def testStateTupleToCudnnLstmState(self):
with self.test_session():
h, c = lstm_utils.state_tuples_to_cudnn_lstm_state(
(rnn.LSTMStateTuple(h=np.arange(10).reshape(5, 2),
c=np.arange(10, 20).reshape(5, 2)),))
self.assertAllEqual(np.arange(10).reshape(1, 5, 2), h.eval())
self.assertAllEqual(np.arange(10, 20).reshape(1, 5, 2), c.eval())
h, c = lstm_utils.state_tuples_to_cudnn_lstm_state(
(rnn.LSTMStateTuple(h=np.arange(10).reshape(5, 2),
c=np.arange(20, 30).reshape(5, 2)),
rnn.LSTMStateTuple(h=np.arange(10, 20).reshape(5, 2),
c=np.arange(30, 40).reshape(5, 2))))
self.assertAllEqual(np.arange(20).reshape(2, 5, 2), h.eval())
self.assertAllEqual(np.arange(20, 40).reshape(2, 5, 2), c.eval())
def testStateTupleToCudnnLstmState(self):
with self.test_session():
h, c = lstm_utils.state_tuples_to_cudnn_lstm_state(
(rnn.LSTMStateTuple(h=np.arange(10).reshape(5, 2),
c=np.arange(10, 20).reshape(5, 2)), ))
self.assertAllEqual(np.arange(10).reshape(1, 5, 2), h.eval())
self.assertAllEqual(np.arange(10, 20).reshape(1, 5, 2), c.eval())
h, c = lstm_utils.state_tuples_to_cudnn_lstm_state(
(rnn.LSTMStateTuple(h=np.arange(10).reshape(5, 2),
c=np.arange(20, 30).reshape(5, 2)),
rnn.LSTMStateTuple(h=np.arange(10, 20).reshape(5, 2),
c=np.arange(30, 40).reshape(5, 2))))
self.assertAllEqual(np.arange(20).reshape(2, 5, 2), h.eval())
self.assertAllEqual(np.arange(20, 40).reshape(2, 5, 2), c.eval())
def _decode(self, z, helper, input_shape, max_length=None):
"""Decodes the given batch of latent vectors vectors, which may be 0-length.
Args:
z: Batch of latent vectors, sized `[batch_size, z_size]`, where `z_size`
may be 0 for unconditioned decoding.
helper: A seq2seq.Helper to use. If a TrainingHelper is passed and a
CudnnLSTM has previously been defined, it will be used instead.
input_shape: The shape of each model input vector passed to the decoder.
max_length: (Optional) The maximum iterations to decode.
Returns:
results: The LstmDecodeResults.
"""
initial_state = lstm_utils.initial_cell_state_from_embedding(
self._dec_cell, z, name='decoder/z_to_initial_state')
# CudnnLSTM does not support sampling so it can only replace TrainingHelper.
if self._cudnn_dec_lstm and type(helper) is seq2seq.TrainingHelper: # pylint:disable=unidiomatic-typecheck
rnn_output, _ = self._cudnn_dec_lstm(
tf.transpose(helper.inputs, [1, 0, 2]),
initial_state=lstm_utils.state_tuples_to_cudnn_lstm_state(
initial_state),
training=self._is_training)
with tf.variable_scope('decoder'):
rnn_output = self._output_layer(rnn_output)
results = lstm_utils.LstmDecodeResults(
rnn_input=helper.inputs[:, :, :self._output_depth],
rnn_output=tf.transpose(rnn_output, [1, 0, 2]),
samples=tf.zeros([z.shape[0], 0]),
# TODO(adarob): Pass the final state when it is valid (fixed-length).
final_state=None,
final_sequence_lengths=helper.sequence_length)
else:
if self._cudnn_dec_lstm:
tf.logging.warning(
'CudnnLSTM does not support sampling. Using `dynamic_decode` '
'instead.')
decoder = lstm_utils.Seq2SeqLstmDecoder(
self._dec_cell,
helper,
initial_state=initial_state,
input_shape=input_shape,
output_layer=self._output_layer)
final_output, final_state, final_lengths = seq2seq.dynamic_decode(
decoder,
maximum_iterations=max_length,
swap_memory=True,
scope='decoder')
results = lstm_utils.LstmDecodeResults(
rnn_input=final_output.rnn_input[:, :, :self._output_depth],
rnn_output=final_output.rnn_output,
samples=final_output.sample_id,
final_state=final_state,
final_sequence_lengths=final_lengths)
return results