log_dir,
'ep{epoch:03d}-val_loss{val_loss:.3f}-val_acc{val_acc:.3f}.h5'),
monitor='val_loss',
save_weights_only=True,
save_best_only=True,
period=1)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=3,
verbose=1)
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=5,
verbose=1)
training_generator = reader.generate_batch(X_train, y_train,
args.batch_size)
validation_generator = reader.generate_batch(X_val, y_val, args.batch_size)
print('\nRun: `tensorboard --logdir={}`\n'.format(log_dir))
model.fit_generator(
generator=training_generator,
steps_per_epoch=train_samples // args.batch_size,
epochs=args.epochs,
validation_data=validation_generator,
validation_steps=val_samples // args.batch_size,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
else:
model, encoder_model, decoder_model = nmt.build(inference=True)
assert (model.layers[7] == model.get_layer('attention'))
