MLP_training.py

customAdam = keras.optimizers.Adam(lr=0.001)
model.compile(optimizer=customAdam,  # Optimizer
              # Loss function to minimize
              loss="mean_squared_error",
              # List of metrics to monitor
              metrics=["binary_crossentropy","mean_squared_error"])
              
print('# Fit model on training data')

history = model.fit(x_train, 
                    labels_train, 
                    batch_size=32, 
                    shuffle = True, #important since we loaded cats first, dogs second.
                    epochs=3,
                    validation_data=(x_valid, labels_valid))
                    
#Train on 4096 samples, validate on 2048 samples
#loss: 0.5000 - binary_crossentropy: 8.0590 - mean_squared_error: 0.5000 - val_loss: 0.5000 - val_binary_crossentropy: 8.0591 - val_mean_squared_error: 0.5000