481 lines
22 KiB
Python
481 lines
22 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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"""Contains the normalization layer classes and their functional aliases."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import warnings
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import tensorflow.compat.v2 as tf
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from keras.layers.normalization import batch_normalization_v1
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from keras.legacy_tf_layers import base
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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from tensorflow.python.util.tf_export import tf_export
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@keras_export(v1=["keras.__internal__.legacy.layers.BatchNormalization"])
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@tf_export(v1=["layers.BatchNormalization"])
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class BatchNormalization(batch_normalization_v1.BatchNormalization, base.Layer):
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"""Batch Normalization layer from (Ioffe et al., 2015).
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Keras APIs handle BatchNormalization updates to the moving_mean and
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moving_variance as part of their `fit()` and `evaluate()` loops. However, if
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a custom training loop is used with an instance of `Model`, these updates
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need to be explicitly included. Here's a simple example of how it can be
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done:
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```python
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# model is an instance of Model that contains BatchNormalization layer.
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update_ops = model.get_updates_for(None) + model.get_updates_for(features)
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train_op = optimizer.minimize(loss)
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train_op = tf.group([train_op, update_ops])
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```
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Args:
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axis: An `int` or list of `int`, the axis or axes that should be
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normalized, typically the features axis/axes. For instance, after a
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`Conv2D` layer with `data_format="channels_first"`, set `axis=1`. If a
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list of axes is provided, each axis in `axis` will be normalized
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simultaneously. Default is `-1` which uses the last axis. Note: when
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using multi-axis batch norm, the `beta`, `gamma`, `moving_mean`, and
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`moving_variance` variables are the same rank as the input Tensor, with
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dimension size 1 in all reduced (non-axis) dimensions).
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momentum: Momentum for the moving average.
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epsilon: Small float added to variance to avoid dividing by zero.
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center: If True, add offset of `beta` to normalized tensor. If False,
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`beta` is ignored.
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scale: If True, multiply by `gamma`. If False, `gamma` is not used. When
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the next layer is linear (also e.g. `nn.relu`), this can be disabled
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since the scaling can be done by the next layer.
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beta_initializer: Initializer for the beta weight.
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gamma_initializer: Initializer for the gamma weight.
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moving_mean_initializer: Initializer for the moving mean.
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moving_variance_initializer: Initializer for the moving variance.
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beta_regularizer: Optional regularizer for the beta weight.
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gamma_regularizer: Optional regularizer for the gamma weight.
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beta_constraint: An optional projection function to be applied to the
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`beta` weight after being updated by an `Optimizer` (e.g. used to
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implement norm constraints or value constraints for layer weights). The
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function must take as input the unprojected variable and must return the
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projected variable (which must have the same shape). Constraints are not
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safe to use when doing asynchronous distributed training.
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gamma_constraint: An optional projection function to be applied to the
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`gamma` weight after being updated by an `Optimizer`.
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renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds
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extra variables during training. The inference is the same for either
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value of this parameter.
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renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
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scalar `Tensors` used to clip the renorm correction. The correction `(r,
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d)` is used as `corrected_value = normalized_value * r + d`, with `r`
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clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
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dmax are set to inf, 0, inf, respectively.
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renorm_momentum: Momentum used to update the moving means and standard
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deviations with renorm. Unlike `momentum`, this affects training and
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should be neither too small (which would add noise) nor too large (which
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would give stale estimates). Note that `momentum` is still applied to
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get the means and variances for inference.
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fused: if `None` or `True`, use a faster, fused implementation if
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possible. If `False`, use the system recommended implementation.
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trainable: Boolean, if `True` also add variables to the graph collection
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`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
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virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
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which means batch normalization is performed across the whole batch.
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When `virtual_batch_size` is not `None`, instead perform "Ghost Batch
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Normalization", which creates virtual sub-batches which are each
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normalized separately (with shared gamma, beta, and moving statistics).
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Must divide the actual batch size during execution.
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adjustment: A function taking the `Tensor` containing the (dynamic) shape
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of the input tensor and returning a pair (scale, bias) to apply to the
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normalized values (before gamma and beta), only during training. For
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example, if axis==-1,
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`adjustment = lambda shape: (
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tf.random.uniform(shape[-1:], 0.93, 1.07),
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tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
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value by up to 7% up or down, then shift the result by up to 0.1
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(with independent scaling and bias for each feature but shared
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across all examples), and finally apply gamma and/or beta. If
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`None`, no adjustment is applied. Cannot be specified if
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virtual_batch_size is specified.
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name: A string, the name of the layer.
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References:
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Batch Normalization - Accelerating Deep Network Training by Reducing
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Internal Covariate Shift:
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[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
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([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
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Batch Renormalization - Towards Reducing Minibatch Dependence in
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Batch-Normalized Models:
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[Ioffe,
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2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models)
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([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf))
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@compatibility(TF2)
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This API is a legacy api that is only compatible with eager execution and
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`tf.function` if you combine it with
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`tf.compat.v1.keras.utils.track_tf1_style_variables`
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Please refer to [tf.layers model mapping section of the migration guide]
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(https://www.tensorflow.org/guide/migrate/model_mapping)
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to learn how to use your TensorFlow v1 model in TF2 with Keras.
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The corresponding TensorFlow v2 layer is
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`tf.keras.layers.BatchNormalization`.
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#### Structural Mapping to Native TF2
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None of the supported arguments have changed name.
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Before:
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```python
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bn = tf.compat.v1.layers.BatchNormalization()
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```
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After:
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```python
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bn = tf.keras.layers.BatchNormalization()
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```
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#### How to Map Arguments
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TF1 Arg Name | TF2 Arg Name | Note
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:------------------------ | :------------------------ | :---------------
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`name` | `name` | Layer base class
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`trainable` | `trainable` | Layer base class
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`axis` | `axis` | -
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`momentum` | `momentum` | -
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`epsilon` | `epsilon` | -
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`center` | `center` | -
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`scale` | `scale` | -
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`beta_initializer` | `beta_initializer` | -
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`gamma_initializer` | `gamma_initializer` | -
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`moving_mean_initializer` | `moving_mean_initializer` | -
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`beta_regularizer` | `beta_regularizer' | -
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`gamma_regularizer` | `gamma_regularizer' | -
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`beta_constraint` | `beta_constraint' | -
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`gamma_constraint` | `gamma_constraint' | -
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`renorm` | Not supported | -
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`renorm_clipping` | Not supported | -
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`renorm_momentum` | Not supported | -
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`fused` | Not supported | -
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`virtual_batch_size` | Not supported | -
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`adjustment` | Not supported | -
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@end_compatibility
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"""
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def __init__(
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self,
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axis=-1,
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momentum=0.99,
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epsilon=1e-3,
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center=True,
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scale=True,
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beta_initializer=tf.compat.v1.zeros_initializer(),
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gamma_initializer=tf.compat.v1.ones_initializer(),
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moving_mean_initializer=tf.compat.v1.zeros_initializer(),
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moving_variance_initializer=tf.compat.v1.ones_initializer(),
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beta_regularizer=None,
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gamma_regularizer=None,
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beta_constraint=None,
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gamma_constraint=None,
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renorm=False,
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renorm_clipping=None,
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renorm_momentum=0.99,
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fused=None,
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trainable=True,
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virtual_batch_size=None,
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adjustment=None,
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name=None,
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**kwargs
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):
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super().__init__(
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axis=axis,
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momentum=momentum,
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epsilon=epsilon,
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center=center,
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scale=scale,
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beta_initializer=beta_initializer,
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gamma_initializer=gamma_initializer,
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moving_mean_initializer=moving_mean_initializer,
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moving_variance_initializer=moving_variance_initializer,
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beta_regularizer=beta_regularizer,
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gamma_regularizer=gamma_regularizer,
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beta_constraint=beta_constraint,
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gamma_constraint=gamma_constraint,
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renorm=renorm,
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renorm_clipping=renorm_clipping,
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renorm_momentum=renorm_momentum,
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fused=fused,
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trainable=trainable,
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virtual_batch_size=virtual_batch_size,
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adjustment=adjustment,
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name=name,
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**kwargs
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)
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def call(self, inputs, training=False, mask=None):
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return super().call(inputs, training=training, mask=mask)
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@keras_export(v1=["keras.__internal__.legacy.layers.batch_normalization"])
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@tf_export(v1=["layers.batch_normalization"])
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def batch_normalization(
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inputs,
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axis=-1,
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momentum=0.99,
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epsilon=1e-3,
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center=True,
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scale=True,
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beta_initializer=tf.compat.v1.zeros_initializer(),
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gamma_initializer=tf.compat.v1.ones_initializer(),
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moving_mean_initializer=tf.compat.v1.zeros_initializer(),
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moving_variance_initializer=tf.compat.v1.ones_initializer(),
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beta_regularizer=None,
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gamma_regularizer=None,
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beta_constraint=None,
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gamma_constraint=None,
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training=False,
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trainable=True,
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name=None,
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reuse=None,
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renorm=False,
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renorm_clipping=None,
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renorm_momentum=0.99,
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fused=None,
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virtual_batch_size=None,
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adjustment=None,
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):
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"""Functional interface for the batch normalization layer from_config(Ioffe
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et al., 2015).
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Note: when training, the moving_mean and moving_variance need to be updated.
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By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
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need to be executed alongside the `train_op`. Also, be sure to add any
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batch_normalization ops before getting the update_ops collection. Otherwise,
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update_ops will be empty, and training/inference will not work properly. For
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example:
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```python
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x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)
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# ...
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update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
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train_op = optimizer.minimize(loss)
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train_op = tf.group([train_op, update_ops])
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```
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Args:
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inputs: Tensor input.
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axis: An `int`, the axis that should be normalized (typically the features
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axis). For instance, after a `Convolution2D` layer with
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`data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
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momentum: Momentum for the moving average.
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epsilon: Small float added to variance to avoid dividing by zero.
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center: If True, add offset of `beta` to normalized tensor. If False,
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`beta` is ignored.
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scale: If True, multiply by `gamma`. If False, `gamma` is not used. When
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the next layer is linear (also e.g. `nn.relu`), this can be disabled
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since the scaling can be done by the next layer.
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beta_initializer: Initializer for the beta weight.
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gamma_initializer: Initializer for the gamma weight.
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moving_mean_initializer: Initializer for the moving mean.
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moving_variance_initializer: Initializer for the moving variance.
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beta_regularizer: Optional regularizer for the beta weight.
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gamma_regularizer: Optional regularizer for the gamma weight.
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beta_constraint: An optional projection function to be applied to the
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`beta` weight after being updated by an `Optimizer` (e.g. used to
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implement norm constraints or value constraints for layer weights). The
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function must take as input the unprojected variable and must return the
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projected variable (which must have the same shape). Constraints are not
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safe to use when doing asynchronous distributed training.
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gamma_constraint: An optional projection function to be applied to the
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`gamma` weight after being updated by an `Optimizer`.
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training: Either a Python boolean, or a TensorFlow boolean scalar tensor
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(e.g. a placeholder). Whether to return the output in training mode
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(normalized with statistics of the current batch) or in inference mode
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(normalized with moving statistics). **NOTE**: make sure to set this
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parameter correctly, or else your training/inference will not work
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properly.
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trainable: Boolean, if `True` also add variables to the graph collection
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`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
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name: String, the name of the layer.
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reuse: Boolean, whether to reuse the weights of a previous layer by the
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same name.
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renorm: Whether to use Batch Renormalization (Ioffe, 2017). This adds
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extra variables during training. The inference is the same for either
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value of this parameter.
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renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
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scalar `Tensors` used to clip the renorm correction. The correction `(r,
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d)` is used as `corrected_value = normalized_value * r + d`, with `r`
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clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
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dmax are set to inf, 0, inf, respectively.
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renorm_momentum: Momentum used to update the moving means and standard
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deviations with renorm. Unlike `momentum`, this affects training and
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|
|
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 a legacy api that is only compatible with eager execution and
|
||
|
|
`tf.function` if you combine it with
|
||
|
|
`tf.compat.v1.keras.utils.track_tf1_style_variables`
|
||
|
|
|
||
|
|
Please refer to [tf.layers model mapping section of the migration guide]
|
||
|
|
(https://www.tensorflow.org/guide/migrate/model_mapping)
|
||
|
|
to learn how to use your TensorFlow v1 model in TF2 with 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).",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
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(inputs, training=training)
|
||
|
|
|
||
|
|
|
||
|
|
# Aliases
|
||
|
|
|
||
|
|
BatchNorm = BatchNormalization
|
||
|
|
batch_norm = batch_normalization
|