Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
The forward element-wise sum 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 K of size n 1 x ... x n p that stores the input data for the forward element-wise sum layer. This input can be an object of any class derived from Tensor. |
|
coefficients |
Pointer to the tensor of size
K that stores the coefficients |
The forward element-wise sum layer has the following parameters:
Parameter |
Default Value |
Description |
|
---|---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
|
method |
defaultDense |
Performance-oriented computation method, the only method supported by the layer. |
The forward element-wise sum 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 Y of size n 1 x ... x n p that stores the result of the forward element-wise sum layer. This result can be an object of any class derived from Tensor. |
|
resultForBackward |
Element ID |
Element |
auxCoefficients |
Pointer to the tensor of size
K that stores the coefficients |
|
auxNumberOfCoefficients |
If the result auxCoefficients is a null pointer, then this result stores the numeric table of size 1 x 1 with the number of coefficients K; otherwise this result is an null pointer. |