model_compile_train_augment_kaggle_mnist.py

# Compile the model
batch_size = 86

opt_rms = keras.optimizers.RMSprop(lr=0.0001, rho=0.9, epsilon=1e-08, decay=0.0)

# opt_rms = keras.optimizers.Adam(lr=0.001, decay=1e-6)
model.compile(loss='categorical_crossentropy', 
              optimizer=opt_rms, 
              metrics=['accuracy'])

from time import time
epochs = 50

tbCallBack = keras.callbacks.TensorBoard(log_dir='./Graph/{}'.format(time()), 
                                         histogram_freq=0, 
                                         write_graph=True, 
                                         write_images=True)
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5', 
                             verbose=1, 
                             monitor='val_acc', 
                             save_best_only=True, 
                             mode='auto')
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', 
                                            patience=3, 
                                            verbose=1, 
                                            factor=0.5, 
                                            min_lr=0.00001)

model.fit(x_train, 
          y_train, 
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test), 
          callbacks=[tbCallBack, checkpoint])