tfp.bijectors.Pad

See Stable See Nightly

Pads a value to the event_shape of a Tensor.

Inherits From: Bijector

tfp.bijectors.Pad(
    paddings=((0, 1),), mode='CONSTANT', constant_values=0, axis=None,
    validate_args=False, name=None
)

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.

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').

Attributes
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. Note: Jacobian matrix is either constant for both forward and inverse or neither.
mode
name	Returns the string name of this Bijector.
name_scope	Returns a tf.name_scope instance for this class.
paddings
parameters	Dictionary of parameters used to instantiate this Bijector.
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_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.
validate_args	Returns True if Tensor arguments will be validated.
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.

Methods

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_dtype

View source

forward_dtype(
    dtype, name='forward_dtype', **kwargs
)

Returns the dtype of the output of the forward transformation.

Args
dtype	tf.dtype, or nested structure of tf.dtypes, of the input to forward.
name	The name to give this op.
**kwargs	Named arguments forwarded to subclass implementation.

Returns
tf.dtype or nested structure of tf.dtypes of the output of forward.

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_dtype

View source

inverse_dtype(
    dtype, name='inverse_dtype', **kwargs
)

Returns the dtype of the output of the inverse transformation.

Args
dtype	tf.dtype, or nested structure of tf.dtypes, of the input to inverse.
name	The name to give this op.
**kwargs	Named arguments forwarded to subclass implementation.

Returns
tf.dtype or nested structure of tf.dtypes of the output of inverse.

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.

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__

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.])