Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 2

Batch Processing

Layer Input

The forward two-dimensional transposed 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 × l2 × l3 × l4 that stores the input data of the forward two-dimensional transposed convolution layer. This input can be an object of any class derived from Tensor.

weighs

Pointer to the tensor K of size l2 × nKernels/nGroups × m3 × m4 that stores a set of kernel weights. This input can be an object of any class derived from Tensor.

biases

Pointer to the 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 transposed 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 transposed 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 transposed convolution is performed.

valueSizes

ValueSizes(0, 0)

Data structure representing the dimension sizes ni, i ∈ {3, 4}, of the value tensor Y. If this value contains (0, 0), the size of value dimensions are computed automatically.

nKernels

n/a

Number of kernels applied to the layer input data.

groupDimension

1

Dimension to 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 transposed 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 x nKernels x n3 x n4 that stores the result of the forward two-dimensional transposed convolution layer. This result can be an object of any class derived from Tensor.

resutForBackward

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

Element ID

Element

auxData

Pointer to tensor X of size n1 × l2 × l3 × l4 that stores the input data for the forward two-dimensional transposed convolution layer. This result can be an object of any class derived from Tensor.

auxWeights

Pointer to tensor K of size l2 × nKernels/nGroups × m3 × m4 that stores a set of kernel weights. This result can be an object of any class derived from Tensor.

See Also