def testLastEncoding(self):
self._testLastEncoding(encoders.UnidirectionalRNNEncoder(
1, 10, cell_class=tf.contrib.rnn.LSTMCell))
self._testLastEncoding(encoders.UnidirectionalRNNEncoder(
3, 10, cell_class=tf.contrib.rnn.LSTMCell))
self._testLastEncoding(encoders.UnidirectionalRNNEncoder(
1, 10, cell_class=tf.contrib.rnn.GRUCell))
self._testLastEncoding(encoders.UnidirectionalRNNEncoder(
3, 10, cell_class=tf.contrib.rnn.GRUCell))
def testParallelEncoderSameInput(self):
sequence_length = [17, 21, 20]
inputs = _build_dummy_sequences(sequence_length)
encoder = encoders.ParallelEncoder([
encoders.UnidirectionalRNNEncoder(1, 20),
encoders.UnidirectionalRNNEncoder(1, 20)],
outputs_reducer=reducer.ConcatReducer())
outputs, _, encoded_length = encoder.encode(
inputs, sequence_length=sequence_length)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs, encoded_length = sess.run([outputs, encoded_length])
self.assertAllEqual([3, 21, 40], outputs.shape)
self.assertAllEqual(sequence_length, encoded_length)
def _encodeInParallel(self,
inputs,
sequence_length=None,
outputs_layer_fn=None,
combined_output_layer_fn=None):
columns = [
encoders.UnidirectionalRNNEncoder(1, 20),
encoders.UnidirectionalRNNEncoder(1, 20)]
encoder = encoders.ParallelEncoder(
columns,
outputs_reducer=reducer.ConcatReducer(),
outputs_layer_fn=outputs_layer_fn,
combined_output_layer_fn=combined_output_layer_fn)
return encoder.encode(inputs, sequence_length=sequence_length)
def testParallelEncoder(self):
sequence_lengths = [[17, 21, 20], [10, 9, 15]]
inputs = [
_build_dummy_sequences(length) for length in sequence_lengths]
encoder = encoders.ParallelEncoder([
encoders.UnidirectionalRNNEncoder(1, 20),
encoders.UnidirectionalRNNEncoder(1, 20)],
outputs_reducer=reducer.ConcatReducer(axis=1))
outputs, _, encoded_length = encoder.encode(
inputs, sequence_length=sequence_lengths)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs, encoded_length = sess.run([outputs, encoded_length])
self.assertAllEqual([3, 35, 20], outputs.shape)
self.assertAllEqual([27, 30, 35], encoded_length)
def testSequentialEncoder(self):
sequence_length = [17, 21, 20]
inputs = _build_dummy_sequences(sequence_length)
encoder = encoders.SequentialEncoder([
encoders.UnidirectionalRNNEncoder(1, 20),
encoders.PyramidalRNNEncoder(3, 10, reduction_factor=2)])
_, _, encoded_length = encoder.encode(
inputs, sequence_length=sequence_length)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
encoded_length = sess.run(encoded_length)
self.assertAllEqual([4, 5, 5], encoded_length)
def testSequentialEncoder(self):
sequence_length = [17, 21, 20]
inputs = _build_dummy_sequences(sequence_length)
encoder = encoders.SequentialEncoder([
encoders.UnidirectionalRNNEncoder(1, 20),
encoders.PyramidalRNNEncoder(3, 10, reduction_factor=2)
])
_, state, encoded_length = encoder.encode(
inputs, sequence_length=sequence_length)
self.assertEqual(4, len(state))
for s in state:
self.assertIsInstance(s, tf.contrib.rnn.LSTMStateTuple)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
encoded_length = sess.run(encoded_length)
self.assertAllEqual([4, 5, 5], encoded_length)
