Developer Guide for Intel® Data Analytics Acceleration Library 2018

Batch Processing

Layer Input

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 . This input can be an object of any class derived from Tensor. If you pass a null pointer, the algorithm uses coefficients equal to 1.

Layer Parameters

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.

Layer Output

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 . This input can be an object of any class derived from Tensor. If you pass a null pointer, the algorithm sets the result auxNumberOfCoefficients.

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.

Examples

C++: eltwise_sum_layer_dense_batch.cpp

Java*: EltwiseSumLayerDenseBatch.java

Python*: eltwise_sum_layer_dense_batch.py