Batch Processing

Layer Input

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.

Layer Parameters

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.

engine

SharePtr< engines:: mt19937:: Batch>()

Pointer to the random number generator engine that is used internally for random generation of mask elements.

Layer Output

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.

For more complete information about compiler optimizations, see our Optimization Notice.
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