def testLuongScaledDType(self):
# Test case for GitHub issue 18099
for dt in [np.float16, np.float32, np.float64]:
num_units = 128
encoder_outputs = array_ops.placeholder(dt, shape=[64, None, 256])
encoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
decoder_inputs = array_ops.placeholder(dt, shape=[64, None, 128])
decoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
batch_size = 64
attention_mechanism = wrapper.LuongAttention(
num_units=num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length,
scale=True,
dtype=dt,
)
cell = rnn_cell.LSTMCell(num_units)
cell = wrapper.AttentionWrapper(cell, attention_mechanism)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dt, batch_size=batch_size))
final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual(final_outputs.rnn_output.dtype, dt)
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
def _testDynamicDecodeRNNWithTrainingHelperMatchesDynamicRNN(
self, use_sequence_length):
sequence_length = [3, 4, 3, 1, 0]
batch_size = 5
max_time = 8
input_depth = 7
cell_depth = 10
max_out = max(sequence_length)
with self.session(use_gpu=True) as sess:
inputs = np.random.randn(batch_size, max_time,
input_depth).astype(np.float32)
cell = rnn_cell.LSTMCell(cell_depth)
zero_state = cell.zero_state(dtype=dtypes.float32, batch_size=batch_size)
helper = helper_py.TrainingHelper(inputs, sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell, helper=helper, initial_state=zero_state)
# Match the variable scope of dynamic_rnn below so we end up
# using the same variables
with vs.variable_scope("root") as scope:
final_decoder_outputs, final_decoder_state, _ = decoder.dynamic_decode(
my_decoder,
# impute_finished=True ensures outputs and final state
# match those of dynamic_rnn called with sequence_length not None
impute_finished=use_sequence_length,
scope=scope)
with vs.variable_scope(scope, reuse=True) as scope:
final_rnn_outputs, final_rnn_state = rnn.dynamic_rnn(
cell,
inputs,
sequence_length=sequence_length if use_sequence_length else None,
initial_state=zero_state,
scope=scope)
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"final_decoder_outputs": final_decoder_outputs,
"final_decoder_state": final_decoder_state,
"final_rnn_outputs": final_rnn_outputs,
"final_rnn_state": final_rnn_state
})
# Decoder only runs out to max_out; ensure values are identical
# to dynamic_rnn, which also zeros out outputs and passes along state.
self.assertAllClose(sess_results["final_decoder_outputs"].rnn_output,
sess_results["final_rnn_outputs"][:, 0:max_out, :])
if use_sequence_length:
self.assertAllClose(sess_results["final_decoder_state"],
sess_results["final_rnn_state"])
def _testWithMaybeMultiAttention(self,
is_multi,
create_attention_mechanisms,
expected_final_output,
expected_final_state,
attention_mechanism_depths,
alignment_history=False,
expected_final_alignment_history=None,
attention_layer_sizes=None,
attention_layers=None,
name=''):
# Allow is_multi to be True with a single mechanism to enable test for
# passing in a single mechanism in a list.
assert len(create_attention_mechanisms) == 1 or is_multi
encoder_sequence_length = [3, 2, 3, 1, 1]
decoder_sequence_length = [2, 0, 1, 2, 3]
batch_size = 5
encoder_max_time = 8
decoder_max_time = 4
input_depth = 7
encoder_output_depth = 10
cell_depth = 9
if attention_layer_sizes is not None:
# Compute sum of attention_layer_sizes. Use encoder_output_depth if None.
attention_depth = sum(attention_layer_size or encoder_output_depth
for attention_layer_size in attention_layer_sizes)
elif attention_layers is not None:
# Compute sum of attention_layers output depth.
attention_depth = sum(
attention_layer.compute_output_shape(
[batch_size, cell_depth + encoder_output_depth]).dims[-1].value
for attention_layer in attention_layers)
else:
attention_depth = encoder_output_depth * len(create_attention_mechanisms)
decoder_inputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32),
shape=(None, None, input_depth))
encoder_outputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, encoder_max_time,
encoder_output_depth).astype(np.float32),
shape=(None, None, encoder_output_depth))
attention_mechanisms = [
creator(num_units=depth,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length)
for creator, depth in zip(create_attention_mechanisms,
attention_mechanism_depths)]
with self.session(use_gpu=True) as sess:
with vs.variable_scope(
'root',
initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
attention_layer_size = attention_layer_sizes
attention_layer = attention_layers
if not is_multi:
if attention_layer_size is not None:
attention_layer_size = attention_layer_size[0]
if attention_layer is not None:
attention_layer = attention_layer[0]
cell = rnn_cell.LSTMCell(cell_depth)
cell = wrapper.AttentionWrapper(
cell,
attention_mechanisms if is_multi else attention_mechanisms[0],
attention_layer_size=attention_layer_size,
alignment_history=alignment_history,
attention_layer=attention_layer)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dtypes.float32, batch_size=batch_size))
final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
self.assertEqual((batch_size, None, attention_depth),
tuple(final_outputs.rnn_output.get_shape().as_list()))
self.assertEqual((batch_size, None),
tuple(final_outputs.sample_id.get_shape().as_list()))
self.assertEqual((batch_size, attention_depth),
tuple(final_state.attention.get_shape().as_list()))
self.assertEqual((batch_size, cell_depth),
tuple(final_state.cell_state.c.get_shape().as_list()))
self.assertEqual((batch_size, cell_depth),
tuple(final_state.cell_state.h.get_shape().as_list()))
if alignment_history:
if is_multi:
state_alignment_history = []
for history_array in final_state.alignment_history:
history = history_array.stack()
self.assertEqual(
(None, batch_size, None),
tuple(history.get_shape().as_list()))
state_alignment_history.append(history)
state_alignment_history = tuple(state_alignment_history)
else:
state_alignment_history = final_state.alignment_history.stack()
self.assertEqual(
(None, batch_size, None),
tuple(state_alignment_history.get_shape().as_list()))
nest.assert_same_structure(
cell.state_size,
cell.zero_state(batch_size, dtypes.float32))
# Remove the history from final_state for purposes of the
# remainder of the tests.
final_state = final_state._replace(alignment_history=()) # pylint: disable=protected-access
else:
state_alignment_history = ()
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
'final_outputs': final_outputs,
'final_state': final_state,
'state_alignment_history': state_alignment_history,
})
final_output_info = nest.map_structure(get_result_summary,
sess_results['final_outputs'])
final_state_info = nest.map_structure(get_result_summary,
sess_results['final_state'])
print(name)
print('Copy/paste:\nexpected_final_output = %s' % str(final_output_info))
print('expected_final_state = %s' % str(final_state_info))
nest.map_structure(self.assertAllCloseOrEqual, expected_final_output,
final_output_info)
nest.map_structure(self.assertAllCloseOrEqual, expected_final_state,
final_state_info)
if alignment_history: # by default, the wrapper emits attention as output
final_alignment_history_info = nest.map_structure(
get_result_summary, sess_results['state_alignment_history'])
print('expected_final_alignment_history = %s' %
str(final_alignment_history_info))
nest.map_structure(
self.assertAllCloseOrEqual,
# outputs are batch major but the stacked TensorArray is time major
expected_final_alignment_history,
final_alignment_history_info)
def _testDynamicDecodeRNN(self, time_major, maximum_iterations=None):
sequence_length = [3, 4, 3, 1, 0]
batch_size = 5
max_time = 8
input_depth = 7
cell_depth = 10
max_out = max(sequence_length)
with self.session(use_gpu=True) as sess:
if time_major:
inputs = np.random.randn(max_time, batch_size,
input_depth).astype(np.float32)
else:
inputs = np.random.randn(batch_size, max_time,
input_depth).astype(np.float32)
cell = rnn_cell.LSTMCell(cell_depth)
helper = helper_py.TrainingHelper(
inputs, sequence_length, time_major=time_major)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dtypes.float32, batch_size=batch_size))
final_outputs, final_state, final_sequence_length = (
decoder.dynamic_decode(my_decoder, output_time_major=time_major,
maximum_iterations=maximum_iterations))
def _t(shape):
if time_major:
return (shape[1], shape[0]) + shape[2:]
return shape
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertTrue(isinstance(final_state, rnn_cell.LSTMStateTuple))
self.assertEqual(
(batch_size,),
tuple(final_sequence_length.get_shape().as_list()))
self.assertEqual(
_t((batch_size, None, cell_depth)),
tuple(final_outputs.rnn_output.get_shape().as_list()))
self.assertEqual(
_t((batch_size, None)),
tuple(final_outputs.sample_id.get_shape().as_list()))
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"final_outputs": final_outputs,
"final_state": final_state,
"final_sequence_length": final_sequence_length,
})
# Mostly a smoke test
time_steps = max_out
expected_length = sequence_length
if maximum_iterations is not None:
time_steps = min(max_out, maximum_iterations)
expected_length = [min(x, maximum_iterations) for x in expected_length]
self.assertEqual(
_t((batch_size, time_steps, cell_depth)),
sess_results["final_outputs"].rnn_output.shape)
self.assertEqual(
_t((batch_size, time_steps)),
sess_results["final_outputs"].sample_id.shape)
self.assertItemsEqual(expected_length,
sess_results["final_sequence_length"])
def testStepWithInferenceHelperMultilabel(self):
batch_size = 5
vocabulary_size = 7
cell_depth = vocabulary_size
start_token = 0
end_token = 6
start_inputs = array_ops.one_hot(
np.ones(batch_size) * start_token, vocabulary_size)
# The sample function samples independent bernoullis from the logits.
sample_fn = (
lambda x: helper_py.bernoulli_sample(logits=x, dtype=dtypes.bool))
# The next inputs are a one-hot encoding of the sampled labels.
next_inputs_fn = math_ops.to_float
end_fn = lambda sample_ids: sample_ids[:, end_token]
with self.session(use_gpu=True) as sess:
with variable_scope.variable_scope(
"testStepWithInferenceHelper",
initializer=init_ops.constant_initializer(0.01)):
cell = rnn_cell.LSTMCell(vocabulary_size)
helper = helper_py.InferenceHelper(
sample_fn,
sample_shape=[cell_depth],
sample_dtype=dtypes.bool,
start_inputs=start_inputs,
end_fn=end_fn,
next_inputs_fn=next_inputs_fn)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(cell_depth, cell_depth),
output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32,
dtypes.bool),
output_dtype)
(first_finished, first_inputs,
first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(constant_op.constant(0),
first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertTrue(
isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state,
rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, cell_depth),
step_outputs[0].get_shape())
self.assertEqual((batch_size, cell_depth),
step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[1].get_shape())
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
})
sample_ids = sess_results["step_outputs"].sample_id
self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
expected_step_finished = sample_ids[:, end_token]
expected_step_next_inputs = sample_ids.astype(np.float32)
self.assertAllEqual(expected_step_finished,
sess_results["step_finished"])
self.assertAllEqual(expected_step_next_inputs,
sess_results["step_next_inputs"])
def testStepWithInferenceHelperCategorical(self):
batch_size = 5
vocabulary_size = 7
cell_depth = vocabulary_size
start_token = 0
end_token = 6
start_inputs = array_ops.one_hot(
np.ones(batch_size) * start_token, vocabulary_size)
# The sample function samples categorically from the logits.
sample_fn = lambda x: helper_py.categorical_sample(logits=x)
# The next inputs are a one-hot encoding of the sampled labels.
next_inputs_fn = (lambda x: array_ops.one_hot(
x, vocabulary_size, dtype=dtypes.float32))
end_fn = lambda sample_ids: math_ops.equal(sample_ids, end_token)
with self.session(use_gpu=True) as sess:
with variable_scope.variable_scope(
"testStepWithInferenceHelper",
initializer=init_ops.constant_initializer(0.01)):
cell = rnn_cell.LSTMCell(vocabulary_size)
helper = helper_py.InferenceHelper(
sample_fn,
sample_shape=(),
sample_dtype=dtypes.int32,
start_inputs=start_inputs,
end_fn=end_fn,
next_inputs_fn=next_inputs_fn)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(
cell_depth, tensor_shape.TensorShape([])), output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32,
dtypes.int32),
output_dtype)
(first_finished, first_inputs,
first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(constant_op.constant(0),
first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertTrue(
isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state,
rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, cell_depth),
step_outputs[0].get_shape())
self.assertEqual((batch_size, ), step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[1].get_shape())
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
})
sample_ids = sess_results["step_outputs"].sample_id
self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
expected_step_finished = (sample_ids == end_token)
expected_step_next_inputs = np.zeros(
(batch_size, vocabulary_size))
expected_step_next_inputs[np.arange(batch_size),
sample_ids] = 1.0
self.assertAllEqual(expected_step_finished,
sess_results["step_finished"])
self.assertAllEqual(expected_step_next_inputs,
sess_results["step_next_inputs"])
def _testStepWithTrainingHelper(self, use_output_layer):
sequence_length = [3, 4, 3, 1, 0]
batch_size = 5
max_time = 8
input_depth = 7
cell_depth = 10
output_layer_depth = 3
with self.session(use_gpu=True) as sess:
inputs = np.random.randn(batch_size, max_time,
input_depth).astype(np.float32)
cell = rnn_cell.LSTMCell(cell_depth)
helper = helper_py.TrainingHelper(inputs,
sequence_length,
time_major=False)
if use_output_layer:
output_layer = layers_core.Dense(output_layer_depth,
use_bias=False)
expected_output_depth = output_layer_depth
else:
output_layer = None
expected_output_depth = cell_depth
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size),
output_layer=output_layer)
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(expected_output_depth,
tensor_shape.TensorShape([])),
output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32, dtypes.int32),
output_dtype)
(first_finished, first_inputs,
first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(constant_op.constant(0),
first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, expected_output_depth),
step_outputs[0].get_shape())
self.assertEqual((batch_size, ), step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[1].get_shape())
if use_output_layer:
# The output layer was accessed
self.assertEqual(len(output_layer.variables), 1)
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
})
self.assertAllEqual([False, False, False, False, True],
sess_results["first_finished"])
self.assertAllEqual([False, False, False, True, True],
sess_results["step_finished"])
self.assertEqual(output_dtype.sample_id,
sess_results["step_outputs"].sample_id.dtype)
self.assertAllEqual(
np.argmax(sess_results["step_outputs"].rnn_output, -1),
sess_results["step_outputs"].sample_id)
def _testStepWithScheduledOutputTrainingHelper(self, sampling_probability,
use_next_inputs_fn,
use_auxiliary_inputs):
sequence_length = [3, 4, 3, 1, 0]
batch_size = 5
max_time = 8
input_depth = 7
cell_depth = input_depth
if use_auxiliary_inputs:
auxiliary_input_depth = 4
auxiliary_inputs = np.random.randn(
batch_size, max_time, auxiliary_input_depth).astype(np.float32)
else:
auxiliary_inputs = None
with self.session(use_gpu=True) as sess:
inputs = np.random.randn(batch_size, max_time,
input_depth).astype(np.float32)
cell = rnn_cell.LSTMCell(cell_depth)
sampling_probability = constant_op.constant(sampling_probability)
if use_next_inputs_fn:
def next_inputs_fn(outputs):
# Use deterministic function for test.
samples = math_ops.argmax(outputs, axis=1)
return array_ops.one_hot(samples,
cell_depth,
dtype=dtypes.float32)
else:
next_inputs_fn = None
helper = helper_py.ScheduledOutputTrainingHelper(
inputs=inputs,
sequence_length=sequence_length,
sampling_probability=sampling_probability,
time_major=False,
next_inputs_fn=next_inputs_fn,
auxiliary_inputs=auxiliary_inputs)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(cell_depth,
tensor_shape.TensorShape([])),
output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32, dtypes.int32),
output_dtype)
(first_finished, first_inputs,
first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(constant_op.constant(0),
first_inputs, first_state)
if use_next_inputs_fn:
output_after_next_inputs_fn = next_inputs_fn(
step_outputs.rnn_output)
batch_size_t = my_decoder.batch_size
self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, cell_depth),
step_outputs[0].get_shape())
self.assertEqual((batch_size, ), step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[1].get_shape())
sess.run(variables.global_variables_initializer())
fetches = {
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
}
if use_next_inputs_fn:
fetches[
"output_after_next_inputs_fn"] = output_after_next_inputs_fn
sess_results = sess.run(fetches)
self.assertAllEqual([False, False, False, False, True],
sess_results["first_finished"])
self.assertAllEqual([False, False, False, True, True],
sess_results["step_finished"])
sample_ids = sess_results["step_outputs"].sample_id
self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
batch_where_not_sampling = np.where(np.logical_not(sample_ids))
batch_where_sampling = np.where(sample_ids)
auxiliary_inputs_to_concat = (
auxiliary_inputs[:, 1] if use_auxiliary_inputs else np.array(
[]).reshape(batch_size, 0).astype(np.float32))
expected_next_sampling_inputs = np.concatenate(
(sess_results["output_after_next_inputs_fn"]
[batch_where_sampling] if use_next_inputs_fn else
sess_results["step_outputs"].rnn_output[batch_where_sampling],
auxiliary_inputs_to_concat[batch_where_sampling]),
axis=-1)
self.assertAllClose(
sess_results["step_next_inputs"][batch_where_sampling],
expected_next_sampling_inputs)
self.assertAllClose(
sess_results["step_next_inputs"][batch_where_not_sampling],
np.concatenate(
(np.squeeze(inputs[batch_where_not_sampling, 1], axis=0),
auxiliary_inputs_to_concat[batch_where_not_sampling]),
axis=-1))
def testStepWithScheduledEmbeddingTrainingHelper(self):
sequence_length = [3, 4, 3, 1, 0]
batch_size = 5
max_time = 8
input_depth = 7
vocabulary_size = 10
with self.session(use_gpu=True) as sess:
inputs = np.random.randn(batch_size, max_time,
input_depth).astype(np.float32)
embeddings = np.random.randn(vocabulary_size,
input_depth).astype(np.float32)
half = constant_op.constant(0.5)
cell = rnn_cell.LSTMCell(vocabulary_size)
helper = helper_py.ScheduledEmbeddingTrainingHelper(
inputs=inputs,
sequence_length=sequence_length,
embedding=embeddings,
sampling_probability=half,
time_major=False)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(vocabulary_size,
tensor_shape.TensorShape([])),
output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32, dtypes.int32),
output_dtype)
(first_finished, first_inputs,
first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(constant_op.constant(0),
first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, vocabulary_size),
step_outputs[0].get_shape())
self.assertEqual((batch_size, ), step_outputs[1].get_shape())
self.assertEqual((batch_size, vocabulary_size),
first_state[0].get_shape())
self.assertEqual((batch_size, vocabulary_size),
first_state[1].get_shape())
self.assertEqual((batch_size, vocabulary_size),
step_state[0].get_shape())
self.assertEqual((batch_size, vocabulary_size),
step_state[1].get_shape())
self.assertEqual((batch_size, input_depth),
step_next_inputs.get_shape())
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
})
self.assertAllEqual([False, False, False, False, True],
sess_results["first_finished"])
self.assertAllEqual([False, False, False, True, True],
sess_results["step_finished"])
sample_ids = sess_results["step_outputs"].sample_id
self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
batch_where_not_sampling = np.where(sample_ids == -1)
batch_where_sampling = np.where(sample_ids > -1)
self.assertAllClose(
sess_results["step_next_inputs"][batch_where_sampling],
embeddings[sample_ids[batch_where_sampling]])
self.assertAllClose(
sess_results["step_next_inputs"][batch_where_not_sampling],
np.squeeze(inputs[batch_where_not_sampling, 1]))
def testStepWithSampleEmbeddingHelper(self):
batch_size = 5
vocabulary_size = 7
cell_depth = vocabulary_size # cell's logits must match vocabulary size
input_depth = 10
np.random.seed(0)
start_tokens = np.random.randint(0, vocabulary_size, size=batch_size)
end_token = 1
with self.session(use_gpu=True) as sess:
with variable_scope.variable_scope(
"testStepWithSampleEmbeddingHelper",
initializer=init_ops.constant_initializer(0.01)):
embeddings = np.random.randn(vocabulary_size,
input_depth).astype(np.float32)
cell = rnn_cell.LSTMCell(vocabulary_size)
helper = helper_py.SampleEmbeddingHelper(embeddings,
start_tokens,
end_token,
seed=0)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(
cell_depth, tensor_shape.TensorShape([])), output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32,
dtypes.int32),
output_dtype)
(first_finished, first_inputs,
first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(constant_op.constant(0),
first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertTrue(
isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state,
rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, cell_depth),
step_outputs[0].get_shape())
self.assertEqual((batch_size, ), step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth),
step_state[1].get_shape())
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
})
sample_ids = sess_results["step_outputs"].sample_id
self.assertEqual(output_dtype.sample_id, sample_ids.dtype)
expected_step_finished = (sample_ids == end_token)
expected_step_next_inputs = embeddings[sample_ids]
self.assertAllEqual(expected_step_finished,
sess_results["step_finished"])
self.assertAllEqual(expected_step_next_inputs,
sess_results["step_next_inputs"])
