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tfp.bijectors.AutoregressiveNetwork

View source on GitHub

Masked Autoencoder for Distribution Estimation [Germain et al. (2015)][1].

tfp.bijectors.AutoregressiveNetwork(
    params, event_shape=None, hidden_units=None, input_order='left-to-right',
    hidden_degrees='equal', activation=None, use_bias=True,
    kernel_initializer='glorot_uniform', bias_initializer='zeros',
    kernel_regularizer=None, bias_regularizer=None, kernel_constraint=None,
    bias_constraint=None, validate_args=False, **kwargs
)

A AutoregressiveNetwork takes as input a Tensor of shape [..., event_size] and returns a Tensor of shape [..., event_size, params].

The output satisfies the autoregressive property. That is, the layer is configured with some permutation ord of {0, ..., event_size-1} (i.e., an ordering of the input dimensions), and the output output[batch_idx, i, ...] for input dimension i depends only on inputs x[batch_idx, j] where ord(j) < ord(i). The autoregressive property allows us to use output[batch_idx, i] to parameterize conditional distributions: p(x[batch_idx, i] | x[batch_idx, ] for ord(j) < ord(i)) which give us a tractable distribution over input x[batch_idx]: p(x[batch_idx]) = prod_i p(x[batch_idx, ord(i)] | x[batch_idx, ord(0:i)])

For example, when params is 2, the output of the layer can parameterize the location and log-scale of an autoregressive Gaussian distribution.

Example

# Generate data -- as in Figure 1 in [Papamakarios et al. (2017)][2]).
n = 2000
x2 = np.random.randn(n).astype(dtype=np.float32) * 2.
x1 = np.random.randn(n).astype(dtype=np.float32) + (x2 * x2 / 4.)
data = np.stack([x1, x2], axis=-1)

# Density estimation with MADE.
made = tfb.AutoregressiveNetwork(params=2, hidden_units=[10, 10])

distribution = tfd.TransformedDistribution(
    distribution=tfd.Normal(loc=0., scale=1.),
    bijector=tfb.MaskedAutoregressiveFlow(made),
    event_shape=[2])

# Construct and fit model.
x_ = tfkl.Input(shape=(2,), dtype=tf.float32)
log_prob_ = distribution.log_prob(x_)
model = tfk.Model(x_, log_prob_)

model.compile(optimizer=tf.optimizers.Adam(),
              loss=lambda _, log_prob: -log_prob)

batch_size = 25
model.fit(x=data,
          y=np.zeros((n, 0), dtype=np.float32),
          batch_size=batch_size,
          epochs=1,
          steps_per_epoch=1,  # Usually `n // batch_size`.
          shuffle=True,
          verbose=True)

# Use the fitted distribution.
distribution.sample((3, 1))
distribution.log_prob(np.ones((3, 2), dtype=np.float32))

Examples: Handling Rank-2+ Tensors

AutoregressiveNetwork can be used as a building block to achieve different autoregressive structures over rank-2+ tensors. For example, suppose we want to build an autoregressive distribution over images with dimension [weight, height, channels] with channels = 3:

  1. We can parameterize a 'fully autoregressive' distribution, with cross-channel and within-pixel autoregressivity:
    r0    g0   b0     r0    g0   b0       r0   g0    b0
    ^   ^      ^         ^   ^   ^         ^      ^   ^
    |  /  ____/           \  |  /           \____  \  |
    | /__/                 \ | /                 \__\ |
    r1    g1   b1     r1 <- g1   b1       r1   g1 <- b1
                                         ^          |
                                          \_________/
as:
# Generate random images for training data.
images = np.random.uniform(size=(100, 8, 8, 3)).astype(np.float32)
n, width, height, channels = images.shape

# Reshape images to achieve desired autoregressivity.
event_shape = [height * width * channels]
reshaped_images = tf.reshape(images, [n, event_shape])

# Density estimation with MADE.
made = tfb.AutoregressiveNetwork(params=2, event_shape=event_shape,
                                 hidden_units=[20, 20], activation='relu')
distribution = tfd.TransformedDistribution(
    distribution=tfd.Normal(loc=0., scale=1.),
    bijector=tfb.MaskedAutoregressiveFlow(made),
    event_shape=event_shape)

# Construct and fit model.
x_ = tfkl.Input(shape=event_shape, dtype=tf.float32)
log_prob_ = distribution.log_prob(x_)
model = tfk.Model(x_, log_prob_)

model.compile(optimizer=tf.optimizers.Adam(),
              loss=lambda _, log_prob: -log_prob)

batch_size = 10
model.fit(x=data,
          y=np.zeros((n, 0), dtype=np.float32),
          batch_size=batch_size,
          epochs=10,
          steps_per_epoch=n // batch_size,
          shuffle=True,
          verbose=True)

# Use the fitted distribution.
distribution.sample((3, 1))
distribution.log_prob(np.ones((5, 8, 8, 3), dtype=np.float32))
  1. We can parameterize a distribution with neither cross-channel nor within-pixel autoregressivity:
    r0    g0   b0
    ^     ^    ^
    |     |    |
    |     |    |
    r1    g1   b1
as:
# Generate fake images.
images = np.random.choice([0, 1], size=(100, 8, 8, 3))
n, width, height, channels = images.shape

# Reshape images to achieve desired autoregressivity.
reshaped_images = np.transpose(
    np.reshape(images, [n, width * height, channels]),
    axes=[0, 2, 1])

made = tfb.AutoregressiveNetwork(params=1, event_shape=[width * height],
                                 hidden_units=[20, 20], activation='relu')

# Density estimation with MADE.
#
# NOTE: Parameterize an autoregressive distribution over an event_shape of
# [channels, width * height], with univariate Bernoulli conditional
# distributions.
distribution = tfd.Autoregressive(
    lambda x: tfd.Independent(
        tfd.Bernoulli(logits=tf.unstack(made(x), axis=-1)[0],
                      dtype=tf.float32),
        reinterpreted_batch_ndims=2),
    sample0=tf.zeros([channels, width * height], dtype=tf.float32))

# Construct and fit model.
x_ = tfkl.Input(shape=(channels, width * height), dtype=tf.float32)
log_prob_ = distribution.log_prob(x_)
model = tfk.Model(x_, log_prob_)

model.compile(optimizer=tf.optimizers.Adam(),
              loss=lambda _, log_prob: -log_prob)

batch_size = 10
model.fit(x=reshaped_images,
          y=np.zeros((n, 0), dtype=np.float32),
          batch_size=batch_size,
          epochs=10,
          steps_per_epoch=n // batch_size,
          shuffle=True,
          verbose=True)

distribution.sample(7)
distribution.log_prob(np.ones((4, 8, 8, 3), dtype=np.float32))
Note that one set of weights is shared for the mapping for each channel
from image to distribution parameters -- i.e., the mapping
`layer(reshaped_images[..., channel, :])`, where `channel` is 0, 1, or 2.

To use separate weights for each channel, we could construct an
`AutoregressiveNetwork` and `TransformedDistribution` for each channel,
and combine them with a `tfd.Blockwise` distribution.

References

[1]: Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE: Masked Autoencoder for Distribution Estimation. In International Conference on Machine Learning, 2015. arxiv.org/abs/1502.03509

[2]: George Papamakarios, Theo Pavlakou, Iain Murray, Masked Autoregressive Flow for Density Estimation. In Neural Information Processing Systems, 2017. arxiv.org/abs/1705.07057

Arguments:

  • params: Python integer specifying the number of parameters to output per input.
  • event_shape: Python list-like of positive integers (or a single int), specifying the shape of the input to this layer, which is also the event_shape of the distribution parameterized by this layer. Currently only rank-1 shapes are supported. That is, event_shape must be a single integer. If not specified, the event shape is inferred when this layer is first called or built.
  • hidden_units: Python list-like of non-negative integers, specifying the number of units in each hidden layer.
  • input_order: Order of degrees to the input units: 'random', 'left-to-right', 'right-to-left', or an array of an explicit order. For example, 'left-to-right' builds an autoregressive model: p(x) = p(x1) p(x2 | x1) ... p(xD | x<D). Default: 'left-to-right'.
  • hidden_degrees: Method for assigning degrees to the hidden units: 'equal', 'random'. If 'equal', hidden units in each layer are allocated equally (up to a remainder term) to each degree. Default: 'equal'.
  • activation: An activation function. See tf.keras.layers.Dense. Default: None.
  • use_bias: Whether or not the dense layers constructed in this layer should have a bias term. See tf.keras.layers.Dense. Default: True.
  • kernel_initializer: Initializer for the Dense kernel weight matrices. Default: 'glorot_uniform'.
  • bias_initializer: Initializer for the Dense bias vectors. Default: 'zeros'.
  • kernel_regularizer: Regularizer function applied to the Dense kernel weight matrices. Default: None.
  • bias_regularizer: Regularizer function applied to the Dense bias weight vectors. Default: None.
  • kernel_constraint: Constraint function applied to the Dense kernel weight matrices. Default: None.
  • bias_constraint: Constraint function applied to the Dense bias weight vectors. Default: None.
  • validate_args: Python bool, default False. When True, layer parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
  • **kwargs: Additional keyword arguments passed to this layer (but not to the tf.keras.layer.Dense layers constructed by this layer).

Attributes:

  • activity_regularizer: Optional regularizer function for the output of this layer.
  • dtype
  • dynamic
  • event_shape
  • 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.

  • params

  • 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.

  • 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
)

See tfkl.Layer.build.

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
)

See tfkl.Layer.compute_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

@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. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Arguments:

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

Returns:

A layer instance.

get_config

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.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

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.