Bijector which computes `Y = g(X) = Log[1 + exp(X)]`.

The softplus `Bijector` has the following two useful properties:

* The domain is the positive real numbers
* `softplus(x) approx x`, for large `x`, so it does not overflow as easily as
  the `Exp` `Bijector`.

  Example Use:

  ```python
  # Create the Y=g(X)=softplus(X) transform which works only on Tensors with 1
  # batch ndim and 2 event ndims (i.e., vector of matrices).
  softplus = Softplus(batch_ndims=1, event_ndims=2)
  x = [[[1., 2],
         [3, 4]],
        [[5, 6],
         [7, 8]]]
  log(1 + exp(x)) == softplus.forward(x)
  log(exp(x) - 1) == softplus.inverse(x)
  ```

  Note: log(.) and exp(.) are applied element-wise but the Jacobian is a
  reduction over the event space.
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#### `tf.contrib.distributions.bijector.Softplus.__init__(event_ndims=0, validate_args=False, name='softplus')` {#Softplus.__init__}




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#### `tf.contrib.distributions.bijector.Softplus.dtype` {#Softplus.dtype}

dtype of `Tensor`s transformable by this distribution.


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#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward', **condition_kwargs)` {#Softplus.forward}

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

##### Args:


*  <b>`x`</b>: `Tensor`. The input to the "forward" evaluation.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  `Tensor`.

##### Raises:


*  <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not
    `self.dtype`.
*  <b>`NotImplementedError`</b>: if `_forward` is not implemented.


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#### `tf.contrib.distributions.bijector.Softplus.forward_event_shape(input_shape, name='forward_event_shape')` {#Softplus.forward_event_shape}

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

##### Args:


*  <b>`input_shape`</b>: `Tensor`, `int32` vector indicating event-portion shape
    passed into `forward` function.
*  <b>`name`</b>: name to give to the op

##### Returns:


*  <b>`forward_event_shape`</b>: `Tensor`, `int32` vector indicating event-portion
    shape after applying `forward`.


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#### `tf.contrib.distributions.bijector.Softplus.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Softplus.forward_log_det_jacobian}

Returns both the forward_log_det_jacobian.

##### Args:


*  <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  `Tensor`.

##### Raises:


*  <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not
    `self.dtype`.
*  <b>`NotImplementedError`</b>: if neither `_forward_log_det_jacobian`
    nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented.


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#### `tf.contrib.distributions.bijector.Softplus.get_forward_event_shape(input_shape)` {#Softplus.get_forward_event_shape}

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

Same meaning as `forward_event_shape`. May be only partially defined.

##### Args:


*  <b>`input_shape`</b>: `TensorShape` indicating event-portion shape passed into
    `forward` function.

##### Returns:


*  <b>`forward_event_shape`</b>: `TensorShape` indicating event-portion shape after
    applying `forward`. Possibly unknown.


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#### `tf.contrib.distributions.bijector.Softplus.get_inverse_event_shape(output_shape)` {#Softplus.get_inverse_event_shape}

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

Same meaning as `inverse_event_shape`. May be only partially defined.

##### Args:


*  <b>`output_shape`</b>: `TensorShape` indicating event-portion shape passed into
    `inverse` function.

##### Returns:


*  <b>`inverse_event_shape`</b>: `TensorShape` indicating event-portion shape after
    applying `inverse`. Possibly unknown.


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#### `tf.contrib.distributions.bijector.Softplus.graph_parents` {#Softplus.graph_parents}

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


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#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse', **condition_kwargs)` {#Softplus.inverse}

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

##### Args:


*  <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  `Tensor`.

##### Raises:


*  <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not
    `self.dtype`.
*  <b>`NotImplementedError`</b>: if neither `_inverse` nor
    `_inverse_and_inverse_log_det_jacobian` are implemented.


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#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_and_inverse_log_det_jacobian}

Returns both the inverse evaluation and inverse_log_det_jacobian.

Enables possibly more efficient calculation when both inverse and
corresponding Jacobian are needed.

See `inverse()`, `inverse_log_det_jacobian()` for more details.

##### Args:


*  <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  `Tensor`.

##### Raises:


*  <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not
    `self.dtype`.
*  <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian`
    nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented.


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#### `tf.contrib.distributions.bijector.Softplus.inverse_event_shape(output_shape, name='inverse_event_shape')` {#Softplus.inverse_event_shape}

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

##### Args:


*  <b>`output_shape`</b>: `Tensor`, `int32` vector indicating event-portion shape
    passed into `inverse` function.
*  <b>`name`</b>: name to give to the op

##### Returns:


*  <b>`inverse_event_shape`</b>: `Tensor`, `int32` vector indicating event-portion
    shape after applying `inverse`.


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#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.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:


*  <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation.
*  <b>`name`</b>: The name to give this op.
*  <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation.

##### Returns:

  `Tensor`.

##### Raises:


*  <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not
    `self.dtype`.
*  <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor
    `_inverse_and_inverse_log_det_jacobian` are implemented.


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#### `tf.contrib.distributions.bijector.Softplus.is_constant_jacobian` {#Softplus.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:

  `Boolean`.


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#### `tf.contrib.distributions.bijector.Softplus.name` {#Softplus.name}

Returns the string name of this `Bijector`.


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#### `tf.contrib.distributions.bijector.Softplus.shaper` {#Softplus.shaper}

Returns shape object used to manage shape constraints.


- - -

#### `tf.contrib.distributions.bijector.Softplus.validate_args` {#Softplus.validate_args}

Returns True if Tensor arguments will be validated.


