Batch Processing

Layer Input

The forward three-dimensional max 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

data

Pointer to tensor X of size n1 x ... x np that stores the input data for the forward three-dimensional max pooling layer. This input can be an object of any class derived from Tensor.

Layer Parameters

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

In addition to the common parameters, the forward three-dimensional max 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, 2)

Data structure representing the size of the three-dimensional subtensor from which the maximum element is selected.

strides

Strides(2, 2, 2)

Data structure representing intervals s1, s2, s3 on which the subtensors for max pooling are selected.

paddings

Paddings(0, 0, 0)

Data structure representing the number of data elements to implicitly add to each side of the three-dimensional subtensor along which max pooling is performed.

indices

Indices(p-3, p-2, p-1)

Indices k1, k2, k3 of the dimensions along which max pooling is performed.

Layer Output

The forward three-dimensional max 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

value

Pointer to tensor Y of size l1 x ... x lp that stores the result of the forward three-dimensional max pooling layer. This input can be an object of any class derived from Tensor.

resultForBackward

Collection of data needed for the backward three-dimensional max pooling layer.

Element ID

Element

auxSelectedIndices

Tensor T of size l1 x ... x lp that stores indices of maximum elements.

auxInputDimensions

NumericTable of size 1 x p that stores the sizes of the dimensions of input data tensor X: n1, n2, …, np.

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