tensorflow:: ops:: BroadcastTo

#include <array_ops.h>

Broadcast an array for a compatible shape.

Summary

Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a Tensor to a shape, it starts with the trailing dimensions, and works its way forward.

For example,

x = tf.constant([1, 2, 3]) y = tf.broadcast_to(x, [3, 3]) print(y) tf.Tensor( [[1 2 3] [1 2 3] [1 2 3]], shape=(3, 3), dtype=int32)

In the above example, the input Tensor with the shape of [1, 3] is broadcasted to output Tensor with shape of [3, 3] .

When doing broadcasted operations such as multiplying a tensor by a scalar, broadcasting (usually) confers some time or space benefit, as the broadcasted tensor is never materialized.

However, broadcast_to does not carry with it any such benefits. The newly-created tensor takes the full memory of the broadcasted shape. (In a graph context, broadcast_to might be fused to subsequent operation and then be optimized away, however.)

Args:

Returns:

Constructors and Destructors

BroadcastTo (const :: tensorflow::Scope & scope, :: tensorflow::Input input, :: tensorflow::Input shape)

Public attributes

operation
output

Public functions

node () const
::tensorflow::Node *
operator::tensorflow::Input () const
operator::tensorflow::Output () const

Public attributes

operation 

Operation operation

output 

::tensorflow::Output output

Public functions

BroadcastTo 

 BroadcastTo(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input shape
)

node 

::tensorflow::Node * node() const

operator::tensorflow::Input 

 operator::tensorflow::Input() const

operator::tensorflow::Output 

 operator::tensorflow::Output() const