Dropout Layer - Dropout layer - Simulink (original) (raw)

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Dropout layer

Since R2024b

Libraries:
Deep Learning Toolbox / Deep Learning Layers / Utility Layers

Description

The Dropout Layer block represents a dropout layer in a deep learning network. At training time, a dropout layer randomly sets input elements to zero with a given probability. At prediction time, the output of a dropout layer is equal to its input.

The exportNetworkToSimulink function generates this block to represent a dropoutLayer object. Because deep learning layer blocks can be used only for prediction, this block has no effect.

Ports

Input

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Input data.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | fixed point

Output

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Output data that is equal to the input data.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | fixed point

Parameters

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To edit block parameters interactively, use theProperty Inspector. From the Simulink® Toolstrip, on the Simulation tab, in thePrepare gallery, select Property Inspector.

Execution

Specify the discrete interval between sample time hits or specify another type of sample time, such as continuous (0) or inherited (-1). For more options, see Types of Sample Time (Simulink).

By default, the block inherits its sample time based on the context of the block within the model.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Extended Capabilities

Version History

Introduced in R2024b