Getting Started Guide

Contents

Batch Processing

Layer Input

The backward dropout 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
inputGradient
Pointer to the tensor of size
n
1
x
n
2
x ... x
n
p
that stores the input gradient computed on the preceding layer. This input can be an object of any class derived from
Tensor
.
inputFromForward
Collection of input data needed for the backward dropout layer. This collection can contain objects of any class derived from
Tensor
.
Element ID
Element
auxRetainMask
Pointer to tensor
M
of size
n
1
x
n
2
x ... x
n
p
that stores Bernoulli random variable values (
0
on positions that were dropped,
1
on the others) divided by the probability that any particular element is retained. 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 backward dropout 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.

Layer Output

The backward dropout 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
gradient
Pointer to the tensor of size
n
1
x
n
2
x ... x
n
p
that stores the result of the backward dropout 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