steps_per_epoch = len(dataset_flow.input_tensor) // BATCH_SIZE
timer = Timer()
previous_loss = 1e5
for epoch in range(EPOCHS):
timer.start()
enc_hidden = encoder.initialize_hidden_state()
total_loss = 0
print('Something', end='\r')
for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
print("Epoch {} batch [{}/{}]".format(epoch + 1, batch + 1,
steps_per_epoch),
end='\r',
flush=True)
batch_loss = train_step(inp, targ, enc_hidden)
total_loss += batch_loss
total_loss = total_loss / steps_per_epoch
if total_loss < previous_loss:
print_str = 'Loss improved from {:.4f} to {:.4f} Saving model to file...'.format(
previous_loss, total_loss)
print(print_str)
encoder.save_weights('model_checkpoint/encoder.h5')
decoder.save_weights('model_checkpoint/decoder.h5')
previous_loss = total_loss
print('Epoch {} Loss {:.4f} : {:.0f} s'.format(epoch + 1, total_loss,
timer.stop()))
for epoch in range(EPOCHS):
start = time.time()
enc_hidden = encoder.initialize_hidden_state()
total_loss = 0
for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
batch_loss = train_step(inp, targ, enc_hidden)
total_loss += batch_loss
if batch % 100 == 0:
print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
batch,
batch_loss.numpy()))
# saving (checkpoint) the model every 2 epochs
if (epoch + 1) % 2 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
print('Epoch {} Loss {:.4f}'.format(epoch + 1,
total_loss / steps_per_epoch))
print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
# saving encoder and decoder weights
encoder.save_weights('./encoder', save_format='tf')
decoder.save_weights('./decoder', save_format='tf')
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
# final call to translation model
image_to_txt()
