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tfp.experimental.substrates.jax.bijectors.Pad

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Class Pad

Pads a value to the event_shape of a Tensor.

Inherits From: Bijector

The semantics of tfp.bijectors.Pad generally follow that of tf.pad() except that tfp.bijectors.Pad's paddings argument applies to the rightmost dimensions. Additionally, the new argument axis enables overriding the dimensions to which paddings is applied. Like paddings, the axis argument is also relative to the rightmost dimension and must therefore be negative.

The argument paddings is a vector of int pairs each representing the number of left and/or right constant_values to pad to the corresponding righmost dimensions. That is, unless axis is specified, specifiyingkdifferentpaddingsmeans the rightmostkdimensions will be "grown" by the sum of the respectivepaddingsrow. Whenaxisis specified, it indicates the dimension to which the correspondingpaddingselement is applied. By defaultaxisisNonewhich means it is logically equivalent torange(start=-len(paddings), limit=0)`, i.e., the rightmost dimensions.

Example usage:

b = tfp.bijectors.Pad()  # Default arguments.

b.forward([3., 4.])      # shape: [2]
# ==> [[3., 4., 0.]]     # shape: [3]

b.forward([[1., 2.],
           [3., 4.]])    # shape: [2, 2]
# ==> [[1., 2., 0.],
#      [3., 4., 0.]]     # shape: [2, 3]

b.inverse([3., 4., 0.])  # shape: [3]
# ==> [3., 4.]           # shape: [2]

b.forward_log_det_jacobian(any_value)
# ==> 0.

b.inverse_log_det_jacobian(any_value)
# ==> 0.
b = tfp.bijectors.Pad(axis=-2)  # With non-default `axis` arg.

b.forward([[3., 4.]])    # shape: [1, 2]
# ==> [[3., 4.],         # shape: [2, 2]
#      [0., 0.]]

b.inverse([[3., 4.],     # shape: [2, 2]
           [0., 0.]])
# ==> [[3., 4.]]         # shape: [1, 2]

b.forward_log_det_jacobian(any_value)
# ==> 0.

b.inverse_log_det_jacobian(any_value)
# ==> 0.

__init__

View source

__init__(
    paddings=((0, 1),),
    mode='CONSTANT',
    constant_values=0,
    axis=None,
    validate_args=False,
    name=None
)

Initializes the Pad bijector.

Args:

  • paddings: A vector-shaped Tensor of int pairs representing the number of elements to pad on the left and right, respectively. Default value: ((0, 1),).
  • mode: One of 'CONSTANT', 'REFLECT', or 'SYMMETRIC' (case-insensitive). For more details, see tf.pad.
  • constant_values: In "CONSTANT" mode, the scalar pad value to use. Must be same type as tensor. For more details, see tf.pad.
  • axis: The dimensions for which paddings are applied. Must be 1:1 with paddings or None. Default value: None (i.e., tf.range(start=-len(paddings), limit=0)).
  • validate_args: Python bool indicating whether arguments should be checked for correctness. Default value: False.
  • name: Python str, name given to ops managed by this object. Default value: None (i.e., 'pad').

Properties

axis

constant_values

dtype

dtype of Tensors transformable by this distribution.

forward_min_event_ndims

Returns the minimal number of dimensions bijector.forward operates on.

graph_parents

Returns this Bijector's graph_parents as a Python list.

inverse_min_event_ndims

Returns the minimal number of dimensions bijector.inverse operates on.

is_constant_jacobian

Returns true iff the Jacobian matrix is not a function of x.

Returns:

  • is_constant_jacobian: Python bool.

mode

name

Returns the string name of this Bijector.

paddings

trainable_variables

validate_args

Returns True if Tensor arguments will be validated.

variables

Methods

__call__

View source

__call__(
    value,
    name=None,
    **kwargs
)

Applies or composes the Bijector, depending on input type.

This is a convenience function which applies the Bijector instance in three different ways, depending on the input:

  1. If the input is a tfd.Distribution instance, return tfd.TransformedDistribution(distribution=input, bijector=self).
  2. If the input is a tfb.Bijector instance, return tfb.Chain([self, input]).
  3. Otherwise, return self.forward(input)

Args:

  • value: A tfd.Distribution, tfb.Bijector, or a Tensor.
  • name: Python str name given to ops created by this function.
  • **kwargs: Additional keyword arguments passed into the created tfd.TransformedDistribution, tfb.Bijector, or self.forward.

Returns:

  • composition: A tfd.TransformedDistribution if the input was a tfd.Distribution, a tfb.Chain if the input was a tfb.Bijector, or a Tensor computed by self.forward.

Examples

sigmoid = tfb.Reciprocal()(
    tfb.AffineScalar(shift=1.)(
      tfb.Exp()(
        tfb.AffineScalar(scale=-1.))))
# ==> `tfb.Chain([
#         tfb.Reciprocal(),
#         tfb.AffineScalar(shift=1.),
#         tfb.Exp(),
#         tfb.AffineScalar(scale=-1.),
#      ])`  # ie, `tfb.Sigmoid()`

log_normal = tfb.Exp()(tfd.Normal(0, 1))
# ==> `tfd.TransformedDistribution(tfd.Normal(0, 1), tfb.Exp())`

tfb.Exp()([-1., 0., 1.])
# ==> tf.exp([-1., 0., 1.])

forward

View source

forward(
    x,
    name='forward',
    **kwargs
)

Returns the forward Bijector evaluation, i.e., X = g(Y).

Args:

  • x: Tensor. The input to the 'forward' evaluation.
  • name: The name to give this op.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor.

Raises:

  • TypeError: if self.dtype is specified and x.dtype is not self.dtype.
  • NotImplementedError: if _forward is not implemented.

forward_event_shape

View source

forward_event_shape(input_shape)

Shape of a single sample from a single batch as a TensorShape.

Same meaning as forward_event_shape_tensor. May be only partially defined.

Args:

  • input_shape: TensorShape indicating event-portion shape passed into forward function.

Returns:

  • forward_event_shape_tensor: TensorShape indicating event-portion shape after applying forward. Possibly unknown.

forward_event_shape_tensor

View source

forward_event_shape_tensor(
    input_shape,
    name='forward_event_shape_tensor'
)

Shape of a single sample from a single batch as an int32 1D Tensor.

Args:

  • input_shape: Tensor, int32 vector indicating event-portion shape passed into forward function.
  • name: name to give to the op

Returns:

  • forward_event_shape_tensor: Tensor, int32 vector indicating event-portion shape after applying forward.

forward_log_det_jacobian

View source

forward_log_det_jacobian(
    x,
    event_ndims,
    name='forward_log_det_jacobian',
    **kwargs
)

Returns both the forward_log_det_jacobian.

Args:

  • x: Tensor. The input to the 'forward' Jacobian determinant evaluation.
  • event_ndims: Number of dimensions in the probabilistic events being transformed. Must be greater than or equal to self.forward_min_event_ndims. The result is summed over the final dimensions to produce a scalar Jacobian determinant for each event, i.e. it has shape rank(x) - event_ndims dimensions.
  • name: The name to give this op.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor, if this bijector is injective. If not injective this is not implemented.

Raises:

  • TypeError: if self.dtype is specified and y.dtype is not self.dtype.
  • NotImplementedError: if neither _forward_log_det_jacobian nor {_inverse, _inverse_log_det_jacobian} are implemented, or this is a non-injective bijector.

inverse

View source

inverse(
    y,
    name='inverse',
    **kwargs
)

Returns the inverse Bijector evaluation, i.e., X = g^{-1}(Y).

Args:

  • y: Tensor. The input to the 'inverse' evaluation.
  • name: The name to give this op.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor, if this bijector is injective. If not injective, returns the k-tuple containing the unique k points (x1, ..., xk) such that g(xi) = y.

Raises:

  • TypeError: if self.dtype is specified and y.dtype is not self.dtype.
  • NotImplementedError: if _inverse is not implemented.

inverse_event_shape

View source

inverse_event_shape(output_shape)

Shape of a single sample from a single batch as a TensorShape.

Same meaning as inverse_event_shape_tensor. May be only partially defined.

Args:

  • output_shape: TensorShape indicating event-portion shape passed into inverse function.

Returns:

  • inverse_event_shape_tensor: TensorShape indicating event-portion shape after applying inverse. Possibly unknown.

inverse_event_shape_tensor

View source

inverse_event_shape_tensor(
    output_shape,
    name='inverse_event_shape_tensor'
)

Shape of a single sample from a single batch as an int32 1D Tensor.

Args:

  • output_shape: Tensor, int32 vector indicating event-portion shape passed into inverse function.
  • name: name to give to the op

Returns:

  • inverse_event_shape_tensor: Tensor, int32 vector indicating event-portion shape after applying inverse.

inverse_log_det_jacobian

View source

inverse_log_det_jacobian(
    y,
    event_ndims,
    name='inverse_log_det_jacobian',
    **kwargs
)

Returns the (log o det o Jacobian o inverse)(y).

Mathematically, returns: log(det(dX/dY))(Y). (Recall that: X=g^{-1}(Y).)

Note that forward_log_det_jacobian is the negative of this function, evaluated at g^{-1}(y).

Args:

  • y: Tensor. The input to the 'inverse' Jacobian determinant evaluation.
  • event_ndims: Number of dimensions in the probabilistic events being transformed. Must be greater than or equal to self.inverse_min_event_ndims. The result is summed over the final dimensions to produce a scalar Jacobian determinant for each event, i.e. it has shape rank(y) - event_ndims dimensions.
  • name: The name to give this op.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • ildj: Tensor, if this bijector is injective. If not injective, returns the tuple of local log det Jacobians, log(det(Dg_i^{-1}(y))), where g_i is the restriction of g to the ith partition Di.

Raises:

  • TypeError: if self.dtype is specified and y.dtype is not self.dtype.
  • NotImplementedError: if _inverse_log_det_jacobian is not implemented.