Getting Started Guide

Contents

Batch Processing

Layer Input

The forward pReLU 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 the tensor of size
n
1
x
n
2
x ... x
n
p
that stores the input data for the forward pReLU layer. This input can be an object of any class derived from
Tensor
.
weights
Pointer to the tensor of size
n
k
x
n
k
+ 1
x ... x
n
k
+
q
- 1
that stores weights for the forward pReLU 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 pReLU 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.
dataDimension
0
Starting index of data dimension of type
size_t
to apply a weight.
weightsDimension
1
Number of weight dimensions of type
size_t
.

Layer Output

The forward pReLU 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 the tensor of size
n
1
x
n
2
x ... x
n
p
that stores the results of the forward pReLU layer. This input can be an object of any class derived from
Tensor
.
resultForBackward
Collection of data needed for the backward pReLU layer.
Element ID
Element
auxData
Pointer to the tensor of size
n
1
x
n
2
x ... x
n
p
that stores the input data for the forward pReLU layer. This input can be an object of any class derived from
Tensor
.
auxWeights
Pointer to the tensor of size
n
k
x
n
k
+ 1
x ... x
n
k
+
q
- 1
that stores weights for the forward pReLU layer. This input can be an object of any class derived from
Tensor
.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804