def testParallelEncoderReuse(self): lengths = [tf.constant([2, 5, 4], dtype=tf.int32), tf.constant([6, 6, 3], dtype=tf.int32)] inputs = [tf.zeros([3, 5, 10]), tf.zeros([3, 6, 10])] encoder = encoders.ParallelEncoder(DenseEncoder(2, 20), outputs_reducer=None) outputs, _, _ = encoder(inputs, sequence_length=lengths) outputs = self.evaluate(outputs) self.assertIsInstance(outputs, tuple) self.assertEqual(len(outputs), 2)
def testParallelEncoderReuse(self):
lengths = [tf.constant([2, 5, 4], dtype=tf.int32), tf.constant([6, 6, 3], dtype=tf.int32)]
inputs = [tf.zeros([3, 5, 10]), tf.zeros([3, 6, 10])]
encoder = encoders.ParallelEncoder(DenseEncoder(2, 20), outputs_reducer=None)
outputs, _, _ = encoder.encode(inputs, sequence_length=lengths)
if not compat.is_tf2():
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs = self.evaluate(outputs)
self.assertIsInstance(outputs, tuple)
self.assertEqual(len(outputs), 2)
def testParallelEncoder(self):
sequence_lengths = [[3, 5, 2], [6, 6, 4]]
inputs = [tf.zeros([3, 5, 10]), tf.zeros([3, 6, 10])]
encoder = encoders.ParallelEncoder(
[DenseEncoder(1, 20), DenseEncoder(2, 20)],
outputs_reducer=reducer.ConcatReducer(axis=1))
outputs, state, encoded_length = encoder(
inputs, sequence_length=sequence_lengths)
self.assertEqual(len(state), 3)
outputs, encoded_length = self.evaluate([outputs, encoded_length])
self.assertAllEqual([3, 11, 20], outputs.shape)
self.assertAllEqual([9, 11, 6], encoded_length)
def _encodeInParallel(self,
inputs,
sequence_length=None,
outputs_layer_fn=None,
combined_output_layer_fn=None):
columns = [DenseEncoder(1, 20), DenseEncoder(1, 20)]
encoder = encoders.ParallelEncoder(
columns,
outputs_reducer=reducer.ConcatReducer(),
outputs_layer_fn=outputs_layer_fn,
combined_output_layer_fn=combined_output_layer_fn)
outputs, _, _ = encoder(inputs, sequence_length=sequence_length)
return self.evaluate(outputs)
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 testParallelEncoder(self):
sequence_lengths = [[3, 5, 2], [6, 6, 4]]
inputs = [tf.zeros([3, 5, 10]), tf.zeros([3, 6, 10])]
encoder = encoders.ParallelEncoder(
[DenseEncoder(1, 20), DenseEncoder(2, 20)],
outputs_reducer=reducer.ConcatReducer(axis=1))
outputs, state, encoded_length = encoder.encode(
inputs, sequence_length=sequence_lengths)
self.assertEqual(len(state), 3)
if not compat.is_tf2():
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs, encoded_length = self.evaluate([outputs, encoded_length])
self.assertAllEqual([3, 11, 20], outputs.shape)
self.assertAllEqual([9, 11, 6], encoded_length)
def _encodeInParallel(self,
inputs,
sequence_length=None,
outputs_layer_fn=None,
combined_output_layer_fn=None):
columns = [DenseEncoder(1, 20), DenseEncoder(1, 20)]
encoder = encoders.ParallelEncoder(
columns,
outputs_reducer=reducer.ConcatReducer(),
outputs_layer_fn=outputs_layer_fn,
combined_output_layer_fn=combined_output_layer_fn)
outputs, _, _ = encoder.encode(inputs, sequence_length=sequence_length)
if not compat.is_tf2():
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
return self.evaluate(outputs)
