.gitlab-ci.yml

-image: docker:git
-before_script:
-  - docker login -u gitlab-ci-token -p $CI_BUILD_TOKEN gitlab-registry.nautilus.optiputer.net
-
+image: gcr.io/kaniko-project/executor:debug-v0.16.0
+   
 stages:
   - build-and-push
-
+   
 build-and-push-job:
   stage: build-and-push
-  tags:
-    - build-as-docker
+  except:
+    changes:
+      - "**/*.yaml"
+      - "README.md"
+      - "screenshots/*.PNG"
   script:
-    - docker build -t gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}:${CI_COMMIT_SHA:0:8} .
-    - docker tag gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}:${CI_COMMIT_SHA:0:8} gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}:latest
-    - docker push gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}
+    - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
+    - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:${CI_COMMIT_SHA:0:8} --destination $CI_REGISTRY_IMAGE:latest
\ No newline at end of file

ClassificationExample.ipynb

+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf; print(tf.__version__)
+```
+
+%% Output
+
+    2.0.0
+
+%% Cell type:code id: tags:
+
+``` python
+import keras
+```
+
+%% Output
+
+    Using TensorFlow backend.
+
+%% Cell type:code id: tags:
+
+``` python
+tf.test.gpu_device_name()
+```
+
+%% Output
+
+    '/device:GPU:0'
+
+%% Cell type:code id: tags:
+
+``` python
+tf.test.is_built_with_cuda()
+tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)
+```
+
+%% Output
+
+    True
+
+%% Cell type:markdown id: tags:
+
+#### GPU Hickups
+
+%% Cell type:code id: tags:
+
+``` python
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+# physical_devices = tf.config.experimental.list_physical_device
+
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+assert tf.config.experimental.get_memory_growth(physical_devices[0])
+```
+
+%% Cell type:markdown id: tags:
+
+#### GPU-Tests
+
+%% Cell type:code id: tags:
+
+``` python
+from keras import backend as K
+K.tensorflow_backend._get_available_gpus()
+```
+
+%% Output
+
+    ['/job:localhost/replica:0/task:0/device:GPU:0']
+
+%% Cell type:code id: tags:
+
+``` python
+from tensorflow.python.client import device_lib
+
+print(device_lib.list_local_devices())
+```
+
+%% Output
+
+    [name: "/device:CPU:0"
+    device_type: "CPU"
+    memory_limit: 268435456
+    locality {
+    }
+    incarnation: 1235784618601914649
+    , name: "/device:XLA_CPU:0"
+    device_type: "XLA_CPU"
+    memory_limit: 17179869184
+    locality {
+    }
+    incarnation: 15452097616079958181
+    physical_device_desc: "device: XLA_CPU device"
+    , name: "/device:XLA_GPU:0"
+    device_type: "XLA_GPU"
+    memory_limit: 17179869184
+    locality {
+    }
+    incarnation: 12893943707023111449
+    physical_device_desc: "device: XLA_GPU device"
+    , name: "/device:GPU:0"
+    device_type: "GPU"
+    memory_limit: 29032448
+    locality {
+      bus_id: 1
+      links {
+      }
+    }
+    incarnation: 4085920435716643793
+    physical_device_desc: "device: 0, name: GeForce GTX 1080, pci bus id: 0000:09:00.0, compute capability: 6.1"
+    ]
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### Detection CNN
+
+%% Cell type:code id: tags:
+
+``` python
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import SeparableConv2D
+from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import Flatten
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import Dense
+```
+
+%% Cell type:code id: tags:
+
+``` python
+class FireDetectionNet:
+    @staticmethod
+    def build(width, height, depth, classes):
+        # initialize the model along with the input shape to be
+        # "channels last" and the channels dimension itself
+        model = Sequential()
+        inputShape = (height, width, depth)
+        chanDim = -1
+
+        model.add(SeparableConv2D(16, (7, 7), padding="same",
+                                  input_shape=inputShape))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+
+        model.add(SeparableConv2D(32, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+
+        model.add(SeparableConv2D(64, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(SeparableConv2D(64, (3, 3), padding="same"))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization(axis=chanDim))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+
+        model.add(Flatten())
+        model.add(Dense(128))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization())
+        model.add(Dropout(0.5))
+
+        # second set of FC => RELU layers
+        model.add(Dense(128))
+        model.add(Activation("relu"))
+        model.add(BatchNormalization())
+        model.add(Dropout(0.5))
+
+        # softmax classifier
+        model.add(Dense(classes))
+        model.add(Activation("softmax"))
+
+        # return the constructed network architecture
+        return model
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### Learning Rate Finder
+
+%% Cell type:code id: tags:
+
+``` python
+from tensorflow.keras.callbacks import LambdaCallback
+from tensorflow.keras import backend as K
+import matplotlib.pyplot as plt
+import numpy as np
+import tempfile
+import cv2
+```
+
+%% Cell type:code id: tags:
+
+``` python
+class LearningRateFinder:
+    def __init__(self, model, stopFactor=4, beta=0.98):
+        # store the model, stop factor, and beta value (for computing
+        # a smoothed, average loss)
+        self.model = model
+        self.stopFactor = stopFactor
+        self.beta = beta
+
+        # initialize our list of learning rates and losses,
+        # respectively
+        self.lrs = []
+        self.losses = []
+
+        # initialize our learning rate multiplier, average loss, best
+        # loss found thus far, current batch number, and weights file
+        self.lrMult = 1
+        self.avgLoss = 0
+        self.bestLoss = 1e9
+        self.batchNum = 0
+        self.weightsFile = None
+
+    def reset(self):
+        # re-initialize all variables from our constructor
+        self.lrs = []
+        self.losses = []
+        self.lrMult = 1
+        self.avgLoss = 0
+        self.bestLoss = 1e9
+        self.batchNum = 0
+        self.weightsFile = None
+
+    def is_data_iter(self, data):
+        # define the set of class types we will check for
+        iterClasses = ["NumpyArrayIterator", "DirectoryIterator",
+             "DataFrameIterator", "Iterator", "Sequence"]
+
+        # return whether our data is an iterator
+        return data.__class__.__name__ in iterClasses
+
+    def on_batch_end(self, batch, logs):
+        # grab the current learning rate and add log it to the list of
+        # learning rates that we've tried
+        lr = K.get_value(self.model.optimizer.lr)
+        self.lrs.append(lr)
+
+        # grab the loss at the end of this batch, increment the total
+        # number of batches processed, compute the average average
+        # loss, smooth it, and update the losses list with the
+        # smoothed value
+        l = logs["loss"]
+        self.batchNum += 1
+        self.avgLoss = (self.beta * self.avgLoss) + ((1 - self.beta) * l)
+        smooth = self.avgLoss / (1 - (self.beta ** self.batchNum))
+        self.losses.append(smooth)
+
+        # compute the maximum loss stopping factor value
+        stopLoss = self.stopFactor * self.bestLoss
+
+        # check to see whether the loss has grown too large
+        if self.batchNum > 1 and smooth > stopLoss:
+            # stop returning and return from the method
+            self.model.stop_training = True
+            return
+
+        # check to see if the best loss should be updated
+        if self.batchNum == 1 or smooth < self.bestLoss:
+            self.bestLoss = smooth
+
+        # increase the learning rate
+        lr *= self.lrMult
+        K.set_value(self.model.optimizer.lr, lr)
+
+    def find(self, trainData, startLR, endLR, epochs=None,
+        stepsPerEpoch=None, batchSize=32, sampleSize=2048,
+        classWeight=None, verbose=1):
+        # reset our class-specific variables
+        self.reset()
+
+        # determine if we are using a data generator or not
+        useGen = self.is_data_iter(trainData)
+
+        # if we're using a generator and the steps per epoch is not
+        # supplied, raise an error
+        if useGen and stepsPerEpoch is None:
+            msg = "Using generator without supplying stepsPerEpoch"
+            raise Exception(msg)
+
+        # if we're not using a generator then our entire dataset must
+        # already be in memory
+        elif not useGen:
+            # grab the number of samples in the training data and
+            # then derive the number of steps per epoch
+            numSamples = len(trainData[0])
+            stepsPerEpoch = np.ceil(numSamples / float(batchSize))
+
+        # if no number of training epochs are supplied, compute the
+        # training epochs based on a default sample size
+        if epochs is None:
+            epochs = int(np.ceil(sampleSize / float(stepsPerEpoch)))
+
+        # compute the total number of batch updates that will take
+        # place while we are attempting to find a good starting
+        # learning rate
+        numBatchUpdates = epochs * stepsPerEpoch
+
+        # derive the learning rate multiplier based on the ending
+        # learning rate, starting learning rate, and total number of
+        # batch updates
+        self.lrMult = (endLR / startLR) ** (1.0 / numBatchUpdates)
+
+        # create a temporary file path for the model weights and
+        # then save the weights (so we can reset the weights when we
+        # are done)
+        self.weightsFile = tempfile.mkstemp()[1]
+        self.model.save_weights(self.weightsFile)
+
+        # grab the *original* learning rate (so we can reset it
+        # later), and then set the *starting* learning rate
+        origLR = K.get_value(self.model.optimizer.lr)
+        K.set_value(self.model.optimizer.lr, startLR)
+
+        # construct a callback that will be called at the end of each
+        # batch, enabling us to increase our learning rate as training
+        # progresses
+        callback = LambdaCallback(on_batch_end=lambda batch, logs:
+            self.on_batch_end(batch, logs))
+
+        # check to see if we are using a data iterator
+        if useGen:
+            self.model.fit_generator(
+                trainData,
+                steps_per_epoch=stepsPerEpoch,
+                epochs=epochs,
+                class_weight=classWeight,
+                verbose=verbose,
+                callbacks=[callback])
+
+        # otherwise, our entire training data is already in memory
+        else:
+            # train our model using Keras' fit method
+            self.model.fit(
+                trainData[0], trainData[1],
+                batch_size=batchSize,
+                epochs=epochs,
+                class_weight=classWeight,
+                callbacks=[callback],
+                verbose=verbose)
+
+        # restore the original model weights and learning rate
+        self.model.load_weights(self.weightsFile)
+        K.set_value(self.model.optimizer.lr, origLR)
+
+    def plot_loss(self, skipBegin=10, skipEnd=1, title=""):
+        # grab the learning rate and losses values to plot
+        lrs = self.lrs[skipBegin:-skipEnd]
+        losses = self.losses[skipBegin:-skipEnd]
+
+        # plot the learning rate vs. loss
+        plt.plot(lrs, losses)
+        plt.xscale("log")
+        plt.xlabel("Learning Rate (Log Scale)")
+        plt.ylabel("Loss")
+
+        # if the title is not empty, add it to the plot
+        if title != "":
+            plt.title(title)
+```
+
+%% Cell type:markdown id: tags:
+
+### Traning/Test image base and graph outputs
+
+%% Cell type:code id: tags:
+
+``` python
+# import the necessary packages
+import os
+
+# initialize the path to the fire and non-fire dataset directories
+Image_Path = "/userdata/kerasData/images/hpwren.ucsd.edu/HWB/HPWREN-FIgLib"
+# NON_FIRE_PATH = "/userdata/kerasData/HPWREN-data/BEFORE/images"
+
+# initialize the class labels in the dataset
+CLASSES = ["Non-Fire", "Fire"]
+
+# define the size of the training and testing split
+TRAIN_SPLIT = 0.85
+TEST_SPLIT = 0.15
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# define the initial learning rate, batch size, and number of epochs
+INIT_LR = 1e-4
+BATCH_SIZE = 64
+NUM_EPOCHS = 1000
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+# set the path to the serialized model after training
+MODEL_PATH = os.path.sep.join(["output", "fire_detection.model"])
+
+# define the path to the output learning rate finder plot and
+# training history plot
+LRFIND_PLOT_PATH = os.path.sep.join(["/userdata/kerasData",
+                                     "output", "lrfindd_plot.png"])
+TRAINING_PLOT_PATH = os.path.sep.join(["/userdata/kerasData",
+                                       "output", "training_plot.png"])
+
+# define the path to the output directory that will store our final
+# output with labels/annotations along with the number of iamges to
+# sample
+OUTPUT_IMAGE_PATH = os.path.sep.join(["output", "examples"])
+SAMPLE_SIZE = 500
+```
+
+%% Cell type:code id: tags:
+
+``` python
+LRFIND_PLOT_PATH
+```
+
+%% Output
+
+    '/userdata/kerasData/output/lrfindd_plot.png'
+
+%% Cell type:markdown id: tags:
+
+### Loading Data
+
+%% Cell type:code id: tags:
+
+``` python
+import matplotlib
+matplotlib.use("Agg")
+
+# import the necessary packages
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+# from imutils import paths
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import cv2
+import os
+import sys
+import re
+from PIL import Image
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def loadData(pathToFiles):
+    Xtrain = np.load(f"{pathToFiles}trainX.npy")
+    Xtest = np.load(f"{pathToFiles}testX.npy")
+    XValidation = np.load(f"{pathToFiles}validationX.npy")
+
+    Ytrain = np.load(f"{pathToFiles}trainY.npy")
+    Ytest = np.load(f"{pathToFiles}testY.npy")
+    YValidation = np.load(f"{pathToFiles}validationY.npy")
+
+    classWeight = np.load(f"{pathToFiles}classWeight.npy")
+    return Xtrain, Xtest, XValidation, Ytrain, Ytest, YValidation, classWeight
+
+mypath = "/userdata/kerasData/"
+Xtrain, Xtest, Xvalidation, Ytrain, Ytest, Yvalidation, classWeight = loadData(mypath)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# (trainX, testX, trainY, testY) = train_test_split(data, labels,
+#     test_size=0.1, random_state=42)
+
+# trainY[1]
+```
+
+%% Cell type:markdown id: tags:
+
+#### Image Load Tester
+
+%% Cell type:markdown id: tags:
+
+## Start of Model
+
+%% Cell type:code id: tags:
+
+``` python
+model = FireDetectionNet.build(width=128, height=128, depth=3,
+    classes=2)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# serialize model to JSON
+model_json = model.to_json()
+with open("model.json", "w") as json_file:
+    json_file.write(model_json)
+# serialize weights to HDF5
+# model.save_weights("model.h5")
+# print("Saved model to disk")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+!pwd
+```
+
+%% Cell type:code id: tags:
+
+``` python
+opt = SGD(lr=INIT_LR, momentum=0.9,
+    decay=INIT_LR / NUM_EPOCHS)
+model.compile(loss="binary_crossentropy", optimizer=opt,
+    metrics=["accuracy"])
+aug = ImageDataGenerator(
+    rotation_range=30,
+    zoom_range=0.15,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.15,
+    horizontal_flip=True,
+    fill_mode="nearest")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+BATCH_SIZE = 64
+```
+
+%% Cell type:code id: tags:
+
+``` python
+classWeight
+```
+
+%% Cell type:markdown id: tags:
+
+#### Learning Rate Finder
+
+%% Cell type:code id: tags:
+
+``` python
+lrf = LearningRateFinder(model)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+classWeight
+```
+
+%% Output
+
+    array([1.0218488, 1.       ], dtype=float32)
+
+%% Cell type:code id: tags:
+
+``` python
+classWeight
+```
+
+%% Output
+
+    array([1.0218488, 1.       ], dtype=float32)
+
+%% Cell type:code id: tags:
+
+``` python
+lrf.find(
+    aug.flow(Xtrain, Ytrain, batch_size=BATCH_SIZE),
+    1e-10, 1e+1,
+    stepsPerEpoch=np.ceil((Xtrain.shape[0] / float(BATCH_SIZE))),
+    epochs=100,
+    batchSize=BATCH_SIZE, classWeight=classWeight)
+```
+
+%% Output
+
+    Epoch 1/100
+    72/72 [==============================] - 20s 281ms/step - loss: 1.4097 - accuracy: 0.5026
+    Epoch 2/100
+    72/72 [==============================] - 20s 276ms/step - loss: 1.4250 - accuracy: 0.4943
+    Epoch 3/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3768 - accuracy: 0.5013
+    Epoch 4/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3722 - accuracy: 0.5105
+    Epoch 5/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.4293 - accuracy: 0.4871
+    Epoch 6/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3842 - accuracy: 0.5048
+    Epoch 7/100
+    72/72 [==============================] - 20s 276ms/step - loss: 1.3648 - accuracy: 0.5002
+    Epoch 8/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3873 - accuracy: 0.5072
+    Epoch 9/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3943 - accuracy: 0.5072
+    Epoch 10/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.4238 - accuracy: 0.4989
+    Epoch 11/100
+    72/72 [==============================] - 20s 276ms/step - loss: 1.4098 - accuracy: 0.4943
+    Epoch 12/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.4326 - accuracy: 0.4899
+    Epoch 13/100
+    72/72 [==============================] - 20s 275ms/step - loss: 1.4246 - accuracy: 0.4867
+    Epoch 14/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3950 - accuracy: 0.5035
+    Epoch 15/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3861 - accuracy: 0.5028
+    Epoch 16/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.4210 - accuracy: 0.5007
+    Epoch 17/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3840 - accuracy: 0.5022
+    Epoch 18/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.4085 - accuracy: 0.4985
+    Epoch 19/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3753 - accuracy: 0.4952
+    Epoch 20/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3995 - accuracy: 0.5098
+    Epoch 21/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3685 - accuracy: 0.4985
+    Epoch 22/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3683 - accuracy: 0.5026
+    Epoch 23/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.4103 - accuracy: 0.4891
+    Epoch 24/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3732 - accuracy: 0.5114
+    Epoch 25/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.4197 - accuracy: 0.4910
+    Epoch 26/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3837 - accuracy: 0.5103
+    Epoch 27/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.3695 - accuracy: 0.5011
+    Epoch 28/100
+    72/72 [==============================] - 20s 275ms/step - loss: 1.3478 - accuracy: 0.5105
+    Epoch 29/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.3546 - accuracy: 0.5118
+    Epoch 30/100
+    72/72 [==============================] - 19s 271ms/step - loss: 1.3953 - accuracy: 0.4961
+    Epoch 31/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.4179 - accuracy: 0.5050
+    Epoch 32/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.4327 - accuracy: 0.4873
+    Epoch 33/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.4244 - accuracy: 0.4974
+    Epoch 34/100
+    72/72 [==============================] - 20s 276ms/step - loss: 1.3839 - accuracy: 0.4937
+    Epoch 35/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.4101 - accuracy: 0.4923
+    Epoch 36/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3957 - accuracy: 0.5017
+    Epoch 37/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3864 - accuracy: 0.4961
+    Epoch 38/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.4591 - accuracy: 0.4799
+    Epoch 39/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3961 - accuracy: 0.5004
+    Epoch 40/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.3980 - accuracy: 0.5044
+    Epoch 41/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3543 - accuracy: 0.5033
+    Epoch 42/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3675 - accuracy: 0.4958
+    Epoch 43/100
+    72/72 [==============================] - 20s 276ms/step - loss: 1.3606 - accuracy: 0.4967
+    Epoch 44/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3665 - accuracy: 0.4998
+    Epoch 45/100
+    72/72 [==============================] - 20s 276ms/step - loss: 1.3334 - accuracy: 0.5050
+    Epoch 46/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.3550 - accuracy: 0.5004
+    Epoch 47/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.3514 - accuracy: 0.4910
+    Epoch 48/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3047 - accuracy: 0.5171
+    Epoch 49/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.3082 - accuracy: 0.5009
+    Epoch 50/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.2869 - accuracy: 0.4965
+    Epoch 51/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.2972 - accuracy: 0.5011
+    Epoch 52/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.2294 - accuracy: 0.5048
+    Epoch 53/100
+    72/72 [==============================] - 20s 273ms/step - loss: 1.2256 - accuracy: 0.5052
+    Epoch 54/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.1805 - accuracy: 0.5009
+    Epoch 55/100
+    72/72 [==============================] - 20s 275ms/step - loss: 1.1205 - accuracy: 0.5146
+    Epoch 56/100
+    72/72 [==============================] - 20s 271ms/step - loss: 1.1258 - accuracy: 0.5059
+    Epoch 57/100
+    72/72 [==============================] - 19s 271ms/step - loss: 1.0955 - accuracy: 0.5140
+    Epoch 58/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.0621 - accuracy: 0.4972
+    Epoch 59/100
+    72/72 [==============================] - 20s 272ms/step - loss: 1.0330 - accuracy: 0.5050
+    Epoch 60/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.9453 - accuracy: 0.5059
+    Epoch 61/100
+    72/72 [==============================] - 20s 274ms/step - loss: 0.8832 - accuracy: 0.5079
+    Epoch 62/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.8503 - accuracy: 0.5015
+    Epoch 63/100
+    72/72 [==============================] - 19s 270ms/step - loss: 0.7816 - accuracy: 0.5085
+    Epoch 64/100
+    72/72 [==============================] - 20s 274ms/step - loss: 0.7358 - accuracy: 0.5127
+    Epoch 65/100
+    72/72 [==============================] - 20s 274ms/step - loss: 0.7171 - accuracy: 0.5072
+    Epoch 66/100
+    72/72 [==============================] - 20s 274ms/step - loss: 0.7035 - accuracy: 0.5017
+    Epoch 67/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.7017 - accuracy: 0.5179
+    Epoch 68/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.7039 - accuracy: 0.5077
+    Epoch 69/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.7050 - accuracy: 0.5153
+    Epoch 70/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.7104 - accuracy: 0.5136
+    Epoch 71/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.7179 - accuracy: 0.5079
+    Epoch 72/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.7276 - accuracy: 0.5144
+    Epoch 73/100
+    72/72 [==============================] - 20s 271ms/step - loss: 0.7355 - accuracy: 0.5131
+    Epoch 74/100
+    72/72 [==============================] - 20s 271ms/step - loss: 0.7578 - accuracy: 0.5061
+    Epoch 75/100
+    72/72 [==============================] - 20s 275ms/step - loss: 0.7719 - accuracy: 0.5168
+    Epoch 76/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.8077 - accuracy: 0.5028
+    Epoch 77/100
+    72/72 [==============================] - 20s 271ms/step - loss: 0.8474 - accuracy: 0.4932
+    Epoch 78/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.8713 - accuracy: 0.4967
+    Epoch 79/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.9173 - accuracy: 0.4967
+    Epoch 80/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.8814 - accuracy: 0.5013
+    Epoch 81/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.8125 - accuracy: 0.5024
+    Epoch 82/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.7698 - accuracy: 0.5009
+    Epoch 83/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.7215 - accuracy: 0.5074
+    Epoch 84/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.7084 - accuracy: 0.4985
+    Epoch 85/100
+    72/72 [==============================] - 20s 274ms/step - loss: 0.7034 - accuracy: 0.4937
+    Epoch 86/100
+    72/72 [==============================] - 20s 274ms/step - loss: 0.7030 - accuracy: 0.5079
+    Epoch 87/100
+    72/72 [==============================] - 20s 273ms/step - loss: 0.7125 - accuracy: 0.5098
+    Epoch 88/100
+    72/72 [==============================] - 20s 272ms/step - loss: 0.7313 - accuracy: 0.5074
+    Epoch 89/100
+    72/72 [==============================] - 19s 271ms/step - loss: 0.8058 - accuracy: 0.4919
+    Epoch 90/100
+    72/72 [==============================] - 20s 274ms/step - loss: 1.6105 - accuracy: 0.5125
+    Epoch 91/100
+    16/72 [=====>........................] - ETA: 15s - loss: 5.8092 - accuracy: 0.5146
+
+%% Cell type:code id: tags:
+
+``` python
+len(trainY[0])
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    NameError                                 Traceback (most recent call last)
+    <ipython-input-81-db40db286bba> in <module>
+    ----> 1 len(trainY[0])
+
+    NameError: name 'trainY' is not defined
+
+%% Cell type:code id: tags:
+
+``` python
+lrf.plot_loss()
+plt.savefig(LRFIND_PLOT_PATH)
+print("Loss Rate finder complete")
+```
+
+%% Output
+
+    Loss Rate finder complete
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.display import Image
+learningRateGraph = Image(filename=LRFIND_PLOT_PATH)
+learningRateGraph
+```
+
+%% Output
+
+
+    <IPython.core.display.Image object>
+
+%% Cell type:code id: tags:
+
+``` python
+bestIters=[]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# define the initial learning rate, batch size, and number of epochs
+INIT_LR = 1e-5
+BATCH_SIZE = 64
+NUM_EPOCHS = 800
+```
+
+%% Cell type:code id: tags:
+
+``` python
+!pwd
+```
+
+%% Output
+
+    /userdata/kerasData
+
+%% Cell type:markdown id: tags:
+
+### Start of Training
+
+%% Cell type:code id: tags:
+
+``` python
+test = tf.keras.models.load_model("/userdata/kerasData/fire_detection.model")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+aug = ImageDataGenerator(
+    rotation_range=30,
+    zoom_range=0.15,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.15,
+    horizontal_flip=True,
+    fill_mode="nearest")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
+mc = tf.keras.callbacks.ModelCheckpoint('best_model.h5', monitor='val_loss', mode='auto',  save_freq='epoch', verbose=1)
+early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=200)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+type(test)
+```
+
+%% Output
+
+    tensorflow.python.keras.saving.saved_model.load.Sequential
+
+%% Cell type:code id: tags:
+
+``` python
+H = test.fit_generator(
+    aug.flow(Xtrain, Ytrain, batch_size=BATCH_SIZE),
+    validation_data=(Xvalidation, Yvalidation),
+    steps_per_epoch=Xtrain.shape[0] // BATCH_SIZE,
+    epochs=500,
+    class_weight=classWeight,
+    callbacks=[mc, early_stopping_callback],
+    verbose=1)
+```
+
+%% Output
+
+    Epoch 1/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.9049 - accuracy: 0.4964
+    Epoch 00001: saving model to best_model.h5
+    71/71 [==============================] - 22s 307ms/step - loss: 0.9018 - accuracy: 0.4976 - val_loss: 0.7018 - val_accuracy: 0.5528
+    Epoch 2/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7397 - accuracy: 0.5002
+    Epoch 00002: saving model to best_model.h5
+    71/71 [==============================] - 20s 286ms/step - loss: 0.7390 - accuracy: 0.5011 - val_loss: 0.6877 - val_accuracy: 0.5330
+    Epoch 3/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7463 - accuracy: 0.5083
+    Epoch 00003: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.7472 - accuracy: 0.5075 - val_loss: 0.7107 - val_accuracy: 0.4867
+    Epoch 4/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7436 - accuracy: 0.5067
+    Epoch 00004: saving model to best_model.h5
+    71/71 [==============================] - 20s 285ms/step - loss: 0.7434 - accuracy: 0.5067 - val_loss: 0.7039 - val_accuracy: 0.5056
+    Epoch 5/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7471 - accuracy: 0.4856
+    Epoch 00005: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.7485 - accuracy: 0.4849 - val_loss: 0.7203 - val_accuracy: 0.4524
+    Epoch 6/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7458 - accuracy: 0.5047
+    Epoch 00006: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7470 - accuracy: 0.5035 - val_loss: 0.6981 - val_accuracy: 0.5270
+    Epoch 7/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7366 - accuracy: 0.4942
+    Epoch 00007: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.7377 - accuracy: 0.4933 - val_loss: 0.6966 - val_accuracy: 0.5124
+    Epoch 8/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7343 - accuracy: 0.5081
+    Epoch 00008: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7341 - accuracy: 0.5084 - val_loss: 0.6990 - val_accuracy: 0.5099
+    Epoch 9/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7317 - accuracy: 0.5148
+    Epoch 00009: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7315 - accuracy: 0.5153 - val_loss: 0.7031 - val_accuracy: 0.5107
+    Epoch 10/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7402 - accuracy: 0.4993
+    Epoch 00010: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7399 - accuracy: 0.5000 - val_loss: 0.7199 - val_accuracy: 0.4927
+    Epoch 11/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7326 - accuracy: 0.4917
+    Epoch 00011: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.7332 - accuracy: 0.4907 - val_loss: 0.7054 - val_accuracy: 0.5039
+    Epoch 12/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7267 - accuracy: 0.5045
+    Epoch 00012: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.7273 - accuracy: 0.5033 - val_loss: 0.6907 - val_accuracy: 0.5356
+    Epoch 13/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7269 - accuracy: 0.5067
+    Epoch 00013: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.7269 - accuracy: 0.5062 - val_loss: 0.6981 - val_accuracy: 0.5030
+    Epoch 14/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7247 - accuracy: 0.5145
+    Epoch 00014: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.7251 - accuracy: 0.5139 - val_loss: 0.6959 - val_accuracy: 0.5116
+    Epoch 15/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7291 - accuracy: 0.4964
+    Epoch 00015: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7292 - accuracy: 0.4971 - val_loss: 0.7001 - val_accuracy: 0.5210
+    Epoch 16/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7299 - accuracy: 0.5144
+    Epoch 00016: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7300 - accuracy: 0.5137 - val_loss: 0.7094 - val_accuracy: 0.4712
+    Epoch 17/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7293 - accuracy: 0.4964
+    Epoch 00017: saving model to best_model.h5
+    71/71 [==============================] - 20s 284ms/step - loss: 0.7296 - accuracy: 0.4967 - val_loss: 0.6890 - val_accuracy: 0.5494
+    Epoch 18/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7291 - accuracy: 0.5073
+    Epoch 00018: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7286 - accuracy: 0.5074 - val_loss: 0.7020 - val_accuracy: 0.4807
+    Epoch 19/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7190 - accuracy: 0.5193
+    Epoch 00019: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.7186 - accuracy: 0.5197 - val_loss: 0.7086 - val_accuracy: 0.5056
+    Epoch 20/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7276 - accuracy: 0.5098
+    Epoch 00020: saving model to best_model.h5
+    71/71 [==============================] - 20s 286ms/step - loss: 0.7269 - accuracy: 0.5103 - val_loss: 0.6913 - val_accuracy: 0.5056
+    Epoch 21/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7284 - accuracy: 0.4932
+    Epoch 00021: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.7279 - accuracy: 0.4940 - val_loss: 0.6827 - val_accuracy: 0.5708
+    Epoch 22/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7210 - accuracy: 0.5060
+    Epoch 00022: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.7212 - accuracy: 0.5055 - val_loss: 0.7139 - val_accuracy: 0.5296
+    Epoch 23/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7271 - accuracy: 0.5056
+    Epoch 00023: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.7268 - accuracy: 0.5060 - val_loss: 0.7046 - val_accuracy: 0.4635
+    Epoch 24/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7233 - accuracy: 0.5036
+    Epoch 00024: saving model to best_model.h5
+    71/71 [==============================] - 20s 275ms/step - loss: 0.7228 - accuracy: 0.5036 - val_loss: 0.6964 - val_accuracy: 0.5519
+    Epoch 25/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7148 - accuracy: 0.5200
+    Epoch 00025: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7145 - accuracy: 0.5200 - val_loss: 0.6938 - val_accuracy: 0.5605
+    Epoch 26/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7154 - accuracy: 0.5205
+    Epoch 00026: saving model to best_model.h5
+    71/71 [==============================] - 20s 286ms/step - loss: 0.7149 - accuracy: 0.5224 - val_loss: 0.6897 - val_accuracy: 0.5468
+    Epoch 27/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7211 - accuracy: 0.5079
+    Epoch 00027: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7212 - accuracy: 0.5082 - val_loss: 0.6863 - val_accuracy: 0.5373
+    Epoch 28/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7172 - accuracy: 0.5067
+    Epoch 00028: saving model to best_model.h5
+    71/71 [==============================] - 20s 284ms/step - loss: 0.7171 - accuracy: 0.5066 - val_loss: 0.6926 - val_accuracy: 0.5073
+    Epoch 29/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7193 - accuracy: 0.5025
+    Epoch 00029: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7194 - accuracy: 0.5022 - val_loss: 0.7015 - val_accuracy: 0.4798
+    Epoch 30/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7152 - accuracy: 0.5211
+    Epoch 00030: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7153 - accuracy: 0.5217 - val_loss: 0.7053 - val_accuracy: 0.4773
+    Epoch 31/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7154 - accuracy: 0.5171
+    Epoch 00031: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.7151 - accuracy: 0.5177 - val_loss: 0.6944 - val_accuracy: 0.4927
+    Epoch 32/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7130 - accuracy: 0.5163
+    Epoch 00032: saving model to best_model.h5
+    71/71 [==============================] - 20s 284ms/step - loss: 0.7135 - accuracy: 0.5152 - val_loss: 0.6944 - val_accuracy: 0.5554
+    Epoch 33/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7181 - accuracy: 0.4917
+    Epoch 00033: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.7184 - accuracy: 0.4916 - val_loss: 0.6850 - val_accuracy: 0.5408
+    Epoch 34/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7151 - accuracy: 0.5040
+    Epoch 00034: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.7145 - accuracy: 0.5058 - val_loss: 0.7050 - val_accuracy: 0.4755
+    Epoch 35/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7131 - accuracy: 0.5086
+    Epoch 00035: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.7128 - accuracy: 0.5092 - val_loss: 0.6922 - val_accuracy: 0.5030
+    Epoch 36/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7140 - accuracy: 0.5107
+    Epoch 00036: saving model to best_model.h5
+    71/71 [==============================] - 21s 298ms/step - loss: 0.7137 - accuracy: 0.5108 - val_loss: 0.6946 - val_accuracy: 0.5245
+    Epoch 37/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7140 - accuracy: 0.5259
+    Epoch 00037: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7137 - accuracy: 0.5264 - val_loss: 0.6917 - val_accuracy: 0.5262
+    Epoch 38/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7075 - accuracy: 0.5202
+    Epoch 00038: saving model to best_model.h5
+    71/71 [==============================] - 19s 274ms/step - loss: 0.7080 - accuracy: 0.5192 - val_loss: 0.7040 - val_accuracy: 0.4661
+    Epoch 39/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7138 - accuracy: 0.5085
+    Epoch 00039: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.7142 - accuracy: 0.5090 - val_loss: 0.6962 - val_accuracy: 0.4970
+    Epoch 40/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7138 - accuracy: 0.5128
+    Epoch 00040: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.7138 - accuracy: 0.5126 - val_loss: 0.6979 - val_accuracy: 0.5107
+    Epoch 41/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7044 - accuracy: 0.5308
+    Epoch 00041: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.7050 - accuracy: 0.5310 - val_loss: 0.6855 - val_accuracy: 0.5339
+    Epoch 42/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7152 - accuracy: 0.5014
+    Epoch 00042: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.7151 - accuracy: 0.5022 - val_loss: 0.7080 - val_accuracy: 0.4172
+    Epoch 43/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7088 - accuracy: 0.5187
+    Epoch 00043: saving model to best_model.h5
+    71/71 [==============================] - 20s 284ms/step - loss: 0.7094 - accuracy: 0.5191 - val_loss: 0.7010 - val_accuracy: 0.5348
+    Epoch 44/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7049 - accuracy: 0.5219
+    Epoch 00044: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7050 - accuracy: 0.5222 - val_loss: 0.7347 - val_accuracy: 0.5030
+    Epoch 45/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7079 - accuracy: 0.5259
+    Epoch 00045: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.7080 - accuracy: 0.5259 - val_loss: 0.7011 - val_accuracy: 0.5004
+    Epoch 46/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7090 - accuracy: 0.5274
+    Epoch 00046: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.7089 - accuracy: 0.5264 - val_loss: 0.6948 - val_accuracy: 0.5064
+    Epoch 47/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6983 - accuracy: 0.5382
+    Epoch 00047: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6990 - accuracy: 0.5373 - val_loss: 0.7067 - val_accuracy: 0.5210
+    Epoch 48/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6936 - accuracy: 0.5423
+    Epoch 00048: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6935 - accuracy: 0.5421 - val_loss: 0.7464 - val_accuracy: 0.5305
+    Epoch 49/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.7017 - accuracy: 0.5353
+    Epoch 00049: saving model to best_model.h5
+    71/71 [==============================] - 20s 286ms/step - loss: 0.7014 - accuracy: 0.5350 - val_loss: 0.7303 - val_accuracy: 0.5416
+    Epoch 50/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6965 - accuracy: 0.5331
+    Epoch 00050: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6968 - accuracy: 0.5333 - val_loss: 0.7006 - val_accuracy: 0.5545
+    Epoch 51/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6933 - accuracy: 0.5583
+    Epoch 00051: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6942 - accuracy: 0.5572 - val_loss: 0.7248 - val_accuracy: 0.4515
+    Epoch 52/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6979 - accuracy: 0.5339
+    Epoch 00052: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6980 - accuracy: 0.5337 - val_loss: 0.6918 - val_accuracy: 0.5176
+    Epoch 53/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6912 - accuracy: 0.5475
+    Epoch 00053: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6913 - accuracy: 0.5481 - val_loss: 0.7392 - val_accuracy: 0.5082
+    Epoch 54/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6928 - accuracy: 0.5495
+    Epoch 00054: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6933 - accuracy: 0.5490 - val_loss: 0.6895 - val_accuracy: 0.6052
+    Epoch 55/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6930 - accuracy: 0.5449
+    Epoch 00055: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6929 - accuracy: 0.5454 - val_loss: 0.6841 - val_accuracy: 0.5717
+    Epoch 56/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6883 - accuracy: 0.5629
+    Epoch 00056: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6877 - accuracy: 0.5638 - val_loss: 0.7488 - val_accuracy: 0.5356
+    Epoch 57/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6849 - accuracy: 0.5549
+    Epoch 00057: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6850 - accuracy: 0.5541 - val_loss: 0.7146 - val_accuracy: 0.5210
+    Epoch 58/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6855 - accuracy: 0.5616
+    Epoch 00058: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6866 - accuracy: 0.5610 - val_loss: 0.7558 - val_accuracy: 0.5236
+    Epoch 59/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6891 - accuracy: 0.5504
+    Epoch 00059: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6894 - accuracy: 0.5497 - val_loss: 0.7100 - val_accuracy: 0.4979
+    Epoch 60/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6830 - accuracy: 0.5616
+    Epoch 00060: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6832 - accuracy: 0.5619 - val_loss: 0.7235 - val_accuracy: 0.5288
+    Epoch 61/500
+
+    70/71 [============================>.] - ETA: 0s - loss: 0.6820 - accuracy: 0.5556
+    Epoch 00061: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6822 - accuracy: 0.5565 - val_loss: 0.7268 - val_accuracy: 0.5511
+    Epoch 62/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6820 - accuracy: 0.5646
+    Epoch 00062: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6812 - accuracy: 0.5661 - val_loss: 0.7622 - val_accuracy: 0.5536
+    Epoch 63/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6800 - accuracy: 0.5711
+    Epoch 00063: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6798 - accuracy: 0.5714 - val_loss: 0.7287 - val_accuracy: 0.5408
+    Epoch 64/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6743 - accuracy: 0.5753
+    Epoch 00064: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6733 - accuracy: 0.5763 - val_loss: 0.8551 - val_accuracy: 0.5030
+    Epoch 65/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6777 - accuracy: 0.5758
+    Epoch 00065: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6771 - accuracy: 0.5765 - val_loss: 0.7220 - val_accuracy: 0.5519
+    Epoch 66/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6682 - accuracy: 0.5834
+    Epoch 00066: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6679 - accuracy: 0.5838 - val_loss: 0.7122 - val_accuracy: 0.5356
+    Epoch 67/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6766 - accuracy: 0.5711
+    Epoch 00067: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6761 - accuracy: 0.5721 - val_loss: 0.7637 - val_accuracy: 0.5056
+    Epoch 68/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6715 - accuracy: 0.5756
+    Epoch 00068: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6712 - accuracy: 0.5754 - val_loss: 0.7585 - val_accuracy: 0.5399
+    Epoch 69/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6758 - accuracy: 0.5657
+    Epoch 00069: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6763 - accuracy: 0.5645 - val_loss: 0.7701 - val_accuracy: 0.4652
+    Epoch 70/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6768 - accuracy: 0.5688
+    Epoch 00070: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6761 - accuracy: 0.5694 - val_loss: 0.7385 - val_accuracy: 0.5236
+    Epoch 71/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6720 - accuracy: 0.5902
+    Epoch 00071: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6724 - accuracy: 0.5885 - val_loss: 0.7473 - val_accuracy: 0.5468
+    Epoch 72/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6691 - accuracy: 0.5697
+    Epoch 00072: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6681 - accuracy: 0.5707 - val_loss: 0.7721 - val_accuracy: 0.5468
+    Epoch 73/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6676 - accuracy: 0.5922
+    Epoch 00073: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6662 - accuracy: 0.5929 - val_loss: 0.7905 - val_accuracy: 0.5691
+    Epoch 74/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6662 - accuracy: 0.5882
+    Epoch 00074: saving model to best_model.h5
+    71/71 [==============================] - 20s 285ms/step - loss: 0.6653 - accuracy: 0.5891 - val_loss: 0.7583 - val_accuracy: 0.5210
+    Epoch 75/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6681 - accuracy: 0.5804
+    Epoch 00075: saving model to best_model.h5
+    71/71 [==============================] - 21s 294ms/step - loss: 0.6679 - accuracy: 0.5807 - val_loss: 0.7354 - val_accuracy: 0.5614
+    Epoch 76/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6611 - accuracy: 0.5825
+    Epoch 00076: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6611 - accuracy: 0.5834 - val_loss: 0.7537 - val_accuracy: 0.4635
+    Epoch 77/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6629 - accuracy: 0.5922
+    Epoch 00077: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6626 - accuracy: 0.5918 - val_loss: 0.7933 - val_accuracy: 0.5133
+    Epoch 78/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6547 - accuracy: 0.5994
+    Epoch 00078: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6545 - accuracy: 0.5998 - val_loss: 0.8105 - val_accuracy: 0.5167
+    Epoch 79/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6564 - accuracy: 0.5951
+    Epoch 00079: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.6563 - accuracy: 0.5958 - val_loss: 0.8270 - val_accuracy: 0.5502
+    Epoch 80/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6549 - accuracy: 0.6017
+    Epoch 00080: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6550 - accuracy: 0.6018 - val_loss: 0.7971 - val_accuracy: 0.5210
+    Epoch 81/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6582 - accuracy: 0.5906
+    Epoch 00081: saving model to best_model.h5
+    71/71 [==============================] - 20s 285ms/step - loss: 0.6590 - accuracy: 0.5889 - val_loss: 0.7583 - val_accuracy: 0.4953
+    Epoch 82/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6612 - accuracy: 0.5877
+    Epoch 00082: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6619 - accuracy: 0.5871 - val_loss: 0.7608 - val_accuracy: 0.5210
+    Epoch 83/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6522 - accuracy: 0.6010
+    Epoch 00083: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6517 - accuracy: 0.6007 - val_loss: 0.7941 - val_accuracy: 0.5227
+    Epoch 84/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6515 - accuracy: 0.6032
+    Epoch 00084: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6510 - accuracy: 0.6033 - val_loss: 0.7679 - val_accuracy: 0.5107
+    Epoch 85/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6527 - accuracy: 0.6001
+    Epoch 00085: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6531 - accuracy: 0.6000 - val_loss: 0.9893 - val_accuracy: 0.5588
+    Epoch 86/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6622 - accuracy: 0.5911
+    Epoch 00086: saving model to best_model.h5
+    71/71 [==============================] - 21s 289ms/step - loss: 0.6620 - accuracy: 0.5913 - val_loss: 0.7662 - val_accuracy: 0.5039
+    Epoch 87/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6543 - accuracy: 0.6034
+    Epoch 00087: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.6540 - accuracy: 0.6039 - val_loss: 0.8634 - val_accuracy: 0.5193
+    Epoch 88/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6541 - accuracy: 0.5994
+    Epoch 00088: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6544 - accuracy: 0.5996 - val_loss: 0.8192 - val_accuracy: 0.4987
+    Epoch 89/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6486 - accuracy: 0.6059
+    Epoch 00089: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6482 - accuracy: 0.6062 - val_loss: 0.7929 - val_accuracy: 0.5528
+    Epoch 90/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6480 - accuracy: 0.6073
+    Epoch 00090: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6492 - accuracy: 0.6069 - val_loss: 0.7754 - val_accuracy: 0.5657
+    Epoch 91/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6444 - accuracy: 0.6131
+    Epoch 00091: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6448 - accuracy: 0.6122 - val_loss: 0.9606 - val_accuracy: 0.5064
+    Epoch 92/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6452 - accuracy: 0.6091
+    Epoch 00092: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6453 - accuracy: 0.6091 - val_loss: 0.7290 - val_accuracy: 0.4910
+    Epoch 93/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6487 - accuracy: 0.6104
+    Epoch 00093: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6484 - accuracy: 0.6098 - val_loss: 0.7395 - val_accuracy: 0.5399
+    Epoch 94/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6467 - accuracy: 0.6000
+    Epoch 00094: saving model to best_model.h5
+    71/71 [==============================] - 21s 291ms/step - loss: 0.6455 - accuracy: 0.6012 - val_loss: 0.7960 - val_accuracy: 0.4635
+    Epoch 95/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6395 - accuracy: 0.6143
+    Epoch 00095: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6399 - accuracy: 0.6133 - val_loss: 0.7302 - val_accuracy: 0.5451
+    Epoch 96/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6450 - accuracy: 0.6091
+    Epoch 00096: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6454 - accuracy: 0.6091 - val_loss: 0.8491 - val_accuracy: 0.5519
+    Epoch 97/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6340 - accuracy: 0.6142
+    Epoch 00097: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6349 - accuracy: 0.6137 - val_loss: 1.0346 - val_accuracy: 0.5030
+    Epoch 98/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6436 - accuracy: 0.6102
+    Epoch 00098: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6431 - accuracy: 0.6113 - val_loss: 1.1868 - val_accuracy: 0.4979
+    Epoch 99/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6326 - accuracy: 0.6215
+    Epoch 00099: saving model to best_model.h5
+    71/71 [==============================] - 20s 285ms/step - loss: 0.6322 - accuracy: 0.6220 - val_loss: 1.0831 - val_accuracy: 0.5124
+    Epoch 100/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6449 - accuracy: 0.6038
+    Epoch 00100: saving model to best_model.h5
+    71/71 [==============================] - 20s 288ms/step - loss: 0.6444 - accuracy: 0.6041 - val_loss: 0.7710 - val_accuracy: 0.5845
+    Epoch 101/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6339 - accuracy: 0.6188
+    Epoch 00101: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6341 - accuracy: 0.6180 - val_loss: 0.8587 - val_accuracy: 0.5004
+    Epoch 102/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6318 - accuracy: 0.6247
+    Epoch 00102: saving model to best_model.h5
+    71/71 [==============================] - 22s 311ms/step - loss: 0.6306 - accuracy: 0.6249 - val_loss: 1.1032 - val_accuracy: 0.4824
+    Epoch 103/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6425 - accuracy: 0.6138
+    Epoch 00103: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6422 - accuracy: 0.6138 - val_loss: 0.9473 - val_accuracy: 0.4918
+    Epoch 104/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6285 - accuracy: 0.6308
+    Epoch 00104: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.6278 - accuracy: 0.6320 - val_loss: 0.9692 - val_accuracy: 0.5339
+    Epoch 105/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6139 - accuracy: 0.6440
+    Epoch 00105: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6143 - accuracy: 0.6437 - val_loss: 0.9732 - val_accuracy: 0.4893
+    Epoch 106/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6280 - accuracy: 0.6240
+    Epoch 00106: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6284 - accuracy: 0.6235 - val_loss: 1.0815 - val_accuracy: 0.5116
+    Epoch 107/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6303 - accuracy: 0.6300
+    Epoch 00107: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6297 - accuracy: 0.6310 - val_loss: 0.9040 - val_accuracy: 0.4781
+    Epoch 108/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6253 - accuracy: 0.6262
+    Epoch 00108: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6252 - accuracy: 0.6268 - val_loss: 0.9259 - val_accuracy: 0.4953
+    Epoch 109/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6202 - accuracy: 0.6413
+    Epoch 00109: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6206 - accuracy: 0.6412 - val_loss: 1.0740 - val_accuracy: 0.4824
+    Epoch 110/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6207 - accuracy: 0.6328
+    Epoch 00110: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6209 - accuracy: 0.6323 - val_loss: 0.8322 - val_accuracy: 0.4961
+    Epoch 111/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6135 - accuracy: 0.6426
+    Epoch 00111: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6139 - accuracy: 0.6421 - val_loss: 0.9185 - val_accuracy: 0.5245
+    Epoch 112/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6242 - accuracy: 0.6365
+    Epoch 00112: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.6237 - accuracy: 0.6373 - val_loss: 0.7987 - val_accuracy: 0.4378
+    Epoch 113/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6270 - accuracy: 0.6291
+    Epoch 00113: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.6283 - accuracy: 0.6268 - val_loss: 0.9852 - val_accuracy: 0.4996
+    Epoch 114/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6302 - accuracy: 0.6281
+    Epoch 00114: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.6300 - accuracy: 0.6294 - val_loss: 1.0164 - val_accuracy: 0.4987
+    Epoch 115/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6154 - accuracy: 0.6339
+    Epoch 00115: saving model to best_model.h5
+    71/71 [==============================] - 20s 284ms/step - loss: 0.6151 - accuracy: 0.6331 - val_loss: 1.0716 - val_accuracy: 0.4258
+    Epoch 116/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6219 - accuracy: 0.6386
+    Epoch 00116: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6217 - accuracy: 0.6384 - val_loss: 1.0561 - val_accuracy: 0.4558
+    Epoch 117/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6141 - accuracy: 0.6453
+    Epoch 00117: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.6138 - accuracy: 0.6454 - val_loss: 0.7311 - val_accuracy: 0.5485
+    Epoch 118/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6155 - accuracy: 0.6328
+    Epoch 00118: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.6168 - accuracy: 0.6327 - val_loss: 1.0044 - val_accuracy: 0.4961
+    Epoch 119/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6166 - accuracy: 0.6394
+    Epoch 00119: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.6151 - accuracy: 0.6405 - val_loss: 0.9369 - val_accuracy: 0.5090
+    Epoch 120/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6082 - accuracy: 0.6511
+    Epoch 00120: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6088 - accuracy: 0.6503 - val_loss: 0.8189 - val_accuracy: 0.5725
+    Epoch 121/500
+
+    70/71 [============================>.] - ETA: 0s - loss: 0.5980 - accuracy: 0.6613
+    Epoch 00121: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5979 - accuracy: 0.6619 - val_loss: 0.9468 - val_accuracy: 0.4309
+    Epoch 122/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6124 - accuracy: 0.6433
+    Epoch 00122: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.6123 - accuracy: 0.6426 - val_loss: 0.7896 - val_accuracy: 0.4815
+    Epoch 123/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6122 - accuracy: 0.6453
+    Epoch 00123: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.6118 - accuracy: 0.6461 - val_loss: 1.0108 - val_accuracy: 0.4575
+    Epoch 124/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5984 - accuracy: 0.6507
+    Epoch 00124: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5983 - accuracy: 0.6510 - val_loss: 0.9160 - val_accuracy: 0.5545
+    Epoch 125/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5972 - accuracy: 0.6562
+    Epoch 00125: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5974 - accuracy: 0.6564 - val_loss: 0.9534 - val_accuracy: 0.4197
+    Epoch 126/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6018 - accuracy: 0.6437
+    Epoch 00126: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.6021 - accuracy: 0.6443 - val_loss: 1.0389 - val_accuracy: 0.4730
+    Epoch 127/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5956 - accuracy: 0.6585
+    Epoch 00127: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5947 - accuracy: 0.6589 - val_loss: 1.0275 - val_accuracy: 0.4979
+    Epoch 128/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5915 - accuracy: 0.6615
+    Epoch 00128: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5922 - accuracy: 0.6610 - val_loss: 1.0177 - val_accuracy: 0.5004
+    Epoch 129/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5872 - accuracy: 0.6653
+    Epoch 00129: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5874 - accuracy: 0.6656 - val_loss: 1.3468 - val_accuracy: 0.5167
+    Epoch 130/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5998 - accuracy: 0.6673
+    Epoch 00130: saving model to best_model.h5
+    71/71 [==============================] - 21s 293ms/step - loss: 0.5986 - accuracy: 0.6681 - val_loss: 1.0924 - val_accuracy: 0.5167
+    Epoch 131/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5924 - accuracy: 0.6660
+    Epoch 00131: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5921 - accuracy: 0.6654 - val_loss: 1.0798 - val_accuracy: 0.5107
+    Epoch 132/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.6046 - accuracy: 0.6597
+    Epoch 00132: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.6046 - accuracy: 0.6599 - val_loss: 0.9677 - val_accuracy: 0.5124
+    Epoch 133/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5986 - accuracy: 0.6599
+    Epoch 00133: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5980 - accuracy: 0.6608 - val_loss: 1.1492 - val_accuracy: 0.4094
+    Epoch 134/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5925 - accuracy: 0.6777
+    Epoch 00134: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5920 - accuracy: 0.6780 - val_loss: 0.8172 - val_accuracy: 0.5837
+    Epoch 135/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5833 - accuracy: 0.6698
+    Epoch 00135: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5837 - accuracy: 0.6687 - val_loss: 1.1924 - val_accuracy: 0.4918
+    Epoch 136/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5902 - accuracy: 0.6763
+    Epoch 00136: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5890 - accuracy: 0.6778 - val_loss: 1.0252 - val_accuracy: 0.4609
+    Epoch 137/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5714 - accuracy: 0.6766
+    Epoch 00137: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5711 - accuracy: 0.6767 - val_loss: 1.1408 - val_accuracy: 0.4918
+    Epoch 138/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5903 - accuracy: 0.6676
+    Epoch 00138: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.5895 - accuracy: 0.6672 - val_loss: 1.4424 - val_accuracy: 0.5090
+    Epoch 139/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5844 - accuracy: 0.6671
+    Epoch 00139: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5838 - accuracy: 0.6676 - val_loss: 0.9815 - val_accuracy: 0.5631
+    Epoch 140/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5835 - accuracy: 0.6732
+    Epoch 00140: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5838 - accuracy: 0.6732 - val_loss: 1.1517 - val_accuracy: 0.5262
+    Epoch 141/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5945 - accuracy: 0.6536
+    Epoch 00141: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5941 - accuracy: 0.6541 - val_loss: 1.1712 - val_accuracy: 0.5245
+    Epoch 142/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5812 - accuracy: 0.6682
+    Epoch 00142: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5813 - accuracy: 0.6672 - val_loss: 0.9647 - val_accuracy: 0.5288
+    Epoch 143/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5824 - accuracy: 0.6730
+    Epoch 00143: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5812 - accuracy: 0.6747 - val_loss: 0.8276 - val_accuracy: 0.5854
+    Epoch 144/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5799 - accuracy: 0.6678
+    Epoch 00144: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5789 - accuracy: 0.6690 - val_loss: 1.2173 - val_accuracy: 0.4687
+    Epoch 145/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5884 - accuracy: 0.6660
+    Epoch 00145: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5869 - accuracy: 0.6676 - val_loss: 0.9850 - val_accuracy: 0.4815
+    Epoch 146/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5881 - accuracy: 0.6734
+    Epoch 00146: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5887 - accuracy: 0.6725 - val_loss: 1.1846 - val_accuracy: 0.4953
+    Epoch 147/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5866 - accuracy: 0.6689
+    Epoch 00147: saving model to best_model.h5
+    71/71 [==============================] - 20s 285ms/step - loss: 0.5864 - accuracy: 0.6690 - val_loss: 1.0037 - val_accuracy: 0.4567
+    Epoch 148/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5710 - accuracy: 0.6892
+    Epoch 00148: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5704 - accuracy: 0.6898 - val_loss: 1.2442 - val_accuracy: 0.4893
+    Epoch 149/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5665 - accuracy: 0.6905
+    Epoch 00149: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5667 - accuracy: 0.6911 - val_loss: 1.1866 - val_accuracy: 0.4197
+    Epoch 150/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5773 - accuracy: 0.6813
+    Epoch 00150: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5779 - accuracy: 0.6805 - val_loss: 1.0186 - val_accuracy: 0.5090
+    Epoch 151/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5667 - accuracy: 0.6867
+    Epoch 00151: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5673 - accuracy: 0.6860 - val_loss: 0.9821 - val_accuracy: 0.5416
+    Epoch 152/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5738 - accuracy: 0.6851
+    Epoch 00152: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5729 - accuracy: 0.6856 - val_loss: 0.8874 - val_accuracy: 0.4961
+    Epoch 153/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5631 - accuracy: 0.6858
+    Epoch 00153: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5641 - accuracy: 0.6847 - val_loss: 1.0875 - val_accuracy: 0.4970
+    Epoch 154/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5665 - accuracy: 0.6934
+    Epoch 00154: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5661 - accuracy: 0.6929 - val_loss: 1.1861 - val_accuracy: 0.5013
+    Epoch 155/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5670 - accuracy: 0.6815
+    Epoch 00155: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5669 - accuracy: 0.6814 - val_loss: 0.9750 - val_accuracy: 0.5330
+    Epoch 156/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5751 - accuracy: 0.6877
+    Epoch 00156: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5751 - accuracy: 0.6875 - val_loss: 0.8207 - val_accuracy: 0.5433
+    Epoch 157/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5643 - accuracy: 0.6886
+    Epoch 00157: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5633 - accuracy: 0.6895 - val_loss: 1.1411 - val_accuracy: 0.4781
+    Epoch 158/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5704 - accuracy: 0.6846
+    Epoch 00158: saving model to best_model.h5
+    71/71 [==============================] - 20s 284ms/step - loss: 0.5709 - accuracy: 0.6840 - val_loss: 1.1208 - val_accuracy: 0.5039
+    Epoch 159/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5447 - accuracy: 0.7031
+    Epoch 00159: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5455 - accuracy: 0.7029 - val_loss: 1.3517 - val_accuracy: 0.4730
+    Epoch 160/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5505 - accuracy: 0.6986
+    Epoch 00160: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5498 - accuracy: 0.7002 - val_loss: 1.0454 - val_accuracy: 0.4867
+    Epoch 161/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5597 - accuracy: 0.6965
+    Epoch 00161: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5598 - accuracy: 0.6964 - val_loss: 1.3123 - val_accuracy: 0.4738
+    Epoch 162/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5626 - accuracy: 0.6810
+    Epoch 00162: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5625 - accuracy: 0.6807 - val_loss: 1.0149 - val_accuracy: 0.5245
+    Epoch 163/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5485 - accuracy: 0.7031
+    Epoch 00163: saving model to best_model.h5
+    71/71 [==============================] - 19s 273ms/step - loss: 0.5493 - accuracy: 0.7022 - val_loss: 1.6219 - val_accuracy: 0.4755
+    Epoch 164/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5527 - accuracy: 0.6907
+    Epoch 00164: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5518 - accuracy: 0.6922 - val_loss: 1.1331 - val_accuracy: 0.5330
+    Epoch 165/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5589 - accuracy: 0.6991
+    Epoch 00165: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5586 - accuracy: 0.6985 - val_loss: 1.1504 - val_accuracy: 0.5039
+    Epoch 166/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5458 - accuracy: 0.6984
+    Epoch 00166: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5452 - accuracy: 0.6987 - val_loss: 1.1868 - val_accuracy: 0.4790
+    Epoch 167/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5585 - accuracy: 0.6936
+    Epoch 00167: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5582 - accuracy: 0.6935 - val_loss: 0.8705 - val_accuracy: 0.5828
+    Epoch 168/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5492 - accuracy: 0.6907
+    Epoch 00168: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5489 - accuracy: 0.6907 - val_loss: 1.2555 - val_accuracy: 0.4841
+    Epoch 169/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5588 - accuracy: 0.7002
+    Epoch 00169: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5587 - accuracy: 0.6996 - val_loss: 1.0974 - val_accuracy: 0.5193
+    Epoch 170/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5423 - accuracy: 0.7049
+    Epoch 00170: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5420 - accuracy: 0.7051 - val_loss: 1.4447 - val_accuracy: 0.4996
+    Epoch 171/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5398 - accuracy: 0.7076
+    Epoch 00171: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5398 - accuracy: 0.7075 - val_loss: 0.9666 - val_accuracy: 0.4661
+    Epoch 172/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5570 - accuracy: 0.6923
+    Epoch 00172: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5573 - accuracy: 0.6916 - val_loss: 1.0178 - val_accuracy: 0.5614
+    Epoch 173/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5485 - accuracy: 0.7004
+    Epoch 00173: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5484 - accuracy: 0.7011 - val_loss: 1.1527 - val_accuracy: 0.5348
+    Epoch 174/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5370 - accuracy: 0.7081
+    Epoch 00174: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5364 - accuracy: 0.7084 - val_loss: 1.1526 - val_accuracy: 0.4979
+    Epoch 175/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5472 - accuracy: 0.7025
+    Epoch 00175: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5467 - accuracy: 0.7040 - val_loss: 1.5305 - val_accuracy: 0.5107
+    Epoch 176/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5412 - accuracy: 0.7081
+    Epoch 00176: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5415 - accuracy: 0.7069 - val_loss: 1.1317 - val_accuracy: 0.5313
+    Epoch 177/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5277 - accuracy: 0.7087
+    Epoch 00177: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5288 - accuracy: 0.7075 - val_loss: 1.2547 - val_accuracy: 0.5262
+    Epoch 178/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5490 - accuracy: 0.7006
+    Epoch 00178: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5500 - accuracy: 0.7000 - val_loss: 1.0554 - val_accuracy: 0.5828
+    Epoch 179/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5325 - accuracy: 0.7189
+    Epoch 00179: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5324 - accuracy: 0.7187 - val_loss: 1.2799 - val_accuracy: 0.4644
+    Epoch 180/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5378 - accuracy: 0.7203
+    Epoch 00180: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5373 - accuracy: 0.7203 - val_loss: 1.2015 - val_accuracy: 0.4524
+    Epoch 181/500
+
+    70/71 [============================>.] - ETA: 0s - loss: 0.5431 - accuracy: 0.7090
+    Epoch 00181: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5422 - accuracy: 0.7098 - val_loss: 2.2196 - val_accuracy: 0.5270
+    Epoch 182/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5328 - accuracy: 0.7204
+    Epoch 00182: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5337 - accuracy: 0.7191 - val_loss: 1.0959 - val_accuracy: 0.4498
+    Epoch 183/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5460 - accuracy: 0.7031
+    Epoch 00183: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5451 - accuracy: 0.7031 - val_loss: 1.3565 - val_accuracy: 0.5270
+    Epoch 184/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5320 - accuracy: 0.7150
+    Epoch 00184: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5310 - accuracy: 0.7160 - val_loss: 1.0530 - val_accuracy: 0.4438
+    Epoch 185/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5361 - accuracy: 0.7069
+    Epoch 00185: saving model to best_model.h5
+    71/71 [==============================] - 20s 276ms/step - loss: 0.5357 - accuracy: 0.7076 - val_loss: 1.0496 - val_accuracy: 0.4927
+    Epoch 186/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5250 - accuracy: 0.7136
+    Epoch 00186: saving model to best_model.h5
+    71/71 [==============================] - 21s 298ms/step - loss: 0.5247 - accuracy: 0.7137 - val_loss: 1.4406 - val_accuracy: 0.5279
+    Epoch 187/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5171 - accuracy: 0.7233
+    Epoch 00187: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5165 - accuracy: 0.7246 - val_loss: 1.0706 - val_accuracy: 0.5648
+    Epoch 188/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5380 - accuracy: 0.7038
+    Epoch 00188: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5374 - accuracy: 0.7055 - val_loss: 1.1809 - val_accuracy: 0.4678
+    Epoch 189/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5305 - accuracy: 0.7166
+    Epoch 00189: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5302 - accuracy: 0.7169 - val_loss: 1.5912 - val_accuracy: 0.4687
+    Epoch 190/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5301 - accuracy: 0.7242
+    Epoch 00190: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.5295 - accuracy: 0.7253 - val_loss: 1.1200 - val_accuracy: 0.5073
+    Epoch 191/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5410 - accuracy: 0.7074
+    Epoch 00191: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5402 - accuracy: 0.7075 - val_loss: 1.2133 - val_accuracy: 0.4549
+    Epoch 192/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5197 - accuracy: 0.7274
+    Epoch 00192: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5200 - accuracy: 0.7268 - val_loss: 1.2712 - val_accuracy: 0.4910
+    Epoch 193/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5430 - accuracy: 0.7045
+    Epoch 00193: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.5434 - accuracy: 0.7029 - val_loss: 1.1931 - val_accuracy: 0.4824
+    Epoch 194/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5336 - accuracy: 0.7076
+    Epoch 00194: saving model to best_model.h5
+    71/71 [==============================] - 20s 275ms/step - loss: 0.5337 - accuracy: 0.7067 - val_loss: 1.3550 - val_accuracy: 0.4953
+    Epoch 195/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5271 - accuracy: 0.7145
+    Epoch 00195: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5263 - accuracy: 0.7148 - val_loss: 1.2209 - val_accuracy: 0.4944
+    Epoch 196/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5397 - accuracy: 0.7087
+    Epoch 00196: saving model to best_model.h5
+    71/71 [==============================] - 20s 275ms/step - loss: 0.5386 - accuracy: 0.7098 - val_loss: 1.2160 - val_accuracy: 0.5373
+    Epoch 197/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5293 - accuracy: 0.7176
+    Epoch 00197: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5297 - accuracy: 0.7170 - val_loss: 1.5736 - val_accuracy: 0.5021
+    Epoch 198/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5399 - accuracy: 0.7141
+    Epoch 00198: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5389 - accuracy: 0.7151 - val_loss: 1.0983 - val_accuracy: 0.4858
+    Epoch 199/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5223 - accuracy: 0.7272
+    Epoch 00199: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.5236 - accuracy: 0.7257 - val_loss: 1.5026 - val_accuracy: 0.4893
+    Epoch 200/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5386 - accuracy: 0.7063
+    Epoch 00200: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5384 - accuracy: 0.7058 - val_loss: 1.5826 - val_accuracy: 0.5090
+    Epoch 201/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5059 - accuracy: 0.7456
+    Epoch 00201: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5077 - accuracy: 0.7448 - val_loss: 1.3451 - val_accuracy: 0.4944
+    Epoch 202/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5134 - accuracy: 0.7269
+    Epoch 00202: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5133 - accuracy: 0.7279 - val_loss: 0.9784 - val_accuracy: 0.5845
+    Epoch 203/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5141 - accuracy: 0.7206
+    Epoch 00203: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5139 - accuracy: 0.7213 - val_loss: 1.1485 - val_accuracy: 0.4773
+    Epoch 204/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5191 - accuracy: 0.7238
+    Epoch 00204: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5190 - accuracy: 0.7244 - val_loss: 1.0170 - val_accuracy: 0.4455
+    Epoch 205/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5142 - accuracy: 0.7346
+    Epoch 00205: saving model to best_model.h5
+    71/71 [==============================] - 20s 279ms/step - loss: 0.5129 - accuracy: 0.7364 - val_loss: 1.1912 - val_accuracy: 0.4446
+    Epoch 206/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5149 - accuracy: 0.7323
+    Epoch 00206: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5145 - accuracy: 0.7319 - val_loss: 1.2988 - val_accuracy: 0.5107
+    Epoch 207/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5222 - accuracy: 0.7260
+    Epoch 00207: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5226 - accuracy: 0.7264 - val_loss: 1.0564 - val_accuracy: 0.4558
+    Epoch 208/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5118 - accuracy: 0.7308
+    Epoch 00208: saving model to best_model.h5
+    71/71 [==============================] - 21s 293ms/step - loss: 0.5108 - accuracy: 0.7312 - val_loss: 1.0883 - val_accuracy: 0.5245
+    Epoch 209/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5254 - accuracy: 0.7248
+    Epoch 00209: saving model to best_model.h5
+    71/71 [==============================] - 20s 277ms/step - loss: 0.5263 - accuracy: 0.7236 - val_loss: 0.9144 - val_accuracy: 0.4901
+    Epoch 210/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5053 - accuracy: 0.7373
+    Epoch 00210: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5052 - accuracy: 0.7368 - val_loss: 1.0266 - val_accuracy: 0.5365
+    Epoch 211/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5172 - accuracy: 0.7283
+    Epoch 00211: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5170 - accuracy: 0.7286 - val_loss: 1.5774 - val_accuracy: 0.5519
+    Epoch 212/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5244 - accuracy: 0.7218
+    Epoch 00212: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5243 - accuracy: 0.7217 - val_loss: 1.3302 - val_accuracy: 0.4953
+    Epoch 213/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5202 - accuracy: 0.7218
+    Epoch 00213: saving model to best_model.h5
+    71/71 [==============================] - 20s 282ms/step - loss: 0.5190 - accuracy: 0.7224 - val_loss: 1.5494 - val_accuracy: 0.4790
+    Epoch 214/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5212 - accuracy: 0.7218
+    Epoch 00214: saving model to best_model.h5
+    71/71 [==============================] - 20s 281ms/step - loss: 0.5203 - accuracy: 0.7233 - val_loss: 0.9906 - val_accuracy: 0.4850
+    Epoch 215/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5159 - accuracy: 0.7236
+    Epoch 00215: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5161 - accuracy: 0.7228 - val_loss: 1.4467 - val_accuracy: 0.4979
+    Epoch 216/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5023 - accuracy: 0.7301
+    Epoch 00216: saving model to best_model.h5
+    71/71 [==============================] - 20s 280ms/step - loss: 0.5018 - accuracy: 0.7310 - val_loss: 1.2833 - val_accuracy: 0.4798
+    Epoch 217/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5110 - accuracy: 0.7353
+    Epoch 00217: saving model to best_model.h5
+    71/71 [==============================] - 20s 278ms/step - loss: 0.5102 - accuracy: 0.7361 - val_loss: 1.0330 - val_accuracy: 0.4567
+    Epoch 218/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5112 - accuracy: 0.7344
+    Epoch 00218: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5129 - accuracy: 0.7335 - val_loss: 1.5519 - val_accuracy: 0.5210
+    Epoch 219/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5157 - accuracy: 0.7258
+    Epoch 00219: saving model to best_model.h5
+    71/71 [==============================] - 20s 285ms/step - loss: 0.5169 - accuracy: 0.7257 - val_loss: 1.2270 - val_accuracy: 0.5047
+    Epoch 220/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.5007 - accuracy: 0.7350
+    Epoch 00220: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.5013 - accuracy: 0.7344 - val_loss: 1.2868 - val_accuracy: 0.5116
+    Epoch 221/500
+    70/71 [============================>.] - ETA: 0s - loss: 0.4956 - accuracy: 0.7434
+    Epoch 00221: saving model to best_model.h5
+    71/71 [==============================] - 20s 283ms/step - loss: 0.4942 - accuracy: 0.7439 - val_loss: 1.1584 - val_accuracy: 0.5425
+
+%% Cell type:code id: tags:
+
+``` python
+import pickle
+```
+
+%% Cell type:code id: tags:
+
+``` python
+with open("trainingHistory", "wb") as historyFile:
+    pickle.dump(H.history, historyFile)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+history = pickle.load(open('trainingHistory', "rb"))
+history
+
+plt.style.use("ggplot")
+plt.figure()
+# N = np.arange(0, NUM_EPOCHS)
+N = np.arange(0, len(history["loss"]))
+plt.plot(N, history["loss"], label="train_loss")
+plt.plot(N, history["val_loss"], label="val_loss")
+plt.plot(N, history["accuracy"], label="train_acc")
+plt.plot(N, history["val_accuracy"], label="val_acc")
+plt.title("Training Loss and Accuracy")
+plt.xlabel("Epoch #")
+plt.ylabel("Loss/Accuracy")
+plt.legend(loc="lower left")
+plt.savefig("historyGraph.png")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.display import Image
+Image("historyGraph.png")
+```
+
+%% Output
+
+
+    <IPython.core.display.Image object>
+
+%% Cell type:code id: tags:
+
+``` python
+# evaluate the network and show a classification report
+model = FireDetectionNet.build(width=128, height=128, depth=3,
+    classes=2)
+model.load_weights("best_model.h5")
+print("[INFO] evaluating network...")
+predictions = model.predict(Xtest, batch_size=BATCH_SIZE)
+print(classification_report(Ytest.argmax(axis=1),
+    predictions.argmax(axis=1), target_names=CLASSES))
+
+# serialize the model to disk
+# print("[INFO] serializing network to '{}'...".format(MODEL_PATH))
+# model.save(MODEL_PATH)
+
+# construct a plot that plots and saves the training history
+N = np.arange(0, 221)
+plt.style.use("ggplot")
+plt.figure()
+plt.plot(N, H.history["loss"], label="train_loss")
+plt.plot(N, H.history["val_loss"], label="val_loss")
+plt.plot(N, H.history["accuracy"], label="train_acc")
+plt.plot(N, H.history["val_accuracy"], label="val_acc")
+plt.title("Training Loss and Accuracy")
+plt.xlabel("Epoch #")
+plt.ylabel("Loss/Accuracy")
+plt.legend(loc="lower left")
+plt.savefig(TRAINING_PLOT_PATH)
+```
+
+%% Output
+
+    [INFO] evaluating network...
+                  precision    recall  f1-score   support
+    
+        Non-Fire       0.52      0.85      0.64       739
+            Fire       0.57      0.20      0.30       740
+    
+        accuracy                           0.53      1479
+       macro avg       0.54      0.53      0.47      1479
+    weighted avg       0.54      0.53      0.47      1479
+    
+
+%% Cell type:code id: tags:
+
+``` python
+!ls
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import random
+from PIL import Image
+
+firePaths = list(paths.list_images(FIRE_PATH))
+nonFirePaths = list(paths.list_images(NON_FIRE_PATH))
+imagePaths = firePaths + nonFirePaths
+
+imagePath = random.sample(imagePaths, 1)[0]
+
+img = cv2.resize(cv2.imread(imagePath), (128,128))
+img = img.astype("float32")/255
+print(np.argmax(model.predict(np.expand_dims(img, axis=0))[0]), imagePath.split(":")[1][:-4])
+Image.fromarray(cv2.imread(imagePath), "RGB")
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import random
+from PIL import Image
+
+firePaths = list(paths.list_images("20180611fallbrookrmwmobocAFTER"))
+nonFirePaths = list(paths.list_images("20180611fallbrookrmwmobocBEFORE"))
+imagePaths = firePaths + nonFirePaths
+
+MaxnumberofImg = len(imagePaths)
+
+numberofImg = 0
+
+for imagePath in imagePaths:
+    img = cv2.resize(cv2.imread(imagePaths[numberofImg]), (128,128))
+    img = img.astype("float32")/255
+    print(np.argmax(model.predict(np.expand_dims(img, axis=0))[0]), imagePaths[numberofImg].split(":")[1][:-4])
+    Image.fromarray(cv2.imread(imagePath), "RGB")
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+model.load_weights("best_model.h5")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# model.evaluate(testX,testY)
+
+testX
+```
+
+%% Cell type:markdown id: tags:
+
+### Transfer to TF.LITE
+
+%% Cell type:code id: tags:
+
+``` python
+from tensorflow import lite
+```
+
+%% Cell type:code id: tags:
+
+``` python
+converter = lite.TFLiteConverter.from_keras_model(model)
+tflite_model = converter.convert()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+open("wildfiretflite_model.tflite", "wb").write(tflite_model)
+```
+
+%% Output
+
+    8501404
+
+%% Cell type:code id: tags:
+
+``` python
+```

Dockerfile

-FROM tensorflow/tensorflow:2.0.0a0-gpu-py3
-RUN apt-get update
-RUN apt-get install nano
-RUN pip install --upgrade pip
-RUN pip install numpy scipy scikit-learn pillow h5py keras 
-RUN pip install --upgrade imutils 
-RUN pip install --upgrade scikit-learn
-RUN pip install --upgrade matplotlib
-RUN pip install -q tensorflow==2.0.0-beta1
-RUN apt-get install locate
\ No newline at end of file
+ARG cuda_version=10.1
+ARG cudnn_version=7
+FROM nvidia/cuda:${cuda_version}-cudnn${cudnn_version}-devel
+
+ENV NB_USER kerasTester
+ENV NB_UID 1000
+
+# RUN mkdir /userdata/kerasData
+
+RUN apt-get update && \
+      apt-get -y install sudo
+
+RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
+    # chown $NB_USER $CONDA_DIR -R && \
+    # chown $NB_USER /userdata/kerasData -R && \
+    #    chown $NB_USER / -R && \
+    # mkdir -p / && \
+    sh -c 'echo "$NB_USER:test" | chpasswd' && \
+    usermod -aG sudo $NB_USER
+
+WORKDIR /userdata/kerasData
+
+# Install system packages
+RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
+      bzip2 \
+      g++ \
+      git \
+      graphviz \
+      libgl1-mesa-glx \
+      libhdf5-dev \
+      openmpi-bin \
+      xvfb \
+      screen \
+      wget && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install conda
+ENV CONDA_DIR /opt/conda
+ENV PATH $CONDA_DIR/bin:$PATH
+
+RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.12.1-Linux-x86_64.sh -O ~/miniconda.sh && \
+    /bin/bash ~/miniconda.sh -b -p /opt/conda && \
+    rm ~/miniconda.sh && \
+    /opt/conda/bin/conda clean -tipsy && \
+    ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc
+
+# Install Python packages and keras
+
+ARG python_version=3.6
+
+RUN conda config --append channels conda-forge
+RUN conda install -y python=${python_version} && \
+    pip install --upgrade pip && \
+    pip install \
+      sklearn_pandas \
+      opencv-python && \
+
+    conda install \
+      bcolz \
+      h5py \
+      statsmodels \
+      matplotlib \
+      mkl \
+      nose \
+      notebook \
+      Pillow \
+      pandas \
+      pydot \
+      pyyaml \
+      scikit-learn \
+      tensorflow-gpu \
+      six \
+      theano \
+      mkdocs \
+      numpy
+
+RUN pip install keras
+
+# RUN git clone git://github.com/keras-team/keras.git /src && pip install -e /src[tests]
+RUN conda clean -yt
+    # pip install git+git://github.com/keras-team/keras.git && \
+
+USER $NB_USER
+ 
+#ADD theanorc /home/keras/.theanorc
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+ENV PYTHONPATH='/src/:$PYTHONPATH'

Evaluator.ipynb

+%% Cell type:code id: tags:
+
+``` python
+from numpy import loadtxt
+from keras.models import load_model
+import tensorflow as tf
+from keras import backend as K
+```
+
+%% Cell type:code id: tags:
+
+``` python
+!pwd
+```
+
+%% Output
+
+    /userdata/kerasData
+
+%% Cell type:code id: tags:
+
+``` python
+def f1(y_true, y_pred):
+
+    def recall(y_true, y_pred):
+        """Recall metric.
+
+        Only computes a batch-wise average of recall.
+
+        Computes the recall, a metric for multi-label classification of
+        how many relevant items are selected.
+        """
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+        recall = true_positives / (possible_positives + K.epsilon())
+        return recall
+
+    def precision(y_true, y_pred):
+        """Precision metric.
+
+        Only computes a batch-wise average of precision.
+
+        Computes the precision, a metric for multi-label classification of
+        how many selected items are relevant.
+        """
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+        precision = true_positives / (predicted_positives + K.epsilon())
+        return precision
+    precision = precision(y_true, y_pred)
+    recall = recall(y_true, y_pred)
+    return 2*((precision*recall)/(precision+recall+K.epsilon()))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+INIT_LR = 1e-4
+batch_size = 16
+NUM_EPOCHS = 200
+
+image_size=(128, 128)
+class_mode="binary"
+image_generator = tf.keras.preprocessing.image.ImageDataGenerator()
+TestGenerator = image_generator.flow_from_directory(
+    "/userdata/kerasData/preloaded/flowDirectory/validation",
+    target_size=image_size,
+    batch_size=batch_size,
+    seed=1000,
+    class_mode=class_mode)
+```
+
+%% Output
+
+    Found 2781 images belonging to 2 classes.
+
+%% Cell type:code id: tags:
+
+``` python
+model_128 = load_model("/userdata/kerasData/output/model/best_model128_128_e-4.h5", custom_objects={"f1":f1})
+model_128.summary()
+```
+
+%% Output
+
+    Model: "sequential"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    separable_conv2d (SeparableC (None, 128, 128, 16)      211
+    _________________________________________________________________
+    activation (Activation)      (None, 128, 128, 16)      0
+    _________________________________________________________________
+    batch_normalization (BatchNo (None, 128, 128, 16)      64
+    _________________________________________________________________
+    max_pooling2d (MaxPooling2D) (None, 64, 64, 16)        0
+    _________________________________________________________________
+    separable_conv2d_1 (Separabl (None, 64, 64, 32)        688
+    _________________________________________________________________
+    activation_1 (Activation)    (None, 64, 64, 32)        0
+    _________________________________________________________________
+    batch_normalization_1 (Batch (None, 64, 64, 32)        128
+    _________________________________________________________________
+    max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32)        0
+    _________________________________________________________________
+    separable_conv2d_2 (Separabl (None, 32, 32, 64)        2400
+    _________________________________________________________________
+    activation_2 (Activation)    (None, 32, 32, 64)        0
+    _________________________________________________________________
+    batch_normalization_2 (Batch (None, 32, 32, 64)        256
+    _________________________________________________________________
+    separable_conv2d_3 (Separabl (None, 32, 32, 64)        4736
+    _________________________________________________________________
+    activation_3 (Activation)    (None, 32, 32, 64)        0
+    _________________________________________________________________
+    batch_normalization_3 (Batch (None, 32, 32, 64)        256
+    _________________________________________________________________
+    max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64)        0
+    _________________________________________________________________
+    flatten (Flatten)            (None, 16384)             0
+    _________________________________________________________________
+    dense (Dense)                (None, 128)               2097280
+    _________________________________________________________________
+    activation_4 (Activation)    (None, 128)               0
+    _________________________________________________________________
+    batch_normalization_4 (Batch (None, 128)               512
+    _________________________________________________________________
+    dropout (Dropout)            (None, 128)               0
+    _________________________________________________________________
+    dense_1 (Dense)              (None, 128)               16512
+    _________________________________________________________________
+    activation_5 (Activation)    (None, 128)               0
+    _________________________________________________________________
+    batch_normalization_5 (Batch (None, 128)               512
+    _________________________________________________________________
+    dropout_1 (Dropout)          (None, 128)               0
+    _________________________________________________________________
+    dense_2 (Dense)              (None, 1)                 129
+    _________________________________________________________________
+    activation_6 (Activation)    (None, 1)                 0
+    =================================================================
+    Total params: 2,123,684
+    Trainable params: 2,122,820
+    Non-trainable params: 864
+    _________________________________________________________________
+
+%% Cell type:code id: tags:
+
+``` python
+model_128.evaluate(TestGenerator)
+```
+
+%% Output
+
+    174/174 [==============================] - 419s 2s/step - loss: 0.7333 - accuracy: 0.5045 - f1: 0.6095 - recall: 0.8166 - precision: 0.5016
+
+    [0.7332570552825928,
+     0.5044947862625122,
+     0.6094719767570496,
+     0.8166065216064453,
+     0.5015521049499512]
+
+%% Cell type:code id: tags:
+
+``` python
+INIT_LR = 1e-4
+batch_size = 16
+NUM_EPOCHS = 200
+
+image_size=(2048, 1536)
+class_mode="categorical"
+
+
+model_e5 = load_model('/userdata/kerasData/best_model_e5.h5', custom_objects={"f1":f1})
+model_e5.summary()
+
+
+image_generator = tf.keras.preprocessing.image.ImageDataGenerator()
+testGenerator = image_generator.flow_from_directory(
+    "/userdata/kerasData/preloaded/flowDirectory/test",
+    target_size=image_size,
+    seed=1000,
+    batch_size=batch_size,
+    class_mode=class_mode)
+```
+
+%% Output
+
+    Model: "sequential"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    separable_conv2d (SeparableC (None, 128, 128, 16)      211
+    _________________________________________________________________
+    activation (Activation)      (None, 128, 128, 16)      0
+    _________________________________________________________________
+    batch_normalization (BatchNo (None, 128, 128, 16)      64
+    _________________________________________________________________
+    max_pooling2d (MaxPooling2D) (None, 64, 64, 16)        0
+    _________________________________________________________________
+    separable_conv2d_1 (Separabl (None, 64, 64, 32)        688
+    _________________________________________________________________
+    activation_1 (Activation)    (None, 64, 64, 32)        0
+    _________________________________________________________________
+    batch_normalization_1 (Batch (None, 64, 64, 32)        128
+    _________________________________________________________________
+    max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32)        0
+    _________________________________________________________________
+    separable_conv2d_2 (Separabl (None, 32, 32, 64)        2400
+    _________________________________________________________________
+    activation_2 (Activation)    (None, 32, 32, 64)        0
+    _________________________________________________________________
+    batch_normalization_2 (Batch (None, 32, 32, 64)        256
+    _________________________________________________________________
+    separable_conv2d_3 (Separabl (None, 32, 32, 64)        4736
+    _________________________________________________________________
+    activation_3 (Activation)    (None, 32, 32, 64)        0
+    _________________________________________________________________
+    batch_normalization_3 (Batch (None, 32, 32, 64)        256
+    _________________________________________________________________
+    max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64)        0
+    _________________________________________________________________
+    flatten (Flatten)            (None, 16384)             0
+    _________________________________________________________________
+    dense (Dense)                (None, 128)               2097280
+    _________________________________________________________________
+    activation_4 (Activation)    (None, 128)               0
+    _________________________________________________________________
+    batch_normalization_4 (Batch (None, 128)               512
+    _________________________________________________________________
+    dropout (Dropout)            (None, 128)               0
+    _________________________________________________________________
+    dense_1 (Dense)              (None, 128)               16512
+    _________________________________________________________________
+    activation_5 (Activation)    (None, 128)               0
+    _________________________________________________________________
+    batch_normalization_5 (Batch (None, 128)               512
+    _________________________________________________________________
+    dropout_1 (Dropout)          (None, 128)               0
+    _________________________________________________________________
+    dense_2 (Dense)              (None, 2)                 258
+    _________________________________________________________________
+    activation_6 (Activation)    (None, 2)                 0
+    =================================================================
+    Total params: 2,123,813
+    Trainable params: 2,122,949
+    Non-trainable params: 864
+    _________________________________________________________________
+    Found 3485 images belonging to 2 classes.
+
+%% Cell type:code id: tags:
+
+``` python
+
+image_size=(2048, 1536)
+class_mode="categorical"
+image_generator = tf.keras.preprocessing.image.ImageDataGenerator()
+testGenerator = image_generator.flow_from_directory(
+    "/userdata/kerasData/preloaded/flowDirectory/test",
+    target_size=image_size,
+    seed=1000,
+    batch_size=batch_size,
+    class_mode=class_mode)
+```
+
+%% Output
+
+    Found 3485 images belonging to 2 classes.
+
+%% Cell type:code id: tags:
+
+``` python
+model_e5.evaluate(testGenerator)
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    InvalidArgumentError                      Traceback (most recent call last)
+    <ipython-input-8-6de600d60988> in <module>
+    ----> 1 model_e5.evaluate(testGenerator)
+
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
+         64   def _method_wrapper(self, *args, **kwargs):
+         65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
+    ---> 66       return method(self, *args, **kwargs)
+         67
+         68     # Running inside `run_distribute_coordinator` already.
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
+       1079                 step_num=step):
+       1080               callbacks.on_test_batch_begin(step)
+    -> 1081               tmp_logs = test_function(iterator)
+       1082               # Catch OutOfRangeError for Datasets of unknown size.
+       1083               # This blocks until the batch has finished executing.
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
+        578         xla_context.Exit()
+        579     else:
+    --> 580       result = self._call(*args, **kwds)
+        581
+        582     if tracing_count == self._get_tracing_count():
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
+        642         # Lifting succeeded, so variables are initialized and we can run the
+        643         # stateless function.
+    --> 644         return self._stateless_fn(*args, **kwds)
+        645     else:
+        646       canon_args, canon_kwds = \
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
+       2418     with self._lock:
+       2419       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
+    -> 2420     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
+       2421
+       2422   @property
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs)
+       1663          if isinstance(t, (ops.Tensor,
+       1664                            resource_variable_ops.BaseResourceVariable))),
+    -> 1665         self.captured_inputs)
+       1666
+       1667   def _call_flat(self, args, captured_inputs, cancellation_manager=None):
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
+       1744       # No tape is watching; skip to running the function.
+       1745       return self._build_call_outputs(self._inference_function.call(
+    -> 1746           ctx, args, cancellation_manager=cancellation_manager))
+       1747     forward_backward = self._select_forward_and_backward_functions(
+       1748         args,
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
+        596               inputs=args,
+        597               attrs=attrs,
+    --> 598               ctx=ctx)
+        599         else:
+        600           outputs = execute.execute_with_cancellation(
+    /opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
+         58     ctx.ensure_initialized()
+         59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
+    ---> 60                                         inputs, attrs, num_outputs)
+         61   except core._NotOkStatusException as e:
+         62     if name is not None:
+    InvalidArgumentError: 2 root error(s) found.
+  (0) Invalid argument:  Incompatible shapes: [16,2] vs. [3072,2]
+	 [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]
+	 [[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_39]]
+  (1) Invalid argument:  Incompatible shapes: [16,2] vs. [3072,2]
+	 [[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]
+0 successful operations.
+0 derived errors ignored. [Op:__inference_test_function_2377]
+
+Errors may have originated from an input operation.
+Input Source operations connected to node mul_1:
+ sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)
+
+Input Source operations connected to node mul_1:
+ sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)
+
+Function call stack:
+test_function -> test_function
+
+%% Cell type:code id: tags:
+
+``` python
+```

README.md

+# Keras Container on Nautilus
+
+This project allows for the usage of Keras on a jupter notebook in Nautilus (as an importable package). With this project, we are able to train keras models on the Nautilus cloud. 
+
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your namespace. 
+
+### Prerequisites
+
+Nautilus namespace
+Nvidia GPU
+
+## Components
+
+The project has the following components:
+```
+- Dockerfile (Dockerfile)
+- Continous Integration Yaml (.gitlab-ci.yml)
+- An example jupter notebook (ClassificationExample.ipynb)
+- Nautilus deployment Yaml (kerasDeloyment.yaml)
+```
+
+### Dockerfile
+```
+This file is used to make the enviroment necessary to run Keras on Jupyter Notebook. Unless 
+truely needed, please avoid editing this file.
+```
+
+### Continous Integration Yaml
+```
+This file is used to utilize gitlab's continous integration feature. Nautilus uses kaniko instead of docker, which can be changed back into using a docker image by replacing the current .gitlab-ci.yml with the "dockerBased-ci.yml" file.  
+```
+
+### Jupter notebook
+```
+This was the notebook I used to train an wildfire classification model. The structure and import commands can be used to utilize keras in 
+other notebooks. I will go over the specific details below.  
+```
+
+### Nautilus Deployment Yaml
+
+If you are planning to use this implementation on another Nautilus namespace, this portion of the readme is especially important. Here are the important aspects of this yaml:
+
+
+1. Changing namespace address <br /> <br />
+![Changing the names](screenshots/nautilusDeploymentNamespaceName.PNG) <br />
+**Change the name and the namespace entries to the current working namespace and a suitable name**
+
+2. Change the resource requests <br /> <br />
+![Change the resource limits and requests](screenshots/nautilusDeploymentResources.PNG) <br />
+**Change the numbers to suit the task**
+
+
+3. Mount volumne <br /><br />
+![Mount Volume onto a path if already created. To find out how to create a persistent volumne claim, refer to Nautilus documentation](screenshots/nautilusDeploymentStorage.PNG) <br />
+**Very important for crash-resistance. I highly recommend saving all work onto mounted directory** 
+
+
+4. Choose GPU type <br /><br />
+![Choose correctly](screenshots/nautilusDeploymentGPUrequest.PNG)  <br />
+If doing intensive training, choose larger/more expensive GPUs
+
+## Using the Components
+
+### Starting the development and accessing jupyter notebook 
+1. Go into kerasDeloyment.yaml file
+2. Choose the RAW file format <br />
+![](screenshots/rawfile.PNG) <br />
+
+3. copy url of RAW file <br />
+![](screenshots/rawaddress.PNG) <br />
+
+4. execute yaml file on nautilius namespace <br />
+![](screenshots/kubectinit.PNG)
+
+5. exec into nautilus pod <br />
+![](screenshots/execinto.PNG)
+
+6. Navigate to /userdata/kerasData and Start Jupyter Notebook <br /><br />
+![](screenshots/startjupyter.PNG)<br />
+**Note: The port number choice does not matter, as long as there are not other processes running on that port. If a port is already in use, jupyter will automatically assign another port. Make sure to match the port number in the next step** <br /> <br />
+![](screenshots/usingotherports.PNG)<br />
+_What happens when a wrong port is chosen_ <br />
+
+7. Go to your computer terminal and start port-forward, matching the port in the pod <br />
+![](screenshots/portforward.PNG)<br />
+
+8. Go to the localhost address<br />
+![](screenshots/localhostaddress.png)<br />
+
+9. Test for keras <br />
+- Create a new notebook or use the ClassificationExample.ipynb file 
+- Run the following tests <br />
+![](screenshots/firstBatch.PNG) <br /><br />
+<!-- ![](screenshots/secondBatch.PNG)<br /><br /> -->
+**_Make sure that the outputs return True or some name._**<br />
+**You are now ready to use Keras on a jupyter notebook hosted on Kubernetes**
+
+### Using Keras in Notebook
+
+#### EXTREMELY IMPORTANT!
+In order to prevent Keras from assigning too much GPU memory and stalling training efforts later on, run this:
+![](screenshots/hickups.PNG) <br />
+If you see an error, shutdown the network server and try again <br />
+![](screenshots/toolate.PNG)<br/>
+If you see nvidia-smi memory allocation at 0/- you have suceeded in reseting the GPU <br />
+![](screenshots/nvidiasmireg.PNG)<br />
+
+Please refer to [Keras Documentation](https://keras.io/) for instructions and information on using Keras
+
+I used the notebook for the following:
+- Training a CNN on the notebook for reference
+- Using a LearningRateFinder to find the optimal learning rate
+
+
+## Using the Fire-Classification training
+
+1. Write the network using Keras layers  <br /> 
+![](screenshots/modelbuild.PNG) <br /> <br />
+2. Set the paths  <br />
+![](screenshots/pathfields.PNG) <br />
+The following must be set
+- FIRE_PATH = Path of the directory with the fire images
+- Non_FIRE_PATH = Path of the directory with images without fire
+- MODEL_PATH = Path where the saved model file should go
+- LRFIND_PLOT_PATH = Where the learning rate finder graph should go
+- TRAINING_PLOT_PATH = Where the training plot graph (loss & accuracy graphs) shoud go 
+3. Loading Data
+- Use the load_dataset() function of the load_data notebook ONLY for HPWREN HWB Database 
+- Otherwise, change the script so that the following numpy arrays are generated, if you want to use the prebuild loader in the Training notebook 
+MAKE SURE THESE in load_data.ipynb ![](screenshots/loading_in.PNG)<br /> MATCH THESE in ClassificationExample.ipynb <br /> ![](screenshot/THEOTHEREND.PNG)
+- Use the def loadData(pathToFiles) in ClassificationExample.ipynb
+
+
+4. Image Load Tester
+Tests the images to see if the loading worked
+5. Model Initialization <br />
+![](screenshots/init.png)<br />
+- The width, height and depth is the data format. Classes are the number of condiitons in the data. In our case: ["Fire", "Not-Fire"]
+- Change the optimization function if you know what you are doing. We are using a starndard SDG
+6. Learning Rate Finder <br />
+Run to find the place where the Network starts to learn
+![](screenshots/lrf.png) <br />
+![](screenshots/lrfplot.png) <br />
+More information is availbe here [pyimagesearch](https://www.pyimagesearch.com/2019/08/05/keras-learning-rate-finder/)
+
+Finally, fill out the INIT_LR from what you learned from above
+![](screenshots/initlr.png)<br />
+7. Train <br />
+![](screenshots/startTraining.PNG) <br />
+8. Get results <br />
+![](screenshots/results.PNG) <br />
+You will find the accuracy measures in the table. Find the model in fire_detection.model
+
+
+## TF Versions
+- TF 2.0 should be on pip path
+- TF1.15 is available in the conda environment tf15
+    - To start type: conda activate tf15
+
+
+
+
+## Contributors
+
+* **Byungheon Jeong**  - [byungheon-jeong](https://gitlab.nautilus.optiputer.net/byungheon-jeong)
+* **Spence Chen** - [Spencer](https://gitlab.nautilus.optiputer.net/Spencer123)
+* **Isaac Nealey** - [Isacc](https://gitlab.nautilus.optiputer.net/inealey)
+* **John Graham** - [John](https://gitlab.nautilus.optiputer.net/jjgraham)
+
+## Acknowledgments
+
+* The Dockerfile is from the Dockerhub of the Keras team
+* The Fire CNN and the Learning Rate finder is adapted from Adrain's excellent blog on first-detection - [Pyimagesearch](https://www.pyimagesearch.com/2019/11/18/fire-and-smoke-detection-with-keras-and-deep-learning/)
+

dockerBasedci.yaml

+image: docker:git
+before_script:
+  - docker login -u gitlab-ci-token -p $CI_BUILD_TOKEN gitlab-registry.nautilus.optiputer.net
+
+stages:
+  - build-and-push
+
+build-and-push-job:
+  stage: build-and-push
+  tags:
+     - build-as-docker
+  script:
+    - docker build --no-cache -t gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}:${CI_COMMIT_SHA:0:8} .
+    - docker tag gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}:${CI_COMMIT_SHA:0:8} gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}:latest
+    - docker push gitlab-registry.nautilus.optiputer.net/${CI_PROJECT_NAMESPACE}/${CI_PROJECT_NAME}

extraction.sh

+ls |grep -v ".*-c$"|xargs -I {} rm -rf {}
+find /userdata/kerasData/hpwren.ucsd.edu/HWB/HPWREN-FIgLib/ -type f -name ".*.mp4" -print
+
+find $PWD -maxdepth 2|grep ".*html.*"|xargs -I {} rm -rf {}
\ No newline at end of file

imageLoader.py

+#(128,128)
+
+def load_dataset(datasetPath, image_dimensions):
+    # grab the paths to all images in our dataset directory, then
+    # initialize our lists of images
+    imagePaths = os.listdir(datasetPath)
+    trainXList = []
+    testXList = []
+    testX = np.array([])
+    trainY = np.array([])
+    trainY = np.array([])
+    testY = np.array([])
+
+    testI = 0 
+    
+    # loop over the image paths
+    for directories in imagePaths:
+        tempF= []
+        tempNF = []
+        
+        for element in os.listdir(datasetPath + "/"+ directories):
+            if re.search(".jpg", element):
+                image = cv2.imread(datasetPath + "/"+ directories + "/" + element)
+                image = cv2.resize(image, image_dimensions)
+            if "+" in element:
+                tempF.append(image)
+            else:
+                tempNF.append(image)
+                
+        tempF = np.array(tempF, dtype="float32")
+        tempNF = np.array(tempNF,  dtype="float32")
+        
+        fireLabels = np.ones((tempF.shape[0],))
+        nonFireLabels = np.zeros((tempNF.shape[0],))
+        data = np.vstack([tempF, tempNF])
+        labels = np.hstack([fireLabels, nonFireLabels])
+        labels = to_categorical(labels, num_classes=2)
+        
+        print(labels)
+        
+        data /= 255
+
+        (t_trainX, t_testX, t_trainY, t_testY) = train_test_split(data, labels,
+    test_size=0.2, random_state=42)
+        
+        trainXList.append(t_trainX)
+        testXList.append(t_testX)
+        print(t_trainY.shape, trainY.shape)
+        
+        if trainY.size == 0:
+            trainY = t_trainY
+            testY = t_testY
+        else:
+            trainY = np.append(trainY, t_trainY, axis = 0)
+            testY = np.append(testY, t_testY, axis = 0)
+
+    
+    trainX = np.vstack(trainXList)
+    testX = np.vstack(testXList)
+#     trainY = np.hstack(trainYList)
+#     testY = np.hstack(testYList)
+    
+    labels = np.append(trainY, testY)
+    labels = to_categorical(labels, num_classes=2)
+    classTotals = labels.sum(axis=0)
+    classWeight = classTotals.max() / classTotals
+    
+    print(trainX.shape, testX.shape, trainY.shape, testY.shape)
+        
+    return trainX, testX, trainY, testY, classWeight
\ No newline at end of file

kerasDeloyment.yaml

+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: keras-2
+  namespace: digits
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      k8s-app: keras-2
+  template:
+    metadata:
+      labels:
+        k8s-app: keras-2
+    spec:
+      tolerations:
+      - key: "region"
+        operator: "Equal"
+        value: "allow"
+        effect: "NoSchedule"
+      containers:
+      - name: keras-kube
+        image: gitlab-registry.nautilus.optiputer.net/ar-noc/keras-smoke-detection:latest
+        securityContext:
+          capabilities:
+            add:
+              - NET_ADMIN
+        command: ["/bin/sh", "-c"]
+        args: ["sleep infinity"]
+        resources:
+          limits:
+            memory: "64Gi"
+            cpu: "8"
+            nvidia.com/gpu: 1
+          requests:
+            memory: "32Gi"
+            cpu: "2"
+            nvidia.com/gpu: 1
+
+        volumeMounts:
+        - mountPath: /userdata/kerasData
+          name: modeltraining
+      volumes:
+        - name: modeltraining
+          persistentVolumeClaim:
+            claimName: modeltraining
+            
+      # affinity:
+      #   nodeAffinity:
+      #     requiredDuringSchedulingIgnoredDuringExecution:
+      #       nodeSelectorTerms:
+      #       - matchExpressions:
+      #         - key: gpu-type
+      #           operator: In # Use NotIn for other types
+      #           values:
+      #           - K40
+      #           - V100
+