tf.contrib.distributions.bijectors.Inline Bijector constructed from custom callables. Inherits From: Bijector 
tf.contrib.distributions.bijectors.Inline(
    forward_fn=None, inverse_fn=None, inverse_log_det_jacobian_fn=None,
    forward_log_det_jacobian_fn=None, forward_event_shape_fn=None,
    forward_event_shape_tensor_fn=None, inverse_event_shape_fn=None,
    inverse_event_shape_tensor_fn=None, is_constant_jacobian=False,
    validate_args=False, forward_min_event_ndims=None, inverse_min_event_ndims=None,
    name='inline'
)
 Example Use: exp = Inline(
  forward_fn=tf.exp,
  inverse_fn=tf.math.log,
  inverse_log_det_jacobian_fn=(
    lambda y: -tf.reduce_sum(tf.math.log(y), axis=-1)),
  name="exp")
 The above example is equivalent to the Bijector Exp().
 


 Args
  forward_fn   Python callable implementing the forward transformation.  
  inverse_fn   Python callable implementing the inverse transformation.  
  inverse_log_det_jacobian_fn   Python callable implementing the log o det o jacobian of the inverse transformation.  
  forward_log_det_jacobian_fn   Python callable implementing the log o det o jacobian of the forward transformation.  
  forward_event_shape_fn   Python callable implementing non-identical static event shape changes. Default: shape is assumed unchanged.  
  forward_event_shape_tensor_fn   Python callable implementing non-identical event shape changes. Default: shape is assumed unchanged.  
  inverse_event_shape_fn   Python callable implementing non-identical static event shape changes. Default: shape is assumed unchanged.  
  inverse_event_shape_tensor_fn   Python callable implementing non-identical event shape changes. Default: shape is assumed unchanged.  
  is_constant_jacobian   Python bool indicating that the Jacobian is constant for all input arguments.  
  validate_args   Python bool indicating whether arguments should be checked for correctness.  
  forward_min_event_ndims   Python int indicating the minimal dimensionality this bijector acts on.  
  inverse_min_event_ndims   Python int indicating the minimal dimensionality this bijector acts on.  
  name   Python str, name given to ops managed by this object.   
 


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

 
  name   Returns the string name of this Bijector.  
  validate_args   Returns True if Tensor arguments will be validated.    Methods forward View source 
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 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'
)
 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 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.    inverse View source 
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, 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_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'
)
 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 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.  
  NotImplementedError   if _inverse_log_det_jacobian is not implemented.     

    
{'path': 'public/docs/tensorflow~1.15/contrib/distributions/bijectors/inline.html', 'source': 'DevDocs.IO'}