def train(self, corpus, epochs=10, direction="pronounce", window=-1):
"""Runs training."""
# Create training log that also redirects to stdout.
stdout_file = sys.stdout
logfile = os.path.join(self._checkpoint_dir, "train.log")
print("Training log: {}".format(logfile))
sys.stdout = utils.DualLogger(logfile)
# Dump some parameters.
print(" Direction: {}".format(direction))
print(" # Epochs: {}".format(epochs))
print(" Batch size: {}".format(self._batch_size))
print(" Window size: {}".format(window))
print(" Max written len: {}".format(corpus.max_written_len))
print(" Max pron len: {}".format(corpus.max_pronounce_len))
print("Max written word len: {}".format(corpus.max_written_word_len))
print(" Max pron word len: {}".format(corpus.max_pronounce_word_len))
print(" Use residuals: {}".format(self._use_residuals))
# Perform training.
best_total_loss = 1000000
nbatches = data.num_batches(corpus, self._batch_size, direction=direction,
window=window)
for epoch in range(epochs):
self._train_accuracy.reset_states()
start = time.time()
total_loss = 0
steps = 0
batches = data.batchify(corpus, self._batch_size, direction,
window=window)
batch, (inputs, targ) = next(batches)
while batch > -1:
bos = np.expand_dims(
[self._output_symbols.find("<s>")] * np.shape(targ)[0], 1)
targets = np.concatenate((bos, targ), axis=-1)
batch_loss = self._train_step(inputs, targets)
total_loss += batch_loss
if batch % 10 == 0:
print("Epoch {} Batch {} (/{}) Loss {:.4f}".format(
epoch + 1,
batch,
nbatches,
batch_loss))
steps += 1
batch, (inputs, targ) = next(batches)
total_loss /= steps
print("Epoch {} Loss {:.4f} Accuracy {:.4f}".format(
epoch + 1, total_loss, self._train_accuracy.result()))
if total_loss < best_total_loss:
self._checkpoint.save(file_prefix=self._checkpoint_prefix)
print("Saved checkpoint to {}".format(self._checkpoint_prefix))
best_total_loss = total_loss
print("Time taken for 1 epoch {} sec\n".format(
time.time() - start))
print("Best total loss: {:.4f}".format(best_total_loss))
# Restore stdout.
sys.stdout = stdout_file
def train(self, corpus, epochs=10, direction="pronounce", window=-1):
"""Main entry point for running the training."""
# Create training log that also redirects to stdout.
stdout_file = sys.stdout
logfile = os.path.join(self._checkpoint_dir, "train.log")
print("Training log: {}".format(logfile))
sys.stdout = utils.DualLogger(logfile)
# Dump some parameters.
print(" Direction: {}".format(direction))
print(" # Epochs: {}".format(epochs))
print(" Batch size: {}".format(self._batch_size))
print(" Window size: {}".format(window))
print(" Max written len: {}".format(corpus.max_written_len))
print(" Max pron len: {}".format(corpus.max_pronounce_len))
print("Max written word len: {}".format(corpus.max_written_word_len))
print(" Max pron word len: {}".format(corpus.max_pronounce_word_len))
# Perform training.
best_total_loss = 1000000
nbatches = data.num_batches(corpus,
self._batch_size,
direction=direction,
window=window)
for epoch in range(epochs):
start = time.time()
total_loss = 0
steps = 0
batches = data.batchify(corpus,
self._batch_size,
direction,
window=window)
batch, (inp, targ) = next(batches)
enc_hidden = self._encoder.initialize_hidden_state(
self._batch_size)
if self._input_length == -1:
# TODO(agutkin,rws): Following two lines will break if batchify()
# returns an empty tuple.
if isinstance(inp, np.ndarray):
self._input_length = inp.shape[1]
if isinstance(targ, np.ndarray):
self._output_length = targ.shape[1]
while batch > -1:
batch_loss = self._train_step(inp, targ, enc_hidden)
total_loss += batch_loss
if batch % 10 == 0:
print("Epoch {} Batch {} (/{}) Loss {:.4f}".format(
epoch + 1, batch, nbatches, batch_loss.numpy()))
steps += 1
batch, (inp, targ) = next(batches)
total_loss /= steps
print("Epoch {} Loss {:.4f}".format(epoch + 1, total_loss))
if total_loss < best_total_loss:
self._checkpoint.save(file_prefix=self._checkpoint_prefix)
print("Saved checkpoint to {}".format(self._checkpoint_prefix))
best_total_loss = total_loss
print("Time taken for 1 epoch {} sec\n".format(time.time() -
start))
print("Best total loss: {:.4f}".format(best_total_loss))
# Restore stdout.
sys.stdout = stdout_file