Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3

Batch Processing

Layer Input

The forward two-dimensional locally-connected 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 locally-connected layer. This input can be an object of any class derived from Tensor.

weights

Pointer to tensor K of size nKernels × l3 × l4 × 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 × l3 × l4 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 locally-connected 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 kernels of the forward locally-connected layer.

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 to which the kernel are applied. Only symmetric padding is currently supported.

nKernels

n/a

Number of kernels applied to the input layer data.

groupDimension

1

Dimension n2 for which grouping is applied. Only groupDimension = 1 is currently supported.

nGroups

1

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

Layer Output

The forward two-dimensional locally-connected 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 locally-connected layer. This input can be an object of any class derived from Tensor.

layerData

Collection of data received on the forward two-dimensional locally-connected 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 locally-connected layer. This input can be an object of any class derived from Tensor.

auxWeights

Pointer to tensor K of size nKernels × l3 × l4 × m2 × m3 × m4 that stores a set of kernel weights. This input can be an object of any class derived from Tensor.

Examples

C++: locallycon2d_layer_dense_batch.cpp

Java*: Locallycon2DLayerDenseBatch.java

Python*: locallycon2d_layer_dense_batch.py

See Also