hamelsmu · January 15, 2018 21:17
diff --git a/train_model.py b/train_model.py
 from keras.callbacks import CSVLogger, ModelCheckpoint

 #setup callbacks for model logging
 script_name_base = 'tutorial_seq2seq'
 csv_logger = CSVLogger('{:}.log'.format(script_name_base))
 model_checkpoint = ModelCheckpoint('{:}.epoch{{epoch:02d}}-val{{val_loss:.5f}}.hdf5'.format(script_name_base),
                                   save_best_only=True)

 # pass arguments to model.fit
 batch_size = 1200
 epochs = 7
 history = seq2seq_Model.fit([encoder_input_data, decoder_input_data], np.expand_dims(decoder_target_data, -1),
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.12, callbacks=[csv_logger, model_checkpoint])
diff --git a/training_progress.md b/training_progress.md
	from keras.callbacks import CSVLogger, ModelCheckpoint

	#setup callbacks for model logging
	script_name_base = 'tutorial_seq2seq'
	csv_logger = CSVLogger('{:}.log'.format(script_name_base))
	model_checkpoint = ModelCheckpoint('{:}.epoch{{epoch:02d}}-val{{val_loss:.5f}}.hdf5'.format(script_name_base),
	save_best_only=True)

	# pass arguments to model.fit
	batch_size = 1200
	epochs = 7
	history = seq2seq_Model.fit([encoder_input_data, decoder_input_data], np.expand_dims(decoder_target_data, -1),
	batch_size=batch_size,
	epochs=epochs,
	validation_split=0.12, callbacks=[csv_logger, model_checkpoint])