Module keras.legacy_tf_layers.normalization
Contains the normalization layer classes and their functional aliases.
Expand source code
# 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
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# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# =============================================================================
# pylint: disable=g-classes-have-attributes
"""Contains the normalization layer classes and their functional aliases."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v2 as tf
import warnings
from keras.layers.normalization import batch_normalization_v1
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export
@keras_export(v1=['keras.__internal__.legacy.layers.BatchNormalization'])
@tf_export(v1=['layers.BatchNormalization'])
class BatchNormalization(batch_normalization_v1.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])
```
Args:
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))
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.BatchNormalization`.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
bn = tf.compat.v1.layers.BatchNormalization()
```
After:
```python
bn = tf.keras.layers.BatchNormalization()
```
#### How to Map Arguments
TF1 Arg Name | TF2 Arg Name | Note
:------------------------ | :------------------------ | :---------------
`name` | `name` | Layer base class
`trainable` | `trainable` | Layer base class
`axis` | `axis` | -
`momentum` | `momentum` | -
`epsilon` | `epsilon` | -
`center` | `center` | -
`scale` | `scale` | -
`beta_initializer` | `beta_initializer` | -
`gamma_initializer` | `gamma_initializer` | -
`moving_mean_initializer` | `moving_mean_initializer` | -
`beta_regularizer` | `beta_regularizer' | -
`gamma_regularizer` | `gamma_regularizer' | -
`beta_constraint` | `beta_constraint' | -
`gamma_constraint` | `gamma_constraint' | -
`renorm` | Not supported | -
`renorm_clipping` | Not supported | -
`renorm_momentum` | Not supported | -
`fused` | Not supported | -
`virtual_batch_size` | Not supported | -
`adjustment` | Not supported | -
@end_compatibility
"""
def __init__(self,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=tf.compat.v1.zeros_initializer(),
gamma_initializer=tf.compat.v1.ones_initializer(),
moving_mean_initializer=tf.compat.v1.zeros_initializer(),
moving_variance_initializer=tf.compat.v1.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)
@keras_export(v1=['keras.__internal__.legacy.layers.batch_normalization'])
@tf_export(v1=['layers.batch_normalization'])
def batch_normalization(inputs,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=tf.compat.v1.zeros_initializer(),
gamma_initializer=tf.compat.v1.ones_initializer(),
moving_mean_initializer=tf.compat.v1.zeros_initializer(),
moving_variance_initializer=tf.compat.v1.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])
```
Args:
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))
@compatibility(TF2)
This API is not compatible with eager execution or `tf.function`.
Please refer to [tf.layers section of the migration guide]
(https://www.tensorflow.org/guide/migrate#models_based_on_tflayers)
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is `tf.keras.layers.BatchNormalization`.
The batch updating pattern with
`tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used in
native TF2. Consult the `tf.keras.layers.BatchNormalization` documentation
for further information.
#### Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
```python
x_norm = tf.compat.v1.layers.batch_normalization(x)
```
After:
To migrate code using TF1 functional layers use the [Keras Functional API]
(https://www.tensorflow.org/guide/keras/functional):
```python
x = tf.keras.Input(shape=(28, 28, 1),)
y = tf.keras.layers.BatchNormalization()(x)
model = tf.keras.Model(x, y)
```
#### How to Map Arguments
TF1 Arg Name | TF2 Arg Name | Note
:------------------------ | :------------------------ | :---------------
`name` | `name` | Layer base class
`trainable` | `trainable` | Layer base class
`axis` | `axis` | -
`momentum` | `momentum` | -
`epsilon` | `epsilon` | -
`center` | `center` | -
`scale` | `scale` | -
`beta_initializer` | `beta_initializer` | -
`gamma_initializer` | `gamma_initializer` | -
`moving_mean_initializer` | `moving_mean_initializer` | -
`beta_regularizer` | `beta_regularizer' | -
`gamma_regularizer` | `gamma_regularizer' | -
`beta_constraint` | `beta_constraint' | -
`gamma_constraint` | `gamma_constraint' | -
`renorm` | Not supported | -
`renorm_clipping` | Not supported | -
`renorm_momentum` | Not supported | -
`fused` | Not supported | -
`virtual_batch_size` | Not supported | -
`adjustment` | Not supported | -
@end_compatibility
"""
warnings.warn(
'`tf.layers.batch_normalization` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.BatchNormalization` instead. '
'In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` '
'should not be used (consult the `tf.keras.layers.BatchNormalization` '
'documentation).')
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
Functions
def batch_norm(inputs, axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>, moving_mean_initializer=<tensorflow.python.ops.init_ops.Zeros object>, moving_variance_initializer=<tensorflow.python.ops.init_ops.Ones object>, 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 thetrain_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: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])
Args
inputs
- Tensor input.
axis
- An
int
, the axis that should be normalized (typically the features axis). For instance, after aConvolution2D
layer withdata_format="channels_first"
, setaxis=1
inBatchNormalization
. 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 anOptimizer
(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 anOptimizer
. 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 collectionGraphKeys.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)<code> is used as </code>corrected_value = normalized_value * r + d<code>, with </code>r
clipped to [rmin, rmax], andd
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 thatmomentum
is still applied to get the means and variances for inference. fused
- if
None
orTrue
, use a faster, fused implementation if possible. IfFalse
, use the system recommended implementation. virtual_batch_size
- An
int
. By default,virtual_batch_size
isNone
, which means batch normalization is performed across the whole batch. Whenvirtual_batch_size
is notNone
, 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. IfNone
, 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 (pdf) Batch Renormalization - Towards Reducing Minibatch Dependence in Batch-Normalized Models: Ioffe, 2017 (pdf)
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.BatchNormalization
.The batch updating pattern with
tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)
should not be used in native TF2. Consult thetf.keras.layers.BatchNormalization
documentation for further information.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
x_norm = tf.compat.v1.layers.batch_normalization(x)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input(shape=(28, 28, 1),) y = tf.keras.layers.BatchNormalization()(x) model = tf.keras.Model(x, y)
How to Map Arguments
TF1 Arg Name TF2 Arg Name Note name
name
Layer base class trainable
trainable
Layer base class axis
axis
- momentum
momentum
- epsilon
epsilon
- center
center
- scale
scale
- beta_initializer
beta_initializer
- gamma_initializer
gamma_initializer
- moving_mean_initializer
moving_mean_initializer
- beta_regularizer
`beta_regularizer' - gamma_regularizer
`gamma_regularizer' - beta_constraint
`beta_constraint' - gamma_constraint
`gamma_constraint' - renorm
Not supported - renorm_clipping
Not supported - renorm_momentum
Not supported - fused
Not supported - virtual_batch_size
Not supported - adjustment
Not supported - @end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.batch_normalization']) @tf_export(v1=['layers.batch_normalization']) def batch_normalization(inputs, axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True, beta_initializer=tf.compat.v1.zeros_initializer(), gamma_initializer=tf.compat.v1.ones_initializer(), moving_mean_initializer=tf.compat.v1.zeros_initializer(), moving_variance_initializer=tf.compat.v1.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]) ``` Args: 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)) @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.BatchNormalization`. The batch updating pattern with `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used in native TF2. Consult the `tf.keras.layers.BatchNormalization` documentation for further information. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python x_norm = tf.compat.v1.layers.batch_normalization(x) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input(shape=(28, 28, 1),) y = tf.keras.layers.BatchNormalization()(x) model = tf.keras.Model(x, y) ``` #### How to Map Arguments TF1 Arg Name | TF2 Arg Name | Note :------------------------ | :------------------------ | :--------------- `name` | `name` | Layer base class `trainable` | `trainable` | Layer base class `axis` | `axis` | - `momentum` | `momentum` | - `epsilon` | `epsilon` | - `center` | `center` | - `scale` | `scale` | - `beta_initializer` | `beta_initializer` | - `gamma_initializer` | `gamma_initializer` | - `moving_mean_initializer` | `moving_mean_initializer` | - `beta_regularizer` | `beta_regularizer' | - `gamma_regularizer` | `gamma_regularizer' | - `beta_constraint` | `beta_constraint' | - `gamma_constraint` | `gamma_constraint' | - `renorm` | Not supported | - `renorm_clipping` | Not supported | - `renorm_momentum` | Not supported | - `fused` | Not supported | - `virtual_batch_size` | Not supported | - `adjustment` | Not supported | - @end_compatibility """ warnings.warn( '`tf.layers.batch_normalization` is deprecated and ' 'will be removed in a future version. ' 'Please use `tf.keras.layers.BatchNormalization` instead. ' 'In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` ' 'should not be used (consult the `tf.keras.layers.BatchNormalization` ' 'documentation).') 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)
def batch_normalization(inputs, axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>, moving_mean_initializer=<tensorflow.python.ops.init_ops.Zeros object>, moving_variance_initializer=<tensorflow.python.ops.init_ops.Ones object>, 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 thetrain_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: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])
Args
inputs
- Tensor input.
axis
- An
int
, the axis that should be normalized (typically the features axis). For instance, after aConvolution2D
layer withdata_format="channels_first"
, setaxis=1
inBatchNormalization
. 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 anOptimizer
(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 anOptimizer
. 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 collectionGraphKeys.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)<code> is used as </code>corrected_value = normalized_value * r + d<code>, with </code>r
clipped to [rmin, rmax], andd
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 thatmomentum
is still applied to get the means and variances for inference. fused
- if
None
orTrue
, use a faster, fused implementation if possible. IfFalse
, use the system recommended implementation. virtual_batch_size
- An
int
. By default,virtual_batch_size
isNone
, which means batch normalization is performed across the whole batch. Whenvirtual_batch_size
is notNone
, 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. IfNone
, 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 (pdf) Batch Renormalization - Towards Reducing Minibatch Dependence in Batch-Normalized Models: Ioffe, 2017 (pdf)
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.BatchNormalization
.The batch updating pattern with
tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)
should not be used in native TF2. Consult thetf.keras.layers.BatchNormalization
documentation for further information.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
x_norm = tf.compat.v1.layers.batch_normalization(x)
After:
To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional):
x = tf.keras.Input(shape=(28, 28, 1),) y = tf.keras.layers.BatchNormalization()(x) model = tf.keras.Model(x, y)
How to Map Arguments
TF1 Arg Name TF2 Arg Name Note name
name
Layer base class trainable
trainable
Layer base class axis
axis
- momentum
momentum
- epsilon
epsilon
- center
center
- scale
scale
- beta_initializer
beta_initializer
- gamma_initializer
gamma_initializer
- moving_mean_initializer
moving_mean_initializer
- beta_regularizer
`beta_regularizer' - gamma_regularizer
`gamma_regularizer' - beta_constraint
`beta_constraint' - gamma_constraint
`gamma_constraint' - renorm
Not supported - renorm_clipping
Not supported - renorm_momentum
Not supported - fused
Not supported - virtual_batch_size
Not supported - adjustment
Not supported - @end_compatibility
Expand source code
@keras_export(v1=['keras.__internal__.legacy.layers.batch_normalization']) @tf_export(v1=['layers.batch_normalization']) def batch_normalization(inputs, axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True, beta_initializer=tf.compat.v1.zeros_initializer(), gamma_initializer=tf.compat.v1.ones_initializer(), moving_mean_initializer=tf.compat.v1.zeros_initializer(), moving_variance_initializer=tf.compat.v1.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]) ``` Args: 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)) @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.BatchNormalization`. The batch updating pattern with `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used in native TF2. Consult the `tf.keras.layers.BatchNormalization` documentation for further information. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python x_norm = tf.compat.v1.layers.batch_normalization(x) ``` After: To migrate code using TF1 functional layers use the [Keras Functional API] (https://www.tensorflow.org/guide/keras/functional): ```python x = tf.keras.Input(shape=(28, 28, 1),) y = tf.keras.layers.BatchNormalization()(x) model = tf.keras.Model(x, y) ``` #### How to Map Arguments TF1 Arg Name | TF2 Arg Name | Note :------------------------ | :------------------------ | :--------------- `name` | `name` | Layer base class `trainable` | `trainable` | Layer base class `axis` | `axis` | - `momentum` | `momentum` | - `epsilon` | `epsilon` | - `center` | `center` | - `scale` | `scale` | - `beta_initializer` | `beta_initializer` | - `gamma_initializer` | `gamma_initializer` | - `moving_mean_initializer` | `moving_mean_initializer` | - `beta_regularizer` | `beta_regularizer' | - `gamma_regularizer` | `gamma_regularizer' | - `beta_constraint` | `beta_constraint' | - `gamma_constraint` | `gamma_constraint' | - `renorm` | Not supported | - `renorm_clipping` | Not supported | - `renorm_momentum` | Not supported | - `fused` | Not supported | - `virtual_batch_size` | Not supported | - `adjustment` | Not supported | - @end_compatibility """ warnings.warn( '`tf.layers.batch_normalization` is deprecated and ' 'will be removed in a future version. ' 'Please use `tf.keras.layers.BatchNormalization` instead. ' 'In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` ' 'should not be used (consult the `tf.keras.layers.BatchNormalization` ' 'documentation).') 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)
Classes
class BatchNormalization (axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>, moving_mean_initializer=<tensorflow.python.ops.init_ops.Zeros object>, moving_variance_initializer=<tensorflow.python.ops.init_ops.Ones object>, 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)
-
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()
andevaluate()
loops. However, if a custom training loop is used with an instance ofModel
, these updates need to be explicitly included. Here's a simple example of how it can be done:# 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])
Args
axis
- An
int
or list ofint
, the axis or axes that should be normalized, typically the features axis/axes. For instance, after aConv2D
layer withdata_format="channels_first"
, setaxis=1
. If a list of axes is provided, each axis inaxis
will be normalized simultaneously. Default is-1
which uses the last axis. Note: when using multi-axis batch norm, thebeta
,gamma
,moving_mean
, andmoving_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 anOptimizer
(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 anOptimizer
. 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)<code> is used as </code>corrected_value = normalized_value * r + d<code>, with </code>r
clipped to [rmin, rmax], andd
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 thatmomentum
is still applied to get the means and variances for inference. fused
- if
None
orTrue
, use a faster, fused implementation if possible. IfFalse
, use the system recommended implementation. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). virtual_batch_size
- An
int
. By default,virtual_batch_size
isNone
, which means batch normalization is performed across the whole batch. Whenvirtual_batch_size
is notNone
, 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. IfNone
, 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 (pdf) Batch Renormalization - Towards Reducing Minibatch Dependence in Batch-Normalized Models: Ioffe, 2017 (pdf)
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.BatchNormalization
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
bn = tf.compat.v1.layers.BatchNormalization()
After:
bn = tf.keras.layers.BatchNormalization()
How to Map Arguments
TF1 Arg Name TF2 Arg Name Note name
name
Layer base class trainable
trainable
Layer base class axis
axis
- momentum
momentum
- epsilon
epsilon
- center
center
- scale
scale
- beta_initializer
beta_initializer
- gamma_initializer
gamma_initializer
- moving_mean_initializer
moving_mean_initializer
- beta_regularizer
`beta_regularizer' - gamma_regularizer
`gamma_regularizer' - beta_constraint
`beta_constraint' - gamma_constraint
`gamma_constraint' - renorm
Not supported - renorm_clipping
Not supported - renorm_momentum
Not supported - fused
Not supported - virtual_batch_size
Not supported - adjustment
Not supported - @end_compatibility
Expand source code
class BatchNormalization(batch_normalization_v1.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]) ``` Args: 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)) @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.BatchNormalization`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python bn = tf.compat.v1.layers.BatchNormalization() ``` After: ```python bn = tf.keras.layers.BatchNormalization() ``` #### How to Map Arguments TF1 Arg Name | TF2 Arg Name | Note :------------------------ | :------------------------ | :--------------- `name` | `name` | Layer base class `trainable` | `trainable` | Layer base class `axis` | `axis` | - `momentum` | `momentum` | - `epsilon` | `epsilon` | - `center` | `center` | - `scale` | `scale` | - `beta_initializer` | `beta_initializer` | - `gamma_initializer` | `gamma_initializer` | - `moving_mean_initializer` | `moving_mean_initializer` | - `beta_regularizer` | `beta_regularizer' | - `gamma_regularizer` | `gamma_regularizer' | - `beta_constraint` | `beta_constraint' | - `gamma_constraint` | `gamma_constraint' | - `renorm` | Not supported | - `renorm_clipping` | Not supported | - `renorm_momentum` | Not supported | - `fused` | Not supported | - `virtual_batch_size` | Not supported | - `adjustment` | Not supported | - @end_compatibility """ def __init__(self, axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True, beta_initializer=tf.compat.v1.zeros_initializer(), gamma_initializer=tf.compat.v1.ones_initializer(), moving_mean_initializer=tf.compat.v1.zeros_initializer(), moving_variance_initializer=tf.compat.v1.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)
Ancestors
- BatchNormalization
- BatchNormalizationBase
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Methods
def call(self, inputs, training=False)
-
This is where the layer's logic lives.
Note here that
call()
method intf.keras
is little bit different fromkeras
API. Inkeras
API, you can pass support masking for layers as additional arguments. Whereastf.keras
hascompute_mask()
method to support masking.Args
inputs
- Input tensor, or dict/list/tuple of input tensors.
The first positional
inputs
argument is subject to special rules: -inputs
must be explicitly passed. A layer cannot have zero arguments, andinputs
cannot be provided via the default value of a keyword argument. - NumPy array or Python scalar values ininputs
get cast as tensors. - Keras mask metadata is only collected frominputs
. - Layers are built (build(input_shape)
method) using shape info frominputs
only. -input_spec
compatibility is only checked againstinputs
. - Mixed precision input casting is only applied toinputs
. If a layer has tensor arguments in*args
or**kwargs
, their casting behavior in mixed precision should be handled manually. - The SavedModel input specification is generated usinginputs
only. - Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported forinputs
and not for tensors in positional and keyword arguments. *args
- Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs
- Additional keyword arguments. May contain tensors, although
this is not recommended, for the reasons above.
The following optional keyword arguments are reserved:
-
training
: Boolean scalar tensor of Python boolean indicating whether thecall
is meant for training or inference. -mask
: Boolean input mask. If the layer'scall()
method takes amask
argument, its default value will be set to the mask generated forinputs
by the previous layer (ifinput
did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).
Returns
A tensor or list/tuple of tensors.
Expand source code
def call(self, inputs, training=False): return super(BatchNormalization, self).call(inputs, training=training)
class BatchNorm (axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>, moving_mean_initializer=<tensorflow.python.ops.init_ops.Zeros object>, moving_variance_initializer=<tensorflow.python.ops.init_ops.Ones object>, 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)
-
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()
andevaluate()
loops. However, if a custom training loop is used with an instance ofModel
, these updates need to be explicitly included. Here's a simple example of how it can be done:# 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])
Args
axis
- An
int
or list ofint
, the axis or axes that should be normalized, typically the features axis/axes. For instance, after aConv2D
layer withdata_format="channels_first"
, setaxis=1
. If a list of axes is provided, each axis inaxis
will be normalized simultaneously. Default is-1
which uses the last axis. Note: when using multi-axis batch norm, thebeta
,gamma
,moving_mean
, andmoving_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 anOptimizer
(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 anOptimizer
. 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)<code> is used as </code>corrected_value = normalized_value * r + d<code>, with </code>r
clipped to [rmin, rmax], andd
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 thatmomentum
is still applied to get the means and variances for inference. fused
- if
None
orTrue
, use a faster, fused implementation if possible. IfFalse
, use the system recommended implementation. trainable
- Boolean, if
True
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). virtual_batch_size
- An
int
. By default,virtual_batch_size
isNone
, which means batch normalization is performed across the whole batch. Whenvirtual_batch_size
is notNone
, 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. IfNone
, 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 (pdf) Batch Renormalization - Towards Reducing Minibatch Dependence in Batch-Normalized Models: Ioffe, 2017 (pdf)
@compatibility(TF2) This API is not compatible with eager execution or
tf.function
.Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is
tf.keras.layers.BatchNormalization
.Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
bn = tf.compat.v1.layers.BatchNormalization()
After:
bn = tf.keras.layers.BatchNormalization()
How to Map Arguments
TF1 Arg Name TF2 Arg Name Note name
name
Layer base class trainable
trainable
Layer base class axis
axis
- momentum
momentum
- epsilon
epsilon
- center
center
- scale
scale
- beta_initializer
beta_initializer
- gamma_initializer
gamma_initializer
- moving_mean_initializer
moving_mean_initializer
- beta_regularizer
`beta_regularizer' - gamma_regularizer
`gamma_regularizer' - beta_constraint
`beta_constraint' - gamma_constraint
`gamma_constraint' - renorm
Not supported - renorm_clipping
Not supported - renorm_momentum
Not supported - fused
Not supported - virtual_batch_size
Not supported - adjustment
Not supported - @end_compatibility
Expand source code
class BatchNormalization(batch_normalization_v1.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]) ``` Args: 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)) @compatibility(TF2) This API is not compatible with eager execution or `tf.function`. Please refer to [tf.layers section of the migration guide] (https://www.tensorflow.org/guide/migrate#models_based_on_tflayers) to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is `tf.keras.layers.BatchNormalization`. #### Structural Mapping to Native TF2 None of the supported arguments have changed name. Before: ```python bn = tf.compat.v1.layers.BatchNormalization() ``` After: ```python bn = tf.keras.layers.BatchNormalization() ``` #### How to Map Arguments TF1 Arg Name | TF2 Arg Name | Note :------------------------ | :------------------------ | :--------------- `name` | `name` | Layer base class `trainable` | `trainable` | Layer base class `axis` | `axis` | - `momentum` | `momentum` | - `epsilon` | `epsilon` | - `center` | `center` | - `scale` | `scale` | - `beta_initializer` | `beta_initializer` | - `gamma_initializer` | `gamma_initializer` | - `moving_mean_initializer` | `moving_mean_initializer` | - `beta_regularizer` | `beta_regularizer' | - `gamma_regularizer` | `gamma_regularizer' | - `beta_constraint` | `beta_constraint' | - `gamma_constraint` | `gamma_constraint' | - `renorm` | Not supported | - `renorm_clipping` | Not supported | - `renorm_momentum` | Not supported | - `fused` | Not supported | - `virtual_batch_size` | Not supported | - `adjustment` | Not supported | - @end_compatibility """ def __init__(self, axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True, beta_initializer=tf.compat.v1.zeros_initializer(), gamma_initializer=tf.compat.v1.ones_initializer(), moving_mean_initializer=tf.compat.v1.zeros_initializer(), moving_variance_initializer=tf.compat.v1.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)
Ancestors
- BatchNormalization
- BatchNormalizationBase
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
BatchNormalization
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights