Developer Guide for Intel® Data Analytics Acceleration Library 2018
The backward two-dimensional average pooling 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 m1 x m2 x ... x mp that stores the input gradient g computed on the preceding layer. This input can be an object of any class derived from Tensor. |
|
inputFromForward |
Collection of data needed for the backward two-dimensional average pooling layer. |
|
Element ID |
Element |
|
auxInputDimensions |
Collection that contains the size of the dimensions of the input data tensor in the forward computation step: n1, n2, ..., np. |
For common parameters of neural network layers, see Common Parameters.
In addition to the common parameters, the backward two-dimensional average pooling 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. |
|
kernelSizes |
KernelSizes(2, 2) |
Data structure representing the size of the two-dimensional subtensor from which the average element is computed. |
|
strides |
Strides(2, 2) |
Data structure representing the intervals on which the subtensors for pooling are selected. |
|
paddings |
Paddings(0, 0) |
Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which pooling is performed. |
|
indices |
HomogenNumericTable(p-2, p-1) |
Indices of the two dimensions on which pooling is performed, stored in HomogenNumericTable. |
The backward two-dimensional average pooling 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 |
|
---|---|---|
gradient |
Pointer to the tensor of size n1 x n2 x ... x np that stores the result of the backward two-dimensional average pooling layer. This input can be an object of any class derived from Tensor. |
C++: ave_pool2d_layer_dense_batch.cpp
Java*: AvePool2DLayerDenseBatch.java
Python*: ave_pool2d_layer_dense_batch.py