diff --git a/Evaluator.ipynb b/Evaluator.ipynb
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@@ -0,0 +1,387 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from numpy import loadtxt\n",
+ "from keras.models import load_model\n",
+ "import tensorflow as tf\n",
+ "from keras import backend as K\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/userdata/kerasData\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pwd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 2781 images belonging to 2 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "INIT_LR = 1e-4\n",
+ "batch_size = 16\n",
+ "NUM_EPOCHS = 200\n",
+ "\n",
+ "image_size=(128, 128)\n",
+ "class_mode=\"binary\"\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
+ "TestGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/validation\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d (SeparableC (None, 128, 128, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation (Activation) (None, 128, 128, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization (BatchNo (None, 128, 128, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_1 (Separabl (None, 64, 64, 32) 688 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 64, 64, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 64, 64, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_2 (Separabl (None, 32, 32, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 32, 32, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 32, 32, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_3 (Separabl (None, 32, 32, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 32, 32, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 32, 32, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten (Flatten) (None, 16384) 0 \n",
+ "_________________________________________________________________\n",
+ "dense (Dense) (None, 128) 2097280 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 128) 16512 \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, 1) 129 \n",
+ "_________________________________________________________________\n",
+ "activation_6 (Activation) (None, 1) 0 \n",
+ "=================================================================\n",
+ "Total params: 2,123,684\n",
+ "Trainable params: 2,122,820\n",
+ "Non-trainable params: 864\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_128 = load_model(\"/userdata/kerasData/output/model/best_model128_128_e-4.h5\", custom_objects={\"f1\":f1})\n",
+ "model_128.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "174/174 [==============================] - 419s 2s/step - loss: 0.7333 - accuracy: 0.5045 - f1: 0.6095 - recall: 0.8166 - precision: 0.5016\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[0.7332570552825928,\n",
+ " 0.5044947862625122,\n",
+ " 0.6094719767570496,\n",
+ " 0.8166065216064453,\n",
+ " 0.5015521049499512]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_128.evaluate(TestGenerator)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d (SeparableC (None, 128, 128, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation (Activation) (None, 128, 128, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization (BatchNo (None, 128, 128, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_1 (Separabl (None, 64, 64, 32) 688 \n",
+ "_________________________________________________________________\n",
+ "activation_1 (Activation) (None, 64, 64, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 64, 64, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_2 (Separabl (None, 32, 32, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_2 (Activation) (None, 32, 32, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_2 (Batch (None, 32, 32, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_3 (Separabl (None, 32, 32, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_3 (Activation) (None, 32, 32, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_3 (Batch (None, 32, 32, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten (Flatten) (None, 16384) 0 \n",
+ "_________________________________________________________________\n",
+ "dense (Dense) (None, 128) 2097280 \n",
+ "_________________________________________________________________\n",
+ "activation_4 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_4 (Batch (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 128) 16512 \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: 2,123,813\n",
+ "Trainable params: 2,122,949\n",
+ "Non-trainable params: 864\n",
+ "_________________________________________________________________\n",
+ "Found 3485 images belonging to 2 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "INIT_LR = 1e-4\n",
+ "batch_size = 16\n",
+ "NUM_EPOCHS = 200\n",
+ "\n",
+ "image_size=(2048, 1536)\n",
+ "class_mode=\"categorical\"\n",
+ "\n",
+ "\n",
+ "model_e5 = load_model('/userdata/kerasData/best_model_e5.h5', custom_objects={\"f1\":f1})\n",
+ "model_e5.summary()\n",
+ "\n",
+ "\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
+ "testGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
+ " target_size=image_size,\n",
+ " seed=1000,\n",
+ " batch_size=batch_size,\n",
+ " class_mode=class_mode)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 3485 images belonging to 2 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "image_size=(2048, 1536)\n",
+ "class_mode=\"categorical\"\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
+ "testGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
+ " target_size=image_size,\n",
+ " seed=1000,\n",
+ " batch_size=batch_size,\n",
+ " class_mode=class_mode)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "InvalidArgumentError",
+ "evalue": "2 root error(s) found.\n (0) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n\t [[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_39]]\n (1) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_test_function_2377]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nFunction call stack:\ntest_function -> test_function\n",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-8-6de600d60988>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_e5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestGenerator\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 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\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 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Running inside `run_distribute_coordinator` already.\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/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)\u001b[0m\n\u001b[1;32m 1079\u001b[0m step_num=step):\n\u001b[1;32m 1080\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_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-> 1081\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_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 1082\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 1083\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 642\u001b[0m \u001b[0;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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 645\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 646\u001b[0m \u001b[0mcanon_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanon_kwds\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\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__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2418\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[1;32m 2419\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[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[0;32m-> 2420\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\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;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2422\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\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_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 1663\u001b[0m if isinstance(t, (ops.Tensor,\n\u001b[1;32m 1664\u001b[0m resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m self.captured_inputs)\n\u001b[0m\u001b[1;32m 1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1667\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m \u001b[0margs\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;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 599\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 600\u001b[0m outputs = execute.execute_with_cancellation(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\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[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mInvalidArgumentError\u001b[0m: 2 root error(s) found.\n (0) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n\t [[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_39]]\n (1) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]\n\t [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_test_function_2377]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nInput Source operations connected to node mul_1:\n sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)\n\nFunction call stack:\ntest_function -> test_function\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_e5.evaluate(testGenerator)"
+ ]
+ },
+ {
+ "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/ImageLoader.ipynb b/ImageLoader.ipynb
index af44233961b74b710e3446e6c71916b1217eae6c..26837f7a3d7e2e4bf834bbbb032877ab138afff3 100644
--- a/ImageLoader.ipynb
+++ b/ImageLoader.ipynb
@@ -2,161 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 12,
- "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": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -169,6 +15,7 @@
"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",
@@ -178,166 +25,402 @@
"import sys\n",
"import re\n",
"from PIL import Image\n",
- "import tensorflow as tf"
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "from os import listdir\n",
- "from os.path import isdir, join, isfile\n",
- "from numpy import asarray\n",
- "from numpy import save\n",
- "\n",
- "pixelSize = {}\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",
- "\n",
- " train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.8, shuffle=True, random_state = 200)\n",
- " train, validation = train_test_split(train, test_size = 0.2, train_size = 0.8, shuffle=True, random_state = 200)\n",
- " print(len(train), len(test), len(validation))\n",
- "\n",
- " for fire in test:\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",
- " fire_im = fire_im.resize((128,128))\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",
- " \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((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",
- " 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((128,128))\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",
+ "# pixelSize = {}\n",
+ "# pixelAmount = []\n",
+ "# mypath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "# onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "# for fire in onlyfiles:\n",
+ "# pixelSize.setdefault(fire, set([]))\n",
+ "# for element in os.listdir(mypath + \"/\"+ fire):\n",
+ "# fire_im = Image.open(mypath + \"/\" + fire + \"/\" + element)\n",
+ "# num_pixels = fire_im.size[0] * fire_im.size[1]\n",
+ "# pixelSize[fire].add(fire_im.size[0])\n",
+ "# pixelSize[fire].add(fire_im.size[1])\n",
+ "# pixelAmount.append(num_pixels)\n",
+ "# break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# height = []\n",
+ "# width = []\n",
"\n",
- " save(\"trainX.npy\", trainX)\n",
- " save(\"testX.npy\", testX)\n",
- " save(\"validationX\", 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",
+ "# for thing in list(pixelSize.values()):\n",
+ "# height.append(list(thing)[0])\n",
+ "# width.append(list(thing)[1])\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",
- " "
+ "# height = np.array(height)\n",
+ "# width = np.array(width)"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 5,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "59 19 15\n",
- "20180614-Bridle-hp-n-mobo-c - fire number 1\n",
- "1\n",
- "2\n",
- "3\n",
- "4\n",
- "5\n",
- "6\n",
- "7\n",
- "8\n",
- "9\n",
- "10\n",
- "11\n",
- "12\n",
+ "outputs": [],
+ "source": [
+ "# np.array(list(pixelSize.values()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# height"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig, ax = plt.subplots()\n",
+ "\n",
+ "# ax.scatter(height,width)\n",
+ "# plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# x, y = np.random.rand(2, 2)\n",
+ "# width"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
+ "\n",
+ "# axs[0].hist(pixelAmount, bins=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mypath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\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": 12,
+ "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": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(dimentionsEE.values())"
+ ]
+ },
+ {
+ "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, 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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "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",
+ "1\n",
+ "2\n",
+ "3\n",
+ "4\n",
+ "5\n",
+ "6\n",
+ "7\n",
+ "8\n",
+ "9\n",
+ "10\n",
+ "11\n",
+ "12\n",
"13\n",
"14\n",
"15\n",
@@ -406,8 +489,8 @@
"78\n",
"79\n",
"80\n",
+ "20190805_FIRE_sp-e-mobo-c - test fire number 2\n",
"81\n",
- "20170826-FIRE-tp-s-mobo-c - fire number 2\n",
"82\n",
"83\n",
"84\n",
@@ -484,12 +567,12 @@
"155\n",
"156\n",
"157\n",
+ "20170711_FIRE_bm-s-mobo-c - test fire number 3\n",
"158\n",
"159\n",
"160\n",
"161\n",
"162\n",
- "20180704-Benton-hp-n-mobo-c - fire number 3\n",
"163\n",
"164\n",
"165\n",
@@ -549,6 +632,7 @@
"219\n",
"220\n",
"221\n",
+ "20170711_FIRE_bl-e-mobo-c - test fire number 4\n",
"222\n",
"223\n",
"224\n",
@@ -571,7 +655,6 @@
"241\n",
"242\n",
"243\n",
- "20170613-FIRE-hp-n-mobo-c - fire number 4\n",
"244\n",
"245\n",
"246\n",
@@ -631,6 +714,7 @@
"300\n",
"301\n",
"302\n",
+ "20180704_Benton_hp-n-mobo-c - test fire number 5\n",
"303\n",
"304\n",
"305\n",
@@ -653,7 +737,6 @@
"322\n",
"323\n",
"324\n",
- "20190715-MLOSouth1-lo-s-mobo-c - fire number 5\n",
"325\n",
"326\n",
"327\n",
@@ -713,6 +796,7 @@
"381\n",
"382\n",
"383\n",
+ "20190924_FIRE_ml-w-mobo-c - test fire number 6\n",
"384\n",
"385\n",
"386\n",
@@ -735,7 +819,6 @@
"403\n",
"404\n",
"405\n",
- "20161113-FIRE-bm-w-mobo-c - fire number 6\n",
"406\n",
"407\n",
"408\n",
@@ -794,6 +877,7 @@
"461\n",
"462\n",
"463\n",
+ "20190716_FIRE_bl-s-mobo-c - test fire number 7\n",
"464\n",
"465\n",
"466\n",
@@ -817,7 +901,6 @@
"484\n",
"485\n",
"486\n",
- "20170708-Whittier-syp-n-mobo-c - fire number 7\n",
"487\n",
"488\n",
"489\n",
@@ -865,6 +948,7 @@
"531\n",
"532\n",
"533\n",
+ "20191007_FIRE_sm-s-mobo-c - test fire number 8\n",
"534\n",
"535\n",
"536\n",
@@ -899,7 +983,6 @@
"565\n",
"566\n",
"567\n",
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- ]
- },
- {
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+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
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+ "20171207_FIRE_smer-tcs8-mobo-c - test fire number 23\n",
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+ ]
+ },
+ {
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+ {
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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)\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": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "20180614-Bridle-hp-n-mobo-c : (2048, 1536)\n",
- "20170826-FIRE-tp-s-mobo-c : (2048, 1536)\n",
- "20180704-Benton-hp-n-mobo-c : (2048, 1536)\n",
- "20170613-FIRE-hp-n-mobo-c : (2048, 1536)\n",
- "20190715-MLOSouth1-lo-s-mobo-c : (2048, 1536)\n",
- "20161113-FIRE-bm-w-mobo-c : (2048, 1536)\n",
- "20170708-Whittier-syp-n-mobo-c : (3072, 2048)\n",
- "20170821-FIRE-lo-s-mobo-c : (2048, 1536)\n",
- "20160619-FIRE-lp-e-iqeye : (1600, 1200)\n",
- "20170609-FIRE-sm-n-mobo-c : (2048, 1536)\n",
- "20170627-FIRE-om-e-mobo-c : (2048, 1536)\n",
- "20170711-FIRE-bm-s-mobo-c : (2048, 1536)\n",
- "20170519-FIRE-rm-w-mobo-c : (2048, 1536)\n",
- "20170711-FIRE-bl-s-mobo-c : (2048, 1536)\n",
- "20190803-OtaySouth-lp-s-mobo : (3072, 2048)\n",
- "20180611-fallbrook-rm-w-mobo-c : (2048, 1536)\n",
- "20190610-Pauma-bh-w-mobo-c : (2048, 1536)\n",
- "20170625-FIRE-mg-s-iqeye : (1600, 1200)\n",
- "20190813-Topanga-69bravo-n-mobo : (3072, 2048)\n",
- "20180602-Alison-sp-w-mobo-c : (3072, 2048)\n",
- "20160711-FIRE-ml-n-mobo-c : (2048, 1536)\n",
- "20171021-FIRE-pi-e-mobo-c : (2048, 1536)\n",
- "20171026-FIRE-rm-n-mobo-c : (2048, 1536)\n",
- "20170722-FIRE-hp-e-mobo-c : (2048, 1536)\n",
- "20160718-FIRE-mg-s-iqeye : (1600, 1200)\n",
- "20171010-FIRE-hp-w-mobo-c : (2048, 1536)\n",
- "20190714-MLOSouth-lo-s-mobo-c : (2048, 1536)\n",
- "20180719-Skyline-sp-n-mobo-c : (3072, 2048)\n",
- "20170722-FIRE-mg-n-iqeye : (2560, 1920)\n",
- "20171207-FIRE-bh-w-mobo-c : (2048, 1536)\n",
- "20171010-FIRE-rm-e-mobo-c : (2048, 1536)\n",
- "20190803-OtaySouth-om-s-mobo : (3072, 2048)\n",
- "20170901-FIRE-om-s-mobo-c : (2048, 1536)\n",
- "20170520-FIRE-lp-s-iqeye : (1600, 1200)\n",
- "20170711-FIRE-bl-e-mobo-c : (2048, 1536)\n",
- "20160619-FIRE-pi-s-mobo-c : (2048, 1536)\n",
- "20190814-Border-lp-s-mobo : (3072, 2048)\n",
- "20170927-FIRE-smer-tcs9-mobo-c : (3072, 2048)\n",
- "20171207-FIRE-bh-n-mobo-c : (2048, 1536)\n",
- "20190610-Pauma-bh-w-mobo-m : (1280, 960)\n",
- "20190529-94Fire-lp-s-mobo-c : (3072, 2048)\n",
- "20190715-MLOSouth2-lo-s-mobo-c : (2048, 1536)\n",
- "20181112-house-wc-n-mobo-c : (3072, 2048)\n",
- "20170722-FIRE-bm-n-mobo-c : (2048, 1536)\n",
- "20190714-PinosSouth-pi-s-mobo-c : (2048, 1536)\n",
- "20180827-Holyflareup-sp-e-mobo-c : (3072, 2048)\n",
- "20190529-94Fire-om-n-mobo-c : (3072, 2048)\n",
- "20170520-FIRE-pi-w-mobo-c : (2048, 1536)\n",
- "20170520-FIRE-om-s-mobo-c : (2048, 1536)\n",
- "20171207-FIRE-smer-tcs8-mobo-c : (3072, 2048)\n",
- "20180706-West-lp-n-mobo-c : (3072, 2048)\n",
- "20170711-FIRE-sm-n-mobo-c : (2048, 1536)\n",
- "20171017-FIRE-smer-tcs3-mobo-c : (3072, 2048)\n",
- "20170627-FIRE-lp-n-iqeye : (1600, 1200)\n",
- "20190803-Sage-om-n-mobo : (3072, 2048)\n",
- "20170807-FIRE-bh-n-mobo-c : (2048, 1536)\n",
- "20160619-FIRE-om-e-mobo-c : (2048, 1536)\n",
- "20170711-FIRE-sdsc-e-mobo-c : (2048, 1536)\n",
- "20161113-FIRE-bl-n-mobo-c : (2048, 1536)\n",
- "20180806-Holy-sp-s-mobo-m : (3072, 2048)\n",
- "20160718-FIRE-lp-n-iqeye : (1600, 1200)\n",
- "20170713-FIRE-smer-tcs8-mobo-c : (3072, 2048)\n",
- "20160722-FIRE-mg-s-iqeye : (1600, 1200)\n",
- "20190716-Meadowfire-hp-n-mobo-c : (2048, 1536)\n",
- "20180725-Cranston-sp-e-mobo-c : (3072, 2048)\n",
- "20180725-Cranston-hp-n-mobo-c : (2048, 1536)\n",
- "20170625-BBM-bm-n-mobo : (2048, 1536)\n",
- "20190716-Riverfire-rm-w-mobo-c : (2048, 1536)\n",
- "20161113-FIRE-bm-n-mobo-c : (2048, 1536)\n",
- "20171010-FIRE-hp-n-mobo-c : (2048, 1536)\n",
- "20160604-FIRE-smer-tcs3-mobo-c : (3072, 2048)\n",
- "20170722-FIRE-so-s-mobo-c : (2048, 1536)\n",
- "20190712-CottonwoodFire-lp-s-mobo-c : (3072, 2048)\n",
- "20160718-FIRE-mw-e-mobo-c : (2048, 1536)\n",
- "20190712-RockHouse-wc-e-mobo-c : (3072, 2048)\n",
- "20160722-FIRE-mw-e-mobo-c : (2048, 1536)\n",
- "20180614-Hope-wc-e-mobo-c : (3072, 2048)\n",
- "20170708-Whittier-syp-n-mobo-m : (3072, 2048)\n",
- "20190728-Dehesa-lp-n-mobo : (3072, 2048)\n",
- "20171026-FIRE-smer-tcs8-mobo-c : (3072, 2048)\n",
- "20170520-FIRE-pi-s-mobo-c : (2048, 1536)\n",
- "20190809-PinosSouth-pi-s-mobo : (2048, 1536)\n",
- "20180717-otay-om-s-mobo-c : (2048, 1536)\n",
- "20171016-FIRE-sdsc-e-mobo-c : (2048, 1536)\n",
- "20171207-Lilac-rm-s-mobo : (2048, 1536)\n",
- "20180602-Alison-sp-s-mobo-c : (3072, 2048)\n",
- "20190810-SantaFire-rm-w-mobo : (2048, 1536)\n",
- "20170613-FIRE-bh-w-mobo-c : (2048, 1536)\n",
- "20190801-Caliente-om-w-mobo : (3072, 2048)\n",
- "20180720-Cinnamon-wc-e-mobo-c : (3072, 2048)\n",
- "20160604-FIRE-rm-n-mobo-c : (2048, 1536)\n",
- "20180806-Holy-sp-s-mobo-c : (3072, 2048)\n",
- "20190715-MLOSouth3-lo-s-mobo-c : (2048, 1536)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"test = pixels\n",
"\n",
@@ -7785,115 +17948,361 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'20180614-Bridle-hp-n-mobo-c': set(),\n",
- " '20170826-FIRE-tp-s-mobo-c': set(),\n",
- " '20180704-Benton-hp-n-mobo-c': set(),\n",
- " '20170613-FIRE-hp-n-mobo-c': set(),\n",
- " '20190715-MLOSouth1-lo-s-mobo-c': set(),\n",
- " '20161113-FIRE-bm-w-mobo-c': set(),\n",
- " '20170708-Whittier-syp-n-mobo-c': set(),\n",
- " '20170821-FIRE-lo-s-mobo-c': set(),\n",
- " '20160619-FIRE-lp-e-iqeye': set(),\n",
- " '20170609-FIRE-sm-n-mobo-c': set(),\n",
- " '20170627-FIRE-om-e-mobo-c': set(),\n",
- " '20170711-FIRE-bm-s-mobo-c': set(),\n",
- " '20170519-FIRE-rm-w-mobo-c': set(),\n",
- " '20170711-FIRE-bl-s-mobo-c': set(),\n",
- " '20190803-OtaySouth-lp-s-mobo': set(),\n",
- " '20180611-fallbrook-rm-w-mobo-c': set(),\n",
- " '20190610-Pauma-bh-w-mobo-c': set(),\n",
- " '20170625-FIRE-mg-s-iqeye': set(),\n",
- " '20190813-Topanga-69bravo-n-mobo': set(),\n",
- " '20180602-Alison-sp-w-mobo-c': set(),\n",
- " '20160711-FIRE-ml-n-mobo-c': set(),\n",
- " '20171021-FIRE-pi-e-mobo-c': set(),\n",
- " '20171026-FIRE-rm-n-mobo-c': set(),\n",
- " '20170722-FIRE-hp-e-mobo-c': set(),\n",
- " '20160718-FIRE-mg-s-iqeye': set(),\n",
- " '20171010-FIRE-hp-w-mobo-c': set(),\n",
- " '20190714-MLOSouth-lo-s-mobo-c': set(),\n",
- " '20180719-Skyline-sp-n-mobo-c': set(),\n",
- " '20170722-FIRE-mg-n-iqeye': set(),\n",
- " '20171207-FIRE-bh-w-mobo-c': set(),\n",
- " '20171010-FIRE-rm-e-mobo-c': set(),\n",
- " '20190803-OtaySouth-om-s-mobo': set(),\n",
- " '20170901-FIRE-om-s-mobo-c': set(),\n",
- " '20170520-FIRE-lp-s-iqeye': set(),\n",
- " '20170711-FIRE-bl-e-mobo-c': set(),\n",
- " '20160619-FIRE-pi-s-mobo-c': set(),\n",
- " '20190814-Border-lp-s-mobo': set(),\n",
- " '20170927-FIRE-smer-tcs9-mobo-c': set(),\n",
- " '20171207-FIRE-bh-n-mobo-c': set(),\n",
- " '20190610-Pauma-bh-w-mobo-m': set(),\n",
- " '20190529-94Fire-lp-s-mobo-c': set(),\n",
- " '20190715-MLOSouth2-lo-s-mobo-c': set(),\n",
- " '20181112-house-wc-n-mobo-c': set(),\n",
- " '20170722-FIRE-bm-n-mobo-c': set(),\n",
- " '20190714-PinosSouth-pi-s-mobo-c': set(),\n",
- " '20180827-Holyflareup-sp-e-mobo-c': set(),\n",
- " '20190529-94Fire-om-n-mobo-c': set(),\n",
- " '20170520-FIRE-pi-w-mobo-c': set(),\n",
- " '20170520-FIRE-om-s-mobo-c': set(),\n",
- " '20171207-FIRE-smer-tcs8-mobo-c': set(),\n",
- " '20180706-West-lp-n-mobo-c': set(),\n",
- " '20170711-FIRE-sm-n-mobo-c': set(),\n",
- " '20171017-FIRE-smer-tcs3-mobo-c': set(),\n",
- " '20170627-FIRE-lp-n-iqeye': set(),\n",
- " '20190803-Sage-om-n-mobo': set(),\n",
- " '20170807-FIRE-bh-n-mobo-c': set(),\n",
- " '20160619-FIRE-om-e-mobo-c': set(),\n",
- " '20170711-FIRE-sdsc-e-mobo-c': set(),\n",
- " '20161113-FIRE-bl-n-mobo-c': set(),\n",
- " '20180806-Holy-sp-s-mobo-m': set(),\n",
- " '20160718-FIRE-lp-n-iqeye': set(),\n",
- " '20170713-FIRE-smer-tcs8-mobo-c': set(),\n",
- " '20160722-FIRE-mg-s-iqeye': set(),\n",
- " '20190716-Meadowfire-hp-n-mobo-c': set(),\n",
- " '20180725-Cranston-sp-e-mobo-c': set(),\n",
- " '20180725-Cranston-hp-n-mobo-c': set(),\n",
- " '20170625-BBM-bm-n-mobo': set(),\n",
- " '20190716-Riverfire-rm-w-mobo-c': set(),\n",
- " '20161113-FIRE-bm-n-mobo-c': set(),\n",
- " '20171010-FIRE-hp-n-mobo-c': set(),\n",
- " '20160604-FIRE-smer-tcs3-mobo-c': set(),\n",
- " '20170722-FIRE-so-s-mobo-c': set(),\n",
- " '20190712-CottonwoodFire-lp-s-mobo-c': set(),\n",
- " '20160718-FIRE-mw-e-mobo-c': set(),\n",
- " '20190712-RockHouse-wc-e-mobo-c': set(),\n",
- " '20160722-FIRE-mw-e-mobo-c': set(),\n",
- " '20180614-Hope-wc-e-mobo-c': set(),\n",
- " '20170708-Whittier-syp-n-mobo-m': set(),\n",
- " '20190728-Dehesa-lp-n-mobo': set(),\n",
- " '20171026-FIRE-smer-tcs8-mobo-c': set(),\n",
- " '20170520-FIRE-pi-s-mobo-c': set(),\n",
- " '20190809-PinosSouth-pi-s-mobo': set(),\n",
- " '20180717-otay-om-s-mobo-c': set(),\n",
- " '20171016-FIRE-sdsc-e-mobo-c': set(),\n",
- " '20171207-Lilac-rm-s-mobo': set(),\n",
- " '20180602-Alison-sp-s-mobo-c': set(),\n",
- " '20190810-SantaFire-rm-w-mobo': set(),\n",
- " '20170613-FIRE-bh-w-mobo-c': set(),\n",
- " '20190801-Caliente-om-w-mobo': set(),\n",
- " '20180720-Cinnamon-wc-e-mobo-c': set(),\n",
- " '20160604-FIRE-rm-n-mobo-c': set(),\n",
- " '20180806-Holy-sp-s-mobo-c': set(),\n",
- " '20190715-MLOSouth3-lo-s-mobo-c': set()}"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "pixels"
+ "# 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": {
@@ -7912,7 +18321,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.6.10"
}
},
"nbformat": 4,
diff --git a/Test_Loader.ipynb b/Test_Loader.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..78e4c07a8ddc4cd0632247ab0b63916d7e4e8351
--- /dev/null
+++ b/Test_Loader.ipynb
@@ -0,0 +1,4493 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "from os import listdir\n",
+ "from os.path import isdir, join, isfile\n",
+ "from numpy import asarray\n",
+ "from numpy import save\n",
+ "from PIL import Image\n",
+ "from os import listdir\n",
+ "import os\n",
+ "import tensorflow as tf; print(tf.__version__)\n",
+ "from tensorflow.keras.callbacks import LambdaCallback\n",
+ "from tensorflow.keras import backend as K\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import tempfile\n",
+ "import cv2\n",
+ "from sklearn.model_selection import train_test_split\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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pixelSize = {}\n",
+ "\n",
+ "testX = []\n",
+ "trainX = []\n",
+ "validationX = []\n",
+ "\n",
+ "test_label = []\n",
+ "train_label = []\n",
+ "validation_label = []\n",
+ " \n",
+ "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 load_dataset(savePath):\n",
+ " if len(os.listdir(savePath)) != 1:\n",
+ " print(len(os.listdir(savePath)))\n",
+ " raise ImportError(\"Save Directory is not empty\")\n",
+ " \n",
+ " firstTrigger = True\n",
+ " \n",
+ " count = 0\n",
+ " fireCount = 0\n",
+ "\n",
+ " \n",
+ " FIRE_PATH = \"/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire\"\n",
+ " NON_FIRE_PATH = \"/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories\"\n",
+ " \n",
+ " \n",
+ " firefiles = [FIRE_PATH+\"/\"+f for f in listdir(FIRE_PATH)]\n",
+ " nonfires = [NON_FIRE_PATH+\"/\"+f for f in listdir(NON_FIRE_PATH)]\n",
+ " onlyfiles = firefiles + nonfires\n",
+ " \n",
+ " train, test = train_test_split(onlyfiles, test_size = 0.2, train_size = 0.8, shuffle=True, random_state = 200)\n",
+ " train, validation = train_test_split(train, test_size = 0.2, train_size = 0.8, shuffle=True, random_state = 200)\n",
+ " \n",
+ " print(len(train), len(test), len(validation))\n",
+ "\n",
+ " i = 0\n",
+ " \n",
+ " for fire in test:\n",
+ " if is_jpg(fire):\n",
+ " fireCount +=1\n",
+ " print(f\"{fire} TEST\")\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",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " testX.append(inArrayim)\n",
+ " if fire in firefiles:\n",
+ " test_label.append(1)\n",
+ " else:\n",
+ " test_label.append(0)\n",
+ "# image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# image = cv2.resize(image, (128,128))\n",
+ " \n",
+ " for fire in train:\n",
+ " if is_jpg(fire):\n",
+ " print(f\"{fire} TRAIN\")\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",
+ " trainX.append(inArrayim)\n",
+ " if fire in firefiles:\n",
+ " train_label.append(1)\n",
+ " else:\n",
+ " train_label.append(0)\n",
+ "\n",
+ "# a = fireCount \n",
+ " for fire in validation:\n",
+ " if is_jpg(fire):\n",
+ " print(f\"{fire} validate\")\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",
+ " shape = inArrayim.shape\n",
+ " if(shape == (128, 128, 3)):\n",
+ " validationX.append(inArrayim)\n",
+ " if fire in firefiles:\n",
+ " validation_label.append(1)\n",
+ " else:\n",
+ " validation_label.append(0)\n",
+ "\n",
+ " \n",
+ "\n",
+ "# image = cv2.imread(datasetPath + \"/\"+ directories + \"/\" + element)\n",
+ "# image = cv2.resize(image, (128,128))\n",
+ "# print(validationX)\n",
+ "\n",
+ " save(f\"{savePath}trainX.npy\", trainX)\n",
+ " save(f\"{savePath}testX.npy\", testX)\n",
+ " save(f\"{savePath}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(f\"{savePath}trainY.npy\", trainY)\n",
+ " save(f\"{savePath}testY.npy\", testY)\n",
+ " save(f\"{savePath}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(f\"{savePath}classWeight.npy\", classWeight)\n",
+ "\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": 160,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2562 801 641\n",
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+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/446.jpg validate\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/tallbuilding_urb899.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/coast_land309.jpg validate\n",
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+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/coast_n203066.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/tallbuilding_art1027.jpg validate\n",
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+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/opencountry_cdmc795.jpg validate\n",
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+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/opencountry_land764.jpg validate\n",
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+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/street_par143.jpg validate\n",
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+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/617.jpg validate\n",
+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/1308.jpg validate\n",
+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/20.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/coast_nat901.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/tallbuilding_art1750.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/forest_land809.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/insidecity_urb781.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/highway_gre685.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/coast_n291076.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/opencountry_fie33.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/highway_natu787.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/tallbuilding_sky34.jpg validate\n",
+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/98.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/opencountry_nat190.jpg validate\n",
+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/412.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/coast_natu975.jpg validate\n",
+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/1267.jpg validate\n",
+ "/userdata/kerasData/Robbery_Accident_Fire_Database2/Fire/1247.jpg validate\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/opencountry_land903.jpg validate\n",
+ "/userdata/kerasData/spatial_envelope_256x256_static_8outdoorcategories/street_street3.jpg validate\n"
+ ]
+ }
+ ],
+ "source": [
+ "ahh = load_dataset(\"/userdata/kerasData/preloaded/pyimageSearch/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "791"
+ ]
+ },
+ "execution_count": 161,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(testX)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "791"
+ ]
+ },
+ "execution_count": 162,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(test_label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "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": "3.6.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/expTest.ipynb b/expTest.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c75d2ba737145bfbb86112409a0aadc191553972
--- /dev/null
+++ b/expTest.ipynb
@@ -0,0 +1,2352 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.2.0\n",
+ "WARNING:tensorflow:From <ipython-input-1-a1b0cb06b492>:5: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Use `tf.config.list_physical_devices('GPU')` instead.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import tensorflow as tf; print(tf.__version__)\n",
+ "import keras\n",
+ "tf.test.gpu_device_name()\n",
+ "tf.test.is_built_with_cuda()\n",
+ "tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)\n",
+ "\n",
+ "tf.config.list_physical_devices('GPU')\n",
+ "tf.test.is_built_with_cuda()\n",
+ "tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import keras\n",
+ "from keras import backend as K\n",
+ "# K.tensorflow_backend._get_available_gpus()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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: 8194322282635050876\n",
+ ", name: \"/device:XLA_CPU:0\"\n",
+ "device_type: \"XLA_CPU\"\n",
+ "memory_limit: 17179869184\n",
+ "locality {\n",
+ "}\n",
+ "incarnation: 2870010530879842282\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: 12055439666602751637\n",
+ "physical_device_desc: \"device: XLA_GPU device\"\n",
+ ", name: \"/device:GPU:0\"\n",
+ "device_type: \"GPU\"\n",
+ "memory_limit: 31732076416\n",
+ "locality {\n",
+ " bus_id: 1\n",
+ " links {\n",
+ " }\n",
+ "}\n",
+ "incarnation: 201013145255581170\n",
+ "physical_device_desc: \"device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:61:00.0, compute capability: 7.0\"\n",
+ "]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.python.client import device_lib\n",
+ "\n",
+ "print(device_lib.list_local_devices())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['20160722_FIRE_mw-e-mobo-c',\n",
+ " '20190922_FIRE_ml-w-mobo-c',\n",
+ " '20200521_VEGMGMT_wc-e-mobo-c',\n",
+ " '20171016_FIRE_sdsc-e-mobo-c',\n",
+ " '20190620_FIRE_smer-tcs9-mobo-c',\n",
+ " '20190716_FIRE_mg-n-mobo-c',\n",
+ " '20200521_FIRE_om-n-mobo-c',\n",
+ " '20180727_FIRE_bh-n-mobo-c',\n",
+ " '20180919_FIRE_rm-e-mobo-c',\n",
+ " '20180728_FIRE_rm-w-mobo-c',\n",
+ " '20180727_FIRE_mg-w-mobo-c',\n",
+ " '20190715_MLOSouth2_lo-s-mobo-c',\n",
+ " '20170722_FIRE_bm-n-mobo-c',\n",
+ " '20191006_FIRE_lp-e-mobo-c',\n",
+ " '20180706_West_lp-n-mobo-c',\n",
+ " '20190716_Riverfire_rm-w-mobo-c',\n",
+ " '20180602_FIRE_smer-tcs9-mobo-c',\n",
+ " '20170613_FIRE_bh-w-mobo-c',\n",
+ " '20180603_FIRE_smer-tcs9-mobo-c',\n",
+ " '20190924_FIRE_pi-w-mobo-c',\n",
+ " '20190915_FIRE_rm-n-mobo-c',\n",
+ " '20191006_FIRE_ml-w-mobo-c',\n",
+ " '20191003_FIRE_rm-w-mobo-c',\n",
+ " '20171207_FIRE_bh-w-mobo-c',\n",
+ " '20190714_PinosSouth_pi-s-mobo-c',\n",
+ " '20180603_FIRE_smer-tcs8-mobo-c',\n",
+ " '20170708_Whittier_syp-n-mobo-c',\n",
+ " '20190924_FIRE_lp-n-mobo-c',\n",
+ " '20180522_FIRE_rm-e-mobo-c',\n",
+ " '20190925_FIRE_wc-s-mobo-c',\n",
+ " '20171010_FIRE_rm-e-mobo-c',\n",
+ " '20180809_FIRE_bh-s-mobo-c',\n",
+ " '20180603_FIRE_sm-n-mobo-c',\n",
+ " '20190826_FIRE_smer-tcs9-mobo-c',\n",
+ " '20190924_FIRE_bl-s-mobo-c',\n",
+ " '20190716_FIRE_bl-s-mobo-c',\n",
+ " '20170520_FIRE_pi-s-mobo-c',\n",
+ " '20190712_CottonwoodFire_lp-s-mobo-c',\n",
+ " '20191001_FIRE_rm-w-mobo-c',\n",
+ " '20180726_FIRE_so-n-mobo-c',\n",
+ " '20170520_FIRE_om-s-mobo-c',\n",
+ " '20190829_FIRE_bl-n-mobo-c',\n",
+ " '20191006_FIRE_lp-s-mobo-c',\n",
+ " '20190826_FIRE_rm-w-mobo-c',\n",
+ " '20200608-FIRE-rm-w-mobo-c',\n",
+ " '20190728_FIRE_sp-n-mobo-c',\n",
+ " '20191006_FIRE_pi-s-mobo-c',\n",
+ " '20190716_FIRE_so-w-mobo-c',\n",
+ " '20180706_FIRE_sm-e-mobo-c',\n",
+ " '20191007_FIRE_lp-s-mobo-c',\n",
+ " '20191005_FIRE_vo-n-mobo-c',\n",
+ " '20180719_Skyline_sp-n-mobo-c',\n",
+ " '20191007_FIRE_sm-s-mobo-c',\n",
+ " '20181112_house_wc-n-mobo-c',\n",
+ " '20170807_FIRE_bh-n-mobo-c',\n",
+ " '20200615-Rainbow-rm-e-mobo-c',\n",
+ " '20170609_FIRE_sm-n-mobo-c',\n",
+ " '20180614_Bridle_hp-n-mobo-c',\n",
+ " '20190712_RockHouse_wc-e-mobo-c',\n",
+ " '20170520_FIRE_pi-w-mobo-c',\n",
+ " '20190805_FIRE_sp-e-mobo-c',\n",
+ " '20200202_FIRE_hp-w-mobo-c',\n",
+ " '20191001_FIRE_smer-tcs9-mobo-c',\n",
+ " '20180725_Cranston_hp-n-mobo-c',\n",
+ " '20200618-FIRE-om-w-mobo-c',\n",
+ " '20191001_FIRE_bh-w-mobo-c',\n",
+ " '20170927_FIRE_smer-tcs9-mobo-c',\n",
+ " '20180504_FIRE_smer-tcs10-mobo-c',\n",
+ " '20160619_FIRE_pi-s-mobo-c',\n",
+ " '20190629_FIRE_hp-n-mobo-c',\n",
+ " '20180717_otay_om-s-mobo-c',\n",
+ " '20191007_FIRE_om-s-mobo-c',\n",
+ " '20180614_FIRE_hp-s-mobo-c',\n",
+ " '20200601_WILDLAND-DRILLS_mlo-s-mobo-c',\n",
+ " '20200306_FIRE_pi-n-mobo-c',\n",
+ " '20180602_FIRE_smer-tcs8-mobo-c',\n",
+ " '20190717_FIRE_lp-n-mobo-c',\n",
+ " '20180813_FIRE_bl-n-mobo-c',\n",
+ " '20170826_FIRE_tp-s-mobo-c',\n",
+ " '20200601_WILDLAND-DRILLS_ml-s-mobo-c',\n",
+ " '20200529_StructFire_wc-e-mobo-c',\n",
+ " '20190924_FIRE_sm-n-mobo-c',\n",
+ " '20171017_FIRE_smer-tcs3-mobo-c',\n",
+ " '20170713_FIRE_smer-tcs8-mobo-c',\n",
+ " '20190717_FIRE_pi-w-mobo-c',\n",
+ " '20190924_FIRE_ml-w-mobo-c',\n",
+ " '20190930_FIRE_om-s-mobo-c',\n",
+ " '20171026_FIRE_rm-n-mobo-c',\n",
+ " '20170711_FIRE_bl-e-mobo-c',\n",
+ " '20191003_FIRE_smer-tcs9-mobo-c',\n",
+ " '20161113_FIRE_bm-w-mobo-c',\n",
+ " '20171026_FIRE_smer-tcs8-mobo-c',\n",
+ " '20170711_FIRE_bm-s-mobo-c',\n",
+ " '20180614_Hope_wc-e-mobo-c',\n",
+ " '20170613_FIRE_hp-n-mobo-c',\n",
+ " '20190716_Meadowfire_hp-n-mobo-c',\n",
+ " '20190529_94Fire_om-n-mobo-c',\n",
+ " '20190715_MLOSouth1_lo-s-mobo-c',\n",
+ " '20170722_FIRE_hp-e-mobo-c',\n",
+ " '20191003_FIRE_om-s-mobo-c',\n",
+ " '20180606_FIRE_pi-e-mobo-c',\n",
+ " '20190814_FIRE_om-e-mobo-c',\n",
+ " '20200521_VEGMGMT_ml-w-mobo-c',\n",
+ " '20190829_FIRE_rm-w-mobo-c',\n",
+ " '20191030_CopperCanyon_om-s-mobo-c',\n",
+ " '20180612_FIRE_rm-w-mobo-c',\n",
+ " '20191005_FIRE_bm-e-mobo-c',\n",
+ " '20200601_WILDLAND-DRILLS_mlo-e-mobo-c',\n",
+ " '20180517_FIRE_rm-n-mobo-c',\n",
+ " '20180612_FIRE_smer-tcs9-mobo-c',\n",
+ " '20170901_FIRE_om-s-mobo-c',\n",
+ " '20190610_FIRE_bh-w-mobo-c',\n",
+ " '20170821_FIRE_lo-s-mobo-c',\n",
+ " '20170722_FIRE_so-s-mobo-c',\n",
+ " '20171010_FIRE_hp-w-mobo-c',\n",
+ " '20160718_FIRE_mw-e-mobo-c',\n",
+ " '20180602_FIRE_rm-n-mobo-c',\n",
+ " '20200705-FIRE-wc-n-mobo-c',\n",
+ " '20180806_Holy_sp-s-mobo-c',\n",
+ " '20180806_FIRE_vo-w-mobo-c',\n",
+ " '20200206_FIRE_ml-s-mobo-c',\n",
+ " '20191006_FIRE_om-s-mobo-c',\n",
+ " '20190825_FIRE_sm-w-mobo-c',\n",
+ " '20180827_Holyflareup_sp-e-mobo-c',\n",
+ " '20170711_FIRE_bl-s-mobo-c',\n",
+ " '20190827_FIRE_so-w-mobo-c',\n",
+ " '20190913_FIRE_lp-n-mobo-c',\n",
+ " '20171010_FIRE_hp-n-mobo-c',\n",
+ " '20180602_Alison_sp-s-mobo-c',\n",
+ " '20180723_FIRE_tp-e-mobo-c',\n",
+ " '20190924_FIRE_wc-e-mobo-c',\n",
+ " '20180809_FIRE_mg-w-mobo-c',\n",
+ " '20200521_FIRE_om-s-mobo-c',\n",
+ " '20180603_FIRE_rm-w-mobo-c',\n",
+ " '20180606_FIRE_ml-s-mobo-c',\n",
+ " '20200611-skyline-lp-n-mobo-c',\n",
+ " '20180504_FIRE_smer-tcs8-mobo-c',\n",
+ " '20190829_FIRE_smer-tcs8-mobo-c',\n",
+ " '20190924_FIRE_wc-s-mobo-c',\n",
+ " '20160604_FIRE_rm-n-mobo-c',\n",
+ " '20200226_FIRE_rm-e-mobo-c',\n",
+ " '20180605_FIRE_rm-w-mobo-c',\n",
+ " '20180910_FIRE_smer-tcs8-mobo-c',\n",
+ " '20191001_FIRE_om-e-mobo-c',\n",
+ " '20160711_FIRE_ml-n-mobo-c',\n",
+ " '20190813_FIRE_69bravo-e-mobo-c',\n",
+ " '20180504_FIRE_bh-n-mobo-c',\n",
+ " '20190924_FIRE_hp-s-mobo-c',\n",
+ " '20190924_FIRE_lo-w-mobo-c',\n",
+ " '20180809_FIRE_bl-e-mobo-c',\n",
+ " '20190529_94Fire_lp-s-mobo-c',\n",
+ " '20180726_FIRE_so-w-mobo-c',\n",
+ " '20200521_VEGMGMT_bm-s-mobo-c',\n",
+ " '20200304_FIRE_rm-w-mobo-c',\n",
+ " '20180605_FIRE_smer-tcs9-mobo-c',\n",
+ " '20200205_FIRE_hp-w-mobo-c',\n",
+ " '20191006_FIRE_lp-n-mobo-c',\n",
+ " '20200306_FIRE_mlo-n-mobo-c',\n",
+ " '20180728_FIRE_smer-tcs9-mobo-c',\n",
+ " '20161113_FIRE_bl-n-mobo-c',\n",
+ " '20191006_FIRE_lo-s-mobo-c',\n",
+ " '20200614-DrumCanyon-syp-w-mobo-c',\n",
+ " '20160604_FIRE_smer-tcs3-mobo-c',\n",
+ " '20200521_FIRE_om-w-mobo-c',\n",
+ " '20180718_FIRE_syp-w-mobo-c',\n",
+ " '20180720_Cinnamon_wc-e-mobo-c',\n",
+ " '20171207_FIRE_smer-tcs8-mobo-c',\n",
+ " '20200601_WILDLAND-DRILLS_om-e-mobo-c',\n",
+ " '20180603_FIRE_sm-w-mobo-c',\n",
+ " '20200709-Tripp-hp-n-mobo-c',\n",
+ " '20191006_FIRE_lo-w-mobo-c',\n",
+ " '20180806_FIRE_mg-s-mobo-c',\n",
+ " '20170711_FIRE_sdsc-e-mobo-c',\n",
+ " '20170627_FIRE_om-e-mobo-c',\n",
+ " '20180602_Alison_sp-w-mobo-c',\n",
+ " '20190829_FIRE_pi-e-mobo-c',\n",
+ " '20180504_FIRE_rm-n-mobo-c',\n",
+ " '20180727_FIRE_wc-n-mobo-c',\n",
+ " '20180603_FIRE_bl-s-mobo-c',\n",
+ " '20190712_FIRE_om-e-mobo-c',\n",
+ " '20180611_fallbrook_rm-w-mobo-c',\n",
+ " '20191005_FIRE_hp-s-mobo-c',\n",
+ " '20180727_FIRE_bl-e-mobo-c',\n",
+ " '20171021_FIRE_pi-e-mobo-c',\n",
+ " '20190728_FIRE_om-n-mobo-c',\n",
+ " '20191006_FIRE_om-n-mobo-c',\n",
+ " '20180813_FIRE_mg-w-mobo-c',\n",
+ " '20170711_FIRE_sm-n-mobo-c',\n",
+ " '20190715_MLOSouth3_lo-s-mobo-c',\n",
+ " '20180606_FIRE_lo-s-mobo-c',\n",
+ " '20160619_FIRE_om-e-mobo-c',\n",
+ " '20200601_WILDLAND-DRILLS_pi-e-mobo-c',\n",
+ " '20190825_FIRE-smer-tcs8-mobo-c',\n",
+ " '20170519_FIRE_rm-w-mobo-c',\n",
+ " '20190814_FIRE-pi-s-mobo-c',\n",
+ " '20191001_FIRE_lp-s-mobo-c',\n",
+ " '20191001_FIRE_om-s-mobo-c',\n",
+ " '20190826_FIRE_pi-s-mobo-c',\n",
+ " '20190925_FIRE_wc-e-mobo-c',\n",
+ " '20190610_Pauma_bh-w-mobo-c',\n",
+ " '20180706_FIRE_sm-n-mobo-c',\n",
+ " '20200705-FIRE-bm-w-mobo-c',\n",
+ " '20190714_MLOSouth_lo-s-mobo-c',\n",
+ " '20180517_FIRE_smer-tcs10-mobo-c',\n",
+ " '20191005_FIRE_wc-e-mobo-c',\n",
+ " '20161113_FIRE_bm-n-mobo-c',\n",
+ " '20171207_FIRE_bh-n-mobo-c',\n",
+ " '20191005_FIRE_wc-n-mobo-c',\n",
+ " '20200306_FIRE_ml-s-mobo-c',\n",
+ " '20180813_FIRE_bh-s-mobo-c',\n",
+ " '20180725_Cranston_sp-e-mobo-c',\n",
+ " '20180720_FIRE_syp-w-mobo-c',\n",
+ " '20180704_Benton_hp-n-mobo-c',\n",
+ " '20180706_FIRE_wc-e-mobo-c',\n",
+ " '20190620_FIRE_rm-w-mobo-c',\n",
+ " '20180725_FIRE_smer-tcs10-mobo-c',\n",
+ " '20180727_FIRE_bh-s-mobo-c']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mypath = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "savepath = \"/userdata/kerasData/preloaded\"\n",
+ "onlyfiles = [f for f in listdir(mypath) if isdir(join(mypath, f))]\n",
+ "onlyfiles"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class expFireDetectionNet:\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(1))\n",
+ " model.add(Activation(\"sigmoid\"))\n",
+ "\n",
+ " # return the constructed network architecture\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import the necessary packages\n",
+ "import os\n",
+ "\n",
+ "# initialize the path to the fire and non-fire dataset directories\n",
+ "Image_Path = \"/userdata/kerasData/images/hpwren.ucsd.edu/HWB/HPWREN-FIgLib\"\n",
+ "# NON_FIRE_PATH = \"/userdata/kerasData/HPWREN-data/BEFORE/images\"\n",
+ "\n",
+ "# initialize the class labels in the dataset\n",
+ "CLASSES = [\"Non-Fire\", \"Fire\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# validationData_generator\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7fcbbc050be0>"
+ ]
+ },
+ "execution_count": 10,
+ "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": [
+ "from PIL import Image\n",
+ "from matplotlib.pyplot import imshow\n",
+ "%matplotlib inline\n",
+ "image_size=(2048, 1536)\n",
+ "addres = \"/userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib/20190829_FIRE_rm-w-mobo-c/1567103106_+02100.jpg\"\n",
+ "\n",
+ "im = Image.open(addres)\n",
+ "resized = im.resize(image_size)\n",
+ "imshow(np.asarray(resized))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define the initial learning rate, batch size, and number of epochs\n",
+ "INIT_LR = 1e-4\n",
+ "batch_size = 16\n",
+ "NUM_EPOCHS = 200"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set the path to the serialized model after training\n",
+ "MODEL_PATH = os.path.sep.join([\"output\", \"fire_detection_128_v1.model\"])\n",
+ "\n",
+ "# define the path to the output learning rate finder plot and\n",
+ "# training history plot\n",
+ "LRFIND_PLOT_PATH = os.path.sep.join([\"/userdata/kerasData\",\n",
+ " \"output\", \"lrfindd_128_v1_plot.png\"])\n",
+ "TRAINING_PLOT_PATH = os.path.sep.join([\"/userdata/kerasData\",\n",
+ " \"output\", \"training_128_v1_plot.png\"])\n",
+ "\n",
+ "# define the path to the output directory that will store our final\n",
+ "# output with labels/annotations along with the number of iamges to\n",
+ "# sample\n",
+ "OUTPUT_IMAGE_PATH = os.path.sep.join([\"output\", \"examples\"])\n",
+ "SAMPLE_SIZE = 500"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 3485 images belonging to 2 classes.\n",
+ "Found 10958 images belonging to 2 classes.\n",
+ "Found 2781 images belonging to 2 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "image_size=(128, 128)\n",
+ "class_mode=\"binary\"\n",
+ "\n",
+ "testGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
+ " target_size=image_size,\n",
+ " seed=1000,\n",
+ " batch_size=batch_size,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "trainGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/train\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "validationGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/validation\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_3\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d_12 (Separab (None, 1536, 2048, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation_21 (Activation) (None, 1536, 2048, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_18 (Batc (None, 1536, 2048, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_9 (MaxPooling2 (None, 768, 1024, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_13 (Separab (None, 768, 1024, 32) 688 \n",
+ "_________________________________________________________________\n",
+ "activation_22 (Activation) (None, 768, 1024, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_19 (Batc (None, 768, 1024, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_10 (MaxPooling (None, 384, 512, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_14 (Separab (None, 384, 512, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_23 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_20 (Batc (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_15 (Separab (None, 384, 512, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_24 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_21 (Batc (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_11 (MaxPooling (None, 192, 256, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_3 (Flatten) (None, 3145728) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_9 (Dense) (None, 128) 402653312 \n",
+ "_________________________________________________________________\n",
+ "activation_25 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_22 (Batc (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_6 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_10 (Dense) (None, 128) 16512 \n",
+ "_________________________________________________________________\n",
+ "activation_26 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_23 (Batc (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_7 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_11 (Dense) (None, 1) 129 \n",
+ "_________________________________________________________________\n",
+ "activation_27 (Activation) (None, 1) 0 \n",
+ "=================================================================\n",
+ "Total params: 402,679,716\n",
+ "Trainable params: 402,678,852\n",
+ "Non-trainable params: 864\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = expFireDetectionNet.build(width=image_size[0], height=image_size[1], depth=3)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def dice_loss(y_true, y_pred):\n",
+ "# numerator = 2 * tf.reduce_sum(y_true * y_pred, axis=-1)\n",
+ "# denominator = tf.reduce_sum(y_true + y_pred, axis=-1)\n",
+ "\n",
+ "# return 1 - (numerator + 1) / (denominator + 1)\n",
+ "\n",
+ "\n",
+ "# def dice_coef_loss(y_true, y_pred):\n",
+ "# return -dice_coef(y_true, y_pred)\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Alt implentation \n",
+ "# def dice_coef(y_true, y_pred, smooth=1):\n",
+ "# \"\"\"\n",
+ "# Dice = (2*|X & Y|)/ (|X|+ |Y|)\n",
+ "# = 2*sum(|A*B|)/(sum(A^2)+sum(B^2))\n",
+ "# ref: https://arxiv.org/pdf/1606.04797v1.pdf\n",
+ "# \"\"\"\n",
+ "# intersection = K.sum(K.abs(y_true * y_pred), axis=-1)\n",
+ "# return (2. * intersection + smooth) / (K.sum(K.square(y_true),-1) + K.sum(K.square(y_pred),-1) + smooth)\n",
+ "\n",
+ "# def dice_coef_loss(y_true, y_pred):\n",
+ "# return 1-dice_coef(y_true, y_pred)\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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ " decay=INIT_LR / NUM_EPOCHS)\n",
+ "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", f1, tf.keras.metrics.Recall(), tf.keras.metrics.Precision()])\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\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ADD OTHER METRICS TO RECORD\n",
+ "name = \"128_128_e-4\"\n",
+ "mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/output/model/best_model{name}.h5', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, min_delta=0.1 )\n",
+ "validation_steps = validationGenerator.n // batch_size\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From <ipython-input-20-aff66eb8379b>:8: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Please use Model.fit, which supports generators.\n",
+ "Epoch 1/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 1.0189 - accuracy: 0.5074 - f1: 0.4816 - recall: 0.5118 - precision: 0.4771\n",
+ "Epoch 00001: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 620s 9s/step - loss: 1.0189 - accuracy: 0.5074 - f1: 0.4816 - recall: 0.5118 - precision: 0.4771 - val_loss: 0.6977 - val_accuracy: 0.5022 - val_f1: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00\n",
+ "Epoch 2/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9746 - accuracy: 0.5037 - f1: 0.4761 - recall: 0.4963 - precision: 0.4944\n",
+ "Epoch 00002: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 405s 6s/step - loss: 0.9746 - accuracy: 0.5037 - f1: 0.4761 - recall: 0.4963 - precision: 0.4944 - val_loss: 0.7690 - val_accuracy: 0.5014 - val_f1: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00\n",
+ "Epoch 3/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9578 - accuracy: 0.5202 - f1: 0.5095 - recall: 0.5181 - precision: 0.5277\n",
+ "Epoch 00003: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 421s 6s/step - loss: 0.9578 - accuracy: 0.5202 - f1: 0.5095 - recall: 0.5181 - precision: 0.5277 - val_loss: 0.7617 - val_accuracy: 0.5025 - val_f1: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00\n",
+ "Epoch 4/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8966 - accuracy: 0.5331 - f1: 0.5159 - recall: 0.5379 - precision: 0.5182\n",
+ "Epoch 00004: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 361s 5s/step - loss: 0.8966 - accuracy: 0.5331 - f1: 0.5159 - recall: 0.5379 - precision: 0.5182 - val_loss: 0.8248 - val_accuracy: 0.4960 - val_f1: 0.0313 - val_recall: 0.0174 - val_precision: 0.3750\n",
+ "Epoch 5/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9249 - accuracy: 0.5165 - f1: 0.4953 - recall: 0.4892 - precision: 0.5272\n",
+ "Epoch 00005: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 353s 5s/step - loss: 0.9249 - accuracy: 0.5165 - f1: 0.4953 - recall: 0.4892 - precision: 0.5272 - val_loss: 0.8795 - val_accuracy: 0.4971 - val_f1: 0.1159 - val_recall: 0.0755 - val_precision: 0.4685\n",
+ "Epoch 6/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9138 - accuracy: 0.5018 - f1: 0.4809 - recall: 0.4898 - precision: 0.4962\n",
+ "Epoch 00006: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 337s 5s/step - loss: 0.9138 - accuracy: 0.5018 - f1: 0.4809 - recall: 0.4898 - precision: 0.4962 - val_loss: 0.8162 - val_accuracy: 0.5025 - val_f1: 0.0702 - val_recall: 0.0413 - val_precision: 0.5135\n",
+ "Epoch 7/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8830 - accuracy: 0.5175 - f1: 0.5057 - recall: 0.5363 - precision: 0.5106\n",
+ "Epoch 00007: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 330s 5s/step - loss: 0.8830 - accuracy: 0.5175 - f1: 0.5057 - recall: 0.5363 - precision: 0.5106 - val_loss: 0.7307 - val_accuracy: 0.5137 - val_f1: 0.2803 - val_recall: 0.2109 - val_precision: 0.5310\n",
+ "Epoch 8/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8737 - accuracy: 0.5184 - f1: 0.5075 - recall: 0.5183 - precision: 0.5202\n",
+ "Epoch 00008: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 307s 5s/step - loss: 0.8737 - accuracy: 0.5184 - f1: 0.5075 - recall: 0.5183 - precision: 0.5202 - val_loss: 0.7169 - val_accuracy: 0.5152 - val_f1: 0.5749 - val_recall: 0.7012 - val_precision: 0.5105\n",
+ "Epoch 9/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8809 - accuracy: 0.5276 - f1: 0.5081 - recall: 0.5110 - precision: 0.5285\n",
+ "Epoch 00009: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 318s 5s/step - loss: 0.8809 - accuracy: 0.5276 - f1: 0.5081 - recall: 0.5110 - precision: 0.5285 - val_loss: 0.7604 - val_accuracy: 0.5145 - val_f1: 0.5274 - val_recall: 0.5714 - val_precision: 0.5114\n",
+ "Epoch 10/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9401 - accuracy: 0.4890 - f1: 0.4761 - recall: 0.5009 - precision: 0.4794\n",
+ "Epoch 00010: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 321s 5s/step - loss: 0.9401 - accuracy: 0.4890 - f1: 0.4761 - recall: 0.5009 - precision: 0.4794 - val_loss: 0.7234 - val_accuracy: 0.5311 - val_f1: 0.5770 - val_recall: 0.6872 - val_precision: 0.5221\n",
+ "Epoch 11/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8632 - accuracy: 0.5386 - f1: 0.5265 - recall: 0.5463 - precision: 0.5344\n",
+ "Epoch 00011: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 345s 5s/step - loss: 0.8632 - accuracy: 0.5386 - f1: 0.5265 - recall: 0.5463 - precision: 0.5344 - val_loss: 0.7477 - val_accuracy: 0.4917 - val_f1: 0.6115 - val_recall: 0.8481 - val_precision: 0.4934\n",
+ "Epoch 12/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8592 - accuracy: 0.5441 - f1: 0.5281 - recall: 0.5369 - precision: 0.5429\n",
+ "Epoch 00012: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 348s 5s/step - loss: 0.8592 - accuracy: 0.5441 - f1: 0.5281 - recall: 0.5369 - precision: 0.5429 - val_loss: 0.7560 - val_accuracy: 0.4996 - val_f1: 0.6161 - val_recall: 0.8510 - val_precision: 0.4981\n",
+ "Epoch 13/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9050 - accuracy: 0.5147 - f1: 0.4983 - recall: 0.5083 - precision: 0.5121\n",
+ "Epoch 00013: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 352s 5s/step - loss: 0.9050 - accuracy: 0.5147 - f1: 0.4983 - recall: 0.5083 - precision: 0.5121 - val_loss: 0.7388 - val_accuracy: 0.4881 - val_f1: 0.6212 - val_recall: 0.8838 - val_precision: 0.4919\n",
+ "Epoch 14/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8313 - accuracy: 0.5304 - f1: 0.5164 - recall: 0.5503 - precision: 0.5151\n",
+ "Epoch 00014: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 366s 5s/step - loss: 0.8313 - accuracy: 0.5304 - f1: 0.5164 - recall: 0.5503 - precision: 0.5151 - val_loss: 0.9459 - val_accuracy: 0.5033 - val_f1: 0.5801 - val_recall: 0.7353 - val_precision: 0.5010\n",
+ "Epoch 15/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8889 - accuracy: 0.4890 - f1: 0.4682 - recall: 0.4860 - precision: 0.4824\n",
+ "Epoch 00015: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 360s 5s/step - loss: 0.8889 - accuracy: 0.4890 - f1: 0.4682 - recall: 0.4860 - precision: 0.4824 - val_loss: 0.7393 - val_accuracy: 0.5329 - val_f1: 0.5992 - val_recall: 0.7388 - val_precision: 0.5218\n",
+ "Epoch 16/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8263 - accuracy: 0.5184 - f1: 0.4946 - recall: 0.5188 - precision: 0.5074\n",
+ "Epoch 00016: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 339s 5s/step - loss: 0.8263 - accuracy: 0.5184 - f1: 0.4946 - recall: 0.5188 - precision: 0.5074 - val_loss: 0.7256 - val_accuracy: 0.5220 - val_f1: 0.5508 - val_recall: 0.6201 - val_precision: 0.5178\n",
+ "Epoch 17/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8290 - accuracy: 0.5074 - f1: 0.4772 - recall: 0.5049 - precision: 0.4787\n",
+ "Epoch 00017: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 358s 5s/step - loss: 0.8290 - accuracy: 0.5074 - f1: 0.4772 - recall: 0.5049 - precision: 0.4787 - val_loss: 0.7280 - val_accuracy: 0.5257 - val_f1: 0.5581 - val_recall: 0.6415 - val_precision: 0.5182\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 18/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8286 - accuracy: 0.5129 - f1: 0.4912 - recall: 0.5199 - precision: 0.4973\n",
+ "Epoch 00018: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 361s 5s/step - loss: 0.8286 - accuracy: 0.5129 - f1: 0.4912 - recall: 0.5199 - precision: 0.4973 - val_loss: 0.7071 - val_accuracy: 0.5499 - val_f1: 0.6006 - val_recall: 0.7165 - val_precision: 0.5361\n",
+ "Epoch 19/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8067 - accuracy: 0.5358 - f1: 0.5267 - recall: 0.5277 - precision: 0.5504\n",
+ "Epoch 00019: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 344s 5s/step - loss: 0.8067 - accuracy: 0.5358 - f1: 0.5267 - recall: 0.5277 - precision: 0.5504 - val_loss: 0.7233 - val_accuracy: 0.5134 - val_f1: 0.6081 - val_recall: 0.7917 - val_precision: 0.5072\n",
+ "Epoch 20/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8409 - accuracy: 0.5193 - f1: 0.5105 - recall: 0.5375 - precision: 0.5098\n",
+ "Epoch 00020: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 355s 5s/step - loss: 0.8409 - accuracy: 0.5193 - f1: 0.5105 - recall: 0.5375 - precision: 0.5098 - val_loss: 0.7491 - val_accuracy: 0.4888 - val_f1: 0.6261 - val_recall: 0.8983 - val_precision: 0.4922\n",
+ "Epoch 21/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8207 - accuracy: 0.5110 - f1: 0.5018 - recall: 0.4973 - precision: 0.5255\n",
+ "Epoch 00021: saving model to /userdata/kerasData/output/model/best_model128_128_e-4.h5\n",
+ "68/68 [==============================] - 374s 5s/step - loss: 0.8207 - accuracy: 0.5110 - f1: 0.5018 - recall: 0.4973 - precision: 0.5255 - val_loss: 0.7334 - val_accuracy: 0.5051 - val_f1: 0.6131 - val_recall: 0.8165 - val_precision: 0.5031\n"
+ ]
+ }
+ ],
+ "source": [
+ "H = model.fit_generator(\n",
+ " trainGenerator,\n",
+ " steps_per_epoch=trainGenerator.n // (batch_size*10),\n",
+ " validation_data=validationGenerator,\n",
+ " validation_steps=validationGenerator.n // batch_size,\n",
+ " epochs=NUM_EPOCHS,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dict_keys(['loss', 'accuracy', 'f1', 'recall', 'precision', 'val_loss', 'val_accuracy', 'val_f1', 'val_recall', 'val_precision'])\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(H.history.keys())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.DataFrame.from_dict(H.history).to_csv(\"/userdata/kerasData/output/results/best_model_{name}.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['loss', 'accuracy', 'f1', 'recall', 'precision', 'val_loss',\n",
+ " 'val_accuracy', 'val_f1', 'val_recall', 'val_precision'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "H = pd.read_csv(\"/userdata/kerasData/output/results/best_model_{name}.csv\")\n",
+ "print(H.keys())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "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"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "plt.plot(H[\"loss\"])\n",
+ "plt.plot(H[\"val_loss\"])\n",
+ "plt.title('model loss functions')\n",
+ "plt.legend(['train', 'validation'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "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"
+ }
+ ],
+ "source": [
+ "plt.plot(H[\"f1\"])\n",
+ "plt.plot(H[\"val_accuracy\"])\n",
+ "plt.title('model validation results')\n",
+ "plt.legend(['accuarcy', 'val_accuracy'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "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"
+ }
+ ],
+ "source": [
+ "plt.plot(H[\"val_f1\"])\n",
+ "plt.plot(H[\"val_recall\"])\n",
+ "plt.plot(H[\"val_precision\"])\n",
+ "\n",
+ "plt.title('model validation results')\n",
+ "plt.legend(['f1', 'recall', 'precission'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 3485 images belonging to 2 classes.\n",
+ "Found 10958 images belonging to 2 classes.\n",
+ "Found 2781 images belonging to 2 classes.\n"
+ ]
+ },
+ {
+ "ename": "ResourceExhaustedError",
+ "evalue": "OOM when allocating tensor with shape[3145728,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:RandomUniform]",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-18-43787fc86836>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpFireDetectionNet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\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 32\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\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 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-15-2833d293e527>\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(width, height, depth)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFlatten\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---> 30\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\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 31\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mActivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"relu\"\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 32\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBatchNormalization\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/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\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--> 456\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\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 457\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 458\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m \u001b[0;31m# pylint: disable=protected-access\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/keras/engine/sequential.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0;31m# If the model is being built continuously on top of an input layer:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[0;31m# refresh its output.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m \u001b[0moutput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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 214\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\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[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSINGLE_LAYER_OUTPUT_ERROR_MSG\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/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 895\u001b[0m \u001b[0;31m# Build layer if applicable (if the `build` method has been\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 896\u001b[0m \u001b[0;31m# overridden).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 897\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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 898\u001b[0m \u001b[0mcast_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_maybe_build\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2414\u001b[0m \u001b[0;31m# operations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2415\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_init_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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-> 2416\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint:disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2417\u001b[0m \u001b[0;31m# We must set also ensure that the layer is marked as built, and the build\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2418\u001b[0m \u001b[0;31m# shape is stored since user defined build functions may not be calling\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/keras/layers/core.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m 1163\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_constraint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1164\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1165\u001b[0;31m trainable=True)\n\u001b[0m\u001b[1;32m 1166\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1167\u001b[0m self.bias = self.add_weight(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36madd_weight\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, synchronization, aggregation, **kwargs)\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m caching_device=caching_device)\n\u001b[0m\u001b[1;32m 578\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mregularizer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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;31m# TODO(fchollet): in the future, this should be handled at the\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/training/tracking/base.py\u001b[0m in \u001b[0;36m_add_variable_with_custom_getter\u001b[0;34m(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)\u001b[0m\n\u001b[1;32m 741\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 743\u001b[0;31m **kwargs_for_getter)\n\u001b[0m\u001b[1;32m 744\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[0;31m# If we set an initializer and the variable processed it, tracking will not\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/keras/engine/base_layer_utils.py\u001b[0m in \u001b[0;36mmake_variable\u001b[0;34m(name, shape, dtype, initializer, trainable, caching_device, validate_shape, constraint, use_resource, collections, synchronization, aggregation, partitioner)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m shape=variable_shape if variable_shape else None)\n\u001b[0m\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mVariableV1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_v1_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[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 260\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_v2_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[0mkwargs\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/ops/variables.py\u001b[0m in \u001b[0;36m_variable_v1_call\u001b[0;34m(cls, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope, constraint, use_resource, synchronization, aggregation, shape)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m shape=shape)\n\u001b[0m\u001b[1;32m 221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m def _variable_v2_call(cls,\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m 196\u001b[0m shape=None):\n\u001b[1;32m 197\u001b[0m \u001b[0;34m\"\"\"Call on Variable class. Useful to force the signature.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m \u001b[0mprevious_getter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdefault_variable_creator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\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 199\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgetter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creator_stack\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0mprevious_getter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprevious_getter\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/ops/variable_scope.py\u001b[0m in \u001b[0;36mdefault_variable_creator\u001b[0;34m(next_creator, **kwargs)\u001b[0m\n\u001b[1;32m 2596\u001b[0m \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2597\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2598\u001b[0;31m shape=shape)\n\u001b[0m\u001b[1;32m 2599\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 2600\u001b[0m return variables.RefVariable(\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_v2_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[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\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--> 263\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVariableMetaclass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\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[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 264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint, distribute_strategy, synchronization, aggregation, shape)\u001b[0m\n\u001b[1;32m 1432\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1434\u001b[0;31m distribute_strategy=distribute_strategy)\n\u001b[0m\u001b[1;32m 1435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1436\u001b[0m def _init_from_args(self,\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36m_init_from_args\u001b[0;34m(self, initial_value, trainable, collections, caching_device, name, dtype, constraint, synchronization, aggregation, distribute_strategy, shape)\u001b[0m\n\u001b[1;32m 1565\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Initializer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\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 1566\u001b[0m initial_value = ops.convert_to_tensor(\n\u001b[0;32m-> 1567\u001b[0;31m \u001b[0minitial_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minit_from_fn\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0minitial_value\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 1568\u001b[0m name=\"initial_value\", dtype=dtype)\n\u001b[1;32m 1569\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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/keras/engine/base_layer_utils.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 119\u001b[0m (type(init_ops.Initializer), type(init_ops_v2.Initializer))):\n\u001b[1;32m 120\u001b[0m \u001b[0minitializer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minitializer\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--> 121\u001b[0;31m \u001b[0minit_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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 122\u001b[0m \u001b[0mvariable_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_resource\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\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/ops/init_ops_v2.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, shape, dtype)\u001b[0m\n\u001b[1;32m 556\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 557\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_random_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_uniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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 559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/ops/init_ops_v2.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(self, shape, minval, maxval, dtype)\u001b[0m\n\u001b[1;32m 1066\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_uniform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1067\u001b[0m return op(\n\u001b[0;32m-> 1068\u001b[0;31m shape=shape, minval=minval, maxval=maxval, dtype=dtype, seed=self.seed)\n\u001b[0m\u001b[1;32m 1069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1070\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtruncated_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstddev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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/ops/random_ops.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, minval, maxval, dtype, seed, name)\u001b[0m\n\u001b[1;32m 294\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 295\u001b[0m result = gen_random_ops.random_uniform(\n\u001b[0;32m--> 296\u001b[0;31m shape, dtype, seed=seed1, seed2=seed2)\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mminval_is_zero\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmaxval_is_one\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/ops/gen_random_ops.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, dtype, seed, seed2, name)\u001b[0m\n\u001b[1;32m 722\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\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[0;32m--> 724\u001b[0;31m \u001b[0m_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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 725\u001b[0m \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dtype\"\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/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m 6651\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\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 6652\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6653\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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 6654\u001b[0m \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
+ "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[3145728,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:RandomUniform]"
+ ]
+ }
+ ],
+ "source": [
+ "# define the path to the output directory that will store our final\n",
+ "# output with labels/annotations along with the number of iamges to\n",
+ "# sample\n",
+ "OUTPUT_IMAGE_PATH = os.path.sep.join([\"output\", \"examples\"])\n",
+ "SAMPLE_SIZE = 500\n",
+ "image_size=(2048, 1536)\n",
+ "class_mode=\"binary\"\n",
+ "\n",
+ "testGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
+ " target_size=image_size,\n",
+ " seed=1000,\n",
+ " batch_size=batch_size,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "trainGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/train\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "validationGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/validation\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "\n",
+ "model = expFireDetectionNet.build(width=image_size[0], height=image_size[1], depth=3)\n",
+ "model.summary()\n",
+ "\n",
+ "opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ " decay=INIT_LR / NUM_EPOCHS)\n",
+ "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", f1, tf.keras.metrics.Recall(), tf.keras.metrics.Precision()])\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",
+ "# ADD OTHER METRICS TO RECORD\n",
+ "name = \"2048_1536_e-5\"\n",
+ "mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/output/model/best_model_2048{name}.h5', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, min_delta=0.1 )\n",
+ "validation_steps = validationGenerator.n // batch_size\n",
+ "\n",
+ "H_2048_1536_e5_v1 = model.fit_generator(\n",
+ " trainGenerator,\n",
+ " steps_per_epoch=trainGenerator.n // (batch_size*10),\n",
+ " validation_data=validationGenerator,\n",
+ " validation_steps=validationGenerator.n // batch_size,\n",
+ " epochs=NUM_EPOCHS,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 3485 images belonging to 2 classes.\n",
+ "Found 10958 images belonging to 2 classes.\n",
+ "Found 2781 images belonging to 2 classes.\n",
+ "Model: \"sequential_2\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "separable_conv2d_8 (Separabl (None, 1536, 2048, 16) 211 \n",
+ "_________________________________________________________________\n",
+ "activation_14 (Activation) (None, 1536, 2048, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_12 (Batc (None, 1536, 2048, 16) 64 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_6 (MaxPooling2 (None, 768, 1024, 16) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_9 (Separabl (None, 768, 1024, 32) 688 \n",
+ "_________________________________________________________________\n",
+ "activation_15 (Activation) (None, 768, 1024, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_13 (Batc (None, 768, 1024, 32) 128 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_7 (MaxPooling2 (None, 384, 512, 32) 0 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_10 (Separab (None, 384, 512, 64) 2400 \n",
+ "_________________________________________________________________\n",
+ "activation_16 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_14 (Batc (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "separable_conv2d_11 (Separab (None, 384, 512, 64) 4736 \n",
+ "_________________________________________________________________\n",
+ "activation_17 (Activation) (None, 384, 512, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_15 (Batc (None, 384, 512, 64) 256 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_8 (MaxPooling2 (None, 192, 256, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 3145728) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 128) 402653312 \n",
+ "_________________________________________________________________\n",
+ "activation_18 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_16 (Batc (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_4 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_7 (Dense) (None, 128) 16512 \n",
+ "_________________________________________________________________\n",
+ "activation_19 (Activation) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_17 (Batc (None, 128) 512 \n",
+ "_________________________________________________________________\n",
+ "dropout_5 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_8 (Dense) (None, 1) 129 \n",
+ "_________________________________________________________________\n",
+ "activation_20 (Activation) (None, 1) 0 \n",
+ "=================================================================\n",
+ "Total params: 402,679,716\n",
+ "Trainable params: 402,678,852\n",
+ "Non-trainable params: 864\n",
+ "_________________________________________________________________\n",
+ "Epoch 1/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9195 - accuracy: 0.5285 - f1: 0.4939 - recall_1: 0.5225 - precision_1: 0.4981\n",
+ "Epoch 00001: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 1123s 17s/step - loss: 0.9195 - accuracy: 0.5285 - f1: 0.4939 - recall_1: 0.5225 - precision_1: 0.4981 - val_loss: 0.7113 - val_accuracy: 0.4978 - val_f1: 0.6541 - val_recall_1: 1.0000 - val_precision_1: 0.4978\n",
+ "Epoch 2/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9324 - accuracy: 0.5101 - f1: 0.5035 - recall_1: 0.5264 - precision_1: 0.4973\n",
+ "Epoch 00002: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 1157s 17s/step - loss: 0.9324 - accuracy: 0.5101 - f1: 0.5035 - recall_1: 0.5264 - precision_1: 0.4973 - val_loss: 0.7336 - val_accuracy: 0.4982 - val_f1: 0.6568 - val_recall_1: 1.0000 - val_precision_1: 0.4982\n",
+ "Epoch 3/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8750 - accuracy: 0.5478 - f1: 0.5327 - recall_1: 0.5537 - precision_1: 0.5355\n",
+ "Epoch 00003: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 950s 14s/step - loss: 0.8750 - accuracy: 0.5478 - f1: 0.5327 - recall_1: 0.5537 - precision_1: 0.5355 - val_loss: 0.7588 - val_accuracy: 0.4971 - val_f1: 0.6537 - val_recall_1: 1.0000 - val_precision_1: 0.4971\n",
+ "Epoch 4/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.9342 - accuracy: 0.5184 - f1: 0.5019 - recall_1: 0.5400 - precision_1: 0.4911\n",
+ "Epoch 00004: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 1046s 15s/step - loss: 0.9342 - accuracy: 0.5184 - f1: 0.5019 - recall_1: 0.5400 - precision_1: 0.4911 - val_loss: 0.7161 - val_accuracy: 0.5210 - val_f1: 0.5681 - val_recall_1: 0.6696 - val_precision_1: 0.5151\n",
+ "Epoch 5/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8950 - accuracy: 0.5469 - f1: 0.5282 - recall_1: 0.5451 - precision_1: 0.5360\n",
+ "Epoch 00005: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 838s 12s/step - loss: 0.8950 - accuracy: 0.5469 - f1: 0.5282 - recall_1: 0.5451 - precision_1: 0.5360 - val_loss: 0.6940 - val_accuracy: 0.5040 - val_f1: 0.6242 - val_recall_1: 0.8585 - val_precision_1: 0.5011\n",
+ "Epoch 6/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8479 - accuracy: 0.5358 - f1: 0.5125 - recall_1: 0.5189 - precision_1: 0.5238\n",
+ "Epoch 00006: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 729s 11s/step - loss: 0.8479 - accuracy: 0.5358 - f1: 0.5125 - recall_1: 0.5189 - precision_1: 0.5238 - val_loss: 0.6999 - val_accuracy: 0.5123 - val_f1: 0.6269 - val_recall_1: 0.8585 - val_precision_1: 0.5060\n",
+ "Epoch 7/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8559 - accuracy: 0.5469 - f1: 0.5187 - recall_1: 0.5392 - precision_1: 0.5159\n",
+ "Epoch 00007: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 733s 11s/step - loss: 0.8559 - accuracy: 0.5469 - f1: 0.5187 - recall_1: 0.5392 - precision_1: 0.5159 - val_loss: 0.7057 - val_accuracy: 0.5242 - val_f1: 0.5418 - val_recall_1: 0.6041 - val_precision_1: 0.5193\n",
+ "Epoch 8/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8601 - accuracy: 0.5460 - f1: 0.5352 - recall_1: 0.5473 - precision_1: 0.5413\n",
+ "Epoch 00008: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 723s 11s/step - loss: 0.8601 - accuracy: 0.5460 - f1: 0.5352 - recall_1: 0.5473 - precision_1: 0.5413 - val_loss: 0.7218 - val_accuracy: 0.5267 - val_f1: 0.5139 - val_recall_1: 0.5253 - val_precision_1: 0.5261\n",
+ "Epoch 9/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8250 - accuracy: 0.5818 - f1: 0.5798 - recall_1: 0.6029 - precision_1: 0.5785\n",
+ "Epoch 00009: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 732s 11s/step - loss: 0.8250 - accuracy: 0.5818 - f1: 0.5798 - recall_1: 0.6029 - precision_1: 0.5785 - val_loss: 0.7216 - val_accuracy: 0.5047 - val_f1: 0.4751 - val_recall_1: 0.4808 - val_precision_1: 0.5030\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 10/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8371 - accuracy: 0.5625 - f1: 0.5443 - recall_1: 0.5547 - precision_1: 0.5589\n",
+ "Epoch 00010: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 714s 10s/step - loss: 0.8371 - accuracy: 0.5625 - f1: 0.5443 - recall_1: 0.5547 - precision_1: 0.5589 - val_loss: 0.7263 - val_accuracy: 0.5134 - val_f1: 0.3960 - val_recall_1: 0.3401 - val_precision_1: 0.5160\n",
+ "Epoch 11/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7992 - accuracy: 0.5910 - f1: 0.5580 - recall_1: 0.5881 - precision_1: 0.5562\n",
+ "Epoch 00011: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 721s 11s/step - loss: 0.7992 - accuracy: 0.5910 - f1: 0.5580 - recall_1: 0.5881 - precision_1: 0.5562 - val_loss: 0.7310 - val_accuracy: 0.4978 - val_f1: 0.4919 - val_recall_1: 0.5225 - val_precision_1: 0.4952\n",
+ "Epoch 12/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.8386 - accuracy: 0.5643 - f1: 0.5499 - recall_1: 0.5427 - precision_1: 0.5843\n",
+ "Epoch 00012: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 715s 11s/step - loss: 0.8386 - accuracy: 0.5643 - f1: 0.5499 - recall_1: 0.5427 - precision_1: 0.5843 - val_loss: 0.7490 - val_accuracy: 0.5199 - val_f1: 0.3617 - val_recall_1: 0.3011 - val_precision_1: 0.5294\n",
+ "Epoch 13/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7898 - accuracy: 0.5919 - f1: 0.5863 - recall_1: 0.5993 - precision_1: 0.5862\n",
+ "Epoch 00013: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 718s 11s/step - loss: 0.7898 - accuracy: 0.5919 - f1: 0.5863 - recall_1: 0.5993 - precision_1: 0.5862 - val_loss: 0.7681 - val_accuracy: 0.5065 - val_f1: 0.3794 - val_recall_1: 0.3229 - val_precision_1: 0.5068\n",
+ "Epoch 14/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7571 - accuracy: 0.5892 - f1: 0.5885 - recall_1: 0.5907 - precision_1: 0.6004\n",
+ "Epoch 00014: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 719s 11s/step - loss: 0.7571 - accuracy: 0.5892 - f1: 0.5885 - recall_1: 0.5907 - precision_1: 0.6004 - val_loss: 0.7408 - val_accuracy: 0.5520 - val_f1: 0.4351 - val_recall_1: 0.3749 - val_precision_1: 0.5777\n",
+ "Epoch 15/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7408 - accuracy: 0.6287 - f1: 0.6159 - recall_1: 0.6096 - precision_1: 0.6490\n",
+ "Epoch 00015: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 704s 10s/step - loss: 0.7408 - accuracy: 0.6287 - f1: 0.6159 - recall_1: 0.6096 - precision_1: 0.6490 - val_loss: 0.7666 - val_accuracy: 0.5137 - val_f1: 0.2659 - val_recall_1: 0.1904 - val_precision_1: 0.5356\n",
+ "Epoch 16/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7672 - accuracy: 0.5974 - f1: 0.5990 - recall_1: 0.5965 - precision_1: 0.6204\n",
+ "Epoch 00016: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 725s 11s/step - loss: 0.7672 - accuracy: 0.5974 - f1: 0.5990 - recall_1: 0.5965 - precision_1: 0.6204 - val_loss: 0.7339 - val_accuracy: 0.5332 - val_f1: 0.4585 - val_recall_1: 0.4262 - val_precision_1: 0.5414\n",
+ "Epoch 17/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7421 - accuracy: 0.5947 - f1: 0.5772 - recall_1: 0.6008 - precision_1: 0.5777\n",
+ "Epoch 00017: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 723s 11s/step - loss: 0.7421 - accuracy: 0.5947 - f1: 0.5772 - recall_1: 0.6008 - precision_1: 0.5777 - val_loss: 0.7434 - val_accuracy: 0.5535 - val_f1: 0.4824 - val_recall_1: 0.4441 - val_precision_1: 0.5656\n",
+ "Epoch 18/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7641 - accuracy: 0.6029 - f1: 0.5852 - recall_1: 0.6080 - precision_1: 0.5835\n",
+ "Epoch 00018: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 723s 11s/step - loss: 0.7641 - accuracy: 0.6029 - f1: 0.5852 - recall_1: 0.6080 - precision_1: 0.5835 - val_loss: 0.7527 - val_accuracy: 0.5238 - val_f1: 0.5915 - val_recall_1: 0.7299 - val_precision_1: 0.5161\n",
+ "Epoch 19/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7206 - accuracy: 0.6287 - f1: 0.6273 - recall_1: 0.6310 - precision_1: 0.6425\n",
+ "Epoch 00019: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 719s 11s/step - loss: 0.7206 - accuracy: 0.6287 - f1: 0.6273 - recall_1: 0.6310 - precision_1: 0.6425 - val_loss: 0.7232 - val_accuracy: 0.5257 - val_f1: 0.5589 - val_recall_1: 0.6350 - val_precision_1: 0.5193\n",
+ "Epoch 20/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7195 - accuracy: 0.6232 - f1: 0.6234 - recall_1: 0.6279 - precision_1: 0.6414\n",
+ "Epoch 00020: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 720s 11s/step - loss: 0.7195 - accuracy: 0.6232 - f1: 0.6234 - recall_1: 0.6279 - precision_1: 0.6414 - val_loss: 0.7552 - val_accuracy: 0.5094 - val_f1: 0.6033 - val_recall_1: 0.7843 - val_precision_1: 0.5044\n",
+ "Epoch 21/200\n",
+ "68/68 [==============================] - ETA: 0s - loss: 0.7100 - accuracy: 0.6314 - f1: 0.6246 - recall_1: 0.6234 - precision_1: 0.6431\n",
+ "Epoch 00021: saving model to /userdata/kerasData/output/model/best_model2048_1536_e-5_V2.h5\n",
+ "68/68 [==============================] - 730s 11s/step - loss: 0.7100 - accuracy: 0.6314 - f1: 0.6246 - recall_1: 0.6234 - precision_1: 0.6431 - val_loss: 0.7734 - val_accuracy: 0.5220 - val_f1: 0.3295 - val_recall_1: 0.2614 - val_precision_1: 0.5437\n"
+ ]
+ }
+ ],
+ "source": [
+ "INIT_LR = 1e-5\n",
+ "batch_size = 16\n",
+ "NUM_EPOCHS = 200\n",
+ "name=\"firstBin\"\n",
+ "\n",
+ "\n",
+ "# define the path to the output directory that will store our final\n",
+ "# output with labels/annotations along with the number of iamges to\n",
+ "# sample\n",
+ "SAMPLE_SIZE = 500\n",
+ "batch_size = 16\n",
+ "\n",
+ "image_size=(2048, 1536)\n",
+ "image_generator = tf.keras.preprocessing.image.ImageDataGenerator()\n",
+ "class_mode=\"binary\"\n",
+ "\n",
+ "testGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/test\",\n",
+ " target_size=image_size,\n",
+ " seed=1000,\n",
+ " batch_size=batch_size,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "trainGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/train\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "validationGenerator = image_generator.flow_from_directory(\n",
+ " \"/userdata/kerasData/preloaded/flowDirectory/validation\",\n",
+ " target_size=image_size,\n",
+ " batch_size=batch_size,\n",
+ " seed=1000,\n",
+ " class_mode=class_mode)\n",
+ "\n",
+ "\n",
+ "model = expFireDetectionNet.build(width=image_size[0], height=image_size[1], depth=3)\n",
+ "model.summary()\n",
+ "\n",
+ "opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ " decay=INIT_LR / NUM_EPOCHS)\n",
+ "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", f1, tf.keras.metrics.Recall(), tf.keras.metrics.Precision()])\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",
+ "# ADD OTHER METRICS TO RECORD\n",
+ "name = \"2048_1536_e-5_V2\"\n",
+ "mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/output/model/best_model{name}.h5', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, min_delta=0.1 )\n",
+ "validation_steps = validationGenerator.n // batch_size\n",
+ "\n",
+ "H_2048_1536 = model.fit_generator(\n",
+ " trainGenerator,\n",
+ " steps_per_epoch=trainGenerator.n // (batch_size*10),\n",
+ " validation_data=validationGenerator,\n",
+ " validation_steps=validationGenerator.n // batch_size,\n",
+ " epochs=NUM_EPOCHS,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define the initial learning rate, batch size, and number of epochs\n",
+ "INIT_LR = 1e-5\n",
+ "batch_size = 16\n",
+ "NUM_EPOCHS = 200"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = expFireDetectionNet.build(width=image_size[0], height=image_size[1], depth=3)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'model' 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-13-1fd1765cb82d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m opt = SGD(lr=INIT_LR, momentum=0.9,\n\u001b[1;32m 2\u001b[0m decay=INIT_LR / NUM_EPOCHS)\n\u001b[0;32m----> 3\u001b[0;31m model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n\u001b[0m\u001b[1;32m 4\u001b[0m metrics=[\"accuracy\", f1, tf.keras.metrics.Recall(), tf.keras.metrics.Precision()])\n\u001b[1;32m 5\u001b[0m aug = ImageDataGenerator(\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ " decay=INIT_LR / NUM_EPOCHS)\n",
+ "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", f1, tf.keras.metrics.Recall(), tf.keras.metrics.Precision()])\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",
+ "name=\"firstBin\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ADD OTHER METRICS TO RECORD\n",
+ "mc = tf.keras.callbacks.ModelCheckpoint(f'/userdata/kerasData/output/model/best_model_2048_v2_{name}.h5', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, min_delta=0.1 )\n",
+ "validation_steps = validationGenerator.n // batch_size\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From <ipython-input-27-46cbd479f685>:8: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Please use Model.fit, which supports generators.\n",
+ "Epoch 1/200\n"
+ ]
+ }
+ ],
+ "source": [
+ "He5 = model.fit_generator(\n",
+ " trainGenerator,\n",
+ " steps_per_epoch=trainGenerator.n // (batch_size*10),\n",
+ " validation_data=validationGenerator,\n",
+ " validation_steps=validationGenerator.n // batch_size,\n",
+ " epochs=NUM_EPOCHS,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.DataFrame.from_dict(He5.history).to_csv(\"e5history.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "He5 = pd.read_csv(\"e5history.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>loss</th>\n",
+ " <th>accuracy</th>\n",
+ " <th>f1</th>\n",
+ " <th>recall_1</th>\n",
+ " <th>precision_1</th>\n",
+ " <th>val_loss</th>\n",
+ " <th>val_accuracy</th>\n",
+ " <th>val_f1</th>\n",
+ " <th>val_recall_1</th>\n",
+ " <th>val_precision_1</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.831668</td>\n",
+ " <td>0.539522</td>\n",
+ " <td>0.539522</td>\n",
+ " <td>0.539522</td>\n",
+ " <td>0.539522</td>\n",
+ " <td>0.724725</td>\n",
+ " <td>0.527457</td>\n",
+ " <td>0.527457</td>\n",
+ " <td>0.527457</td>\n",
+ " <td>0.527457</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.856745</td>\n",
+ " <td>0.500919</td>\n",
+ " <td>0.500919</td>\n",
+ " <td>0.500919</td>\n",
+ " <td>0.500919</td>\n",
+ " <td>0.728873</td>\n",
+ " <td>0.527818</td>\n",
+ " <td>0.527818</td>\n",
+ " <td>0.527818</td>\n",
+ " <td>0.527818</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.851700</td>\n",
+ " <td>0.501838</td>\n",
+ " <td>0.501838</td>\n",
+ " <td>0.501838</td>\n",
+ " <td>0.501838</td>\n",
+ " <td>0.729254</td>\n",
+ " <td>0.522038</td>\n",
+ " <td>0.522038</td>\n",
+ " <td>0.522038</td>\n",
+ " <td>0.522038</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.864165</td>\n",
+ " <td>0.511949</td>\n",
+ " <td>0.511948</td>\n",
+ " <td>0.511949</td>\n",
+ " <td>0.511949</td>\n",
+ " <td>0.732063</td>\n",
+ " <td>0.506864</td>\n",
+ " <td>0.506864</td>\n",
+ " <td>0.506864</td>\n",
+ " <td>0.506864</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.838170</td>\n",
+ " <td>0.535846</td>\n",
+ " <td>0.535846</td>\n",
+ " <td>0.535846</td>\n",
+ " <td>0.535846</td>\n",
+ " <td>0.727758</td>\n",
+ " <td>0.506864</td>\n",
+ " <td>0.506864</td>\n",
+ " <td>0.506864</td>\n",
+ " <td>0.506864</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>0.802232</td>\n",
+ " <td>0.554228</td>\n",
+ " <td>0.554228</td>\n",
+ " <td>0.554228</td>\n",
+ " <td>0.554228</td>\n",
+ " <td>0.725792</td>\n",
+ " <td>0.507225</td>\n",
+ " <td>0.507225</td>\n",
+ " <td>0.507225</td>\n",
+ " <td>0.507225</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>0.844557</td>\n",
+ " <td>0.526654</td>\n",
+ " <td>0.526654</td>\n",
+ " <td>0.526654</td>\n",
+ " <td>0.526654</td>\n",
+ " <td>0.724664</td>\n",
+ " <td>0.507587</td>\n",
+ " <td>0.507587</td>\n",
+ " <td>0.507587</td>\n",
+ " <td>0.507587</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>0.822007</td>\n",
+ " <td>0.556066</td>\n",
+ " <td>0.556066</td>\n",
+ " <td>0.556066</td>\n",
+ " <td>0.556066</td>\n",
+ " <td>0.727122</td>\n",
+ " <td>0.509393</td>\n",
+ " <td>0.509393</td>\n",
+ " <td>0.509393</td>\n",
+ " <td>0.509393</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>0.822612</td>\n",
+ " <td>0.541360</td>\n",
+ " <td>0.541360</td>\n",
+ " <td>0.541360</td>\n",
+ " <td>0.541360</td>\n",
+ " <td>0.728835</td>\n",
+ " <td>0.510116</td>\n",
+ " <td>0.510116</td>\n",
+ " <td>0.510116</td>\n",
+ " <td>0.510116</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>0.814630</td>\n",
+ " <td>0.550551</td>\n",
+ " <td>0.550551</td>\n",
+ " <td>0.550551</td>\n",
+ " <td>0.550551</td>\n",
+ " <td>0.725296</td>\n",
+ " <td>0.505419</td>\n",
+ " <td>0.505419</td>\n",
+ " <td>0.505419</td>\n",
+ " <td>0.505419</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>0.841589</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.727379</td>\n",
+ " <td>0.506503</td>\n",
+ " <td>0.506503</td>\n",
+ " <td>0.506503</td>\n",
+ " <td>0.506503</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>0.802058</td>\n",
+ " <td>0.536765</td>\n",
+ " <td>0.536765</td>\n",
+ " <td>0.536765</td>\n",
+ " <td>0.536765</td>\n",
+ " <td>0.729174</td>\n",
+ " <td>0.511199</td>\n",
+ " <td>0.511199</td>\n",
+ " <td>0.511199</td>\n",
+ " <td>0.511199</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>0.830148</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.537684</td>\n",
+ " <td>0.729422</td>\n",
+ " <td>0.505058</td>\n",
+ " <td>0.505058</td>\n",
+ " <td>0.505058</td>\n",
+ " <td>0.505058</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>0.818458</td>\n",
+ " <td>0.521140</td>\n",
+ " <td>0.521140</td>\n",
+ " <td>0.521140</td>\n",
+ " <td>0.521140</td>\n",
+ " <td>0.727189</td>\n",
+ " <td>0.507225</td>\n",
+ " <td>0.507225</td>\n",
+ " <td>0.507225</td>\n",
+ " <td>0.507225</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>0.799285</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.723485</td>\n",
+ " <td>0.511922</td>\n",
+ " <td>0.511922</td>\n",
+ " <td>0.511922</td>\n",
+ " <td>0.511922</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>0.813551</td>\n",
+ " <td>0.548713</td>\n",
+ " <td>0.548713</td>\n",
+ " <td>0.548713</td>\n",
+ " <td>0.548713</td>\n",
+ " <td>0.726973</td>\n",
+ " <td>0.510477</td>\n",
+ " <td>0.510477</td>\n",
+ " <td>0.510477</td>\n",
+ " <td>0.510477</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>0.791703</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.545956</td>\n",
+ " <td>0.726417</td>\n",
+ " <td>0.510838</td>\n",
+ " <td>0.510838</td>\n",
+ " <td>0.510838</td>\n",
+ " <td>0.510838</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>0.820564</td>\n",
+ " <td>0.527574</td>\n",
+ " <td>0.527573</td>\n",
+ " <td>0.527574</td>\n",
+ " <td>0.527574</td>\n",
+ " <td>0.729048</td>\n",
+ " <td>0.507587</td>\n",
+ " <td>0.507587</td>\n",
+ " <td>0.507587</td>\n",
+ " <td>0.507587</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>0.828426</td>\n",
+ " <td>0.534991</td>\n",
+ " <td>0.534926</td>\n",
+ " <td>0.534991</td>\n",
+ " <td>0.534991</td>\n",
+ " <td>0.726497</td>\n",
+ " <td>0.512283</td>\n",
+ " <td>0.512283</td>\n",
+ " <td>0.512283</td>\n",
+ " <td>0.512283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>0.807585</td>\n",
+ " <td>0.549632</td>\n",
+ " <td>0.549632</td>\n",
+ " <td>0.549632</td>\n",
+ " <td>0.549632</td>\n",
+ " <td>0.724397</td>\n",
+ " <td>0.508309</td>\n",
+ " <td>0.508309</td>\n",
+ " <td>0.508309</td>\n",
+ " <td>0.508309</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>0.801943</td>\n",
+ " <td>0.543199</td>\n",
+ " <td>0.543198</td>\n",
+ " <td>0.543199</td>\n",
+ " <td>0.543199</td>\n",
+ " <td>0.730092</td>\n",
+ " <td>0.511199</td>\n",
+ " <td>0.511199</td>\n",
+ " <td>0.511199</td>\n",
+ " <td>0.511199</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " loss accuracy f1 recall_1 precision_1 val_loss \\\n",
+ "0 0.831668 0.539522 0.539522 0.539522 0.539522 0.724725 \n",
+ "1 0.856745 0.500919 0.500919 0.500919 0.500919 0.728873 \n",
+ "2 0.851700 0.501838 0.501838 0.501838 0.501838 0.729254 \n",
+ "3 0.864165 0.511949 0.511948 0.511949 0.511949 0.732063 \n",
+ "4 0.838170 0.535846 0.535846 0.535846 0.535846 0.727758 \n",
+ "5 0.802232 0.554228 0.554228 0.554228 0.554228 0.725792 \n",
+ "6 0.844557 0.526654 0.526654 0.526654 0.526654 0.724664 \n",
+ "7 0.822007 0.556066 0.556066 0.556066 0.556066 0.727122 \n",
+ "8 0.822612 0.541360 0.541360 0.541360 0.541360 0.728835 \n",
+ "9 0.814630 0.550551 0.550551 0.550551 0.550551 0.725296 \n",
+ "10 0.841589 0.537684 0.537684 0.537684 0.537684 0.727379 \n",
+ "11 0.802058 0.536765 0.536765 0.536765 0.536765 0.729174 \n",
+ "12 0.830148 0.537684 0.537684 0.537684 0.537684 0.729422 \n",
+ "13 0.818458 0.521140 0.521140 0.521140 0.521140 0.727189 \n",
+ "14 0.799285 0.545956 0.545956 0.545956 0.545956 0.723485 \n",
+ "15 0.813551 0.548713 0.548713 0.548713 0.548713 0.726973 \n",
+ "16 0.791703 0.545956 0.545956 0.545956 0.545956 0.726417 \n",
+ "17 0.820564 0.527574 0.527573 0.527574 0.527574 0.729048 \n",
+ "18 0.828426 0.534991 0.534926 0.534991 0.534991 0.726497 \n",
+ "19 0.807585 0.549632 0.549632 0.549632 0.549632 0.724397 \n",
+ "20 0.801943 0.543199 0.543198 0.543199 0.543199 0.730092 \n",
+ "\n",
+ " val_accuracy val_f1 val_recall_1 val_precision_1 \n",
+ "0 0.527457 0.527457 0.527457 0.527457 \n",
+ "1 0.527818 0.527818 0.527818 0.527818 \n",
+ "2 0.522038 0.522038 0.522038 0.522038 \n",
+ "3 0.506864 0.506864 0.506864 0.506864 \n",
+ "4 0.506864 0.506864 0.506864 0.506864 \n",
+ "5 0.507225 0.507225 0.507225 0.507225 \n",
+ "6 0.507587 0.507587 0.507587 0.507587 \n",
+ "7 0.509393 0.509393 0.509393 0.509393 \n",
+ "8 0.510116 0.510116 0.510116 0.510116 \n",
+ "9 0.505419 0.505419 0.505419 0.505419 \n",
+ "10 0.506503 0.506503 0.506503 0.506503 \n",
+ "11 0.511199 0.511199 0.511199 0.511199 \n",
+ "12 0.505058 0.505058 0.505058 0.505058 \n",
+ "13 0.507225 0.507225 0.507225 0.507225 \n",
+ "14 0.511922 0.511922 0.511922 0.511922 \n",
+ "15 0.510477 0.510477 0.510477 0.510477 \n",
+ "16 0.510838 0.510838 0.510838 0.510838 \n",
+ "17 0.507587 0.507587 0.507587 0.507587 \n",
+ "18 0.512283 0.512283 0.512283 0.512283 \n",
+ "19 0.508309 0.508309 0.508309 0.508309 \n",
+ "20 0.511199 0.511199 0.511199 0.511199 "
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "He5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "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"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "plt.plot(He5[\"loss\"])\n",
+ "plt.plot(He5[\"val_loss\"])\n",
+ "plt.title('model loss functions')\n",
+ "plt.legend(['train', 'test'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "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"
+ }
+ ],
+ "source": [
+ "plt.plot(He5[\"accuracy\"])\n",
+ "plt.plot(He5[\"val_accuracy\"])\n",
+ "plt.plot(He5[\"val_f1\"])\n",
+ "plt.title('model validation results')\n",
+ "plt.legend(['val_accuarcy', 'val_f1'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "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": [],
+ "source": [
+ "# define the initial learning rate, batch size, and number of epochs\n",
+ "INIT_LR = 1e-6\n",
+ "batch_size = 16\n",
+ "NUM_EPOCHS = 200"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt = SGD(lr=INIT_LR, momentum=0.9,\n",
+ " decay=INIT_LR / NUM_EPOCHS)\n",
+ "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
+ " metrics=[\"accuracy\", dice_loss])\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\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ADD OTHER METRICS TO RECORD\n",
+ "mc = tf.keras.callbacks.ModelCheckpoint('best_model_e5.h5', monitor='val_loss', mode='auto', save_freq='epoch', verbose=1)\n",
+ "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=100, min_delta=0.1 )\n",
+ "validation_steps = validationGenerator.n // batch_size\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/200\n",
+ "16/17 [===========================>..] - ETA: 5s - loss: 1.2546 - accuracy: 0.5195 - dice_loss: 0.3196 \n",
+ "Epoch 00001: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 730s 43s/step - loss: 1.2428 - accuracy: 0.5184 - dice_loss: 0.3187 - val_loss: 0.6951 - val_accuracy: 0.4975 - val_dice_loss: 0.3334\n",
+ "Epoch 2/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.3117 - accuracy: 0.4883 - dice_loss: 0.3380\n",
+ "Epoch 00002: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 672s 40s/step - loss: 1.2789 - accuracy: 0.5037 - dice_loss: 0.3321 - val_loss: 0.6974 - val_accuracy: 0.4975 - val_dice_loss: 0.3334\n",
+ "Epoch 3/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.2199 - accuracy: 0.5078 - dice_loss: 0.3219\n",
+ "Epoch 00003: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 596s 35s/step - loss: 1.2445 - accuracy: 0.4926 - dice_loss: 0.3269 - val_loss: 0.6977 - val_accuracy: 0.4975 - val_dice_loss: 0.3333\n",
+ "Epoch 4/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9808 - accuracy: 0.5625 - dice_loss: 0.2967\n",
+ "Epoch 00004: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 641s 38s/step - loss: 0.9945 - accuracy: 0.5588 - dice_loss: 0.2977 - val_loss: 0.6972 - val_accuracy: 0.4975 - val_dice_loss: 0.3333\n",
+ "Epoch 5/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9409 - accuracy: 0.5703 - dice_loss: 0.2942\n",
+ "Epoch 00005: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 660s 39s/step - loss: 0.9370 - accuracy: 0.5662 - dice_loss: 0.2964 - val_loss: 0.6997 - val_accuracy: 0.4975 - val_dice_loss: 0.3333\n",
+ "Epoch 6/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9979 - accuracy: 0.5859 - dice_loss: 0.3005\n",
+ "Epoch 00006: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 625s 37s/step - loss: 0.9845 - accuracy: 0.5919 - dice_loss: 0.2983 - val_loss: 0.7026 - val_accuracy: 0.4975 - val_dice_loss: 0.3332\n",
+ "Epoch 7/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0341 - accuracy: 0.5469 - dice_loss: 0.3037\n",
+ "Epoch 00007: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 482s 28s/step - loss: 1.0317 - accuracy: 0.5441 - dice_loss: 0.3031 - val_loss: 0.7090 - val_accuracy: 0.4975 - val_dice_loss: 0.3331\n",
+ "Epoch 8/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0915 - accuracy: 0.5273 - dice_loss: 0.3050\n",
+ "Epoch 00008: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 482s 28s/step - loss: 1.0837 - accuracy: 0.5294 - dice_loss: 0.3056 - val_loss: 0.7135 - val_accuracy: 0.4975 - val_dice_loss: 0.3330\n",
+ "Epoch 9/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0197 - accuracy: 0.5664 - dice_loss: 0.3013\n",
+ "Epoch 00009: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 483s 28s/step - loss: 1.0265 - accuracy: 0.5588 - dice_loss: 0.3034 - val_loss: 0.7201 - val_accuracy: 0.4975 - val_dice_loss: 0.3328\n",
+ "Epoch 10/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.1181 - accuracy: 0.5613 - dice_loss: 0.3231\n",
+ "Epoch 00010: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 478s 28s/step - loss: 1.0916 - accuracy: 0.5688 - dice_loss: 0.3196 - val_loss: 0.7252 - val_accuracy: 0.4975 - val_dice_loss: 0.3324\n",
+ "Epoch 11/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0334 - accuracy: 0.5742 - dice_loss: 0.2951\n",
+ "Epoch 00011: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 480s 28s/step - loss: 1.0218 - accuracy: 0.5846 - dice_loss: 0.2911 - val_loss: 0.7345 - val_accuracy: 0.4978 - val_dice_loss: 0.3319\n",
+ "Epoch 12/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0457 - accuracy: 0.5508 - dice_loss: 0.3081\n",
+ "Epoch 00012: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 541s 32s/step - loss: 1.0614 - accuracy: 0.5551 - dice_loss: 0.3080 - val_loss: 0.7478 - val_accuracy: 0.4978 - val_dice_loss: 0.3318\n",
+ "Epoch 13/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9633 - accuracy: 0.5703 - dice_loss: 0.2824\n",
+ "Epoch 00013: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 476s 28s/step - loss: 0.9496 - accuracy: 0.5699 - dice_loss: 0.2815 - val_loss: 0.7638 - val_accuracy: 0.4978 - val_dice_loss: 0.3318\n",
+ "Epoch 14/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0238 - accuracy: 0.5586 - dice_loss: 0.3092\n",
+ "Epoch 00014: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 673s 40s/step - loss: 1.0293 - accuracy: 0.5588 - dice_loss: 0.3093 - val_loss: 0.7752 - val_accuracy: 0.4978 - val_dice_loss: 0.3312\n",
+ "Epoch 15/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9847 - accuracy: 0.6133 - dice_loss: 0.2747\n",
+ "Epoch 00015: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 539s 32s/step - loss: 1.0199 - accuracy: 0.6103 - dice_loss: 0.2783 - val_loss: 0.7831 - val_accuracy: 0.4978 - val_dice_loss: 0.3300\n",
+ "Epoch 16/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0432 - accuracy: 0.5664 - dice_loss: 0.3061\n",
+ "Epoch 00016: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 582s 34s/step - loss: 1.0184 - accuracy: 0.5735 - dice_loss: 0.3036 - val_loss: 0.8099 - val_accuracy: 0.4978 - val_dice_loss: 0.3295\n",
+ "Epoch 17/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 1.0536 - accuracy: 0.5625 - dice_loss: 0.3028\n",
+ "Epoch 00017: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 582s 34s/step - loss: 1.0481 - accuracy: 0.5688 - dice_loss: 0.3012 - val_loss: 0.8307 - val_accuracy: 0.4978 - val_dice_loss: 0.3287\n",
+ "Epoch 18/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9056 - accuracy: 0.6211 - dice_loss: 0.2666\n",
+ "Epoch 00018: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 483s 28s/step - loss: 0.8838 - accuracy: 0.6287 - dice_loss: 0.2635 - val_loss: 0.8630 - val_accuracy: 0.4978 - val_dice_loss: 0.3282\n",
+ "Epoch 19/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.8323 - accuracy: 0.6055 - dice_loss: 0.2629\n",
+ "Epoch 00019: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 576s 34s/step - loss: 0.8375 - accuracy: 0.5993 - dice_loss: 0.2659 - val_loss: 0.8866 - val_accuracy: 0.4978 - val_dice_loss: 0.3272\n",
+ "Epoch 20/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9872 - accuracy: 0.6094 - dice_loss: 0.2775\n",
+ "Epoch 00020: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 575s 34s/step - loss: 0.9764 - accuracy: 0.6176 - dice_loss: 0.2747 - val_loss: 0.8999 - val_accuracy: 0.4978 - val_dice_loss: 0.3255\n",
+ "Epoch 21/200\n",
+ "16/17 [===========================>..] - ETA: 4s - loss: 0.9002 - accuracy: 0.5703 - dice_loss: 0.2902\n",
+ "Epoch 00021: saving model to best_model_e5.h5\n",
+ "17/17 [==============================] - 572s 34s/step - loss: 0.8998 - accuracy: 0.5662 - dice_loss: 0.2915 - val_loss: 0.9009 - val_accuracy: 0.4975 - val_dice_loss: 0.3234\n"
+ ]
+ }
+ ],
+ "source": [
+ "He6 = model.fit_generator(\n",
+ " trainGenerator,\n",
+ " steps_per_epoch=trainGenerator.n // (batch_size*10),\n",
+ " validation_data=validationGenerator,\n",
+ " validation_steps=validationGenerator.n // batch_size,\n",
+ " epochs=NUM_EPOCHS,\n",
+ " callbacks=[mc, early_stopping_callback],\n",
+ " verbose=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.DataFrame.from_dict(He6.history).to_csv(\"e6history.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "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"
+ }
+ ],
+ "source": [
+ "plt.plot(He6.history[\"loss\"])\n",
+ "plt.plot(He6.history[\"val_loss\"])\n",
+ "plt.title('model loss functions')\n",
+ "plt.legend(['train', 'test'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "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"
+ }
+ ],
+ "source": [
+ "plt.plot(He6.history[\"val_accuracy\"])\n",
+ "plt.plot(He6.history[\"dice_loss\"])\n",
+ "plt.title('model validation results')\n",
+ "plt.legend(['val_accuarcy', 'DICE'], loc='upper left')\n",
+ "plt.ylabel('loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from keras import backend as K\n",
+ "\n",
+ "def mcor(y_true, y_pred):\n",
+ " #matthews_correlation\n",
+ " y_pred_pos = K.round(K.clip(y_pred, 0, 1))\n",
+ " y_pred_neg = 1 - y_pred_pos\n",
+ " y_pos = K.round(K.clip(y_true, 0, 1))\n",
+ " y_neg = 1 - y_pos\n",
+ " tp = K.sum(y_pos * y_pred_pos)\n",
+ " tn = K.sum(y_neg * y_pred_neg)\n",
+ " fp = K.sum(y_neg * y_pred_pos)\n",
+ " fn = K.sum(y_pos * y_pred_neg)\n",
+ " numerator = (tp * tn - fp * fn)\n",
+ " denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n",
+ " return numerator / (denominator + K.epsilon())\n",
+ "\n",
+ "\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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "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
+}