tensorflow::ops::ParallelConcat

Concatenates a list of N tensors along the first dimension.

Summary

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.

Args:

• scope: A Scope object
• 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:

• Output: The concatenated tensor.

Public attributes

Operation operation

::tensorflow::Output output

Public functions

 ParallelConcat(
  const ::tensorflow::Scope & scope,
  ::tensorflow::InputList values,
  PartialTensorShape shape
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const