# tf.contrib.distributions.bijectors.PowerTransform

### `class tf.contrib.distributions.bijectors.PowerTransform`

See the guide: Random variable transformations (contrib) > Bijectors

Compute `Y = g(X) = (1 + X * c)**(1 / c), X >= -1 / c`.

The power transform maps inputs from `[0, inf]` to `[-1/c, inf]`; this is equivalent to the `inverse` of this bijector.

This bijector is equivalent to the `Exp` bijector when `c=0`.

## Properties

### `dtype`

dtype of `Tensor`s transformable by this distribution.

### `event_ndims`

Returns then number of event dimensions this bijector operates on.

### `graph_parents`

Returns this `Bijector`'s graph_parents as a Python list.

### `is_constant_jacobian`

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

Note: Jacobian is either constant for both forward and inverse or neither.

#### Returns:

• `is_constant_jacobian`: Python `bool`.

### `name`

Returns the string name of this `Bijector`.

### `power`

The `c` in: `Y = g(X) = (1 + X * c)**(1 / c)`.

### `validate_args`

Returns True if Tensor arguments will be validated.

## Methods

### `__init__`

```__init__(
power=0.0,
event_ndims=0,
validate_args=False,
name='power_transform'
)
```

Instantiates the `PowerTransform` bijector.

#### Args:

• `power`: Python `float` scalar indicating the transform power, i.e., `Y = g(X) = (1 + X * c)**(1 / c)` where `c` is the `power`.
• `event_ndims`: Python scalar indicating the number of dimensions associated with a particular draw from the distribution.
• `validate_args`: Python `bool` indicating whether arguments should be checked for correctness.
• `name`: Python `str` name given to ops managed by this object.

#### Raises:

• `ValueError`: if `power < 0` or is not known statically.

### `forward`

```forward(
x,
name='forward'
)
```

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.

#### 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`

```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`

```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`

```forward_log_det_jacobian(
x,
name='forward_log_det_jacobian'
)
```

Returns both the forward_log_det_jacobian.

#### Args:

• `x`: `Tensor`. The input to the "forward" Jacobian evaluation.
• `name`: The name to give this op.

#### Returns:

`Tensor`.

#### 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.

### `inverse`

```inverse(
y,
name='inverse'
)
```

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.

#### Returns:

`Tensor`.

#### Raises:

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

### `inverse_event_shape`

```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`

```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`

```inverse_log_det_jacobian(
y,
name='inverse_log_det_jacobian'
)
```

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.

#### Args:

• `y`: `Tensor`. The input to the "inverse" Jacobian evaluation.
• `name`: The name to give this op.

#### Returns:

`Tensor`.

#### Raises:

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

https://www.tensorflow.org/api_docs/python/tf/contrib/distributions/bijectors/PowerTransform

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