def testSetFinal(self):
with self.test_session():
sequences = np.arange(40, dtype=np.float32).reshape(4, 5, 2)
lengths = np.array([0, 1, 2, 5])
final_values = np.arange(40, 48, dtype=np.float32).reshape(4, 2)
expected_result = sequences.copy()
for i, l in enumerate(lengths):
expected_result[i, l:] = 0.0
expected_result[i, max(0, l - 1)] = final_values[i]
expected_result[range(4),
np.maximum(0, lengths - 1)] = final_values
self.assertAllEqual(
expected_result,
lstm_utils.set_final(sequences,
lengths,
final_values,
time_major=False).eval())
self.assertAllEqual(
np.transpose(expected_result, [1, 0, 2]),
lstm_utils.set_final(np.transpose(sequences, [1, 0, 2]),
lengths,
final_values,
time_major=True).eval())
def testSetFinal(self):
with self.test_session():
sequences = np.arange(40, dtype=np.float32).reshape(4, 5, 2)
lengths = np.array([0, 1, 2, 5])
final_values = np.arange(40, 48, dtype=np.float32).reshape(4, 2)
expected_result = sequences.copy()
for i, l in enumerate(lengths):
expected_result[i, l:] = 0.0
expected_result[i, max(0, l-1)] = final_values[i]
expected_result[range(4), np.maximum(0, lengths - 1)] = final_values
self.assertAllEqual(
expected_result,
lstm_utils.set_final(
sequences, lengths, final_values, time_major=False).eval())
self.assertAllEqual(
np.transpose(expected_result, [1, 0, 2]),
lstm_utils.set_final(
np.transpose(sequences, [1, 0, 2]),
lengths,
final_values,
time_major=True).eval())
def base_train_fn(embedding, hier_index):
"""Base function for training hierarchical decoder."""
split_size = self._level_lengths[-1]
split_input = hier_input[hier_index]
split_target = hier_target[hier_index]
split_length = hier_length[hier_index]
split_control = (
hier_control[hier_index] if hier_control is not None else None)
res = self._core_decoder.reconstruction_loss(
split_input, split_target, split_length, embedding, split_control)
loss_outputs.append(res)
decode_results = res[-1]
if self._hierarchical_encoder:
# Get the approximate "sample" from the model.
# Start with the inputs the RNN saw (excluding the start token).
samples = decode_results.rnn_input[:, 1:]
# Pad to be the max length.
samples = tf.pad(
samples,
[(0, 0), (0, split_size - tf.shape(samples)[1]), (0, 0)])
samples.set_shape([batch_size, split_size, self._output_depth])
# Set the final value based on the target, since the scheduled sampling
# helper does not sample the final value.
samples = lstm_utils.set_final(
samples,
split_length,
lstm_utils.get_final(split_target, split_length, time_major=False),
time_major=False)
# Return the re-encoded sample.
return self._hierarchical_encoder.level(0).encode(
sequence=samples,
sequence_length=split_length)
elif self._disable_autoregression:
return None
else:
return tf.concat(tf.nest.flatten(decode_results.final_state), axis=-1)
