Batch Processing

Layer Input

The backward one-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 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 one-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 .

Layer Parameters

For common parameters of neural network layers, see Common Parameters.

In addition to the common parameters, the backward one-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.

kernelSize

KernelSize(2)

Data structure representing the size of the one-dimensional subtensor from which the average element is computed.

stride

Stride(2)

Data structure representing the intervals on which the subtensors for pooling are selected.

padding

Padding(0)

Data structure representing the number of data elements to implicitly add to each size of the one-dimensional subtensor on which pooling is performed.

indices

HomogenNumericTable(p-1)

Indices of the one dimensions on which pooling is performed, stored in HomogenNumericTable.

Layer Output

The backward one-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 one-dimensional average pooling layer. This input can be an object of any class derived from Tensor.

For more complete information about compiler optimizations, see our Optimization Notice.
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