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Move tf.layers to keras folder and export from there.

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PiperOrigin-RevId: 300878712
Change-Id: I3648257c5e49f03a6d163110f4cfed10b57444ea
This commit is contained in:
Scott Zhu 2020-03-13 21:06:01 -07:00 committed by TensorFlower Gardener
parent 62b6926417
commit 2c6bfdd663
47 changed files with 3516 additions and 3265 deletions
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157
tensorflow/python/BUILD
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@ -2062,8 +2062,8 @@ tf_py_test(
main = "framework/registry_test.py",
python_version = "PY3",
deps = [
":client_testlib",
":framework_for_generated_wrappers",
"//tensorflow/python:client_testlib",
"@absl_py//absl/testing:parameterized",
],
)
@ -3895,8 +3895,8 @@ cuda_py_test(
srcs = ["training/experimental/mixed_precision_test.py"],
python_version = "PY3",
deps = [
":client_testlib",
":mixed_precision",
"//tensorflow/python:client_testlib",
"@absl_py//absl/testing:parameterized",
],
)
@ -5879,7 +5879,7 @@ filegroup(
"//tensorflow/core/util:port", # util_port
"//tensorflow/core/util/tensor_bundle", # checkpoint_reader
"//tensorflow/lite/toco/python:toco_python_api", # toco
"//tensorflow/python:tf_session_helper", # tf_session
":tf_session_helper", # tf_session
"//tensorflow/python/eager:pywrap_tfe_lib", # pywrap_tfe_lib
"//tensorflow/stream_executor:stream_executor_pimpl", # stat_summarizer
"//tensorflow/tools/graph_transforms:transform_graph_lib", # transform_graph
@ -7021,6 +7021,7 @@ py_tests(
],
)
# TODO(scottzhu): Move all the tf.layer related targets.
py_library(
name = "layers_base",
srcs = [
@ -7029,19 +7030,7 @@ py_library(
],
srcs_version = "PY2AND3",
deps = [
":array_ops",
":control_flow_ops",
":framework_for_generated_wrappers",
":layers_util",
":platform",
":smart_cond",
":tensor_util",
":util",
":variable_scope",
":variables",
"//tensorflow/python/eager:context",
"//tensorflow/python/keras:engine",
"//third_party/py/numpy",
"//tensorflow/python/keras/legacy_tf_layers:layers_base",
],
)
@ -7070,134 +7059,12 @@ py_library(
],
srcs_version = "PY2AND3",
deps = [
":array_ops",
":array_ops_gen",
":control_flow_ops",
":framework",
":framework_for_generated_wrappers",
":init_ops",
":layers_base",
":math_ops",
":nn",
":nn_ops",
":platform",
":resource_variable_ops",
":resource_variable_ops_gen",
":standard_ops",
":state_ops",
":training",
":util",
":variable_scope",
":variables",
"//tensorflow/python/eager:context",
"//tensorflow/python/keras/layers",
"//third_party/py/numpy",
"@six_archive//:six",
],
)
tf_py_test(
name = "layers_base_test",
size = "small",
srcs = ["layers/base_test.py"],
main = "layers/base_test.py",
python_version = "PY3",
deps = [
":array_ops",
":client_testlib",
":framework_for_generated_wrappers",
":framework_test_lib",
":init_ops",
":layers",
":layers_base",
":math_ops",
":random_ops",
":variable_scope",
"//tensorflow/python/eager:context",
],
)
tf_py_test(
name = "layers_core_test",
size = "small",
srcs = ["layers/core_test.py"],
main = "layers/core_test.py",
python_version = "PY3",
deps = [
":array_ops",
":client_testlib",
":framework_for_generated_wrappers",
":framework_test_lib",
":layers",
":math_ops",
":nn_ops",
":random_ops",
":variable_scope",
":variables",
"//third_party/py/numpy",
],
)
tf_py_test(
name = "layers_convolutional_test",
size = "small",
srcs = ["layers/convolutional_test.py"],
main = "layers/convolutional_test.py",
python_version = "PY3",
deps = [
":client_testlib",
":framework_for_generated_wrappers",
":framework_test_lib",
":layers",
":math_ops",
":nn_ops",
":random_ops",
],
)
tf_py_test(
name = "layers_utils_test",
size = "small",
srcs = ["layers/utils_test.py"],
main = "layers/utils_test.py",
python_version = "PY3",
deps = [
":client_testlib",
":layers",
],
)
tf_py_test(
name = "layers_pooling_test",
size = "small",
srcs = ["layers/pooling_test.py"],
main = "layers/pooling_test.py",
python_version = "PY3",
deps = [
":client_testlib",
":framework_test_lib",
":layers",
":random_ops",
],
)
cuda_py_test(
name = "layers_normalization_test",
size = "medium",
srcs = ["layers/normalization_test.py"],
main = "layers/normalization_test.py",
python_version = "PY3",
shard_count = 10,
deps = [
":array_ops",
":client_testlib",
":framework_for_generated_wrappers",
":framework_test_lib",
":layers",
":math_ops",
":random_ops",
":variables",
"//third_party/py/numpy",
"//tensorflow/python/keras/engine:input_spec",
"//tensorflow/python/keras/legacy_tf_layers:convolutional",
"//tensorflow/python/keras/legacy_tf_layers:core",
"//tensorflow/python/keras/legacy_tf_layers:normalization",
"//tensorflow/python/keras/legacy_tf_layers:pooling",
],
)
@ -7711,7 +7578,7 @@ tf_py_test(
deps = [
":client_testlib",
":graph_placer",
"//tensorflow/python:math_ops",
":math_ops",
],
)
@ -8145,6 +8012,6 @@ cuda_py_test(
srcs = ["ops/raw_ops_test.py"],
python_version = "PY3",
deps = [
"//tensorflow/python:client_testlib",
":client_testlib",
],
)

195
tensorflow/python/keras/legacy_tf_layers/BUILD Normal file
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@ -0,0 +1,195 @@
# Description:
# Contains the legacy TF layers (internal TensorFlow version).
load("//tensorflow:tensorflow.bzl", "tf_py_test")
load("//tensorflow:tensorflow.bzl", "cuda_py_test")
package(
default_visibility = ["//tensorflow:__subpackages__"],
licenses = ["notice"], # Apache 2.0
)
py_library(
name = "layers_base",
srcs = [
"__init__.py",
"base.py",
],
srcs_version = "PY2AND3",
deps = [
"//tensorflow/python:dtypes",
"//tensorflow/python:framework_ops",
"//tensorflow/python:util",
"//tensorflow/python:variable_scope",
"//tensorflow/python:variables",
"//tensorflow/python/eager:context",
"//tensorflow/python/keras:backend",
"//tensorflow/python/keras/engine:base_layer",
"//tensorflow/python/keras/mixed_precision/experimental:policy",
"//tensorflow/python/training/tracking:base",
],
)
py_library(
name = "convolutional",
srcs = ["convolutional.py"],
deps = [
":layers_base",
"//tensorflow/python:init_ops",
"//tensorflow/python:util",
"//tensorflow/python/keras/layers",
],
)
py_library(
name = "core",
srcs = ["core.py"],
deps = [
":layers_base",
"//tensorflow/python:init_ops",
"//tensorflow/python:util",
"//tensorflow/python/keras/layers",
],
)
py_library(
name = "normalization",
srcs = ["normalization.py"],
deps = [
":layers_base",
"//tensorflow/python:init_ops",
"//tensorflow/python:util",
"//tensorflow/python/keras/layers:normalization",
],
)
py_library(
name = "pooling",
srcs = ["pooling.py"],
deps = [
":layers_base",
"//tensorflow/python:util",
"//tensorflow/python/keras/layers",
],
)
tf_py_test(
name = "base_test",
size = "small",
srcs = ["base_test.py"],
main = "base_test.py",
python_version = "PY3",
deps = [
":core",
":layers_base",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",
"//tensorflow/python:constant_op",
"//tensorflow/python:dtypes",
"//tensorflow/python:extra_py_tests_deps",
"//tensorflow/python:framework_ops",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:init_ops",
"//tensorflow/python:math_ops",
"//tensorflow/python:partitioned_variables",
"//tensorflow/python:random_ops",
"//tensorflow/python:state_ops",
"//tensorflow/python:variable_scope",
"//tensorflow/python/eager:context",
"//tensorflow/python/eager:def_function",
"//tensorflow/python/keras/engine:base_layer",
"//tensorflow/python/keras/engine:input_spec",
],
)
tf_py_test(
name = "core_test",
size = "small",
srcs = ["core_test.py"],
main = "core_test.py",
python_version = "PY3",
deps = [
":core",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",
"//tensorflow/python:constant_op",
"//tensorflow/python:dtypes",
"//tensorflow/python:extra_py_tests_deps",
"//tensorflow/python:framework_ops",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:init_ops",
"//tensorflow/python:math_ops",
"//tensorflow/python:nn_ops",
"//tensorflow/python:random_ops",
"//tensorflow/python:tensor_shape",
"//tensorflow/python:variable_scope",
"//tensorflow/python:variables",
"//tensorflow/python/eager:context",
],
)
tf_py_test(
name = "convolutional_test",
size = "small",
srcs = ["convolutional_test.py"],
main = "convolutional_test.py",
python_version = "PY3",
deps = [
":convolutional",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",
"//tensorflow/python:dtypes",
"//tensorflow/python:extra_py_tests_deps",
"//tensorflow/python:framework_ops",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:init_ops",
"//tensorflow/python:math_ops",
"//tensorflow/python:nn_ops",
"//tensorflow/python:random_ops",
"//tensorflow/python:variable_scope",
"//tensorflow/python:variables",
],
)
tf_py_test(
name = "pooling_test",
size = "small",
srcs = ["pooling_test.py"],
main = "pooling_test.py",
python_version = "PY3",
deps = [
":pooling",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",
"//tensorflow/python:extra_py_tests_deps",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:random_ops",
],
)
cuda_py_test(
name = "normalization_test",
size = "medium",
srcs = ["normalization_test.py"],
main = "normalization_test.py",
python_version = "PY3",
shard_count = 10,
deps = [
":convolutional",
":normalization",
"//tensorflow/core:protos_all_py",
"//tensorflow/python:array_ops",
"//tensorflow/python:client_testlib",
"//tensorflow/python:dtypes",
"//tensorflow/python:extra_py_tests_deps",
"//tensorflow/python:framework_ops",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:init_ops",
"//tensorflow/python:math_ops",
"//tensorflow/python:random_ops",
"//tensorflow/python:saver",
"//tensorflow/python:training_lib",
"//tensorflow/python:variable_scope",
"//tensorflow/python:variables",
],
)

0
tensorflow/python/keras/legacy_tf_layers/__init__.py Normal file
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593
tensorflow/python/keras/legacy_tf_layers/base.py Normal file
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@ -0,0 +1,593 @@
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Contains the base Layer class, from which all layers inherit."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend
from tensorflow.python.keras.engine import base_layer
from tensorflow.python.keras.mixed_precision.experimental import policy
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import deprecation
from tensorflow.python.util import function_utils
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.tf_export import tf_export
# Avoid breaking users who directly import this symbol from this file.
# TODO(fchollet): remove this.
InputSpec = base_layer.InputSpec # pylint: disable=invalid-name
_KERAS_STYLE_SCOPE = False
@tf_export(v1=['layers.experimental.keras_style_scope'])
@tf_contextlib.contextmanager
def keras_style_scope():
"""Use Keras-style variable management.
All tf.layers and tf RNN cells created in this scope use Keras-style
variable management. Creating such layers with a scope= argument is
disallowed, and reuse=True is disallowed.
The purpose of this scope is to allow users of existing layers to
slowly transition to a Keras layers API without breaking existing
functionality.
One example of this is when using TensorFlow's RNN classes with Keras
Models or Networks. Because Keras models do not properly set variable
scopes, users of RNNs may either accidentally share scopes between two
different models, or get errors about variables that already exist.
Example:
```python
class RNNModel(tf.keras.Model):
def __init__(self, name):
super(RNNModel, self).__init__(name=name)
self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
[tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)])
def call(self, input, state):
return self.rnn(input, state)
model_1 = RNNModel("model_1")
model_2 = RNNModel("model_2")
# OK
output_1, next_state_1 = model_1(input, state)
# Raises an error about trying to create an already existing variable.
output_2, next_state_2 = model_2(input, state)
```
The solution is to wrap the model construction and execution in a keras-style
scope:
```python
with keras_style_scope():
model_1 = RNNModel("model_1")
model_2 = RNNModel("model_2")
# model_1 and model_2 are guaranteed to create their own variables.
output_1, next_state_1 = model_1(input, state)
output_2, next_state_2 = model_2(input, state)
assert len(model_1.weights) > 0
assert len(model_2.weights) > 0
assert(model_1.weights != model_2.weights)
```
Yields:
A keras layer style scope.
"""
global _KERAS_STYLE_SCOPE
stack = _KERAS_STYLE_SCOPE
_KERAS_STYLE_SCOPE = True
try:
yield
finally:
_KERAS_STYLE_SCOPE = stack
@tf_export(v1=['layers.experimental.set_keras_style'])
def set_keras_style():
"""Use Keras-style variable management.
All tf.layers and tf RNN cells created after keras style ha been enabled
use Keras-style variable management. Creating such layers with a
scope= argument is disallowed, and reuse=True is disallowed.
The purpose of this function is to allow users of existing layers to
slowly transition to Keras layers API without breaking existing
functionality.
For more details, see the documentation for `keras_style_scope`.
Note, once keras style has been set, it is set globally for the entire
program and cannot be unset.
Example:
```python
set_keras_style()
model_1 = RNNModel(name="model_1")
model_2 = RNNModel(name="model_2")
# model_1 and model_2 are guaranteed to create their own variables.
output_1, next_state_1 = model_1(input, state)
output_2, next_state_2 = model_2(input, state)
assert len(model_1.weights) > 0
assert len(model_2.weights) > 0
assert(model_1.weights != model_2.weights)
```
"""
global _KERAS_STYLE_SCOPE
_KERAS_STYLE_SCOPE = True
def _is_in_keras_style_scope():
global _KERAS_STYLE_SCOPE
return _KERAS_STYLE_SCOPE
@tf_export(v1=['layers.Layer'])
class Layer(base_layer.Layer):
"""Base layer class.
It is considered legacy, and we recommend the use of `tf.keras.layers.Layer`
instead.
Arguments:
trainable: Boolean, whether the layer's variables should be trainable.
name: String name of the layer.
dtype: Default dtype of the layer's weights (default of `None` means use the
type of the first input).
Read-only properties:
name: The name of the layer (string).
dtype: Default dtype of the layer's weights (default of `None` means use the
type of the first input).
trainable_variables: List of trainable variables.
non_trainable_variables: List of non-trainable variables.
variables: List of all variables of this layer, trainable and
non-trainable.
updates: List of update ops of this layer.
losses: List of losses added by this layer.
trainable_weights: List of variables to be included in backprop.
non_trainable_weights: List of variables that should not be
included in backprop.
weights: The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).
Mutable properties:
trainable: Whether the layer should be trained (boolean).
input_spec: Optional (list of) `InputSpec` object(s) specifying the
constraints on inputs that can be accepted by the layer.
"""
def __init__(self, trainable=True, name=None, dtype=None,
**kwargs):
# For backwards compatibility, legacy layers do not use `ResourceVariable`
# by default.
self._use_resource_variables = False
scope = kwargs.pop('_scope', None)
self._reuse = kwargs.pop('_reuse', None)
# Avoid an incorrect lint error
self._trainable_weights = []
self.built = False
if dtype is None:
# Indicates to infer dtype from inputs. When the V2 dtype behavior is
# enabled, Keras layers default their dtype to floatx instead, so we pass
# an "_infer" policy to keep the old V1 behavior.
dtype = policy.Policy('_infer')
if 'autocast' not in kwargs:
kwargs['autocast'] = False
super(Layer, self).__init__(trainable=trainable, name=name, dtype=dtype,
**kwargs)
if _is_in_keras_style_scope():
if scope is not None:
raise ValueError(
'scope argument not allowed when keras style layers are enabled, '
'but saw: {}'.format(scope))
if self._reuse is not None:
raise ValueError(
'reuse argument not allowed when keras style layers are enabled, '
'but saw: {}'.format(self._reuse))
self._keras_style = True
else:
self._keras_style = False
self._call_has_scope_arg = 'scope' in self._call_fn_args
if scope:
with vs.variable_scope(scope) as captured_scope:
self._scope = captured_scope
else:
self._scope = None
self._current_scope = None
# We no longer track graph in tf.layers layers. This property is only kept to
# maintain API backward compatibility.
@property
@deprecation.deprecated(
date=None,
instructions='Stop using this property because tf.layers layers no '
'longer track their graph.')
def graph(self):
if context.executing_eagerly():
raise RuntimeError('Layer.graph not supported when executing eagerly.')
return None
def _init_set_name(self, name):
# Determine layer name (non-unique).
if isinstance(name, vs.VariableScope):
base_name = name.name
self._name, _ = self._make_unique_name()
else:
base_name = name
self._name = name
if not name:
self._name, base_name = self._make_unique_name()
self._base_name = base_name
def _make_unique_name(self, name_uid_map=None, avoid_names=None,
namespace='', zero_based=False):
base_name = base_layer.to_snake_case(self.__class__.__name__)
name = backend.unique_object_name(
base_name,
name_uid_map=name_uid_map,
avoid_names=avoid_names,
namespace=namespace,
zero_based=zero_based)
return (name, base_name)
@property
def scope_name(self):
if not self._scope:
raise ValueError('No name available for layer scope because the layer "' +
self._name + '" has not been used yet. The scope name ' +
' is determined the first time the layer instance is ' +
'called. You must therefore call the layer before ' +
'querying `scope_name`.')
return self._scope.name
def add_loss(self, losses, inputs=None):
previous_losses_length = len(self._losses)
previous_callable_losses_length = len(self._callable_losses)
super(Layer, self).add_loss(losses, inputs=inputs)
if not context.executing_eagerly():
# TODO(fchollet): deprecate collection below.
new_losses = self._losses[previous_losses_length:]
new_callable_losses = self._callable_losses[
previous_callable_losses_length:]
for regularizer in new_callable_losses:
loss_tensor = regularizer()
if loss_tensor is not None:
new_losses.append(loss_tensor)
_add_elements_to_collection(
new_losses,
ops.GraphKeys.REGULARIZATION_LOSSES)
def _name_scope(self):
"""Determines op naming for the Layer."""
if self._keras_style:
return super(Layer, self)._name_scope()
return self._current_scope.original_name_scope
def _set_scope(self, scope=None):
if self._scope is None:
# If constructed with _scope=None, lazy setting of scope.
if self._reuse:
with vs.variable_scope(
scope if scope is not None else self._base_name) as captured_scope:
self._scope = captured_scope
else:
with vs.variable_scope(
scope, default_name=self._base_name) as captured_scope:
self._scope = captured_scope
def add_weight(self,
name,
shape,
dtype=None,
initializer=None,
regularizer=None,
trainable=None,
constraint=None,
use_resource=None,
synchronization=vs.VariableSynchronization.AUTO,
aggregation=vs.VariableAggregation.NONE,
partitioner=None,
**kwargs):
"""Adds a new variable to the layer, or gets an existing one; returns it.
Arguments:
name: variable name.
shape: variable shape.
dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
initializer: initializer instance (callable).
regularizer: regularizer instance (callable).
trainable: whether the variable should be part of the layer's
"trainable_variables" (e.g. variables, biases)
or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
Note, if the current variable scope is marked as non-trainable
then this parameter is ignored and any added variables are also
marked as non-trainable. `trainable` defaults to `True` unless
`synchronization` is set to `ON_READ`.
constraint: constraint instance (callable).
use_resource: Whether to use `ResourceVariable`.
synchronization: Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
`tf.VariableSynchronization`. By default the synchronization is set to
`AUTO` and the current `DistributionStrategy` chooses
when to synchronize. If `synchronization` is set to `ON_READ`,
`trainable` must not be set to `True`.
aggregation: Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
`tf.VariableAggregation`.
partitioner: (optional) partitioner instance (callable). If
provided, when the requested variable is created it will be split
into multiple partitions according to `partitioner`. In this case,
an instance of `PartitionedVariable` is returned. Available
partitioners include `tf.compat.v1.fixed_size_partitioner` and
`tf.compat.v1.variable_axis_size_partitioner`. For more details, see
the documentation of `tf.compat.v1.get_variable` and the "Variable
Partitioners and Sharding" section of the API guide.
**kwargs: Additional keyword arguments.
Returns:
The created variable. Usually either a `Variable` or `ResourceVariable`
instance. If `partitioner` is not `None`, a `PartitionedVariable`
instance is returned.
Raises:
RuntimeError: If called with partitioned variable regularization and
eager execution is enabled.
ValueError: When trainable has been set to True with synchronization
set as `ON_READ`.
"""
for kwarg in kwargs:
if kwarg != 'experimental_autocast':
raise TypeError('Unknown keyword argument:', kwarg)
if self._keras_style:
return super(Layer, self).add_weight(
name=name,
shape=shape,
dtype=dtype,
initializer=initializer,
regularizer=regularizer,
trainable=trainable and self.trainable,
constraint=constraint,
use_resource=use_resource,
synchronization=vs.VariableSynchronization.AUTO,
aggregation=vs.VariableAggregation.NONE,
partitioner=partitioner,
**kwargs)
if synchronization == vs.VariableSynchronization.ON_READ:
if trainable:
raise ValueError(
'Synchronization value can be set to '
'VariableSynchronization.ON_READ only for non-trainable variables. '
'You have specified trainable=True and '
'synchronization=VariableSynchronization.ON_READ.')
else:
# Set trainable to be false when variable is to be synced on read.
trainable = False
elif trainable is None:
trainable = True
def _should_add_regularizer(variable, existing_variable_set):
if isinstance(variable, tf_variables.PartitionedVariable):
for var in variable:
if var in existing_variable_set:
return False
return True
else:
return variable not in existing_variable_set
init_graph = None
if not context.executing_eagerly():
default_graph = ops.get_default_graph()
if default_graph.building_function:
with ops.init_scope():
# Retrieve the variables from the graph into which variables
# will be lifted; if initialization ops will be lifted into
# the eager context, then there is nothing to retrieve, since variable
# collections are not supported when eager execution is enabled.
if not context.executing_eagerly():
init_graph = ops.get_default_graph()
existing_variables = set(tf_variables.global_variables())
else:
# Initialization ops will not be lifted out of the default graph.
init_graph = default_graph
existing_variables = set(tf_variables.global_variables())
if dtype is None:
dtype = self.dtype or dtypes.float32
self._set_scope(None)
reuse = self.built or self._reuse
prev_len_trainable = len(self._trainable_weights)
with vs.variable_scope(
self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
self._current_scope = scope
with ops.name_scope(self._name_scope(), skip_on_eager=False):
use_resource = (use_resource or
self._use_resource_variables or
scope.use_resource)
if initializer is None:
initializer = scope.initializer
variable = super(Layer, self).add_weight(
name,
shape,
dtype=dtypes.as_dtype(dtype),
initializer=initializer,
trainable=trainable and self.trainable,
constraint=constraint,
partitioner=partitioner,
use_resource=use_resource,
synchronization=synchronization,
aggregation=aggregation,
getter=vs.get_variable,
**kwargs)
if regularizer:
if (ops.executing_eagerly_outside_functions()
or _should_add_regularizer(variable, existing_variables)):
self._handle_weight_regularization(name, variable, regularizer)
if init_graph is not None:
# Handle edge case where a custom getter has overridden `trainable`.
# There is one known occurrence of this, in unit test
# testBasicRNNCellNotTrainable in
# contrib.rnn.python.kernel_tests.core_rnn_cell_test
with init_graph.as_default():
trainable_variables = tf_variables.trainable_variables()
if (trainable and self.trainable and
variable not in trainable_variables):
# A custom getter / variable scope overrode the trainable flag.
extra_trainable_vars = self._trainable_weights[prev_len_trainable:]
self._trainable_weights = self._trainable_weights[
:prev_len_trainable]
self._non_trainable_weights += extra_trainable_vars
return variable
def __call__(self, inputs, *args, **kwargs):
"""Wraps `call`, applying pre- and post-processing steps.
Arguments:
inputs: input tensor(s).
*args: additional positional arguments to be passed to `self.call`.
**kwargs: additional keyword arguments to be passed to `self.call`.
**Note**: kwarg `scope` is reserved for use by the layer.
Returns:
Output tensor(s).
Note:
- If the layer's `call` method takes a `scope` keyword argument,
this argument will be automatically set to the current variable scope.
- If the layer's `call` method takes a `mask` argument (as some Keras
layers do), its default value will be set to the mask generated
for `inputs` by the previous layer (if `input` did come from
a layer that generated a corresponding mask, i.e. if it came from
a Keras layer with masking support.
Raises:
ValueError: if the layer's `call` method returns None (an invalid value).
"""
scope = kwargs.pop('scope', None)
if self._keras_style:
if scope is not None:
raise ValueError(
'scope argument not allowed when keras style layers are enabled, '
'but saw: {}'.format(scope))
return super(Layer, self).__call__(inputs, *args, **kwargs)
self._set_scope(scope)
if self.built:
try:
# Some classes which inherit from Layer do not use its constructor, so
# rather than initializing to None we check for an AttributeError.
scope_context_manager = self._always_reuse_variable_scope
except AttributeError:
# From this point we will always set reuse=True, so create a "final"
# variable scope with this setting. We avoid re-creating variable scopes
# after this point as an optimization.
self._always_reuse_variable_scope = vs.variable_scope(
self._scope, reuse=True, auxiliary_name_scope=False)
scope_context_manager = self._always_reuse_variable_scope
else:
scope_context_manager = vs.variable_scope(
self._scope, reuse=self._reuse, auxiliary_name_scope=False)
with scope_context_manager as scope:
self._current_scope = scope
try:
call_has_scope_arg = self._call_has_scope_arg
except AttributeError:
self._call_fn_args = function_utils.fn_args(self.call)
self._call_has_scope_arg = 'scope' in self._call_fn_args
call_has_scope_arg = self._call_has_scope_arg
if call_has_scope_arg:
kwargs['scope'] = scope
# Actually call layer
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
if not context.executing_eagerly():
# Update global default collections.
_add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
return outputs
def __deepcopy__(self, memo):
no_copy = set(['_graph', '_thread_local', '_metrics_lock'])
shallow_copy = set(['_scope', '_always_reuse_variable_scope'])
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
for k, v in self.__dict__.items():
if k in no_copy:
setattr(result, k, v)
elif k in shallow_copy:
setattr(result, k, copy.copy(v))
elif base_layer.is_tensor_or_tensor_list(v):
setattr(result, k, v)
else:
setattr(result, k, copy.deepcopy(v, memo))
return result
def __setattr__(self, value, name):
# By-pass the automatic dependency tracking performed by the parent Layer.
super(trackable.Trackable, self).__setattr__(value, name)
@property
def _is_legacy_layer(self):
"""Used by keras to check compatibility. This should not be overridden."""
return True
def _add_elements_to_collection(elements, collection_list):
if context.executing_eagerly():
raise RuntimeError('Using collections from Layers not supported in Eager '
'mode. Tried to add %s to %s' % (elements,
collection_list))
elements = nest.flatten(elements)
collection_list = nest.flatten(collection_list)
for name in collection_list:
collection = ops.get_collection_ref(name)
collection_set = {id(e) for e in collection}
for element in elements:
if id(element) not in collection_set:
collection.append(element)

4
tensorflow/python/layers/base_test.py → tensorflow/python/keras/legacy_tf_layers/base_test.py
View File

@ -30,8 +30,8 @@ from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.keras.engine import base_layer as keras_base_layer
from tensorflow.python.keras.engine import input_spec
from tensorflow.python.layers import base as base_layers
from tensorflow.python.layers import core as core_layers
from tensorflow.python.keras.legacy_tf_layers import base as base_layers
from tensorflow.python.keras.legacy_tf_layers import core as core_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops

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2
tensorflow/python/layers/convolutional_test.py → tensorflow/python/keras/legacy_tf_layers/convolutional_test.py
View File

@ -23,7 +23,7 @@ import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.layers import convolutional as conv_layers
from tensorflow.python.keras.legacy_tf_layers import convolutional as conv_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops

338
tensorflow/python/keras/legacy_tf_layers/core.py Normal file
View File

@ -0,0 +1,338 @@
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Contains the core layers: Dense, Dropout.
Also contains their functional aliases.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
@tf_export(v1=['layers.Dense'])
class Dense(keras_layers.Dense, base.Layer):
"""Densely-connected layer class.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Arguments:
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer. Layers with the same name will
share weights, but to avoid mistakes we require reuse=True in such cases.
_reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Properties:
units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer instance (or name) for the kernel matrix.
bias_initializer: Initializer instance (or name) for the bias.
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
kernel_constraint: Constraint function for the kernel matrix.
bias_constraint: Constraint function for the bias.
kernel: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).
"""
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Dense, self).__init__(units=units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.Dense instead.')
@tf_export(v1=['layers.dense'])
def dense(
inputs, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the densely-connected layer.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Arguments:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor the same shape as `inputs` except the last dimension is of
size `units`.
Raises:
ValueError: if eager execution is enabled.
"""
layer = Dense(units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
@tf_export(v1=['layers.Dropout'])
class Dropout(keras_layers.Dropout, base.Layer):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed`.
for behavior.
name: The name of the layer (string).
"""
def __init__(self, rate=0.5,
noise_shape=None,
seed=None,
name=None,
**kwargs):
super(Dropout, self).__init__(rate=rate,
noise_shape=noise_shape,
seed=seed,
name=name,
**kwargs)
def call(self, inputs, training=False):
return super(Dropout, self).call(inputs, training=training)
@deprecation.deprecated(
date=None,
instructions='Use keras.layers.dropout instead.')
@tf_export(v1=['layers.dropout'])
def dropout(inputs,
rate=0.5,
noise_shape=None,
seed=None,
training=False,
name=None):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
inputs: Tensor input.
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed`
for behavior.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(apply dropout) or in inference mode (return the input untouched).
name: The name of the layer (string).
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
return layer.apply(inputs, training=training)
@tf_export(v1=['layers.Flatten'])
class Flatten(keras_layers.Flatten, base.Layer):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Arguments:
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, ...)`.
Examples:
```
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, 16)`
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, None)`
```
"""
pass
@deprecation.deprecated(
date=None,
instructions='Use keras.layers.Flatten instead.')
@tf_export(v1=['layers.flatten'])
def flatten(inputs, name=None, data_format='channels_last'):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Arguments:
inputs: Tensor input.
name: The name of the layer (string).
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
Returns:
Reshaped tensor.
Examples:
```
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, 16)`
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, None)`
```
"""
layer = Flatten(name=name, data_format=data_format)
return layer.apply(inputs)
# Aliases
FullyConnected = Dense
fully_connected = dense

2
tensorflow/python/layers/core_test.py → tensorflow/python/keras/legacy_tf_layers/core_test.py
View File

@ -29,7 +29,7 @@ from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.layers import core as core_layers
from tensorflow.python.keras.legacy_tf_layers import core as core_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops

342
tensorflow/python/keras/legacy_tf_layers/normalization.py Normal file
View File

@ -0,0 +1,342 @@
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Contains the normalization layer classes and their functional aliases.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.keras.layers import normalization as keras_normalization
from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
@tf_export(v1=['layers.BatchNormalization'])
class BatchNormalization(keras_normalization.BatchNormalization, base.Layer):
"""Batch Normalization layer from (Ioffe et al., 2015).
Keras APIs handle BatchNormalization updates to the moving_mean and
moving_variance as part of their `fit()` and `evaluate()` loops. However, if a
custom training loop is used with an instance of `Model`, these updates need
to be explicitly included. Here's a simple example of how it can be done:
```python
# model is an instance of Model that contains BatchNormalization layer.
update_ops = model.get_updates_for(None) + model.get_updates_for(features)
train_op = optimizer.minimize(loss)
train_op = tf.group([train_op, update_ops])
```
Arguments:
axis: An `int` or list of `int`, the axis or axes that should be normalized,
typically the features axis/axes. For instance, after a `Conv2D` layer
with `data_format="channels_first"`, set `axis=1`. If a list of axes is
provided, each axis in `axis` will be normalized
simultaneously. Default is `-1` which uses the last axis. Note: when
using multi-axis batch norm, the `beta`, `gamma`, `moving_mean`, and
`moving_variance` variables are the same rank as the input Tensor,
with dimension size 1 in all reduced (non-axis) dimensions).
momentum: Momentum for the moving average.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
is ignored.
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
scaling can be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
moving_mean_initializer: Initializer for the moving mean.
moving_variance_initializer: Initializer for the moving variance.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: An optional projection function to be applied to the `beta`
weight after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected variable and must return the projected
variable (which must have the same shape). Constraints are not safe to use
when doing asynchronous distributed training.
gamma_constraint: An optional projection function to be applied to the
`gamma` weight after being updated by an `Optimizer`.
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
variables during training. The inference is the same for either value of
this parameter.
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
scalar `Tensors` used to clip the renorm correction. The correction `(r,
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
dmax are set to inf, 0, inf, respectively.
renorm_momentum: Momentum used to update the moving means and standard
deviations with renorm. Unlike `momentum`, this affects training and
should be neither too small (which would add noise) nor too large (which
would give stale estimates). Note that `momentum` is still applied to get
the means and variances for inference.
fused: if `None` or `True`, use a faster, fused implementation if possible.
If `False`, use the system recommended implementation.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
which means batch normalization is performed across the whole batch. When
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
Normalization", which creates virtual sub-batches which are each
normalized separately (with shared gamma, beta, and moving statistics).
Must divide the actual batch size during execution.
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
the input tensor and returning a pair (scale, bias) to apply to the
normalized values (before gamma and beta), only during training. For
example, if axis==-1,
`adjustment = lambda shape: (
tf.random.uniform(shape[-1:], 0.93, 1.07),
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
value by up to 7% up or down, then shift the result by up to 0.1
(with independent scaling and bias for each feature but shared
across all examples), and finally apply gamma and/or beta. If
`None`, no adjustment is applied. Cannot be specified if
virtual_batch_size is specified.
name: A string, the name of the layer.
References:
Batch Normalization - Accelerating Deep Network Training by Reducing
Internal Covariate Shift:
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
Batch Renormalization - Towards Reducing Minibatch Dependence in
Batch-Normalized Models:
[Ioffe,
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
"""
def __init__(self,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=init_ops.zeros_initializer(),
gamma_initializer=init_ops.ones_initializer(),
moving_mean_initializer=init_ops.zeros_initializer(),
moving_variance_initializer=init_ops.ones_initializer(),
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
trainable=True,
virtual_batch_size=None,
adjustment=None,
name=None,
**kwargs):
super(BatchNormalization, self).__init__(
axis=axis,
momentum=momentum,
epsilon=epsilon,
center=center,
scale=scale,
beta_initializer=beta_initializer,
gamma_initializer=gamma_initializer,
moving_mean_initializer=moving_mean_initializer,
moving_variance_initializer=moving_variance_initializer,
beta_regularizer=beta_regularizer,
gamma_regularizer=gamma_regularizer,
beta_constraint=beta_constraint,
gamma_constraint=gamma_constraint,
renorm=renorm,
renorm_clipping=renorm_clipping,
renorm_momentum=renorm_momentum,
fused=fused,
trainable=trainable,
virtual_batch_size=virtual_batch_size,
adjustment=adjustment,
name=name,
**kwargs)
def call(self, inputs, training=False):
return super(BatchNormalization, self).call(inputs, training=training)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.BatchNormalization instead. In '
'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
'be used (consult the `tf.keras.layers.BatchNormalization` '
'documentation).')
@tf_export(v1=['layers.batch_normalization'])
def batch_normalization(inputs,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=init_ops.zeros_initializer(),
gamma_initializer=init_ops.ones_initializer(),
moving_mean_initializer=init_ops.zeros_initializer(),
moving_variance_initializer=init_ops.ones_initializer(),
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
training=False,
trainable=True,
name=None,
reuse=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
virtual_batch_size=None,
adjustment=None):
"""Functional interface for the batch normalization layer from_config(Ioffe et al., 2015).
Note: when training, the moving_mean and moving_variance need to be updated.
By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
need to be executed alongside the `train_op`. Also, be sure to add any
batch_normalization ops before getting the update_ops collection. Otherwise,
update_ops will be empty, and training/inference will not work properly. For
example:
```python
x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)
# ...
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = optimizer.minimize(loss)
train_op = tf.group([train_op, update_ops])
```
Arguments:
inputs: Tensor input.
axis: An `int`, the axis that should be normalized (typically the features
axis). For instance, after a `Convolution2D` layer with
`data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
momentum: Momentum for the moving average.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
is ignored.
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
scaling can be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
moving_mean_initializer: Initializer for the moving mean.
moving_variance_initializer: Initializer for the moving variance.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: An optional projection function to be applied to the `beta`
weight after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected variable and must return the projected
variable (which must have the same shape). Constraints are not safe to use
when doing asynchronous distributed training.
gamma_constraint: An optional projection function to be applied to the
`gamma` weight after being updated by an `Optimizer`.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(normalized with statistics of the current batch) or in inference mode
(normalized with moving statistics). **NOTE**: make sure to set this
parameter correctly, or else your training/inference will not work
properly.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer by the same
name.
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
variables during training. The inference is the same for either value of
this parameter.
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
scalar `Tensors` used to clip the renorm correction. The correction `(r,
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
dmax are set to inf, 0, inf, respectively.
renorm_momentum: Momentum used to update the moving means and standard
deviations with renorm. Unlike `momentum`, this affects training and
should be neither too small (which would add noise) nor too large (which
would give stale estimates). Note that `momentum` is still applied to get
the means and variances for inference.
fused: if `None` or `True`, use a faster, fused implementation if possible.
If `False`, use the system recommended implementation.
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
which means batch normalization is performed across the whole batch. When
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
Normalization", which creates virtual sub-batches which are each
normalized separately (with shared gamma, beta, and moving statistics).
Must divide the actual batch size during execution.
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
the input tensor and returning a pair (scale, bias) to apply to the
normalized values (before gamma and beta), only during training. For
example, if axis==-1,
`adjustment = lambda shape: (
tf.random.uniform(shape[-1:], 0.93, 1.07),
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
value by up to 7% up or down, then shift the result by up to 0.1
(with independent scaling and bias for each feature but shared
across all examples), and finally apply gamma and/or beta. If
`None`, no adjustment is applied. Cannot be specified if
virtual_batch_size is specified.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
References:
Batch Normalization - Accelerating Deep Network Training by Reducing
Internal Covariate Shift:
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
Batch Renormalization - Towards Reducing Minibatch Dependence in
Batch-Normalized Models:
[Ioffe,
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
"""
layer = BatchNormalization(
axis=axis,
momentum=momentum,
epsilon=epsilon,
center=center,
scale=scale,
beta_initializer=beta_initializer,
gamma_initializer=gamma_initializer,
moving_mean_initializer=moving_mean_initializer,
moving_variance_initializer=moving_variance_initializer,
beta_regularizer=beta_regularizer,
gamma_regularizer=gamma_regularizer,
beta_constraint=beta_constraint,
gamma_constraint=gamma_constraint,
renorm=renorm,
renorm_clipping=renorm_clipping,
renorm_momentum=renorm_momentum,
fused=fused,
trainable=trainable,
virtual_batch_size=virtual_batch_size,
adjustment=adjustment,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs, training=training)
# Aliases
BatchNorm = BatchNormalization
batch_norm = batch_normalization

4
tensorflow/python/layers/normalization_test.py → tensorflow/python/keras/legacy_tf_layers/normalization_test.py
View File

@ -26,8 +26,8 @@ from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.layers import convolutional as conv_layers
from tensorflow.python.layers import normalization as normalization_layers
from tensorflow.python.keras.legacy_tf_layers import convolutional as conv_layers
from tensorflow.python.keras.legacy_tf_layers import normalization as normalization_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops

470
tensorflow/python/keras/legacy_tf_layers/pooling.py Normal file
View File

@ -0,0 +1,470 @@
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""Contains the pooling layer classes and their functional aliases.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
@tf_export(v1=['layers.AveragePooling1D'])
class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
"""Average Pooling layer for 1D inputs.
Arguments:
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(AveragePooling1D, self).__init__(
pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name,
**kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling1D instead.')
@tf_export(v1=['layers.average_pooling1d'])
def average_pooling1d(inputs, pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average Pooling layer for 1D inputs.
Arguments:
inputs: The tensor over which to pool. Must have rank 3.
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
Returns:
The output tensor, of rank 3.
Raises:
ValueError: if eager execution is enabled.
"""
layer = AveragePooling1D(pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.MaxPooling1D'])
class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
"""Max Pooling layer for 1D inputs.
Arguments:
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(MaxPooling1D, self).__init__(
pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name,
**kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling1D instead.')
@tf_export(v1=['layers.max_pooling1d'])
def max_pooling1d(inputs, pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Max Pooling layer for 1D inputs.
Arguments:
inputs: The tensor over which to pool. Must have rank 3.
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
Returns:
The output tensor, of rank 3.
Raises:
ValueError: if eager execution is enabled.
"""
layer = MaxPooling1D(pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.AveragePooling2D'])
class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
"""Average pooling layer for 2D inputs (e.g. images).
Arguments:
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(AveragePooling2D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling2D instead.')
@tf_export(v1=['layers.average_pooling2d'])
def average_pooling2d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average pooling layer for 2D inputs (e.g. images).
Arguments:
inputs: The tensor over which to pool. Must have rank 4.
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = AveragePooling2D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.MaxPooling2D'])
class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
"""Max pooling layer for 2D inputs (e.g. images).
Arguments:
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(MaxPooling2D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling2D instead.')
@tf_export(v1=['layers.max_pooling2d'])
def max_pooling2d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Max pooling layer for 2D inputs (e.g. images).
Arguments:
inputs: The tensor over which to pool. Must have rank 4.
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = MaxPooling2D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.AveragePooling3D'])
class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
"""Average pooling layer for 3D inputs (e.g. volumes).
Arguments:
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(AveragePooling3D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling3D instead.')
@tf_export(v1=['layers.average_pooling3d'])
def average_pooling3d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average pooling layer for 3D inputs (e.g. volumes).
Arguments:
inputs: The tensor over which to pool. Must have rank 5.
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = AveragePooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.MaxPooling3D'])
class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
"""Max pooling layer for 3D inputs (e.g. volumes).
Arguments:
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(MaxPooling3D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling3D instead.')
@tf_export(v1=['layers.max_pooling3d'])
def max_pooling3d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Max pooling layer for 3D inputs (e.g.
volumes).
Arguments:
inputs: The tensor over which to pool. Must have rank 5.
pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height,
pool_width) specifying the size of the pooling window. Can be a single
integer to specify the same value for all spatial dimensions.
strides: An integer or tuple/list of 3 integers, specifying the strides of
the pooling operation. Can be a single integer to specify the same value
for all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape `(batch, depth, height,
width, channels)` while `channels_first` corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = MaxPooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
# Aliases
AvgPool2D = AveragePooling2D
MaxPool2D = MaxPooling2D
max_pool2d = max_pooling2d
avg_pool2d = average_pooling2d

2
tensorflow/python/layers/pooling_test.py → tensorflow/python/keras/legacy_tf_layers/pooling_test.py
View File

@ -19,7 +19,7 @@ from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import test_util
from tensorflow.python.layers import pooling as pooling_layers
from tensorflow.python.keras.legacy_tf_layers import pooling as pooling_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test

577
tensorflow/python/layers/base.py
View File

@ -17,577 +17,10 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from tensorflow.python.keras.legacy_tf_layers import base
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend
from tensorflow.python.keras.engine import base_layer
from tensorflow.python.keras.mixed_precision.experimental import policy
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import deprecation
from tensorflow.python.util import function_utils
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.tf_export import tf_export
InputSpec = base.InputSpec
# Avoid breaking users who directly import this symbol from this file.
# TODO(fchollet): remove this.
InputSpec = base_layer.InputSpec # pylint: disable=invalid-name
_KERAS_STYLE_SCOPE = False
@tf_export(v1=['layers.experimental.keras_style_scope'])
@tf_contextlib.contextmanager
def keras_style_scope():
"""Use Keras-style variable management.
All tf.layers and tf RNN cells created in this scope use Keras-style
variable management. Creating such layers with a scope= argument is
disallowed, and reuse=True is disallowed.
The purpose of this scope is to allow users of existing layers to
slowly transition to a Keras layers API without breaking existing
functionality.
One example of this is when using TensorFlow's RNN classes with Keras
Models or Networks. Because Keras models do not properly set variable
scopes, users of RNNs may either accidentally share scopes between two
different models, or get errors about variables that already exist.
Example:
```python
class RNNModel(tf.keras.Model):
def __init__(self, name):
super(RNNModel, self).__init__(name=name)
self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell(
[tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)])
def call(self, input, state):
return self.rnn(input, state)
model_1 = RNNModel("model_1")
model_2 = RNNModel("model_2")
# OK
output_1, next_state_1 = model_1(input, state)
# Raises an error about trying to create an already existing variable.
output_2, next_state_2 = model_2(input, state)
```
The solution is to wrap the model construction and execution in a keras-style
scope:
```python
with keras_style_scope():
model_1 = RNNModel("model_1")
model_2 = RNNModel("model_2")
# model_1 and model_2 are guaranteed to create their own variables.
output_1, next_state_1 = model_1(input, state)
output_2, next_state_2 = model_2(input, state)
assert len(model_1.weights) > 0
assert len(model_2.weights) > 0
assert(model_1.weights != model_2.weights)
```
Yields:
A keras layer style scope.
"""
global _KERAS_STYLE_SCOPE
stack = _KERAS_STYLE_SCOPE
_KERAS_STYLE_SCOPE = True
try:
yield
finally:
_KERAS_STYLE_SCOPE = stack
@tf_export(v1=['layers.experimental.set_keras_style'])
def set_keras_style():
"""Use Keras-style variable management.
All tf.layers and tf RNN cells created after keras style ha been enabled
use Keras-style variable management. Creating such layers with a
scope= argument is disallowed, and reuse=True is disallowed.
The purpose of this function is to allow users of existing layers to
slowly transition to Keras layers API without breaking existing
functionality.
For more details, see the documentation for `keras_style_scope`.
Note, once keras style has been set, it is set globally for the entire
program and cannot be unset.
Example:
```python
set_keras_style()
model_1 = RNNModel(name="model_1")
model_2 = RNNModel(name="model_2")
# model_1 and model_2 are guaranteed to create their own variables.
output_1, next_state_1 = model_1(input, state)
output_2, next_state_2 = model_2(input, state)
assert len(model_1.weights) > 0
assert len(model_2.weights) > 0
assert(model_1.weights != model_2.weights)
```
"""
global _KERAS_STYLE_SCOPE
_KERAS_STYLE_SCOPE = True
def _is_in_keras_style_scope():
global _KERAS_STYLE_SCOPE
return _KERAS_STYLE_SCOPE
@tf_export(v1=['layers.Layer'])
class Layer(base_layer.Layer):
"""Base layer class.
It is considered legacy, and we recommend the use of `tf.keras.layers.Layer`
instead.
Arguments:
trainable: Boolean, whether the layer's variables should be trainable.
name: String name of the layer.
dtype: Default dtype of the layer's weights (default of `None` means use the
type of the first input).
Read-only properties:
name: The name of the layer (string).
dtype: Default dtype of the layer's weights (default of `None` means use the
type of the first input).
trainable_variables: List of trainable variables.
non_trainable_variables: List of non-trainable variables.
variables: List of all variables of this layer, trainable and
non-trainable.
updates: List of update ops of this layer.
losses: List of losses added by this layer.
trainable_weights: List of variables to be included in backprop.
non_trainable_weights: List of variables that should not be
included in backprop.
weights: The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).
Mutable properties:
trainable: Whether the layer should be trained (boolean).
input_spec: Optional (list of) `InputSpec` object(s) specifying the
constraints on inputs that can be accepted by the layer.
"""
def __init__(self, trainable=True, name=None, dtype=None,
**kwargs):
# For backwards compatibility, legacy layers do not use `ResourceVariable`
# by default.
self._use_resource_variables = False
scope = kwargs.pop('_scope', None)
self._reuse = kwargs.pop('_reuse', None)
# Avoid an incorrect lint error
self._trainable_weights = []
self.built = False
if dtype is None:
# Indicates to infer dtype from inputs. When the V2 dtype behavior is
# enabled, Keras layers default their dtype to floatx instead, so we pass
# an "_infer" policy to keep the old V1 behavior.
dtype = policy.Policy('_infer')
if 'autocast' not in kwargs:
kwargs['autocast'] = False
super(Layer, self).__init__(trainable=trainable, name=name, dtype=dtype,
**kwargs)
if _is_in_keras_style_scope():
if scope is not None:
raise ValueError(
'scope argument not allowed when keras style layers are enabled, '
'but saw: {}'.format(scope))
if self._reuse is not None:
raise ValueError(
'reuse argument not allowed when keras style layers are enabled, '
'but saw: {}'.format(self._reuse))
self._keras_style = True
else:
self._keras_style = False
self._call_has_scope_arg = 'scope' in self._call_fn_args
if scope:
with vs.variable_scope(scope) as captured_scope:
self._scope = captured_scope
else:
self._scope = None
self._current_scope = None
# We no longer track graph in tf.layers layers. This property is only kept to
# maintain API backward compatibility.
@property
@deprecation.deprecated(
date=None,
instructions='Stop using this property because tf.layers layers no '
'longer track their graph.')
def graph(self):
if context.executing_eagerly():
raise RuntimeError('Layer.graph not supported when executing eagerly.')
return None
def _init_set_name(self, name):
# Determine layer name (non-unique).
if isinstance(name, vs.VariableScope):
base_name = name.name
self._name, _ = self._make_unique_name()
else:
base_name = name
self._name = name
if not name:
self._name, base_name = self._make_unique_name()
self._base_name = base_name
def _make_unique_name(self, name_uid_map=None, avoid_names=None,
namespace='', zero_based=False):
base_name = base_layer.to_snake_case(self.__class__.__name__)
name = backend.unique_object_name(
base_name,
name_uid_map=name_uid_map,
avoid_names=avoid_names,
namespace=namespace,
zero_based=zero_based)
return (name, base_name)
@property
def scope_name(self):
if not self._scope:
raise ValueError('No name available for layer scope because the layer "' +
self._name + '" has not been used yet. The scope name ' +
' is determined the first time the layer instance is ' +
'called. You must therefore call the layer before ' +
'querying `scope_name`.')
return self._scope.name
def add_loss(self, losses, inputs=None):
previous_losses_length = len(self._losses)
previous_callable_losses_length = len(self._callable_losses)
super(Layer, self).add_loss(losses, inputs=inputs)
if not context.executing_eagerly():
# TODO(fchollet): deprecate collection below.
new_losses = self._losses[previous_losses_length:]
new_callable_losses = self._callable_losses[
previous_callable_losses_length:]
for regularizer in new_callable_losses:
loss_tensor = regularizer()
if loss_tensor is not None:
new_losses.append(loss_tensor)
_add_elements_to_collection(
new_losses,
ops.GraphKeys.REGULARIZATION_LOSSES)
def _name_scope(self):
"""Determines op naming for the Layer."""
if self._keras_style:
return super(Layer, self)._name_scope()
return self._current_scope.original_name_scope
def _set_scope(self, scope=None):
if self._scope is None:
# If constructed with _scope=None, lazy setting of scope.
if self._reuse:
with vs.variable_scope(
scope if scope is not None else self._base_name) as captured_scope:
self._scope = captured_scope
else:
with vs.variable_scope(
scope, default_name=self._base_name) as captured_scope:
self._scope = captured_scope
def add_weight(self,
name,
shape,
dtype=None,
initializer=None,
regularizer=None,
trainable=None,
constraint=None,
use_resource=None,
synchronization=vs.VariableSynchronization.AUTO,
aggregation=vs.VariableAggregation.NONE,
partitioner=None,
**kwargs):
"""Adds a new variable to the layer, or gets an existing one; returns it.
Arguments:
name: variable name.
shape: variable shape.
dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
initializer: initializer instance (callable).
regularizer: regularizer instance (callable).
trainable: whether the variable should be part of the layer's
"trainable_variables" (e.g. variables, biases)
or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
Note, if the current variable scope is marked as non-trainable
then this parameter is ignored and any added variables are also
marked as non-trainable. `trainable` defaults to `True` unless
`synchronization` is set to `ON_READ`.
constraint: constraint instance (callable).
use_resource: Whether to use `ResourceVariable`.
synchronization: Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
`tf.VariableSynchronization`. By default the synchronization is set to
`AUTO` and the current `DistributionStrategy` chooses
when to synchronize. If `synchronization` is set to `ON_READ`,
`trainable` must not be set to `True`.
aggregation: Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
`tf.VariableAggregation`.
partitioner: (optional) partitioner instance (callable). If
provided, when the requested variable is created it will be split
into multiple partitions according to `partitioner`. In this case,
an instance of `PartitionedVariable` is returned. Available
partitioners include `tf.compat.v1.fixed_size_partitioner` and
`tf.compat.v1.variable_axis_size_partitioner`. For more details, see
the documentation of `tf.compat.v1.get_variable` and the "Variable
Partitioners and Sharding" section of the API guide.
**kwargs: Additional keyword arguments.
Returns:
The created variable. Usually either a `Variable` or `ResourceVariable`
instance. If `partitioner` is not `None`, a `PartitionedVariable`
instance is returned.
Raises:
RuntimeError: If called with partitioned variable regularization and
eager execution is enabled.
ValueError: When trainable has been set to True with synchronization
set as `ON_READ`.
"""
for kwarg in kwargs:
if kwarg != 'experimental_autocast':
raise TypeError('Unknown keyword argument:', kwarg)
if self._keras_style:
return super(Layer, self).add_weight(
name=name,
shape=shape,
dtype=dtype,
initializer=initializer,
regularizer=regularizer,
trainable=trainable and self.trainable,
constraint=constraint,
use_resource=use_resource,
synchronization=vs.VariableSynchronization.AUTO,
aggregation=vs.VariableAggregation.NONE,
partitioner=partitioner,
**kwargs)
if synchronization == vs.VariableSynchronization.ON_READ:
if trainable:
raise ValueError(
'Synchronization value can be set to '
'VariableSynchronization.ON_READ only for non-trainable variables. '
'You have specified trainable=True and '
'synchronization=VariableSynchronization.ON_READ.')
else:
# Set trainable to be false when variable is to be synced on read.
trainable = False
elif trainable is None:
trainable = True
def _should_add_regularizer(variable, existing_variable_set):
if isinstance(variable, tf_variables.PartitionedVariable):
for var in variable:
if var in existing_variable_set:
return False
return True
else:
return variable not in existing_variable_set
init_graph = None
if not context.executing_eagerly():
default_graph = ops.get_default_graph()
if default_graph.building_function:
with ops.init_scope():
# Retrieve the variables from the graph into which variables
# will be lifted; if initialization ops will be lifted into
# the eager context, then there is nothing to retrieve, since variable
# collections are not supported when eager execution is enabled.
if not context.executing_eagerly():
init_graph = ops.get_default_graph()
existing_variables = set(tf_variables.global_variables())
else:
# Initialization ops will not be lifted out of the default graph.
init_graph = default_graph
existing_variables = set(tf_variables.global_variables())
if dtype is None:
dtype = self.dtype or dtypes.float32
self._set_scope(None)
reuse = self.built or self._reuse
prev_len_trainable = len(self._trainable_weights)
with vs.variable_scope(
self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
self._current_scope = scope
with ops.name_scope(self._name_scope(), skip_on_eager=False):
use_resource = (use_resource or
self._use_resource_variables or
scope.use_resource)
if initializer is None:
initializer = scope.initializer
variable = super(Layer, self).add_weight(
name,
shape,
dtype=dtypes.as_dtype(dtype),
initializer=initializer,
trainable=trainable and self.trainable,
constraint=constraint,
partitioner=partitioner,
use_resource=use_resource,
synchronization=synchronization,
aggregation=aggregation,
getter=vs.get_variable,
**kwargs)
if regularizer:
if (ops.executing_eagerly_outside_functions()
or _should_add_regularizer(variable, existing_variables)):
self._handle_weight_regularization(name, variable, regularizer)
if init_graph is not None:
# Handle edge case where a custom getter has overridden `trainable`.
# There is one known occurrence of this, in unit test
# testBasicRNNCellNotTrainable in
# contrib.rnn.python.kernel_tests.core_rnn_cell_test
with init_graph.as_default():
trainable_variables = tf_variables.trainable_variables()
if (trainable and self.trainable and
variable not in trainable_variables):
# A custom getter / variable scope overrode the trainable flag.
extra_trainable_vars = self._trainable_weights[prev_len_trainable:]
self._trainable_weights = self._trainable_weights[
:prev_len_trainable]
self._non_trainable_weights += extra_trainable_vars
return variable
def __call__(self, inputs, *args, **kwargs):
"""Wraps `call`, applying pre- and post-processing steps.
Arguments:
inputs: input tensor(s).
*args: additional positional arguments to be passed to `self.call`.
**kwargs: additional keyword arguments to be passed to `self.call`.
**Note**: kwarg `scope` is reserved for use by the layer.
Returns:
Output tensor(s).
Note:
- If the layer's `call` method takes a `scope` keyword argument,
this argument will be automatically set to the current variable scope.
- If the layer's `call` method takes a `mask` argument (as some Keras
layers do), its default value will be set to the mask generated
for `inputs` by the previous layer (if `input` did come from
a layer that generated a corresponding mask, i.e. if it came from
a Keras layer with masking support.
Raises:
ValueError: if the layer's `call` method returns None (an invalid value).
"""
scope = kwargs.pop('scope', None)
if self._keras_style:
if scope is not None:
raise ValueError(
'scope argument not allowed when keras style layers are enabled, '
'but saw: {}'.format(scope))
return super(Layer, self).__call__(inputs, *args, **kwargs)
self._set_scope(scope)
if self.built:
try:
# Some classes which inherit from Layer do not use its constructor, so
# rather than initializing to None we check for an AttributeError.
scope_context_manager = self._always_reuse_variable_scope
except AttributeError:
# From this point we will always set reuse=True, so create a "final"
# variable scope with this setting. We avoid re-creating variable scopes
# after this point as an optimization.
self._always_reuse_variable_scope = vs.variable_scope(
self._scope, reuse=True, auxiliary_name_scope=False)
scope_context_manager = self._always_reuse_variable_scope
else:
scope_context_manager = vs.variable_scope(
self._scope, reuse=self._reuse, auxiliary_name_scope=False)
with scope_context_manager as scope:
self._current_scope = scope
try:
call_has_scope_arg = self._call_has_scope_arg
except AttributeError:
self._call_fn_args = function_utils.fn_args(self.call)
self._call_has_scope_arg = 'scope' in self._call_fn_args
call_has_scope_arg = self._call_has_scope_arg
if call_has_scope_arg:
kwargs['scope'] = scope
# Actually call layer
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
if not context.executing_eagerly():
# Update global default collections.
_add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
return outputs
def __deepcopy__(self, memo):
no_copy = set(['_graph', '_thread_local', '_metrics_lock'])
shallow_copy = set(['_scope', '_always_reuse_variable_scope'])
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
for k, v in self.__dict__.items():
if k in no_copy:
setattr(result, k, v)
elif k in shallow_copy:
setattr(result, k, copy.copy(v))
elif base_layer.is_tensor_or_tensor_list(v):
setattr(result, k, v)
else:
setattr(result, k, copy.deepcopy(v, memo))
return result
def __setattr__(self, value, name):
# By-pass the automatic dependency tracking performed by the parent Layer.
super(trackable.Trackable, self).__setattr__(value, name)
@property
def _is_legacy_layer(self):
"""Used by keras to check compatibility. This should not be overridden."""
return True
def _add_elements_to_collection(elements, collection_list):
if context.executing_eagerly():
raise RuntimeError('Using collections from Layers not supported in Eager '
'mode. Tried to add %s to %s' % (elements,
collection_list))
elements = nest.flatten(elements)
collection_list = nest.flatten(collection_list)
for name in collection_list:
collection = ops.get_collection_ref(name)
collection_set = {id(e) for e in collection}
for element in elements:
if id(element) not in collection_set:
collection.append(element)
keras_style_scope = base.keras_style_scope
set_keras_style = base.set_keras_style
Layer = base.Layer

1449
tensorflow/python/layers/convolutional.py
View File

File diff suppressed because it is too large Load Diff

315
tensorflow/python/layers/core.py
View File

@ -21,316 +21,15 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.layers import base
from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.keras.legacy_tf_layers import core
@tf_export(v1=['layers.Dense'])
class Dense(keras_layers.Dense, base.Layer):
"""Densely-connected layer class.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Arguments:
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer. Layers with the same name will
share weights, but to avoid mistakes we require reuse=True in such cases.
_reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Properties:
units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer instance (or name) for the kernel matrix.
bias_initializer: Initializer instance (or name) for the bias.
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
kernel_constraint: Constraint function for the kernel matrix.
bias_constraint: Constraint function for the bias.
kernel: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).
"""
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Dense, self).__init__(units=units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
**kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.Dense instead.')
@tf_export(v1=['layers.dense'])
def dense(
inputs, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the densely-connected layer.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Arguments:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor the same shape as `inputs` except the last dimension is of
size `units`.
Raises:
ValueError: if eager execution is enabled.
"""
layer = Dense(units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
@tf_export(v1=['layers.Dropout'])
class Dropout(keras_layers.Dropout, base.Layer):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
rate: The dropout rate, between 0 and 1. E.g. `rate=0.1` would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed`.
for behavior.
name: The name of the layer (string).
"""
def __init__(self, rate=0.5,
noise_shape=None,
seed=None,
name=None,
**kwargs):
super(Dropout, self).__init__(rate=rate,
noise_shape=noise_shape,
seed=seed,
name=name,
**kwargs)
def call(self, inputs, training=False):
return super(Dropout, self).call(inputs, training=training)
@deprecation.deprecated(
date=None,
instructions='Use keras.layers.dropout instead.')
@tf_export(v1=['layers.dropout'])
def dropout(inputs,
rate=0.5,
noise_shape=None,
seed=None,
training=False,
name=None):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
inputs: Tensor input.
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed`
for behavior.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(apply dropout) or in inference mode (return the input untouched).
name: The name of the layer (string).
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
return layer.apply(inputs, training=training)
@tf_export(v1=['layers.Flatten'])
class Flatten(keras_layers.Flatten, base.Layer):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Arguments:
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, ...)`.
Examples:
```
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, 16)`
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
y = Flatten()(x)
# now `y` has shape `(None, None)`
```
"""
pass
@deprecation.deprecated(
date=None,
instructions='Use keras.layers.Flatten instead.')
@tf_export(v1=['layers.flatten'])
def flatten(inputs, name=None, data_format='channels_last'):
"""Flattens an input tensor while preserving the batch axis (axis 0).
Arguments:
inputs: Tensor input.
name: The name of the layer (string).
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
Returns:
Reshaped tensor.
Examples:
```
x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, 16)`
x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
y = flatten(x)
# now `y` has shape `(None, None)`
```
"""
layer = Flatten(name=name, data_format=data_format)
return layer.apply(inputs)
Dense = core.Dense
dense = core.dense
Dropout = core.Dropout
dropout = core.dropout
Flatten = core.Flatten
flatten = core.flatten
# Aliases

317
tensorflow/python/layers/normalization.py
View File

@ -20,322 +20,11 @@ from __future__ import division
from __future__ import print_function
from tensorflow.python.keras.layers import normalization as keras_normalization
from tensorflow.python.layers import base
from tensorflow.python.ops import init_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
@tf_export(v1=['layers.BatchNormalization'])
class BatchNormalization(keras_normalization.BatchNormalization, base.Layer):
"""Batch Normalization layer from (Ioffe et al., 2015).
Keras APIs handle BatchNormalization updates to the moving_mean and
moving_variance as part of their `fit()` and `evaluate()` loops. However, if a
custom training loop is used with an instance of `Model`, these updates need
to be explicitly included. Here's a simple example of how it can be done:
```python
# model is an instance of Model that contains BatchNormalization layer.
update_ops = model.get_updates_for(None) + model.get_updates_for(features)
train_op = optimizer.minimize(loss)
train_op = tf.group([train_op, update_ops])
```
Arguments:
axis: An `int` or list of `int`, the axis or axes that should be normalized,
typically the features axis/axes. For instance, after a `Conv2D` layer
with `data_format="channels_first"`, set `axis=1`. If a list of axes is
provided, each axis in `axis` will be normalized
simultaneously. Default is `-1` which uses the last axis. Note: when
using multi-axis batch norm, the `beta`, `gamma`, `moving_mean`, and
`moving_variance` variables are the same rank as the input Tensor,
with dimension size 1 in all reduced (non-axis) dimensions).
momentum: Momentum for the moving average.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
is ignored.
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
scaling can be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
moving_mean_initializer: Initializer for the moving mean.
moving_variance_initializer: Initializer for the moving variance.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: An optional projection function to be applied to the `beta`
weight after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected variable and must return the projected
variable (which must have the same shape). Constraints are not safe to use
when doing asynchronous distributed training.
gamma_constraint: An optional projection function to be applied to the
`gamma` weight after being updated by an `Optimizer`.
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
variables during training. The inference is the same for either value of
this parameter.
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
scalar `Tensors` used to clip the renorm correction. The correction `(r,
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
dmax are set to inf, 0, inf, respectively.
renorm_momentum: Momentum used to update the moving means and standard
deviations with renorm. Unlike `momentum`, this affects training and
should be neither too small (which would add noise) nor too large (which
would give stale estimates). Note that `momentum` is still applied to get
the means and variances for inference.
fused: if `None` or `True`, use a faster, fused implementation if possible.
If `False`, use the system recommended implementation.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
which means batch normalization is performed across the whole batch. When
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
Normalization", which creates virtual sub-batches which are each
normalized separately (with shared gamma, beta, and moving statistics).
Must divide the actual batch size during execution.
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
the input tensor and returning a pair (scale, bias) to apply to the
normalized values (before gamma and beta), only during training. For
example, if axis==-1,
`adjustment = lambda shape: (
tf.random.uniform(shape[-1:], 0.93, 1.07),
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
value by up to 7% up or down, then shift the result by up to 0.1
(with independent scaling and bias for each feature but shared
across all examples), and finally apply gamma and/or beta. If
`None`, no adjustment is applied. Cannot be specified if
virtual_batch_size is specified.
name: A string, the name of the layer.
References:
Batch Normalization - Accelerating Deep Network Training by Reducing
Internal Covariate Shift:
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
Batch Renormalization - Towards Reducing Minibatch Dependence in
Batch-Normalized Models:
[Ioffe,
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
"""
def __init__(self,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=init_ops.zeros_initializer(),
gamma_initializer=init_ops.ones_initializer(),
moving_mean_initializer=init_ops.zeros_initializer(),
moving_variance_initializer=init_ops.ones_initializer(),
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
trainable=True,
virtual_batch_size=None,
adjustment=None,
name=None,
**kwargs):
super(BatchNormalization, self).__init__(
axis=axis,
momentum=momentum,
epsilon=epsilon,
center=center,
scale=scale,
beta_initializer=beta_initializer,
gamma_initializer=gamma_initializer,
moving_mean_initializer=moving_mean_initializer,
moving_variance_initializer=moving_variance_initializer,
beta_regularizer=beta_regularizer,
gamma_regularizer=gamma_regularizer,
beta_constraint=beta_constraint,
gamma_constraint=gamma_constraint,
renorm=renorm,
renorm_clipping=renorm_clipping,
renorm_momentum=renorm_momentum,
fused=fused,
trainable=trainable,
virtual_batch_size=virtual_batch_size,
adjustment=adjustment,
name=name,
**kwargs)
def call(self, inputs, training=False):
return super(BatchNormalization, self).call(inputs, training=training)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.BatchNormalization instead. In '
'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
'be used (consult the `tf.keras.layers.BatchNormalization` '
'documentation).')
@tf_export(v1=['layers.batch_normalization'])
def batch_normalization(inputs,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=init_ops.zeros_initializer(),
gamma_initializer=init_ops.ones_initializer(),
moving_mean_initializer=init_ops.zeros_initializer(),
moving_variance_initializer=init_ops.ones_initializer(),
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
training=False,
trainable=True,
name=None,
reuse=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
virtual_batch_size=None,
adjustment=None):
"""Functional interface for the batch normalization layer from_config(Ioffe et al., 2015).
Note: when training, the moving_mean and moving_variance need to be updated.
By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
need to be executed alongside the `train_op`. Also, be sure to add any
batch_normalization ops before getting the update_ops collection. Otherwise,
update_ops will be empty, and training/inference will not work properly. For
example:
```python
x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)
# ...
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = optimizer.minimize(loss)
train_op = tf.group([train_op, update_ops])
```
Arguments:
inputs: Tensor input.
axis: An `int`, the axis that should be normalized (typically the features
axis). For instance, after a `Convolution2D` layer with
`data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
momentum: Momentum for the moving average.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor. If False, `beta`
is ignored.
scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
next layer is linear (also e.g. `nn.relu`), this can be disabled since the
scaling can be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
moving_mean_initializer: Initializer for the moving mean.
moving_variance_initializer: Initializer for the moving variance.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: An optional projection function to be applied to the `beta`
weight after being updated by an `Optimizer` (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected variable and must return the projected
variable (which must have the same shape). Constraints are not safe to use
when doing asynchronous distributed training.
gamma_constraint: An optional projection function to be applied to the
`gamma` weight after being updated by an `Optimizer`.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(normalized with statistics of the current batch) or in inference mode
(normalized with moving statistics). **NOTE**: make sure to set this
parameter correctly, or else your training/inference will not work
properly.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer by the same
name.
renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds extra
variables during training. The inference is the same for either value of
this parameter.
renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
scalar `Tensors` used to clip the renorm correction. The correction `(r,
d)` is used as `corrected_value = normalized_value * r + d`, with `r`
clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
dmax are set to inf, 0, inf, respectively.
renorm_momentum: Momentum used to update the moving means and standard
deviations with renorm. Unlike `momentum`, this affects training and
should be neither too small (which would add noise) nor too large (which
would give stale estimates). Note that `momentum` is still applied to get
the means and variances for inference.
fused: if `None` or `True`, use a faster, fused implementation if possible.
If `False`, use the system recommended implementation.
virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
which means batch normalization is performed across the whole batch. When
`virtual_batch_size` is not `None`, instead perform "Ghost Batch
Normalization", which creates virtual sub-batches which are each
normalized separately (with shared gamma, beta, and moving statistics).
Must divide the actual batch size during execution.
adjustment: A function taking the `Tensor` containing the (dynamic) shape of
the input tensor and returning a pair (scale, bias) to apply to the
normalized values (before gamma and beta), only during training. For
example, if axis==-1,
`adjustment = lambda shape: (
tf.random.uniform(shape[-1:], 0.93, 1.07),
tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
value by up to 7% up or down, then shift the result by up to 0.1
(with independent scaling and bias for each feature but shared
across all examples), and finally apply gamma and/or beta. If
`None`, no adjustment is applied. Cannot be specified if
virtual_batch_size is specified.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
References:
Batch Normalization - Accelerating Deep Network Training by Reducing
Internal Covariate Shift:
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
Batch Renormalization - Towards Reducing Minibatch Dependence in
Batch-Normalized Models:
[Ioffe,
2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
"""
layer = BatchNormalization(
axis=axis,
momentum=momentum,
epsilon=epsilon,
center=center,
scale=scale,
beta_initializer=beta_initializer,
gamma_initializer=gamma_initializer,
moving_mean_initializer=moving_mean_initializer,
moving_variance_initializer=moving_variance_initializer,
beta_regularizer=beta_regularizer,
gamma_regularizer=gamma_regularizer,
beta_constraint=beta_constraint,
gamma_constraint=gamma_constraint,
renorm=renorm,
renorm_clipping=renorm_clipping,
renorm_momentum=renorm_momentum,
fused=fused,
trainable=trainable,
virtual_batch_size=virtual_batch_size,
adjustment=adjustment,
name=name,
_reuse=reuse,
_scope=name)
return layer.apply(inputs, training=training)
from tensorflow.python.keras.legacy_tf_layers import normalization
BatchNormalization = normalization.BatchNormalization
batch_normalization = normalization.batch_normalization
# Aliases
BatchNorm = BatchNormalization

453
tensorflow/python/layers/pooling.py
View File

@ -19,448 +19,21 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.keras import layers as keras_layers
from tensorflow.python.layers import base
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.keras.legacy_tf_layers import pooling
@tf_export(v1=['layers.AveragePooling1D'])
class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
"""Average Pooling layer for 1D inputs.
Arguments:
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(AveragePooling1D, self).__init__(
pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name,
**kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling1D instead.')
@tf_export(v1=['layers.average_pooling1d'])
def average_pooling1d(inputs, pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average Pooling layer for 1D inputs.
Arguments:
inputs: The tensor over which to pool. Must have rank 3.
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
Returns:
The output tensor, of rank 3.
Raises:
ValueError: if eager execution is enabled.
"""
layer = AveragePooling1D(pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.MaxPooling1D'])
class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
"""Max Pooling layer for 1D inputs.
Arguments:
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(MaxPooling1D, self).__init__(
pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name,
**kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling1D instead.')
@tf_export(v1=['layers.max_pooling1d'])
def max_pooling1d(inputs, pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Max Pooling layer for 1D inputs.
Arguments:
inputs: The tensor over which to pool. Must have rank 3.
pool_size: An integer or tuple/list of a single integer,
representing the size of the pooling window.
strides: An integer or tuple/list of a single integer, specifying the
strides of the pooling operation.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string, one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, length, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, length)`.
name: A string, the name of the layer.
Returns:
The output tensor, of rank 3.
Raises:
ValueError: if eager execution is enabled.
"""
layer = MaxPooling1D(pool_size=pool_size,
strides=strides,
padding=padding,
data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.AveragePooling2D'])
class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
"""Average pooling layer for 2D inputs (e.g. images).
Arguments:
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(AveragePooling2D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling2D instead.')
@tf_export(v1=['layers.average_pooling2d'])
def average_pooling2d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average pooling layer for 2D inputs (e.g. images).
Arguments:
inputs: The tensor over which to pool. Must have rank 4.
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = AveragePooling2D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.MaxPooling2D'])
class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
"""Max pooling layer for 2D inputs (e.g. images).
Arguments:
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(MaxPooling2D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling2D instead.')
@tf_export(v1=['layers.max_pooling2d'])
def max_pooling2d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Max pooling layer for 2D inputs (e.g. images).
Arguments:
inputs: The tensor over which to pool. Must have rank 4.
pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, height, width, channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = MaxPooling2D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.AveragePooling3D'])
class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
"""Average pooling layer for 3D inputs (e.g. volumes).
Arguments:
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(AveragePooling3D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.AveragePooling3D instead.')
@tf_export(v1=['layers.average_pooling3d'])
def average_pooling3d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Average pooling layer for 3D inputs (e.g. volumes).
Arguments:
inputs: The tensor over which to pool. Must have rank 5.
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = AveragePooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
@tf_export(v1=['layers.MaxPooling3D'])
class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
"""Max pooling layer for 3D inputs (e.g. volumes).
Arguments:
pool_size: An integer or tuple/list of 3 integers:
(pool_depth, pool_height, pool_width)
specifying the size of the pooling window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the pooling operation.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape
`(batch, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
"""
def __init__(self, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
if strides is None:
raise ValueError('Argument `strides` must not be None.')
super(MaxPooling3D, self).__init__(
pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format, name=name, **kwargs)
@deprecation.deprecated(
date=None, instructions='Use keras.layers.MaxPooling3D instead.')
@tf_export(v1=['layers.max_pooling3d'])
def max_pooling3d(inputs,
pool_size, strides,
padding='valid', data_format='channels_last',
name=None):
"""Max pooling layer for 3D inputs (e.g.
volumes).
Arguments:
inputs: The tensor over which to pool. Must have rank 5.
pool_size: An integer or tuple/list of 3 integers: (pool_depth, pool_height,
pool_width) specifying the size of the pooling window. Can be a single
integer to specify the same value for all spatial dimensions.
strides: An integer or tuple/list of 3 integers, specifying the strides of
the pooling operation. Can be a single integer to specify the same value
for all spatial dimensions.
padding: A string. The padding method, either 'valid' or 'same'.
Case-insensitive.
data_format: A string. The ordering of the dimensions in the inputs.
`channels_last` (default) and `channels_first` are supported.
`channels_last` corresponds to inputs with shape `(batch, depth, height,
width, channels)` while `channels_first` corresponds to inputs with shape
`(batch, channels, depth, height, width)`.
name: A string, the name of the layer.
Returns:
Output tensor.
Raises:
ValueError: if eager execution is enabled.
"""
layer = MaxPooling3D(pool_size=pool_size, strides=strides,
padding=padding, data_format=data_format,
name=name)
return layer.apply(inputs)
AveragePooling1D = pooling.AveragePooling1D
average_pooling1d = pooling.average_pooling1d
MaxPooling1D = pooling.MaxPooling1D
max_pooling1d = pooling.max_pooling1d
AveragePooling2D = pooling.AveragePooling2D
average_pooling2d = pooling.average_pooling2d
MaxPooling2D = pooling.MaxPooling2D
max_pooling2d = pooling.max_pooling2d
AveragePooling3D = pooling.AveragePooling3D
average_pooling3d = pooling.average_pooling3d
MaxPooling3D = pooling.MaxPooling3D
max_pooling3d = pooling.max_pooling3d
# Aliases

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-average-pooling1-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.AveragePooling1D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling1D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.AveragePooling1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling1D\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-average-pooling2-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.AveragePooling2D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling2D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.AveragePooling2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling2D\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-average-pooling3-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.AveragePooling3D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling3D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.AveragePooling3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.AveragePooling3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling3D\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-batch-normalization.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.BatchNormalization"
tf_class {
is_instance: "<class \'tensorflow.python.layers.normalization.BatchNormalization\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.normalization.BatchNormalization\'>"
is_instance: "<class \'tensorflow.python.keras.layers.normalization.BatchNormalization\'>"
is_instance: "<class \'tensorflow.python.keras.layers.normalization.BatchNormalizationBase\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-conv1-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.Conv1D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv1D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-conv2-d-transpose.pbtxt
View File

@ -1,10 +1,10 @@
path: "tensorflow.layers.Conv2DTranspose"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2DTranspose\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv2DTranspose\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2DTranspose\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-conv2-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.Conv2D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-conv3-d-transpose.pbtxt
View File

@ -1,10 +1,10 @@
path: "tensorflow.layers.Conv3DTranspose"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3DTranspose\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv3DTranspose\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3DTranspose\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-conv3-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.Conv3D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.Conv3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-dense.pbtxt
View File

@ -1,8 +1,8 @@
path: "tensorflow.layers.Dense"
tf_class {
is_instance: "<class \'tensorflow.python.layers.core.Dense\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.core.Dense\'>"
is_instance: "<class \'tensorflow.python.keras.layers.core.Dense\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-dropout.pbtxt
View File

@ -1,8 +1,8 @@
path: "tensorflow.layers.Dropout"
tf_class {
is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.core.Dropout\'>"
is_instance: "<class \'tensorflow.python.keras.layers.core.Dropout\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-flatten.pbtxt
View File

@ -1,8 +1,8 @@
path: "tensorflow.layers.Flatten"
tf_class {
is_instance: "<class \'tensorflow.python.layers.core.Flatten\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.core.Flatten\'>"
is_instance: "<class \'tensorflow.python.keras.layers.core.Flatten\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.layers.-layer.pbtxt
View File

@ -1,6 +1,6 @@
path: "tensorflow.layers.Layer"
tf_class {
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-max-pooling1-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.MaxPooling1D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling1D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.MaxPooling1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling1D\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-max-pooling2-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.MaxPooling2D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling2D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.MaxPooling2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling2D\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-max-pooling3-d.pbtxt
View File

@ -1,9 +1,9 @@
path: "tensorflow.layers.MaxPooling3D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling3D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.pooling.MaxPooling3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.MaxPooling3D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.pooling.Pooling3D\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-separable-conv1-d.pbtxt
View File

@ -1,10 +1,10 @@
path: "tensorflow.layers.SeparableConv1D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv1D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.SeparableConv1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv1D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

4
tensorflow/tools/api/golden/v1/tensorflow.layers.-separable-conv2-d.pbtxt
View File

@ -1,10 +1,10 @@
path: "tensorflow.layers.SeparableConv2D"
tf_class {
is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv2D\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.convolutional.SeparableConv2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv2D\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.SeparableConv\'>"
is_instance: "<class \'tensorflow.python.keras.layers.convolutional.Conv\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.lite.experimental.nn.-t-f-lite-l-s-t-m-cell.pbtxt
View File

@ -3,7 +3,7 @@ tf_class {
is_instance: "<class \'tensorflow.lite.experimental.examples.lstm.rnn_cell.TFLiteLSTMCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.lite.experimental.nn.-tf-lite-r-n-n-cell.pbtxt
View File

@ -3,7 +3,7 @@ tf_class {
is_instance: "<class \'tensorflow.lite.experimental.examples.lstm.rnn_cell.TfLiteRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
View File

@ -3,7 +3,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
View File

@ -3,7 +3,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.BasicRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
View File

@ -4,7 +4,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.DeviceWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
View File

@ -4,7 +4,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.DropoutWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
View File

@ -3,7 +3,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.GRUCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
View File

@ -3,7 +3,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LSTMCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
View File

@ -2,7 +2,7 @@ path: "tensorflow.nn.rnn_cell.MultiRNNCell"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.MultiRNNCell\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
View File

@ -1,7 +1,7 @@
path: "tensorflow.nn.rnn_cell.RNNCell"
tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
View File

@ -4,7 +4,7 @@ tf_class {
is_instance: "<class \'tensorflow.python.ops.rnn_cell_wrapper_impl.ResidualWrapperBase\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl._RNNCellWrapperV1\'>"
is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.legacy_tf_layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"