The semantics generally follow that of tf.reshape(), with
a few differences:
The user must provide both the input and output shape, so that
the transformation can be inverted. If an input shape is not
specified, the default assumes a vector-shaped input, i.e.,
event_shape_in = (-1,).
The Reshape bijector automatically broadcasts over the leftmost
dimensions of its input (sample_shape and batch_shape); only
the rightmost event_ndims_in dimensions are reshaped. The
number of dimensions to reshape is inferred from the provided
event_shape_in (event_ndims_in = len(event_shape_in)).
An int-like vector-shaped Tensor
representing the event shape of the transformed output.
event_shape_in
An optional int-like vector-shape Tensor
representing the event shape of the input. This is required in
order to define inverse operations; the default of (-1,)
assumes a vector-shaped input.
validate_args
Python bool indicating whether arguments should
be checked for correctness.
name
Python str, name given to ops managed by this object.
Raises
TypeError
if either event_shape_in or event_shape_out has
non-integer dtype.
ValueError
if either of event_shape_in or event_shape_out
has non-vector shape (rank > 1), or if their sizes do not
match.
Attributes
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.
name
Returns the string name of this Bijector.
validate_args
Returns True if Tensor arguments will be validated.
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 x.shape.ndims - event_ndims dimensions.
name
The name to give this op.
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.
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 y.shape.ndims - event_ndims dimensions.
name
The name to give this op.
Returns
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]