tfp.layers.KLDivergenceRegularizer

Regularizer that adds a KL divergence penalty to the model loss.

View aliases

Main aliases

tfp.layers.distribution_layer.KLDivergenceRegularizer

tfp.layers.KLDivergenceRegularizer(
    distribution_b,
    use_exact_kl=False,
    test_points_reduce_axis=(),
    test_points_fn=tfp.experimental.distributions.marginal_fns.ps.convert_to_shape_tensor,
    weight=None
)

When using Monte Carlo approximation (e.g., use_exact=False), it is presumed that the input distribution's concretization (i.e., tf.convert_to_tensor(distribution)) corresponds to a random sample. To override this behavior, set test_points_fn.

Example

tfd = tfp.distributions
tfpl = tfp.layers
tfk = tf.keras
tfkl = tf.keras.layers

# Create a variational encoder and add a KL Divergence penalty to the
# loss that encourages marginal coherence with a unit-MVN (the "prior").
input_shape = [28, 28, 1]
encoded_size = 2
variational_encoder = tfk.Sequential([
    tfkl.InputLayer(input_shape=input_shape),
    tfkl.Flatten(),
    tfkl.Dense(10, activation='relu'),
    tfkl.Dense(tfpl.MultivariateNormalTriL.params_size(encoded_size)),
    tfpl.MultivariateNormalTriL(
        encoded_size,
        lambda s: s.sample(10),
        activity_regularizer=tfpl.KLDivergenceRegularizer(
           tfd.MultivariateNormalDiag(loc=tf.zeros(encoded_size)),
           weight=num_train_samples)),
])

Args
distribution_b	distribution instance corresponding to b as in KL[a, b]. The previous layer's output is presumed to be a Distribution instance and is a).
use_exact_kl	Python bool indicating if KL divergence should be calculated exactly via tfp.distributions.kl_divergence or via Monte Carlo approximation. Default value: False.
test_points_reduce_axis	int vector or scalar representing dimensions over which to reduce_mean while calculating the Monte Carlo approximation of the KL divergence. As is with all tf.reduce_* ops, None means reduce over all dimensions; () means reduce over none of them. Default value: () (i.e., no reduction).
test_points_fn	Python callable taking a Distribution instance and returning a Tensor used for random test points to approximate the KL divergence. Default value: tf.convert_to_tensor.
weight	Multiplier applied to the calculated KL divergence for each Keras batch member. Default value: None (i.e., do not weight each batch member).

Attributes
distribution_b
name	Returns the name of this module as passed or determined in the ctor. Note: This is not the same as the self.name_scope.name which includes parent module names.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
submodules	Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on). a = tf.Module() b = tf.Module() c = tf.Module() a.b = b b.c = c list(a.submodules) == [b, c] True list(b.submodules) == [c] True list(c.submodules) == [] True
test_points_fn
test_points_reduce_axis
trainable_variables	Sequence of trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
use_exact_kl
variables	Sequence of variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
weight

Methods

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.

Args
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.

with_name_scope

@classmethod
with_name_scope(
    method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable &#x27;my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
method	The method to wrap.

Returns
The original method wrapped such that it enters the module's name scope.

__call__

View source

__call__(
    distribution_a
)

Compute a regularization penalty from an input tensor.