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

Batch Processing

Layer Input

The forward local contrast normalization 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 n2 x n3 x n4 that stores the input data for the forward local contrast normalization 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 local contrast normalization 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

Default computation method used by the algorithm, the only method supported by the layer.

kernel

HomogenTensor<float> of size 5 x 5 with values 0.04

Tensor with sizes m1 x m2 of the two-dimensional kernel. Only kernels with odd dimensions are currently supported.

indices

indices(2,3)

Data structure representing dimensions k1 and k2 for kernels.

sumDimension

HomogenNumericTable<float> of size 1 x 1 with value 1

Numeric table of size 1 x 1 that stores dimension f. If it is NULL, there is no summation over this dimension.

Layer Output

The forward local contrast normalization 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 n2 x n3 x n4 that stores the result of the forward local contrast normalization layer. This input can be an object of any class derived from Tensor.

layerData

Collection of input data needed for the backward local contrast normalization layer. This collection can contain objects of any class derived from Tensor.

Element ID

Element

auxInvMax

Pointer to the tensor:

  • 1/mnij of size n1 x n3 x n4 if sumDimension is not NULL
  • 1/mnqij of size n1 x n2 x n3 x n4 if sumDimension is NULL

This tensor stores the inverted max values. This input can be an object of any class derived from Tensor.

auxCenteredData

Pointer to tensor vnqij of size n1 x n2 x n3 x n4 that stores values as shown above. This input can be an object of any class derived from Tensor.

auxSigma

Pointer to tensor σnij of size n1 x n3 x n4 if sumDimension is not NULL, or tensor σnqij of size n1 x n2 x n3 x n4 otherwise, that stores values as shown above. This input can be an object of any class derived from Tensor.

auxC

Pointer to tensor cn of size n1 if sumDimension is not NULL, or tensor cnq of size n1 x n2 otherwise, that stores values as shown above. This input can be an object of any class derived from Tensor.

Examples

C++: lcn_layer_dense_batch.cpp

Java*: LCNLayerDenseBatch.java

Python*: lcn_layer_dense_batch.py

See Also