_shape=inputShape))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+
+ model.add(SeparableConv2D(64, (5,5), padding="same"))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+ model.add(MaxPooling2D(pool_size=(2, 2)))
+
+ model.add(SeparableConv2D(64, (3, 3), padding="same"))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+
+ model.add(SeparableConv2D(128, (3, 3), padding="same"))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization(axis=chanDim))
+ model.add(MaxPooling2D(pool_size=(5,5)))
+
+ model.add(Flatten())
+ model.add(Dense(64))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization())
+ model.add(Dropout(0.5))
+
+ # second set of FC => RELU layers
+ model.add(Dense(128))
+ model.add(Activation("relu"))
+ model.add(BatchNormalization())
+ model.add(Dropout(0.5))
+
+ # softmax classifier
+ model.add(Dense(2))
+ model.add(Activation("softmax"))
+
+ # return the constructed network architecture
+ return model
+
+# This name is really important because the model history and strucuture is indexed by this
+# name = "HPWRENGroundUp_1024_SPLIT2_v1_e3"
+name = "TEST"
+# Parameteres and model initaliazation
+opt = SGD(lr=INIT_LR, momentum=0.9,
+ decay=INIT_LR / NUM_EPOCHS)
+groundUpModel = FireDetectionNet.build(width=1024, height=768, depth=3)
+# Compiles the model
+groundUpModel.compile(loss="binary_crossentropy", optimizer=opt,
+metrics=["accuracy", precision, recall, f1])
+mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/pyimagesearch/output/experimental/{name}HPWREN.model', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)
+early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)
+
+# This trains the model
+history = groundUpModel.fit(
+ trainingGeneratorHPWREN,
+ validation_data=validationGeneratorHPWREN,
+ steps_per_epoch=len(trainingGeneratorHPWREN) // BATCH_SIZE,
+ validation_steps= len(validationGeneratorHPWREN) // BATCH_SIZE,
+ epochs=NUM_EPOCHS,
+ callbacks=[mc, early_stopping_callback],
+ verbose=1
+)
+
+history_df = pd.DataFrame(history.history)
+hist_csv_file=f"/userdata/kerasData/output/recreate/{name}"
+with open(hist_csv_file, mode='w') as f:
+ history_df.to_csv(f)
--
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