x.add(Flatten())
x.add(Dense(16, activation='relu'))
x.add(Dropout(0.5))
x.add(BatchNormalization())
# predictions = Dense(num_classes, activation = 'softmax')(x)
x.add(Dense(len(classes), activation="softmax"))
# Compile
x.compile(optimizer=Adam(lr=0.0001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Let's keep on csv file...
for c in batches.class_indices:
classes[batches.class_indices[c]] = c
x.classes = classes
filename = 'model_train_new.csv'
csv_log = keras.callbacks.CSVLogger(filename, separator=',', append=False)
# Early stopping if loss doesnt improve
early_stopping = EarlyStopping(patience=10)
checkpointer = ModelCheckpoint('resnet_best.h5',
verbose=1,
save_best_only=True)
tensorboard_callback = keras.callbacks.TensorBoard(log_dir='./logs',
histogram_freq=0,
batch_size=BATCH_SIZE,
write_graph=True,
write_grads=False,
write_images=False,
