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Regularizer base class.
Regularizers allow you to apply penalties on layer parameters or layer activity during optimization. These penalties are summed into the loss function that the network optimizes.
Regularization penalties are applied on a per-layer basis. The exact API will depend on the layer, but many layers (e.g. Dense
, Conv1D
, Conv2D
and Conv3D
) have a unified API.
These layers expose 3 keyword arguments:
kernel_regularizer
: Regularizer to apply a penalty on the layer's kernelbias_regularizer
: Regularizer to apply a penalty on the layer's biasactivity_regularizer
: Regularizer to apply a penalty on the layer's outputAll layers (including custom layers) expose activity_regularizer
as a settable property, whether or not it is in the constructor arguments.
The value returned by the activity_regularizer
is divided by the input batch size so that the relative weighting between the weight regularizers and the activity regularizers does not change with the batch size.
You can access a layer's regularization penalties by calling layer.losses
after calling the layer on inputs.
layer = tf.keras.layers.Dense( 5, input_dim=5, kernel_initializer='ones', kernel_regularizer=tf.keras.regularizers.L1(0.01), activity_regularizer=tf.keras.regularizers.L2(0.01)) tensor = tf.ones(shape=(5, 5)) * 2.0 out = layer(tensor)
# The kernel regularization term is 0.25 # The activity regularization term (after dividing by the batch size) is 5 tf.math.reduce_sum(layer.losses) <tf.Tensor: shape=(), dtype=float32, numpy=5.25>
tf.keras.regularizers.L1(0.3) # L1 Regularization Penalty tf.keras.regularizers.L2(0.1) # L2 Regularization Penalty tf.keras.regularizers.L1L2(l1=0.01, l2=0.01) # L1 + L2 penalties
Compute a regularization loss on a tensor by directly calling a regularizer as if it is a one-argument function.
E.g.
regularizer = tf.keras.regularizers.L2(2.) tensor = tf.ones(shape=(5, 5)) regularizer(tensor)
Any function that takes in a weight matrix and returns a scalar tensor can be used as a regularizer, e.g.:
@tf.keras.utils.register_keras_serializable(package='Custom', name='l1') def l1_reg(weight_matrix): return 0.01 * tf.math.reduce_sum(tf.math.abs(weight_matrix)) layer = tf.keras.layers.Dense(5, input_dim=5, kernel_initializer='ones', kernel_regularizer=l1_reg) tensor = tf.ones(shape=(5, 5)) out = layer(tensor) layer.losses [<tf.Tensor: shape=(), dtype=float32, numpy=0.25>]
Alternatively, you can write your custom regularizers in an object-oriented way by extending this regularizer base class, e.g.:
@tf.keras.utils.register_keras_serializable(package='Custom', name='l2') class L2Regularizer(tf.keras.regularizers.Regularizer): def __init__(self, l2=0.): self.l2 = l2 def __call__(self, x): return self.l2 * tf.math.reduce_sum(tf.math.square(x)) def get_config(self): return {'l2': float(self.l2)} layer = tf.keras.layers.Dense( 5, input_dim=5, kernel_initializer='ones', kernel_regularizer=L2Regularizer(l2=0.5))
tensor = tf.ones(shape=(5, 5)) out = layer(tensor) layer.losses [<tf.Tensor: shape=(), dtype=float32, numpy=12.5>]
Registering the regularizers as serializable is optional if you are just training and executing models, exporting to and from SavedModels, or saving and loading weight checkpoints.
Registration is required for Keras model_to_estimator
, saving and loading models to HDF5 formats, Keras model cloning, some visualization utilities, and exporting models to and from JSON. If using this functionality, you must make sure any python process running your model has also defined and registered your custom regularizer.
tf.keras.utils.register_keras_serializable
is only available in TF 2.1 and beyond. In earlier versions of TensorFlow you must pass your custom regularizer to the custom_objects
argument of methods that expect custom regularizers to be registered as serializable.
from_config
@classmethod from_config( config )
Creates a regularizer from its config.
This method is the reverse of get_config
, capable of instantiating the same regularizer from the config dictionary.
This method is used by Keras model_to_estimator
, saving and loading models to HDF5 formats, Keras model cloning, some visualization utilities, and exporting models to and from JSON.
Arguments | |
---|---|
config | A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A regularizer instance. |
get_config
get_config()
Returns the config of the regularizer.
An regularizer config is a Python dictionary (serializable) containing all configuration parameters of the regularizer. The same regularizer can be reinstantiated later (without any saved state) from this configuration.
This method is optional if you are just training and executing models, exporting to and from SavedModels, or using weight checkpoints.
This method is required for Keras model_to_estimator
, saving and loading models to HDF5 formats, Keras model cloning, some visualization utilities, and exporting models to and from JSON.
Returns | |
---|---|
Python dictionary. |
__call__
__call__( x )
Compute a regularization penalty from an input tensor.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/regularizers/Regularizer