# Compile the model with an Adam optimizar and a learning rate of 0.02
model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=0.0001))
checkpoint = ModelCheckpoint('keras_models/weights.hdf5',
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='auto')
# Train the model
history = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
batch_size=4,
epochs=100,
callbacks=[checkpoint])
model.load_weights('keras_models/weights.hdf5')
save_model(model)
else:
model = load_model()
# Make predictions
predictions = model.predict(x_test)
predictions = predictions.round()
# Load prediction data and history to plot interesting graphs
graphics = Graphics()
if option == "2":
graphics.load_data(predictions, y_test)
