Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 2
The backward 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 |
|
---|---|---|
inputGradient |
Pointer to the tensor of size n 1 x ... х n p that stores the input gradient computed on the preceding layer. This input can be an object of any class derived from Tensor. |
|
inputFromForward |
Element ID |
Element |
auxCoefficients |
Pointer to the tensor of size
K that stores the coefficients |
|
auxNumberOfCoefficients |
If the input auxNumberOfCoefficients is a null pointer, this input must be a numeric table of size 1 × 1 that contains the number of coefficients K; otherwise the algorithm ignores the input. |
The backward 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 backward 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 |
|
---|---|---|
resultLayerData |
The collection K of tensors Z (i) of size n 1 x n p that stores the gradients computed on the backward element-wise sum layer. This result can be an object of any class derived from Tensor. |