Train and checkpoint the seq2seq model

train_ckpt_seq2seq.py

# Create an Adam optimizer and clips gradients by norm
optimizer = tf.keras.optimizers.Adam(clipnorm=5.0)
# Create a checkpoint object to save the model
checkpoint_dir = './training_ckpt_seq2seq'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 encoder=encoder,
                                 decoder=decoder)

losses, accuracies = main_train(encoder, decoder, dataset, EPOCHS, BATCH_SIZE, optimizer, checkpoint, checkpoint_prefix)