Inherits From: `Bijector`

The affine autoregressive flow [(Papamakarios et al., 2016)][3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events. Regarding terminology,

'Autoregressive models decompose the joint density as a product of conditionals, and model each conditional in turn. Normalizing flows transform a base density (e.g. a standard Gaussian) into the target density by an invertible transformation with tractable Jacobian.' [(Papamakarios et al., 2016)][3]

In other words, the 'autoregressive property' is equivalent to the decomposition, `p(x) = prod{ p(x[perm[i]] | x[perm[0:i]]) : i=0, ..., d }` where `perm` is some permutation of `{0, ..., d}`. In the simple case where the permutation is identity this reduces to: `p(x) = prod{ p(x[i] | x[0:i]) : i=0, ..., d }`.

In TensorFlow Probability, 'normalizing flows' are implemented as `tfp.bijectors.Bijector`s. The `forward` 'autoregression' is implemented using a `tf.while_loop` and a deep neural network (DNN) with masked weights such that the autoregressive property is automatically met in the `inverse`.

A `TransformedDistribution` using `MaskedAutoregressiveFlow(...)` uses the (expensive) forward-mode calculation to draw samples and the (cheap) reverse-mode calculation to compute log-probabilities. Conversely, a `TransformedDistribution` using `Invert(MaskedAutoregressiveFlow(...))` uses the (expensive) forward-mode calculation to compute log-probabilities and the (cheap) reverse-mode calculation to compute samples. See 'Example Use' [below] for more details.

Given a `shift_and_log_scale_fn`, the forward and inverse transformations are (a sequence of) affine transformations. A 'valid' `shift_and_log_scale_fn` must compute each `shift` (aka `loc` or 'mu' in [Germain et al. (2015)][1]) and `log(scale)` (aka 'alpha' in [Germain et al. (2015)][1]) such that each are broadcastable with the arguments to `forward` and `inverse`, i.e., such that the calculations in `forward`, `inverse` [below] are possible.

For convenience, `tfp.bijectors.AutoregressiveNetwork` is offered as a possible `shift_and_log_scale_fn` function. It implements the MADE architecture [(Germain et al., 2015)][1]. MADE is a feed-forward network that computes a `shift` and `log(scale)` using masked dense layers in a deep neural network. Weights are masked to ensure the autoregressive property. It is possible that this architecture is suboptimal for your task. To build alternative networks, either change the arguments to `tfp.bijectors.AutoregressiveNetwork` or use some other architecture, e.g., using `tf.keras.layers`.

Assuming `shift_and_log_scale_fn` has valid shape and autoregressive semantics, the forward transformation is

``````def forward(x):
y = zeros_like(x)
event_size = x.shape[-event_dims:].num_elements()
for _ in range(event_size):
shift, log_scale = shift_and_log_scale_fn(y)
y = x * tf.exp(log_scale) + shift
return y
``````

and the inverse transformation is

``````def inverse(y):
shift, log_scale = shift_and_log_scale_fn(y)
return (y - shift) / tf.exp(log_scale)
``````

Notice that the `inverse` does not need a for-loop. This is because in the forward pass each calculation of `shift` and `log_scale` is based on the `y` calculated so far (not `x`). In the `inverse`, the `y` is fully known, thus is equivalent to the scaling used in `forward` after `event_size` passes, i.e., the 'last' `y` used to compute `shift`, `log_scale`. (Roughly speaking, this also proves the transform is bijective.)

The `bijector_fn` argument allows specifying a more general coupling relation, such as the LSTM-inspired activation from [4], or Neural Spline Flow [5]. It must logically operate on each element of the input individually, and still obey the 'autoregressive property' described above. The forward transformation is

``````def forward(x):
y = zeros_like(x)
event_size = x.shape[-event_dims:].num_elements()
for _ in range(event_size):
bijector = bijector_fn(y)
y = bijector.forward(x)
return y
``````

and inverse transformation is

``````def inverse(y):
bijector = bijector_fn(y)
return bijector.inverse(y)
``````

#### Examples

``````tfd = tfp.distributions
tfb = tfp.bijectors

dims = 2

# A common choice for a normalizing flow is to use a Gaussian for the base
# distribution.  (However, any continuous distribution would work.) Here, we
# use `tfd.Sample` to create a joint Gaussian distribution with diagonal
# covariance for the base distribution (note that in the Gaussian case,
# `tfd.MultivariateNormalDiag` could also be used.)
maf = tfd.TransformedDistribution(
distribution=tfd.Sample(
tfd.Normal(loc=0., scale=1.), sample_shape=[dims]),
shift_and_log_scale_fn=tfb.AutoregressiveNetwork(
params=2, hidden_units=[512, 512])))

x = maf.sample()  # Expensive; uses `tf.while_loop`, no Bijector caching.
maf.log_prob(x)   # Almost free; uses Bijector caching.
# Cheap; no `tf.while_loop` despite no Bijector caching.
maf.log_prob(tf.zeros(dims))

# [Papamakarios et al. (2016)][3] also describe an Inverse Autoregressive
# Flow [(Kingma et al., 2016)][2]:
iaf = tfd.TransformedDistribution(
distribution=tfd.Sample(
tfd.Normal(loc=0., scale=1.), sample_shape=[dims]),
shift_and_log_scale_fn=tfb.AutoregressiveNetwork(
params=2, hidden_units=[512, 512]))))

x = iaf.sample()  # Cheap; no `tf.while_loop` despite no Bijector caching.
iaf.log_prob(x)   # Almost free; uses Bijector caching.
# Expensive; uses `tf.while_loop`, no Bijector caching.
iaf.log_prob(tf.zeros(dims))

# In many (if not most) cases the default `shift_and_log_scale_fn` will be a
# poor choice.  Here's an example of using a 'shift only' version and with a
# different number/depth of hidden layers.
maf_no_scale_hidden2 = tfd.TransformedDistribution(
distribution=tfd.Sample(
tfd.Normal(loc=0., scale=1.), sample_shape=[dims]),
is_constant_jacobian=True))
# NOTE: The last line ensures that maf_no_scale_hidden2.trainable_variables
# will include all variables from `made`.
``````

#### Variable Tracking

A `tfb.MaskedAutoregressiveFlow` instance saves a reference to the values passed as `shift_and_log_scale_fn` and `bijector_fn` to its constructor. Thus, for most values passed as `shift_and_log_scale_fn` or `bijector_fn`, variables referenced by those values will be found and tracked by the `tfb.MaskedAutoregressiveFlow` instance. Please see the `tf.Module` documentation for further details.

However, if the value passed to `shift_and_log_scale_fn` or `bijector_fn` is a Python function, then `tfb.MaskedAutoregressiveFlow` cannot automatically track variables used inside `shift_and_log_scale_fn` or `bijector_fn`. To get `tfb.MaskedAutoregressiveFlow` to track such variables, either:

1. Replace the Python function with a `tf.Module`, `tf.keras.Layer`, or other callable object through which `tf.Module` can find variables.

2. Or, add a reference to the variables to the `tfb.MaskedAutoregressiveFlow` instance by setting an attribute -- for example:

``````made1 = tfb.AutoregressiveNetwork(params=1, hidden_units=[10, 10])
``````

#### References

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

[2]: Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. Improving Variational Inference with Inverse Autoregressive Flow. In Neural Information Processing Systems, 2016. https://arxiv.org/abs/1606.04934

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

[4]: Diederik P Kingma, Tim Salimans, Max Welling. Improving Variational Inference with Inverse Autoregressive Flow. In Neural Information Processing Systems, 2016. https://arxiv.org/abs/1606.04934

[5]: Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios. Neural Spline Flows, 2019. http://arxiv.org/abs/1906.04032

`shift_and_log_scale_fn` Python `callable` which computes `shift` and `log_scale` from the inverse domain (`y`). Calculation must respect the 'autoregressive property' (see class docstring). Suggested default `tfb.AutoregressiveNetwork(params=2, hidden_layers=...)`. Typically the function contains `tf.Variables`. Returning `None` for either (both) `shift`, `log_scale` is equivalent to (but more efficient than) returning zero. If `shift_and_log_scale_fn` returns a single `Tensor`, the returned value will be unstacked to get the `shift` and `log_scale`: `tf.unstack(shift_and_log_scale_fn(y), num=2, axis=-1)`.
`bijector_fn` Python `callable` which returns a `tfb.Bijector` which transforms event tensor with the signature `(input, **condition_kwargs) -> bijector`. The bijector must operate on scalar events and must not alter the rank of its input. The `bijector_fn` will be called with `Tensors` from the inverse domain (`y`). Calculation must respect the 'autoregressive property' (see class docstring).
`is_constant_jacobian` Python `bool`. Default: `False`. When `True` the implementation assumes `log_scale` does not depend on the forward domain (`x`) or inverse domain (`y`) values. (No validation is made; `is_constant_jacobian=False` is always safe but possibly computationally inefficient.)
`validate_args` Python `bool` indicating whether arguments should be checked for correctness.
`unroll_loop` Python `bool` indicating whether the `tf.while_loop` in `_forward` should be replaced with a static for loop. Requires that the final dimension of `x` be known at graph construction time. Defaults to `False`.
`event_ndims` Python `integer`, the intrinsic dimensionality of this bijector. 1 corresponds to a simple vector autoregressive bijector as implemented by the `tfp.bijectors.AutoregressiveNetwork`, 2 might be useful for a 2D convolutional `shift_and_log_scale_fn` and so on.
`name` Python `str`, name given to ops managed by this object.

`ValueError` If both or none of `shift_and_log_scale_fn` and `bijector_fn` are specified.

`dtype`

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

Multipart bijectors return structured `ndims`, which indicates the expected structure of their inputs. Some multipart bijectors, notably Composites, may return structures of `None`.

`graph_parents` Returns this `Bijector`'s graph_parents as a Python list.
`has_static_min_event_ndims` Returns True if the bijector has statically-known `min_event_ndims`. (deprecated)

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

Multipart bijectors return structured `event_ndims`, which indicates the expected structure of their outputs. Some multipart bijectors, notably Composites, may return structures of `None`.

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

`name` Returns the string name of this `Bijector`.
`name_scope` Returns a `tf.name_scope` instance for this class.
`non_trainable_variables` Sequence of non-trainable variables owned by this module and its submodules.
`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.

`validate_args` Returns True if Tensor arguments will be validated.
`variables` Sequence of variables owned by this module and its submodules.

## Methods

### `copy`

View source

Creates a copy of the bijector.

Args
`**override_parameters_kwargs` String/value dictionary of initialization arguments to override with new values.

Returns
`bijector` A new instance of `type(self)` initialized from the union of self.parameters and override_parameters_kwargs, i.e., `dict(self.parameters, **override_parameters_kwargs)`.

### `experimental_batch_shape`

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Returns the batch shape of this bijector for inputs of the given rank.

The batch shape of a bijector decribes the set of distinct transformations it represents on events of a given size. For example: the bijector `tfb.Scale([1., 2.])` has batch shape `[2]` for scalar events (`event_ndims = 0`), because applying it to a scalar event produces two scalar outputs, the result of two different scaling transformations. The same bijector has batch shape `[]` for vector events, because applying it to a vector produces (via elementwise multiplication) a single vector output.

Bijectors that operate independently on multiple state parts, such as `tfb.JointMap`, must broadcast to a coherent batch shape. Some events may not be valid: for example, the bijector `tfd.JointMap([tfb.Scale([1., 2.]), tfb.Scale([1., 2., 3.])])` does not produce a valid batch shape when `event_ndims = [0, 0]`, since the batch shapes of the two parts are inconsistent. The same bijector does define valid batch shapes of `[]`, `[2]`, and `[3]` if `event_ndims` is `[1, 1]`, `[0, 1]`, or `[1, 0]`, respectively.

Since transforming a single event produces a scalar log-det-Jacobian, the batch shape of a bijector with non-constant Jacobian is expected to equal the shape of `forward_log_det_jacobian(x, event_ndims=x_event_ndims)` or `inverse_log_det_jacobian(y, event_ndims=y_event_ndims)`, for `x` or `y` of the specified `ndims`.

Args
`x_event_ndims` Optional Python `int` (structure) number of dimensions in a probabilistic event passed to `forward`; this must be greater than or equal to `self.forward_min_event_ndims`. If `None`, defaults to `self.forward_min_event_ndims`. Mutually exclusive with `y_event_ndims`. Default value: `None`.
`y_event_ndims` Optional Python `int` (structure) number of dimensions in a probabilistic event passed to `inverse`; this must be greater than or equal to `self.inverse_min_event_ndims`. Mutually exclusive with `x_event_ndims`. Default value: `None`.

Returns
`batch_shape` `TensorShape` batch shape of this bijector for a value with the given event rank. May be unknown or partially defined.

### `experimental_batch_shape_tensor`

View source

Returns the batch shape of this bijector for inputs of the given rank.

The batch shape of a bijector decribes the set of distinct transformations it represents on events of a given size. For example: the bijector `tfb.Scale([1., 2.])` has batch shape `[2]` for scalar events (`event_ndims = 0`), because applying it to a scalar event produces two scalar outputs, the result of two different scaling transformations. The same bijector has batch shape `[]` for vector events, because applying it to a vector produces (via elementwise multiplication) a single vector output.

Bijectors that operate independently on multiple state parts, such as `tfb.JointMap`, must broadcast to a coherent batch shape. Some events may not be valid: for example, the bijector `tfd.JointMap([tfb.Scale([1., 2.]), tfb.Scale([1., 2., 3.])])` does not produce a valid batch shape when `event_ndims = [0, 0]`, since the batch shapes of the two parts are inconsistent. The same bijector does define valid batch shapes of `[]`, `[2]`, and `[3]` if `event_ndims` is `[1, 1]`, `[0, 1]`, or `[1, 0]`, respectively.

Since transforming a single event produces a scalar log-det-Jacobian, the batch shape of a bijector with non-constant Jacobian is expected to equal the shape of `forward_log_det_jacobian(x, event_ndims=x_event_ndims)` or `inverse_log_det_jacobian(y, event_ndims=y_event_ndims)`, for `x` or `y` of the specified `ndims`.

Args
`x_event_ndims` Optional Python `int` (structure) number of dimensions in a probabilistic event passed to `forward`; this must be greater than or equal to `self.forward_min_event_ndims`. If `None`, defaults to `self.forward_min_event_ndims`. Mutually exclusive with `y_event_ndims`. Default value: `None`.
`y_event_ndims` Optional Python `int` (structure) number of dimensions in a probabilistic event passed to `inverse`; this must be greater than or equal to `self.inverse_min_event_ndims`. Mutually exclusive with `x_event_ndims`. Default value: `None`.

Returns
`batch_shape_tensor` integer `Tensor` batch shape of this bijector for a value with the given event rank.

### `forward`

View source

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

Args
`x` `Tensor` (structure). The input to the 'forward' evaluation.
`name` The name to give this op.
`**kwargs` Named arguments forwarded to subclass implementation.

Returns
`Tensor` (structure).

Raises
`TypeError` if `self.dtype` is specified and `x.dtype` is not `self.dtype`.
`NotImplementedError` if `_forward` is not implemented.

### `forward_dtype`

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Returns the dtype returned by `forward` for the provided input.

### `forward_event_ndims`

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Returns the number of event dimensions produced by `forward`.

Args
`event_ndims` Structure of Python and/or Tensor `int`s, and/or `None` values. The structure should match that of `self.forward_min_event_ndims`, and all non-`None` values must be greater than or equal to the corresponding value in `self.forward_min_event_ndims`.
`**kwargs` Optional keyword arguments forwarded to nested bijectors.

Returns
`forward_event_ndims` Structure of integers and/or `None` values matching `self.inverse_min_event_ndims`. These are computed using 'prefer static' semantics: if any inputs are `None`, some or all of the outputs may be `None`, indicating that the output dimension could not be inferred (conversely, if all inputs are non-`None`, all outputs will be non-`None`). If all input `event_ndims` are Python `int`s, all of the (non-`None`) outputs will be Python `int`s; otherwise, some or all of the outputs may be `Tensor` `int`s.

### `forward_event_shape`

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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` (structure) indicating event-portion shape passed into `forward` function.

Returns
`forward_event_shape_tensor` `TensorShape` (structure) indicating event-portion shape after applying `forward`. Possibly unknown.

### `forward_event_shape_tensor`

View source

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

Args
`input_shape` `Tensor`, `int32` vector (structure) indicating event-portion shape passed into `forward` function.
`name` name to give to the op

Returns
`forward_event_shape_tensor` `Tensor`, `int32` vector (structure) indicating event-portion shape after applying `forward`.

### `forward_log_det_jacobian`

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Returns both the forward_log_det_jacobian.

Args
`x` `Tensor` (structure). The input to the 'forward' Jacobian determinant evaluation.
`event_ndims` Optional number of dimensions in the probabilistic events being transformed; this must be greater than or equal to `self.forward_min_event_ndims`. If `event_ndims` is specified, the log Jacobian determinant is summed to produce a scalar log-determinant for each event. Otherwise (if `event_ndims` is `None`), no reduction is performed. Multipart bijectors require structured event_ndims, such that the batch rank `rank(y[i]) - event_ndims[i]` is the same for all elements `i` of the structured input. In most cases (with the exception of `tfb.JointMap`) they further require that `event_ndims[i] - self.inverse_min_event_ndims[i]` is the same for all elements `i` of the structured input. Default value: `None` (equivalent to `self.forward_min_event_ndims`).
`name` The name to give this op.
`**kwargs` Named arguments forwarded to subclass implementation.

Returns
`Tensor` (structure), if this bijector is injective. If not injective this is not implemented.

Raises
`TypeError` if `y`'s dtype is incompatible with the expected output dtype.
`NotImplementedError` if neither `_forward_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented, or this is a non-injective bijector.
`ValueError` if the value of `event_ndims` is not valid for this bijector.

### `inverse`

View source

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

Args
`y` `Tensor` (structure). The input to the 'inverse' evaluation.
`name` The name to give this op.
`**kwargs` Named arguments forwarded to subclass implementation.

Returns
`Tensor` (structure), 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 `y`'s structured dtype is incompatible with the expected output dtype.
`NotImplementedError` if `_inverse` is not implemented.

### `inverse_dtype`

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Returns the dtype returned by `inverse` for the provided input.

### `inverse_event_ndims`

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Returns the number of event dimensions produced by `inverse`.

Args
`event_ndims` Structure of Python and/or Tensor `int`s, and/or `None` values. The structure should match that of `self.inverse_min_event_ndims`, and all non-`None` values must be greater than or equal to the corresponding value in `self.inverse_min_event_ndims`.
`**kwargs` Optional keyword arguments forwarded to nested bijectors.

Returns
`inverse_event_ndims` Structure of integers and/or `None` values matching `self.forward_min_event_ndims`. These are computed using 'prefer static' semantics: if any inputs are `None`, some or all of the outputs may be `None`, indicating that the output dimension could not be inferred (conversely, if all inputs are non-`None`, all outputs will be non-`None`). If all input `event_ndims` are Python `int`s, all of the (non-`None`) outputs will be Python `int`s; otherwise, some or all of the outputs may be `Tensor` `int`s.

### `inverse_event_shape`

View source

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` (structure) indicating event-portion shape passed into `inverse` function.

Returns
`inverse_event_shape_tensor` `TensorShape` (structure) indicating event-portion shape after applying `inverse`. Possibly unknown.

### `inverse_event_shape_tensor`

View source

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

Args
`output_shape` `Tensor`, `int32` vector (structure) indicating event-portion shape passed into `inverse` function.
`name` name to give to the op

Returns
`inverse_event_shape_tensor` `Tensor`, `int32` vector (structure) indicating event-portion shape after applying `inverse`.

### `inverse_log_det_jacobian`

View source

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` (structure). The input to the 'inverse' Jacobian determinant evaluation.
`event_ndims` Optional number of dimensions in the probabilistic events being transformed; this must be greater than or equal to `self.inverse_min_event_ndims`. If `event_ndims` is specified, the log Jacobian determinant is summed to produce a scalar log-determinant for each event. Otherwise (if `event_ndims` is `None`), no reduction is performed. Multipart bijectors require structured event_ndims, such that the batch rank `rank(y[i]) - event_ndims[i]` is the same for all elements `i` of the structured input. In most cases (with the exception of `tfb.JointMap`) they further require that `event_ndims[i] - self.inverse_min_event_ndims[i]` is the same for all elements `i` of the structured input. Default value: `None` (equivalent to `self.inverse_min_event_ndims`).
`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 `x`'s dtype is incompatible with the expected inverse-dtype.
`NotImplementedError` if `_inverse_log_det_jacobian` is not implemented.
`ValueError` if the value of `event_ndims` is not valid for this bijector.

### `parameter_properties`

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Returns a dict mapping constructor arg names to property annotations.

This dict should include an entry for each of the bijector's `Tensor`-valued constructor arguments.

Args
`dtype` Optional float `dtype` to assume for continuous-valued parameters. Some constraining bijectors require advance knowledge of the dtype because certain constants (e.g., `tfb.Softplus.low`) must be instantiated with the same dtype as the values to be transformed.

Returns
`parameter_properties` A `str ->`tfp.python.internal.parameter_properties.ParameterProperties`dict mapping constructor argument names to`ParameterProperties` instances.

### `with_name_scope`

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.Variable`s and `tf.Tensor`s 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

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 (structure of) `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 (structure of) `Tensor` computed by `self.forward`.

#### Examples

``````sigmoid = tfb.Reciprocal()(
tfb.Shift(shift=1.)(
tfb.Exp()(
tfb.Scale(scale=-1.))))
# ==> `tfb.Chain([
#         tfb.Reciprocal(),
#         tfb.Shift(shift=1.),
#         tfb.Exp(),
#         tfb.Scale(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.])
``````

### `__eq__`

View source

Return self==value.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]