tfp.layers.Convolution2DReparameterization

2D convolution layer (e.g. spatial convolution over images).

View aliases

Main aliases

tfp.layers.conv_variational.Convolution2DReparameterization

tfp.layers.Convolution2DReparameterization(
    filters, kernel_size, strides=(1, 1), padding='valid',
    data_format='channels_last', dilation_rate=(1, 1), activation=None,
    activity_regularizer=None,
    kernel_posterior_fn=tfp_layers_util.default_mean_field_normal_fn(),
    kernel_posterior_tensor_fn=(lambda d: d.sample()),
    kernel_prior_fn=tfp.layers.default_multivariate_normal_fn,
    kernel_divergence_fn=(lambda q, p, ignore: kl_lib.kl_divergence(q, p)), bias_pos
    terior_fn=tfp_layers_util.default_mean_field_normal_fn(is_singular=True),
    bias_posterior_tensor_fn=(lambda d: d.sample()), bias_prior_fn=None,
    bias_divergence_fn=(lambda q, p, ignore: kl_lib.kl_divergence(q, p)), **kwargs
)

This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. It may also include a bias addition and activation function on the outputs. It assumes the kernel and/or bias are drawn from distributions.

By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors,

outputs = f(inputs; kernel, bias), kernel, bias ~ posterior

where f denotes the layer's calculation. It uses the reparameterization estimator [(Kingma and Welling, 2014)][1], which performs a Monte Carlo approximation of the distribution integrating over the kernel and bias.

The arguments permit separate specification of the surrogate posterior (q(W|x)), prior (p(W)), and divergence for both the kernel and bias distributions.

Upon being built, this layer adds losses (accessible via the losses property) representing the divergences of kernel and/or bias surrogate posteriors and their respective priors. When doing minibatch stochastic optimization, make sure to scale this loss such that it is applied just once per epoch (e.g. if kl is the sum of losses for each element of the batch, you should pass kl / num_examples_per_epoch to your optimizer).

You can access the kernel and/or bias posterior and prior distributions after the layer is built via the kernel_posterior, kernel_prior, bias_posterior and bias_prior properties.

Examples

We illustrate a Bayesian neural network with variational inference, assuming a dataset of features and labels.

import tensorflow as tf
import tensorflow_probability as tfp

model = tf.keras.Sequential([
    tf.keras.layers.Reshape([32, 32, 3]),
    tfp.layers.Convolution2DReparameterization(
        64, kernel_size=5, padding='SAME', activation=tf.nn.relu),
    tf.keras.layers.MaxPooling2D(pool_size=[2, 2],
                                 strides=[2, 2],
                                 padding='SAME'),
    tf.keras.layers.Flatten(),
    tfp.layers.DenseReparameterization(10),
])

logits = model(features)
neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits(
    labels=labels, logits=logits)
kl = sum(model.losses)
loss = neg_log_likelihood + kl
train_op = tf.train.AdamOptimizer().minimize(loss)

It uses reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer.

References

[1]: Diederik Kingma and Max Welling. Auto-Encoding Variational Bayes. In International Conference on Learning Representations, 2014. https://arxiv.org/abs/1312.6114

Args:

filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding: One of "valid" or "same" (case-insensitive).
data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).
dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
activation: Activation function. Set it to None to maintain a linear activation.
activity_regularizer: Regularizer function for the output.
kernel_posterior_fn: Python callable which creates tfd.Distribution instance representing the surrogate posterior of the kernel parameter. Default value: default_mean_field_normal_fn().
kernel_posterior_tensor_fn: Python callable which takes a tfd.Distribution instance and returns a representative value. Default value: lambda d: d.sample().
kernel_prior_fn: Python callable which creates tfd instance. See default_mean_field_normal_fn docstring for required parameter signature. Default value: tfd.Normal(loc=0., scale=1.).
kernel_divergence_fn: Python callable which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are tfd.Distribution-like instances and the sample is a Tensor.
bias_posterior_fn: Python callable which creates tfd.Distribution instance representing the surrogate posterior of the bias parameter. Default value: default_mean_field_normal_fn(is_singular=True) (which creates an instance of tfd.Deterministic).
bias_posterior_tensor_fn: Python callable which takes a tfd.Distribution instance and returns a representative value. Default value: lambda d: d.sample().
bias_prior_fn: Python callable which creates tfd instance. See default_mean_field_normal_fn docstring for required parameter signature. Default value: None (no prior, no variational inference)
bias_divergence_fn: Python callable which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are tfd.Distribution-like instances and the sample is a Tensor.

Attributes:

activity_regularizer: Optional regularizer function for the output of this layer.
dtype
dynamic
input: Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.
input_mask: Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
input_shape: Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.
input_spec
losses: Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.
metrics
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
non_trainable_weights
output: Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.
output_mask: Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
output_shape: Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.
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
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []

trainable
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.
trainable_weights
updates
variables: Returns the list of all layer variables/weights.

Alias of self.weights.
weights: Returns the list of all layer variables/weights.

Methods

`call`

__call__(
    inputs, *args, **kwargs
)

Wraps call, applying pre- and post-processing steps.

Arguments:

inputs: input tensor(s).
*args: additional positional arguments to be passed to self.call.
**kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

Note:

The following optional keyword arguments are reserved for specific uses:
- training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
- mask: Boolean input mask.
If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.

Raises:

ValueError: if the layer's call method returns None (an invalid value).

`build`

View source

build(
    input_shape
)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments:

input_shape: Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

`compute_mask`

compute_mask(
    inputs, mask=None
)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors.
mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

`compute_output_shape`

View source

compute_output_shape(
    input_shape
)

Computes the output shape of the layer.

Args:

input_shape: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

output_shape: A tuple representing the output shape.

`count_params`

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

ValueError: if the layer isn't yet built (in which case its weights aren't yet defined).

`from_config`

View source

@classmethod
from_config(
    cls, config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary.

Args:

config: A Python dictionary, typically the output of get_config.

Returns:

layer: A layer instance.

`get_config`

View source

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

Returns:

config: A Python dictionary of class keyword arguments and their serialized values.

`get_input_at`

get_input_at(
    node_index
)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises:

RuntimeError: If called in Eager mode.

`get_input_mask_at`

get_input_mask_at(
    node_index
)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

`get_input_shape_at`

get_input_shape_at(
    node_index
)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises:

RuntimeError: If called in Eager mode.

`get_losses_for`

get_losses_for(
    inputs
)

Retrieves losses relevant to a specific set of inputs.

Arguments:

inputs: Input tensor or list/tuple of input tensors.

Returns:

List of loss tensors of the layer that depend on inputs.

`get_output_at`

get_output_at(
    node_index
)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises:

RuntimeError: If called in Eager mode.

`get_output_mask_at`

get_output_mask_at(
    node_index
)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

`get_output_shape_at`

get_output_shape_at(
    node_index
)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises:

RuntimeError: If called in Eager mode.

`get_updates_for`

get_updates_for(
    inputs
)

Retrieves updates relevant to a specific set of inputs.

Arguments:

inputs: Input tensor or list/tuple of input tensors.

Returns:

List of update ops of the layer that depend on inputs.

`get_weights`

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

`set_weights`

set_weights(
    weights
)

Sets the weights of the layer, from Numpy arrays.

Arguments:

weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).

Raises:

ValueError: If the provided weights list does not match the layer's specifications.

`with_name_scope`

@classmethod
with_name_scope(
    cls, 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], 64]))
    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([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>

Args:

method: The method to wrap.

Returns:

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