Dropout Layer

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

In — Input matrix

scalar | vector | matrix

Input data.

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

Output

Out — Output matrix

scalar | vector | matrix

Output data that is equal to the input data.

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

Parameters

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

Execution

Sample time — Option to specify sample time

-1 (default) | scalar

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 upon the context of the block within the model.

Programmatic Use

Block Parameter: SampleTime

Type: character vector

Values: scalar

Default: '-1'

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Version History

Introduced in R2024b