Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
The forward concat layer accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
Input ID |
Input |
|
---|---|---|
inputLayerData |
Collection of tensors of size n1 x ... x nki x ... x np that stores the input data for the forward concat layer. This collection can contain objects of any class derived from Tensor. |
For common parameters of neural network layers, see Common Parameters.
In addition to the common parameters, the forward concat layer has the following parameters:
Parameter |
Default Value |
Description |
|
---|---|---|---|
concatDimension |
0 |
Index of the dimension along which concatenation should be implemented. |
The forward concat layer calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID |
Result |
|
---|---|---|
value |
Pointer to the tensor of size n1 x ... x nk x ... x np that stores the result of the forward concat layer. This input can be an object of any class derived from Tensor. |
|
resultForBackward |
Collection of data needed for the backward concat layer. |
|
Element ID |
Element |
|
auxInputDimensions |
Collection of integers that stores the sizes of the input tensors along concatDimension: nk1, ..., nkt. |
C++: concat_layer_dense_batch.cpp
Java*: ConcatLayerDenseBatch.java
Python*: concat_layer_dense_batch.py