Batch Processing

Layer Input

The forward two-dimensional convolution 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 × n2 × n3 × n4 that stores the input data for the forward two-dimensional convolution layer. This input can be an object of any class derived from Tensor.

weights

Pointer to tensor K of size nKernels × m2 × m3 × m4 that stores a set of kernel weights. This input can be an object of any class derived from Tensor.

biases

Pointer to tensor B of size nKernels that stores a set of biases. This input can be an object of any class derived from Tensor. If you pass a null pointer, the library does not apply a bias-related transformation.

Layer Parameters

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

In addition to the common parameters, the forward two-dimensional convolution 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 sizes mi, i ∈ {3, 4}, of the two-dimensional kernel subtensor.

indices

Indices(2,3)

Data structure representing the dimensions for applying convolution kernels.

strides

Strides(2, 2)

Data structure representing the intervals si, i ∈ {3, 4}, on which the kernel should be applied to the input.

paddings

Paddings(0, 0)

Data structure representing the number of data elements pi, i ∈ {3, 4}, to implicitly add to each side of the two-dimensional subtensor along which forward two-dimensional convolution is performed.

nKernels

n/a

Number of kernels applied to the input layer data.

groupDimension

1

Dimension for which grouping is applied.

nGroups

1

Number of groups into which the input data is split in dimension groupDimension.

Layer Output

The forward two-dimensional convolution 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 n1 × nKernels × l3 × l4 that stores the result of the forward two-dimensional convolution layer. This result can be an object of any class derived from Tensor.

resultForBackward

Collection of data received on the forward two-dimensional convolution layer. This collection can contain objects of any class derived from Tensor.

Element ID

Element

auxData

Pointer to tensor X of size n1 × n2 × n3 × n4 that stores the input data for the forward two-dimensional convolution layer. This result can be an object of any class derived from Tensor.

auxWeights

Pointer to tensor K of size nKernels × m2 × m3 × m4 that stores a set of kernel weights. This result 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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