Spatial Dropout Layer

Spatial dropout layer

Since R2026a

expand all in page
  • Spatial Dropout Layer block

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

Description

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

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

Ports

Input

expand all

Input data.

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

Output

expand all

Output data that is equal to the input data.

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

Parameters

expand all

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

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.

Parameter: SampleTime
Data Types: char
Values: '-1' (default) | scalar

Extended Capabilities

expand all

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

Version History

Introduced in R2026a

See Also

|

Topics