Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
The forward 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 |
|
---|---|---|
data |
Pointer to the tensor of size n1 x n2 x ... x np that stores the input data for the forward dropout layer. This input can be an object of any class derived from Tensor. |
For common parameters of neural network layers, see Common Parameters.
In addition to the common parameters, the forward 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. |
|
retainRatio |
0.5 |
Probability that any particular element is retained. |
|
DEPRECATED: seed |
777 |
NoteThis parameter is deprecated and will be removed in a future release.Seed for random generation of mask elements. |
|
engine |
SharePtr< engines:: mt19937:: Batch>() |
Pointer to the random number generator engine that is used internally for random generation of mask elements. |
The forward 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 |
|
---|---|---|
value |
Pointer to the tensor of size n1 x n2 x ... x np that stores the result of the forward dropout layer. This input can be an object of any class derived from Tensor. |
|
resultForBackward |
Collection of 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 n1 x n2 x ... x np 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. |
C++: dropout_layer_dense_batch.cpp
Java*: DropoutLayerDenseBatch.java
Python*: dropout_layer_dense_batch.py