Warning: This API is deprecated and will be removed in a future version of TensorFlow after the replacement is stable.

ParallelConcat

public final class ParallelConcat

Concatenates a list of `N` tensors along the first dimension.

The input tensors are all required to have size 1 in the first dimension.

For example:

# 'x' is [[1, 4]]
 # 'y' is [[2, 5]]
 # 'z' is [[3, 6]]
 parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
 
The difference between concat and parallel_concat is that concat requires all of the inputs be computed before the operation will begin but doesn't require that the input shapes be known during graph construction. Parallel concat will copy pieces of the input into the output as they become available, in some situations this can provide a performance benefit.

Public Methods

Output <T>
asOutput ()
Returns the symbolic handle of a tensor.
static <T> ParallelConcat <T>
create ( Scope scope, Iterable< Operand <T>> values, Shape shape)
Factory method to create a class wrapping a new ParallelConcat operation.
Output <T>
output ()
The concatenated tensor.

Inherited Methods

Public Methods

public Output <T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static ParallelConcat <T> create ( Scope scope, Iterable< Operand <T>> values, Shape shape)

Factory method to create a class wrapping a new ParallelConcat operation.

Parameters
scope current scope
values Tensors to be concatenated. All must have size 1 in the first dimension and same shape.
shape the final shape of the result; should be equal to the shapes of any input but with the number of input values in the first dimension.
Returns
  • a new instance of ParallelConcat

public Output <T> output ()

The concatenated tensor.