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`	Dtype used by the weights of the layer, set in the constructor.
`dynamic`	Whether the layer is dynamic (eager-only); set in the constructor.
`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_spec`	`InputSpec` instance(s) describing the input format for this layer. When you create a layer subclass, you can set `self.input_spec` to enable the layer to run input compatibility checks when it is called. Consider a `Conv2D` layer: it can only be called on a single input tensor of rank 4. As such, you can set, in `__init__()`: `self.input_spec = tf.keras.layers.InputSpec(ndim=4)` Now, if you try to call the layer on an input that isn't rank 4 (for instance, an input of shape `(2,)`, it will raise a nicely-formatted error: `ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2]` Input checks that can be specified via `input_spec` include: Structure (e.g. a single input, a list of 2 inputs, etc) Shape Rank (ndim) Dtype For more information, see `tf.keras.layers.InputSpec`.
`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`	List of `tf.keras.metrics.Metric` instances tracked by the layer.
`name`	Name of the layer (string), set in the constructor.
`name_scope`	Returns a `tf.name_scope` instance for this class.
`non_trainable_weights`	List of all non-trainable weights tracked by this layer. Non-trainable weights are not updated during training. They are expected to be updated manually in `call()`.
`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.
`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`
`trainable`
`trainable_weights`	List of all trainable weights tracked by this layer. Trainable weights are updated via gradient descent during training.
`weights`	Returns the list of all layer variables/weights.

Methods

`add_loss`

add_loss(
    losses, inputs=None
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model's call function, in which case losses should be a Tensor or list of Tensors.

Example:

class MyLayer(tf.keras.layers.Layer):
  def call(inputs, self):
    self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
    return inputs

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These losses become part of the model's topology and are tracked in get_config.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(x.kernel))

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Arguments

losses Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.

inputs Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

Arguments
`losses`	Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
`inputs`	Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If `None` is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

`add_metric`

add_metric(
    value, aggregation=None, name=None
)

Adds metric tensor to the layer.

Args
`value`	Metric tensor.
`aggregation`	Sample-wise metric reduction function. If `aggregation=None`, it indicates that the metric tensor provided has been aggregated already. eg, `bin_acc = BinaryAccuracy(name='acc')` followed by `model.add_metric(bin_acc(y_true, y_pred))`. If aggregation='mean', the given metric tensor will be sample-wise reduced using `mean` function. eg, `model.add_metric(tf.reduce_sum(outputs), name='output_mean', aggregation='mean')`.
`name`	String metric name.

Raises
`ValueError`	If `aggregation` is anything other than None or `mean`.

`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(
    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_weights`

get_weights()

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values-- per-output weights and the bias value. These can be used to set the weights of another Dense layer:

a = tf.keras.layers.Dense(1,
  kernel_initializer=tf.constant_initializer(1.))
a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
b = tf.keras.layers.Dense(1,
  kernel_initializer=tf.constant_initializer(2.))
b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
b.set_weights(a.get_weights())
b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Returns
Weights values as a list of numpy arrays.

`set_weights`

set_weights(
    weights
)

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values-- per-output weights and the bias value. These can be used to set the weights of another Dense layer:

a = tf.keras.layers.Dense(1,
  kernel_initializer=tf.constant_initializer(1.))
a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
b = tf.keras.layers.Dense(1,
  kernel_initializer=tf.constant_initializer(2.))
b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
b.set_weights(a.get_weights())
b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

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(
    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 '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`

__call__(
    *args, **kwargs
)

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

Arguments
`*args`	Positional arguments to be passed to `self.call`.
`**kwargs`	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).
`RuntimeError`	if `super().__init__()` was not called in the constructor.

tfp.layers.Convolution2DReparameterization

View aliases

Examples

References

Args

Attributes

Methods

add_loss

Example:

Example:

Example:

add_metric

build

compute_mask

compute_output_shape

count_params

from_config

get_config

get_weights

set_weights

with_name_scope

__call__

Note:

`add_loss`

`add_metric`

`build`

`compute_mask`

`compute_output_shape`

`count_params`

`from_config`

`get_config`

`get_weights`

`set_weights`

`with_name_scope`

`call`