本文整理汇总了Python中tensorflow.compat.v1.assert_equal方法的典型用法代码示例。如果您正苦于以下问题:Python v1.assert_equal方法的具体用法?Python v1.assert_equal怎么用?Python v1.assert_equal使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v1
的用法示例。
在下文中一共展示了v1.assert_equal方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testReset
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def testReset(self):
batch_size = 2
key_depth = 3
val_depth = 5
memory_size = 4
memory = transformer_memory.TransformerMemory(
batch_size, key_depth, val_depth, memory_size)
vals = tf.random_uniform([batch_size, memory_size, val_depth], minval=1.0)
logits = tf.random_uniform([batch_size, memory_size], minval=1.0)
update_op = memory.set(vals, logits)
reset_op = memory.reset([1])
mem_vals, mem_logits = memory.get()
assert_op1 = tf.assert_equal(mem_vals[0], vals[0])
assert_op2 = tf.assert_equal(mem_logits[0], logits[0])
with tf.control_dependencies([assert_op1, assert_op2]):
all_zero1 = tf.reduce_sum(tf.abs(mem_vals[1]))
all_zero2 = tf.reduce_sum(tf.abs(mem_logits[1]))
with self.test_session() as session:
session.run(tf.global_variables_initializer())
session.run(update_op)
session.run(reset_op)
zero1, zero2 = session.run([all_zero1, all_zero2])
self.assertAllEqual(0, zero1)
self.assertAllEqual(0, zero2)
示例2: _merge_decode_results
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def _merge_decode_results(self, decode_results):
"""Merge in the output dimension."""
output_axis = -1
assert decode_results
zipped_results = lstm_utils.LstmDecodeResults(*list(zip(*decode_results)))
with tf.control_dependencies([
tf.assert_equal(
zipped_results.final_sequence_lengths, self.hparams.max_seq_len,
message='Variable length not supported by '
'MultiOutCategoricalLstmDecoder.')]):
if zipped_results.final_state[0] is None:
final_state = None
else:
final_state = tf.nest.map_structure(
lambda x: tf.concat(x, axis=output_axis),
zipped_results.final_state)
return lstm_utils.LstmDecodeResults(
rnn_output=tf.concat(zipped_results.rnn_output, axis=output_axis),
rnn_input=tf.concat(zipped_results.rnn_input, axis=output_axis),
samples=tf.concat(zipped_results.samples, axis=output_axis),
final_state=final_state,
final_sequence_lengths=zipped_results.final_sequence_lengths[0])
示例3: _reverse_seq
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def _reverse_seq(sequence, sequence_lengths=None):
"""Reverse sequence along dim 0.
Args:
sequence: Tensor of shape [T, B, ...].
sequence_lengths: (optional) tensor of shape [B]. If `None`, only reverse
along dim 0.
Returns:
Tensor of same shape as sequence with dim 0 reversed up to sequence_lengths.
"""
if sequence_lengths is None:
return tf.reverse(sequence, [0])
sequence_lengths = tf.convert_to_tensor(sequence_lengths)
with tf.control_dependencies(
[tf.assert_equal(sequence.shape[1], sequence_lengths.shape[0])]):
return tf.reverse_sequence(
sequence, sequence_lengths, seq_axis=0, batch_axis=1)
示例4: _assert_correct_number_of_anchors
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def _assert_correct_number_of_anchors(self, anchors_list,
feature_map_shape_list):
"""Assert that correct number of anchors was generated.
Args:
anchors_list: A list of box_list.BoxList object holding anchors generated.
feature_map_shape_list: list of (height, width) pairs in the format
[(height_0, width_0), (height_1, width_1), ...] that the generated
anchors must align with.
Returns:
Op that raises InvalidArgumentError if the number of anchors does not
match the number of expected anchors.
"""
expected_num_anchors = 0
actual_num_anchors = 0
for num_anchors_per_location, feature_map_shape, anchors in zip(
self.num_anchors_per_location(), feature_map_shape_list, anchors_list):
expected_num_anchors += (num_anchors_per_location
* feature_map_shape[0]
* feature_map_shape[1])
actual_num_anchors += anchors.num_boxes()
return tf.assert_equal(expected_num_anchors, actual_num_anchors)
示例5: testAddTimingSignalsGivenPositionsEquivalent
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def testAddTimingSignalsGivenPositionsEquivalent(self):
x = tf.zeros([1, 10, 128], dtype=tf.float32)
positions = tf.expand_dims(tf.range(0, 10, dtype=tf.float32), axis=0)
# The method add_timing_signal_1d_given_position could be replaced by
# add_timing_signals_given_positions:
tf.assert_equal(
common_attention.add_timing_signal_1d_given_position(x, positions),
common_attention.add_timing_signals_given_positions(x, [positions]))
示例6: tflite_compat_mel
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def tflite_compat_mel(wav_audio, hparams):
"""EXPERIMENTAL: Log mel spec with ops that can be made TFLite compatible."""
samples, decoded_sample_rate = tf.audio.decode_wav(
wav_audio, desired_channels=1)
samples = tf.squeeze(samples, axis=1)
# Ensure that we decoded the samples at the expected sample rate.
with tf.control_dependencies(
[tf.assert_equal(decoded_sample_rate, hparams.sample_rate)]):
return tflite_compat_mel_from_samples(samples, hparams)
示例7: assert_shape_equal
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def assert_shape_equal(shape_a, shape_b):
"""Asserts that shape_a and shape_b are equal.
If the shapes are static, raises a ValueError when the shapes
mismatch.
If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
mismatch.
Args:
shape_a: a list containing shape of the first tensor.
shape_b: a list containing shape of the second tensor.
Returns:
Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
when the shapes are dynamic.
Raises:
ValueError: When shapes are both static and unequal.
"""
if (all(isinstance(dim, int) for dim in shape_a) and
all(isinstance(dim, int) for dim in shape_b)):
if shape_a != shape_b:
raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
else: return tf.no_op()
else:
return tf.assert_equal(shape_a, shape_b)
示例8: CenterCropImages
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def CenterCropImages(images, input_shape,
target_shape):
"""Take a central crop of given size from a list of images.
Args:
images: List of tensors of shape [batch_size, h, w, c].
input_shape: Shape [h, w, c] of the input images.
target_shape: Shape [h, w] of the cropped output.
Returns:
crops: List of cropped tensors of shape [batch_size] + target_shape.
"""
if len(input_shape) != 3:
raise ValueError(
'The input shape has to be of the form (height, width, channels) '
'but has len {}'.format(len(input_shape)))
if len(target_shape) != 2:
raise ValueError('The target shape has to be of the form (height, width) '
'but has len {}'.format(len(target_shape)))
if input_shape[0] == target_shape[0] and input_shape[1] == target_shape[1]:
return [image for image in images]
# Assert all images have the same shape.
assert_ops = []
for image in images:
assert_ops.append(
tf.assert_equal(
input_shape[:2],
tf.shape(image)[1:3],
message=('All images must have same width and height'
'for CenterCropImages.')))
offset_y = int(input_shape[0] - target_shape[0]) // 2
offset_x = int(input_shape[1] - target_shape[1]) // 2
with tf.control_dependencies(assert_ops):
crops = [
tf.image.crop_to_bounding_box(image, offset_y, offset_x,
target_shape[0], target_shape[1])
for image in images
]
return crops
示例9: assert_shape_equal_along_first_dimension
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def assert_shape_equal_along_first_dimension(shape_a, shape_b):
"""Asserts that shape_a and shape_b are the same along the 0th-dimension.
If the shapes are static, raises a ValueError when the shapes
mismatch.
If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
mismatch.
Args:
shape_a: a list containing shape of the first tensor.
shape_b: a list containing shape of the second tensor.
Returns:
Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
when the shapes are dynamic.
Raises:
ValueError: When shapes are both static and unequal.
"""
if isinstance(shape_a[0], int) and isinstance(shape_b[0], int):
if shape_a[0] != shape_b[0]:
raise ValueError('Unequal first dimension {}, {}'.format(
shape_a[0], shape_b[0]))
else: return tf.no_op()
else:
return tf.assert_equal(shape_a[0], shape_b[0])
示例10: expand_first_dimension
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def expand_first_dimension(inputs, dims):
"""Expands `K-d` tensor along first dimension to be a `(K+n-1)-d` tensor.
Converts `inputs` with shape [D0, D1, ..., D(K-1)] into a tensor of shape
[dims[0], dims[1], ..., dims[-1], D1, ..., D(k-1)].
Example:
`inputs` is a tensor with shape [50, 20, 20, 3].
new_tensor = expand_first_dimension(inputs, [10, 5]).
new_tensor.shape -> [10, 5, 20, 20, 3].
Args:
inputs: a tensor with shape [D0, D1, ..., D(K-1)].
dims: List with new dimensions to expand first axis into. The length of
`dims` is typically 2 or larger.
Returns:
a tensor with shape [dims[0], dims[1], ..., dims[-1], D1, ..., D(k-1)].
"""
inputs_shape = combined_static_and_dynamic_shape(inputs)
expanded_shape = tf.stack(dims + inputs_shape[1:])
# Verify that it is possible to expand the first axis of inputs.
assert_op = tf.assert_equal(
inputs_shape[0], tf.reduce_prod(tf.stack(dims)),
message=('First dimension of `inputs` cannot be expanded into provided '
'`dims`'))
with tf.control_dependencies([assert_op]):
inputs_reshaped = tf.reshape(inputs, expanded_shape)
return inputs_reshaped
示例11: testStates
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def testStates(self):
batch_size = 1
beam_size = 1
vocab_size = 2
decode_length = 3
initial_ids = tf.constant([0] * batch_size) # GO
probabilities = tf.constant([[[0.7, 0.3]], [[0.4, 0.6]], [[0.5, 0.5]]])
expected_states = tf.constant([[[0.]], [[1.]]])
def symbols_to_logits(ids, _, states):
pos = tf.shape(ids)[1] - 1
# We have to assert the values of state inline here since we can't fetch
# them out of the loop!
with tf.control_dependencies(
[tf.assert_equal(states["state"], expected_states[pos])]):
logits = tf.to_float(tf.log(probabilities[pos, :]))
states["state"] += 1
return logits, states
states = {
"state": tf.zeros((batch_size, 1)),
}
states["state"] = tf.placeholder_with_default(
states["state"], shape=(None, 1))
final_ids, _, _ = beam_search.beam_search(
symbols_to_logits,
initial_ids,
beam_size,
decode_length,
vocab_size,
0.0,
eos_id=1,
states=states)
with self.test_session() as sess:
# Catch and fail so that the testing framework doesn't think it's an error
try:
sess.run(final_ids)
except tf.errors.InvalidArgumentError as e:
raise AssertionError(e.message)
示例12: testStateBeamTwo
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def testStateBeamTwo(self):
batch_size = 1
beam_size = 2
vocab_size = 3
decode_length = 3
initial_ids = tf.constant([0] * batch_size) # GO
probabilities = tf.constant([[[0.1, 0.1, 0.8], [0.1, 0.1, 0.8]],
[[0.4, 0.5, 0.1], [0.2, 0.4, 0.4]],
[[0.05, 0.9, 0.05], [0.4, 0.4, 0.2]]])
# The top beam is always selected so we should see the top beam's state
# at each position, which is the one thats getting 3 added to it each step.
expected_states = tf.constant([[[0.], [0.]], [[3.], [3.]], [[6.], [6.]]])
def symbols_to_logits(ids, _, states):
pos = tf.shape(ids)[1] - 1
# We have to assert the values of state inline here since we can't fetch
# them out of the loop!
with tf.control_dependencies(
[tf.assert_equal(states["state"], expected_states[pos])]):
logits = tf.to_float(tf.log(probabilities[pos, :]))
states["state"] += tf.constant([[3.], [7.]])
return logits, states
states = {
"state": tf.zeros((batch_size, 1)),
}
states["state"] = tf.placeholder_with_default(
states["state"], shape=(None, 1))
final_ids, _, _ = beam_search.beam_search(
symbols_to_logits,
initial_ids,
beam_size,
decode_length,
vocab_size,
0.0,
eos_id=1,
states=states)
with self.test_session() as sess:
# Catch and fail so that the testing framework doesn't think it's an error
try:
sess.run(final_ids)
except tf.errors.InvalidArgumentError as e:
raise AssertionError(e.message)
示例13: testTPUBeam
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def testTPUBeam(self):
batch_size = 1
beam_size = 2
vocab_size = 3
decode_length = 3
initial_ids = tf.constant([0] * batch_size) # GO
probabilities = tf.constant([[[0.1, 0.1, 0.8], [0.1, 0.1, 0.8]],
[[0.4, 0.5, 0.1], [0.2, 0.4, 0.4]],
[[0.05, 0.9, 0.05], [0.4, 0.4, 0.2]]])
# The top beam is always selected so we should see the top beam's state
# at each position, which is the one thats getting 3 added to it each step.
expected_states = tf.constant([[[0.], [0.]], [[3.], [3.]], [[6.], [6.]]])
def symbols_to_logits(_, i, states):
# We have to assert the values of state inline here since we can't fetch
# them out of the loop!
with tf.control_dependencies(
[tf.assert_equal(states["state"], expected_states[i])]):
logits = tf.to_float(tf.log(probabilities[i, :]))
states["state"] += tf.constant([[3.], [7.]])
return logits, states
states = {
"state": tf.zeros((batch_size, 1)),
}
states["state"] = tf.placeholder_with_default(
states["state"], shape=(None, 1))
final_ids, _, _ = beam_search.beam_search(
symbols_to_logits,
initial_ids,
beam_size,
decode_length,
vocab_size,
3.5,
eos_id=1,
states=states,
use_tpu=True)
with self.test_session() as sess:
# Catch and fail so that the testing framework doesn't think it's an error
try:
sess.run(final_ids)
except tf.errors.InvalidArgumentError as e:
raise AssertionError(e.message)
self.assertAllEqual([[[0, 2, 0, 1], [0, 2, 1, 0]]], final_ids)
示例14: encode
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def encode(self, sequence, sequence_length):
"""Hierarchically encodes the input sequences, returning a single embedding.
Each sequence should be padded per-segment. For example, a sequence with
three segments [1, 2, 3], [4, 5], [6, 7, 8 ,9] and a `max_seq_len` of 12
should be input as `sequence = [1, 2, 3, 0, 4, 5, 0, 0, 6, 7, 8, 9]` with
`sequence_length = [3, 2, 4]`.
Args:
sequence: A batch of (padded) sequences, sized
`[batch_size, max_seq_len, input_depth]`.
sequence_length: A batch of sequence lengths. May be sized
`[batch_size, level_lengths[0]]` or `[batch_size]`. If the latter,
each length must either equal `max_seq_len` or 0. In this case, the
segment lengths are assumed to be constant and the total length will be
evenly divided amongst the segments.
Returns:
embedding: A batch of embeddings, sized `[batch_size, N]`.
"""
batch_size = int(sequence.shape[0])
sequence_length = lstm_utils.maybe_split_sequence_lengths(
sequence_length, np.prod(self._level_lengths[1:]),
self._total_length)
for level, (num_splits, h_encoder) in enumerate(
self._hierarchical_encoders):
split_seqs = tf.split(sequence, num_splits, axis=1)
# In the first level, we use the input `sequence_length`. After that,
# we use the full embedding sequences.
if level:
sequence_length = tf.fill(
[batch_size, num_splits], split_seqs[0].shape[1])
split_lengths = tf.unstack(sequence_length, axis=1)
embeddings = [
h_encoder.encode(s, l) for s, l in zip(split_seqs, split_lengths)]
sequence = tf.stack(embeddings, axis=1)
with tf.control_dependencies([tf.assert_equal(tf.shape(sequence)[1], 1)]):
return sequence[:, 0]
# DECODERS
示例15: compute_loss
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import assert_equal [as 别名]
def compute_loss(self, unreduced_loss):
"""Computes scaled loss based on mask out size."""
# construct mask to identify zero padding that was introduced to
# make the batch rectangular
batch_duration = tf.shape(self.pianorolls)[1]
indices = tf.to_float(tf.range(batch_duration))
pad_mask = tf.to_float(
indices[None, :, None, None] < self.lengths[:, None, None, None])
# construct mask and its complement, respecting pad mask
mask = pad_mask * self.masks
unmask = pad_mask * (1. - self.masks)
# Compute numbers of variables
# #timesteps * #variables per timestep
variable_axis = 3 if self.hparams.use_softmax_loss else 2
dd = (
self.lengths[:, None, None, None] * tf.to_float(
tf.shape(self.pianorolls)[variable_axis]))
reduced_dd = tf.reduce_sum(dd)
# Compute numbers of variables to be predicted/conditioned on
mask_size = tf.reduce_sum(mask, axis=[1, variable_axis], keep_dims=True)
unmask_size = tf.reduce_sum(unmask, axis=[1, variable_axis], keep_dims=True)
unreduced_loss *= pad_mask
if self.hparams.rescale_loss:
unreduced_loss *= dd / mask_size
# Compute average loss over entire set of variables
self.loss_total = tf.reduce_sum(unreduced_loss) / reduced_dd
# Compute separate losses for masked/unmasked variables
# NOTE: indexing the pitch dimension with 0 because the mask is constant
# across pitch. Except in the sigmoid case, but then the pitch dimension
# will have been reduced over.
self.reduced_mask_size = tf.reduce_sum(mask_size[:, :, 0, :])
self.reduced_unmask_size = tf.reduce_sum(unmask_size[:, :, 0, :])
assert_partition_op = tf.group(
tf.assert_equal(tf.reduce_sum(mask * unmask), 0.),
tf.assert_equal(self.reduced_mask_size + self.reduced_unmask_size,
reduced_dd))
with tf.control_dependencies([assert_partition_op]):
self.loss_mask = (
tf.reduce_sum(mask * unreduced_loss) / self.reduced_mask_size)
self.loss_unmask = (
tf.reduce_sum(unmask * unreduced_loss) / self.reduced_unmask_size)
# Check which loss to use as objective function.
self.loss = (
self.loss_mask if self.hparams.optimize_mask_only else self.loss_total)