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Copy constraint/initializer/regularizers to frozen_keras.

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This is used by legacy_base_layer and other layer subclasses.

PiperOrigin-RevId: 299410923
Change-Id: I8011fca38347bfb30956d8560ce6c144fd6069b4
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Scott Zhu 2020-03-06 12:23:26 -08:00 committed by TensorFlower Gardener
parent e3c18e7beb
commit 6f795b7539
6 changed files with 849 additions and 6 deletions
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55
tensorflow/python/frozen_keras/BUILD Normal file
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package(
default_visibility = ["//tensorflow:__subpackages__"],
licenses = ["notice"], # Apache 2.0
)
#TODO(scottzhu): Cleanup all the deps to python/keras
py_library(
name = "frozen_keras",
deps = [
":constraint",
":initializers",
":regularizers",
"//tensorflow/python/frozen_keras/engine:legacy_base_layer",
],
)
py_library(
name = "constraint",
srcs = ["constraints.py"],
deps = [
"//tensorflow/python:array_ops",
"//tensorflow/python:control_flow_ops",
"//tensorflow/python:math_ops",
"//tensorflow/python:tensor_shape",
"//tensorflow/python/keras:backend",
"//tensorflow/python/keras/utils:generic_utils",
"@six_archive//:six",
],
)
py_library(
name = "initializers",
srcs = ["initializers.py"],
deps = [
"//tensorflow/python:dtypes",
"//tensorflow/python:init_ops",
"//tensorflow/python:init_ops_v2",
"//tensorflow/python:tf2",
"//tensorflow/python/keras/utils:generic_utils",
"@six_archive//:six",
],
)
py_library(
name = "regularizers",
srcs = ["regularizers.py"],
deps = [
"//tensorflow/python:math_ops",
"//tensorflow/python:util",
"//tensorflow/python/keras:backend",
"//tensorflow/python/keras/utils:generic_utils",
"@six_archive//:six",
],
)

282
tensorflow/python/frozen_keras/constraints.py Normal file
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# 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.
# ==============================================================================
# pylint: disable=invalid-name
# pylint: disable=g-classes-have-attributes
"""Constraints: functions that impose constraints on weight values."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
class Constraint(object):
def __call__(self, w):
return w
def get_config(self):
return {}
class MaxNorm(Constraint):
"""MaxNorm weight constraint.
Constrains the weights incident to each hidden unit
to have a norm less than or equal to a desired value.
Arguments:
m: the maximum norm for the incoming weights.
axis: integer, axis along which to calculate weight norms.
For instance, in a `Dense` layer the weight matrix
has shape `(input_dim, output_dim)`,
set `axis` to `0` to constrain each weight vector
of length `(input_dim,)`.
In a `Conv2D` layer with `data_format="channels_last"`,
the weight tensor has shape
`(rows, cols, input_depth, output_depth)`,
set `axis` to `[0, 1, 2]`
to constrain the weights of each filter tensor of size
`(rows, cols, input_depth)`.
"""
def __init__(self, max_value=2, axis=0):
self.max_value = max_value
self.axis = axis
def __call__(self, w):
norms = K.sqrt(
math_ops.reduce_sum(math_ops.square(w), axis=self.axis, keepdims=True))
desired = K.clip(norms, 0, self.max_value)
return w * (desired / (K.epsilon() + norms))
def get_config(self):
return {'max_value': self.max_value, 'axis': self.axis}
class NonNeg(Constraint):
"""Constrains the weights to be non-negative.
"""
def __call__(self, w):
return w * math_ops.cast(math_ops.greater_equal(w, 0.), K.floatx())
class UnitNorm(Constraint):
"""Constrains the weights incident to each hidden unit to have unit norm.
Arguments:
axis: integer, axis along which to calculate weight norms.
For instance, in a `Dense` layer the weight matrix
has shape `(input_dim, output_dim)`,
set `axis` to `0` to constrain each weight vector
of length `(input_dim,)`.
In a `Conv2D` layer with `data_format="channels_last"`,
the weight tensor has shape
`(rows, cols, input_depth, output_depth)`,
set `axis` to `[0, 1, 2]`
to constrain the weights of each filter tensor of size
`(rows, cols, input_depth)`.
"""
def __init__(self, axis=0):
self.axis = axis
def __call__(self, w):
return w / (
K.epsilon() + K.sqrt(
math_ops.reduce_sum(
math_ops.square(w), axis=self.axis, keepdims=True)))
def get_config(self):
return {'axis': self.axis}
class MinMaxNorm(Constraint):
"""MinMaxNorm weight constraint.
Constrains the weights incident to each hidden unit
to have the norm between a lower bound and an upper bound.
Arguments:
min_value: the minimum norm for the incoming weights.
max_value: the maximum norm for the incoming weights.
rate: rate for enforcing the constraint: weights will be
rescaled to yield
`(1 - rate) * norm + rate * norm.clip(min_value, max_value)`.
Effectively, this means that rate=1.0 stands for strict
enforcement of the constraint, while rate<1.0 means that
weights will be rescaled at each step to slowly move
towards a value inside the desired interval.
axis: integer, axis along which to calculate weight norms.
For instance, in a `Dense` layer the weight matrix
has shape `(input_dim, output_dim)`,
set `axis` to `0` to constrain each weight vector
of length `(input_dim,)`.
In a `Conv2D` layer with `data_format="channels_last"`,
the weight tensor has shape
`(rows, cols, input_depth, output_depth)`,
set `axis` to `[0, 1, 2]`
to constrain the weights of each filter tensor of size
`(rows, cols, input_depth)`.
"""
def __init__(self, min_value=0.0, max_value=1.0, rate=1.0, axis=0):
self.min_value = min_value
self.max_value = max_value
self.rate = rate
self.axis = axis
def __call__(self, w):
norms = K.sqrt(
math_ops.reduce_sum(math_ops.square(w), axis=self.axis, keepdims=True))
desired = (
self.rate * K.clip(norms, self.min_value, self.max_value) +
(1 - self.rate) * norms)
return w * (desired / (K.epsilon() + norms))
def get_config(self):
return {
'min_value': self.min_value,
'max_value': self.max_value,
'rate': self.rate,
'axis': self.axis
}
class RadialConstraint(Constraint):
"""Constrains `Conv2D` kernel weights to be the same for each radius.
For example, the desired output for the following 4-by-4 kernel::
```
kernel = [[v_00, v_01, v_02, v_03],
[v_10, v_11, v_12, v_13],
[v_20, v_21, v_22, v_23],
[v_30, v_31, v_32, v_33]]
```
is this::
```
kernel = [[v_11, v_11, v_11, v_11],
[v_11, v_33, v_33, v_11],
[v_11, v_33, v_33, v_11],
[v_11, v_11, v_11, v_11]]
```
This constraint can be applied to any `Conv2D` layer version, including
`Conv2DTranspose` and `SeparableConv2D`, and with either `"channels_last"` or
`"channels_first"` data format. The method assumes the weight tensor is of
shape `(rows, cols, input_depth, output_depth)`.
"""
def __call__(self, w):
w_shape = w.shape
if w_shape.rank is None or w_shape.rank != 4:
raise ValueError(
'The weight tensor must be of rank 4, but is of shape: %s' % w_shape)
height, width, channels, kernels = w_shape
w = K.reshape(w, (height, width, channels * kernels))
# TODO(cpeter): Switch map_fn for a faster tf.vectorized_map once K.switch
# is supported.
w = K.map_fn(
self._kernel_constraint,
K.stack(array_ops.unstack(w, axis=-1), axis=0))
return K.reshape(K.stack(array_ops.unstack(w, axis=0), axis=-1),
(height, width, channels, kernels))
def _kernel_constraint(self, kernel):
"""Radially constraints a kernel with shape (height, width, channels)."""
padding = K.constant([[1, 1], [1, 1]], dtype='int32')
kernel_shape = K.shape(kernel)[0]
start = K.cast(kernel_shape / 2, 'int32')
kernel_new = K.switch(
K.cast(math_ops.floormod(kernel_shape, 2), 'bool'),
lambda: kernel[start - 1:start, start - 1:start],
lambda: kernel[start - 1:start, start - 1:start] + K.zeros( # pylint: disable=g-long-lambda
(2, 2), dtype=kernel.dtype))
index = K.switch(
K.cast(math_ops.floormod(kernel_shape, 2), 'bool'),
lambda: K.constant(0, dtype='int32'),
lambda: K.constant(1, dtype='int32'))
while_condition = lambda index, *args: K.less(index, start)
def body_fn(i, array):
return i + 1, array_ops.pad(
array,
padding,
constant_values=kernel[start + i, start + i])
_, kernel_new = control_flow_ops.while_loop(
while_condition,
body_fn,
[index, kernel_new],
shape_invariants=[index.get_shape(),
tensor_shape.TensorShape([None, None])])
return kernel_new
# Aliases.
max_norm = MaxNorm
non_neg = NonNeg
unit_norm = UnitNorm
min_max_norm = MinMaxNorm
radial_constraint = RadialConstraint
# Legacy aliases.
maxnorm = max_norm
nonneg = non_neg
unitnorm = unit_norm
def serialize(constraint):
return serialize_keras_object(constraint)
def deserialize(config, custom_objects=None):
return deserialize_keras_object(
config,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='constraint')
def get(identifier):
if identifier is None:
return None
if isinstance(identifier, dict):
return deserialize(identifier)
elif isinstance(identifier, six.string_types):
config = {'class_name': str(identifier), 'config': {}}
return deserialize(config)
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret constraint identifier: ' +
str(identifier))

8
tensorflow/python/frozen_keras/engine/BUILD
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@ -5,6 +5,8 @@ package(
licenses = ["notice"], # Apache 2.0
)
#TODO(scottzhu): Cleanup all the deps to python/keras
py_library(
name = "legacy_base_layer",
srcs = ["legacy_base_layer.py"],
@ -29,11 +31,11 @@ py_library(
"//tensorflow/python/eager:context",
"//tensorflow/python/eager:execute",
"//tensorflow/python/eager:function",
"//tensorflow/python/frozen_keras:constraint",
"//tensorflow/python/frozen_keras:initializers",
"//tensorflow/python/frozen_keras:regularizers",
"//tensorflow/python/keras:backend",
"//tensorflow/python/keras:constraints",
"//tensorflow/python/keras:initializers",
"//tensorflow/python/keras:metrics",
"//tensorflow/python/keras:regularizers",
"//tensorflow/python/keras/engine",
"//tensorflow/python/keras/engine:base_layer_utils",
"//tensorflow/python/keras/engine:input_spec",

6
tensorflow/python/frozen_keras/engine/legacy_base_layer.py
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@ -51,10 +51,10 @@ from tensorflow.python.framework import func_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import tensor_util
from tensorflow.python.frozen_keras import constraints
from tensorflow.python.frozen_keras import initializers
from tensorflow.python.frozen_keras import regularizers
from tensorflow.python.keras import backend
from tensorflow.python.keras import constraints
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.keras.engine import input_spec
from tensorflow.python.keras.engine import node as node_module

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# 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.
# ==============================================================================
"""Keras initializer serialization / deserialization."""
# pylint: disable=g-classes-have-attributes
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python import tf2
from tensorflow.python.framework import dtypes
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.ops import init_ops_v2
# These imports are brought in so that keras.initializers.deserialize
# has them available in module_objects.
from tensorflow.python.ops.init_ops import Constant
from tensorflow.python.ops.init_ops import GlorotNormal
from tensorflow.python.ops.init_ops import GlorotUniform
from tensorflow.python.ops.init_ops import he_normal # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import he_uniform # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import Identity
from tensorflow.python.ops.init_ops import Initializer # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import lecun_normal # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import lecun_uniform # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import Ones
from tensorflow.python.ops.init_ops import Orthogonal
from tensorflow.python.ops.init_ops import RandomNormal as TFRandomNormal
from tensorflow.python.ops.init_ops import RandomUniform as TFRandomUniform
from tensorflow.python.ops.init_ops import TruncatedNormal as TFTruncatedNormal
from tensorflow.python.ops.init_ops import VarianceScaling # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import Zeros
# pylint: disable=unused-import, disable=line-too-long
from tensorflow.python.ops.init_ops_v2 import Constant as ConstantV2
from tensorflow.python.ops.init_ops_v2 import GlorotNormal as GlorotNormalV2
from tensorflow.python.ops.init_ops_v2 import GlorotUniform as GlorotUniformV2
from tensorflow.python.ops.init_ops_v2 import he_normal as he_normalV2
from tensorflow.python.ops.init_ops_v2 import he_uniform as he_uniformV2
from tensorflow.python.ops.init_ops_v2 import Identity as IdentityV2
from tensorflow.python.ops.init_ops_v2 import Initializer as InitializerV2
from tensorflow.python.ops.init_ops_v2 import lecun_normal as lecun_normalV2
from tensorflow.python.ops.init_ops_v2 import lecun_uniform as lecun_uniformV2
from tensorflow.python.ops.init_ops_v2 import Ones as OnesV2
from tensorflow.python.ops.init_ops_v2 import Orthogonal as OrthogonalV2
from tensorflow.python.ops.init_ops_v2 import RandomNormal as RandomNormalV2
from tensorflow.python.ops.init_ops_v2 import RandomUniform as RandomUniformV2
from tensorflow.python.ops.init_ops_v2 import TruncatedNormal as TruncatedNormalV2
from tensorflow.python.ops.init_ops_v2 import VarianceScaling as VarianceScalingV2
from tensorflow.python.ops.init_ops_v2 import Zeros as ZerosV2
# pylint: enable=unused-import, enable=line-too-long
class TruncatedNormal(TFTruncatedNormal):
"""Initializer that generates a truncated normal distribution.
These values are similar to values from a `random_normal_initializer`
except that values more than two standard deviations from the mean
are discarded and re-drawn. This is the recommended initializer for
neural network weights and filters.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values to
generate. Defaults to 0.
stddev: a python scalar or a scalar tensor. Standard deviation of the random
values to generate. Defaults to 0.05.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: The data type. Only floating point types are supported.
Returns:
A TruncatedNormal instance.
"""
def __init__(self, mean=0.0, stddev=0.05, seed=None, dtype=dtypes.float32):
super(TruncatedNormal, self).__init__(
mean=mean, stddev=stddev, seed=seed, dtype=dtype)
class RandomUniform(TFRandomUniform):
"""Initializer that generates tensors with a uniform distribution.
Args:
minval: A python scalar or a scalar tensor. Lower bound of the range of
random values to generate. Defaults to -0.05.
maxval: A python scalar or a scalar tensor. Upper bound of the range of
random values to generate. Defaults to 0.05.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: The data type.
Returns:
A RandomUniform instance.
"""
def __init__(self, minval=-0.05, maxval=0.05, seed=None,
dtype=dtypes.float32):
super(RandomUniform, self).__init__(
minval=minval, maxval=maxval, seed=seed, dtype=dtype)
class RandomNormal(TFRandomNormal):
"""Initializer that generates tensors with a normal distribution.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values to
generate. Defaults to 0.
stddev: a python scalar or a scalar tensor. Standard deviation of the random
values to generate. Defaults to 0.05.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
dtype: The data type. Only floating point types are supported.
Returns:
RandomNormal instance.
"""
def __init__(self, mean=0.0, stddev=0.05, seed=None, dtype=dtypes.float32):
super(RandomNormal, self).__init__(
mean=mean, stddev=stddev, seed=seed, dtype=dtype)
# Compatibility aliases
# pylint: disable=invalid-name
zero = zeros = Zeros
one = ones = Ones
constant = Constant
uniform = random_uniform = RandomUniform
normal = random_normal = RandomNormal
truncated_normal = TruncatedNormal
identity = Identity
orthogonal = Orthogonal
glorot_normal = GlorotNormal
glorot_uniform = GlorotUniform
# Utility functions
def serialize(initializer):
return serialize_keras_object(initializer)
def deserialize(config, custom_objects=None):
"""Return an `Initializer` object from its config."""
if tf2.enabled():
# Class names are the same for V1 and V2 but the V2 classes
# are aliased in this file so we need to grab them directly
# from `init_ops_v2`.
module_objects = {
obj_name: getattr(init_ops_v2, obj_name)
for obj_name in dir(init_ops_v2)
}
else:
module_objects = globals()
return deserialize_keras_object(
config,
module_objects=module_objects,
custom_objects=custom_objects,
printable_module_name='initializer')
def get(identifier):
if identifier is None:
return None
if isinstance(identifier, dict):
return deserialize(identifier)
elif isinstance(identifier, six.string_types):
identifier = str(identifier)
# We have to special-case functions that return classes.
# TODO(omalleyt): Turn these into classes or class aliases.
special_cases = ['he_normal', 'he_uniform', 'lecun_normal', 'lecun_uniform']
if identifier in special_cases:
# Treat like a class.
return deserialize({'class_name': identifier, 'config': {}})
return deserialize(identifier)
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret initializer identifier: ' +
str(identifier))
# pylint: enable=invalid-name

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# 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.
# ==============================================================================
"""Built-in regularizers."""
# pylint: disable=g-classes-have-attributes
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.ops import math_ops
class Regularizer(object):
"""Regularizer base class.
Regularizers allow you to apply penalties on layer parameters or layer
activity during optimization. These penalties are summed into the loss
function that the network optimizes.
Regularization penalties are applied on a per-layer basis. The exact API will
depend on the layer, but many layers (e.g. `Dense`, `Conv1D`, `Conv2D` and
`Conv3D`) have a unified API.
These layers expose 3 keyword arguments:
- `kernel_regularizer`: Regularizer to apply a penalty on the layer's kernel
- `bias_regularizer`: Regularizer to apply a penalty on the layer's bias
- `activity_regularizer`: Regularizer to apply a penalty on the layer's output
All layers (including custom layers) expose `activity_regularizer` as a
settable property, whether or not it is in the constructor arguments.
The value returned by the `activity_regularizer` is divided by the input
batch size so that the relative weighting between the weight regularizers and
the activity regularizers does not change with the batch size.
You can access a layer's regularization penalties by calling `layer.losses`
after calling the layer on inputs.
## Example
>>> layer = tf.keras.layers.Dense(
... 5, input_dim=5,
... kernel_initializer='ones',
... kernel_regularizer=tf.keras.regularizers.l1(0.01),
... activity_regularizer=tf.keras.regularizers.l2(0.01))
>>> tensor = tf.ones(shape=(5, 5)) * 2.0
>>> out = layer(tensor)
>>> # The kernel regularization term is 0.25
>>> # The activity regularization term (after dividing by the batch size) is 5
>>> tf.math.reduce_sum(layer.losses)
<tf.Tensor: shape=(), dtype=float32, numpy=5.25>
## Available penalties
```python
tf.keras.regularizers.l1(0.3) # L1 Regularization Penalty
tf.keras.regularizers.l2(0.1) # L2 Regularization Penalty
tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01) # L1 + L2 penalties
```
## Directly calling a regularizer
Compute a regularization loss on a tensor by directly calling a regularizer
as if it is a one-argument function.
E.g.
>>> regularizer = tf.keras.regularizers.l2(2.)
>>> tensor = tf.ones(shape=(5, 5))
>>> regularizer(tensor)
<tf.Tensor: shape=(), dtype=float32, numpy=50.0>
## Developing new regularizers
Any function that takes in a weight matrix and returns a scalar
tensor can be used as a regularizer, e.g.:
>>> @tf.keras.utils.register_keras_serializable(package='Custom', name='l1')
... def l1_reg(weight_matrix):
... return 0.01 * tf.math.reduce_sum(tf.math.abs(weight_matrix))
...
>>> layer = tf.keras.layers.Dense(5, input_dim=5,
... kernel_initializer='ones', kernel_regularizer=l1_reg)
>>> tensor = tf.ones(shape=(5, 5))
>>> out = layer(tensor)
>>> layer.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=0.25>]
Alternatively, you can write your custom regularizers in an
object-oriented way by extending this regularizer base class, e.g.:
>>> @tf.keras.utils.register_keras_serializable(package='Custom', name='l2')
... class L2Regularizer(tf.keras.regularizers.Regularizer):
... def __init__(self, l2=0.): # pylint: disable=redefined-outer-name
... self.l2 = l2
...
... def __call__(self, x):
... return self.l2 * tf.math.reduce_sum(tf.math.square(x))
...
... def get_config(self):
... return {'l2': float(self.l2)}
...
>>> layer = tf.keras.layers.Dense(
... 5, input_dim=5, kernel_initializer='ones',
... kernel_regularizer=L2Regularizer(l2=0.5))
>>> tensor = tf.ones(shape=(5, 5))
>>> out = layer(tensor)
>>> layer.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=12.5>]
### A note on serialization and deserialization:
Registering the regularizers as serializable is optional if you are just
training and executing models, exporting to and from SavedModels, or saving
and loading weight checkpoints.
Registration is required for Keras `model_to_estimator`, saving and
loading models to HDF5 formats, Keras model cloning, some visualization
utilities, and exporting models to and from JSON. If using this functionality,
you must make sure any python process running your model has also defined
and registered your custom regularizer.
`tf.keras.utils.register_keras_serializable` is only available in TF 2.1 and
beyond. In earlier versions of TensorFlow you must pass your custom
regularizer to the `custom_objects` argument of methods that expect custom
regularizers to be registered as serializable.
"""
def __call__(self, x):
"""Compute a regularization penalty from an input tensor."""
return 0.
@classmethod
def from_config(cls, config):
"""Creates a regularizer from its config.
This method is the reverse of `get_config`,
capable of instantiating the same regularizer from the config
dictionary.
This method is used by Keras `model_to_estimator`, saving and
loading models to HDF5 formats, Keras model cloning, some visualization
utilities, and exporting models to and from JSON.
Arguments:
config: A Python dictionary, typically the output of get_config.
Returns:
A regularizer instance.
"""
return cls(**config)
def get_config(self):
"""Returns the config of the regularizer.
An regularizer config is a Python dictionary (serializable)
containing all configuration parameters of the regularizer.
The same regularizer can be reinstantiated later
(without any saved state) from this configuration.
This method is optional if you are just training and executing models,
exporting to and from SavedModels, or using weight checkpoints.
This method is required for Keras `model_to_estimator`, saving and
loading models to HDF5 formats, Keras model cloning, some visualization
utilities, and exporting models to and from JSON.
Returns:
Python dictionary.
"""
raise NotImplementedError(str(self) + ' does not implement get_config()')
class L1L2(Regularizer):
r"""A regularizer that applies both L1 and L2 regularization penalties.
The L1 regularization penalty is computed as:
$$\ell_1\,\,penalty =\ell_1\sum_{i=0}^n|x_i|$$
The L2 regularization penalty is computed as
$$\ell_2\,\,penalty =\ell_2\sum_{i=0}^nx_i^2$$
Attributes:
l1: Float; L1 regularization factor.
l2: Float; L2 regularization factor.
"""
def __init__(self, l1=0., l2=0.): # pylint: disable=redefined-outer-name
self.l1 = K.cast_to_floatx(l1)
self.l2 = K.cast_to_floatx(l2)
def __call__(self, x):
if not self.l1 and not self.l2:
return K.constant(0.)
regularization = 0.
if self.l1:
regularization += self.l1 * math_ops.reduce_sum(math_ops.abs(x))
if self.l2:
regularization += self.l2 * math_ops.reduce_sum(math_ops.square(x))
return regularization
def get_config(self):
return {'l1': float(self.l1), 'l2': float(self.l2)}
# Aliases.
def l1(l=0.01):
r"""Create a regularizer that applies an L1 regularization penalty.
The L1 regularization penalty is computed as:
$$\ell_1\,\,penalty =\ell_1\sum_{i=0}^n|x_i|$$
Arguments:
l: Float; L1 regularization factor.
Returns:
An L1 Regularizer with the given regularization factor.
"""
return L1L2(l1=l)
def l2(l=0.01):
r"""Create a regularizer that applies an L2 regularization penalty.
The L2 regularization penalty is computed as:
$$\ell_2\,\,penalty =\ell_2\sum_{i=0}^nx_i^2$$
Arguments:
l: Float; L2 regularization factor.
Returns:
An L2 Regularizer with the given regularization factor.
"""
return L1L2(l2=l)
def l1_l2(l1=0.01, l2=0.01): # pylint: disable=redefined-outer-name
r"""Create a regularizer that applies both L1 and L2 penalties.
The L1 regularization penalty is computed as:
$$\ell_1\,\,penalty =\ell_1\sum_{i=0}^n|x_i|$$
The L2 regularization penalty is computed as:
$$\ell_2\,\,penalty =\ell_2\sum_{i=0}^nx_i^2$$
Arguments:
l1: Float; L1 regularization factor.
l2: Float; L2 regularization factor.
Returns:
An L1L2 Regularizer with the given regularization factors.
"""
return L1L2(l1=l1, l2=l2)
def serialize(regularizer):
return serialize_keras_object(regularizer)
def deserialize(config, custom_objects=None):
return deserialize_keras_object(
config,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='regularizer')
def get(identifier):
if identifier is None:
return None
if isinstance(identifier, dict):
return deserialize(identifier)
elif isinstance(identifier, six.string_types):
identifier = str(identifier)
# We have to special-case functions that return classes.
# TODO(omalleyt): Turn these into classes or class aliases.
special_cases = ['l1', 'l2', 'l1_l2']
if identifier in special_cases:
# Treat like a class.
return deserialize({'class_name': identifier, 'config': {}})
return deserialize(str(identifier))
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret regularizer identifier:', identifier)