Concatenates a list of
N tensors along the first dimension.
The input tensors are all required to have size 1 in the first dimension.
# '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.
Output: The concatenated tensor.
|Constructors and Destructors|
| || |
ParallelConcat( const ::tensorflow::Scope & scope, ::tensorflow::InputList values, PartialTensorShape shape )
::tensorflow::Node * node() const
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.