os.makedirs('checkpoints_luong/encoder')
if not os.path.exists('checkpoints_luong/decoder'):
os.makedirs('checkpoints_luong/decoder')
# Uncomment these lines for inference mode
encoder_checkpoint = tf.train.latest_checkpoint('checkpoints_luong/encoder')
decoder_checkpoint = tf.train.latest_checkpoint('checkpoints_luong/decoder')
if encoder_checkpoint is not None and decoder_checkpoint is not None:
encoder.load_weights(encoder_checkpoint)
decoder.load_weights(decoder_checkpoint)
if MODE == 'train':
for e in range(NUM_EPOCHS):
en_initial_states = encoder.init_states(BATCH_SIZE)
encoder.save_weights(
'checkpoints_luong/encoder/encoder_{}.h5'.format(e + 1))
decoder.save_weights(
'checkpoints_luong/decoder/decoder_{}.h5'.format(e + 1))
for batch, (source_seq, target_seq_in,
target_seq_out) in enumerate(dataset.take(-1)):
loss = train_step(source_seq, target_seq_in, target_seq_out,
en_initial_states)
if batch % 100 == 0:
print('Epoch {} Batch {} Loss {:.4f}'.format(
e + 1, batch, loss.numpy()))
try:
predict()
predict("How are you today ?")
reply = Dialog.id2sent(output_word_ids.numpy()[0])
reply = reply.replace("TSTEOSTST", " ]")
reply = reply.replace("TSTSTARTTST", "[ ")
return reply
# 4. Run Model
print("<<<< Start Training >>>>")
for step in range(epoch):
with writer.as_default():
tf.summary.trace_export(
name="my_func_trace",
step=0,
profiler_outdir=logdir)
total_loss = 0.0
num_batches = 0
for index, (X, Y, Y_shifted) in enumerate(data_set):
batch_loss = train(X, Y_shifted, Y)
total_loss += batch_loss
num_batches += 1
total_loss = total_loss
data_set = tf.data.Dataset.zip((data_X, data_Y, data_Y_shifted))
data_set = data_set.shuffle(buffer_size=len(dialog_x)).batch(batch_size, drop_remainder=True)
print("Step: " + str(step) + ", Total Loss: " + str(total_loss))
print("--- Me: How are you doing, my dear?")
print("--- Computer: " + predict("How are you doing, my dear?"))
if step % save_interval == 0:
encoder.save_weights('encoder_weights_saving')
decoder.save_weights('decoder_weights_saving')
print("Model weights saved ....")
