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dropoutLayer

Dropout layer

Description

A dropout layer randomly sets input elements to zero with a given probability.

Creation

Description

layer = dropoutLayer creates a dropout layer.

layer = dropoutLayer(probability) creates a dropout layer and sets the Probability property.

example

layer = dropoutLayer(___,'Name',Name) sets the optional Name property using a name-value pair and any of the arguments in the previous syntaxes. For example, dropoutLayer(0.4,'Name','drop1') creates a dropout layer with dropout probability 0.4 and name 'drop1'. Enclose the property name in single quotes.

Properties

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Dropout

Probability for dropping out input elements, specified as a nonnegative number less than 1.

At training time, the layer randomly sets input elements to zero given by the dropout mask rand(size(X))<Probability, where X is the layer input and then scales the remaining elements by 1/(1-Probability). This operation effectively changes the underlying network architecture between iterations and helps prevent the network from overfitting [1], [2]. A higher number results in more elements being dropped during training. At prediction time, the output of the layer is equal to its input.

For image input, the layer applies a different mask for each channel of each image. For sequence input, the layer applies a different dropout mask for each time step of each sequence.

Example: 0.4

Layer

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork, assembleNetwork, layerGraph, and dlnetwork functions automatically assign names to layers with Name set to ''.

Data Types: char | string

This property is read-only.

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

This property is read-only.

Input names of the layer. This layer accepts a single input only.

Data Types: cell

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types: double

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: cell

Examples

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Create a dropout layer with name 'drop1'.

layer = dropoutLayer('Name','drop1')
layer = 
  DropoutLayer with properties:

           Name: 'drop1'

   Hyperparameters
    Probability: 0.5000

Include a dropout layer in a Layer array.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    dropoutLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer]
layers = 
  7x1 Layer array with layers:

     1   ''   Image Input             28x28x1 images with 'zerocenter' normalization
     2   ''   Convolution             20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   Dropout                 50% dropout
     5   ''   Fully Connected         10 fully connected layer
     6   ''   Softmax                 softmax
     7   ''   Classification Output   crossentropyex

More About

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References

[1] Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov. "Dropout: A Simple Way to Prevent Neural Networks from Overfitting." Journal of Machine Learning Research. Vol. 15, pp. 1929-1958, 2014.

[2] Krizhevsky, A., I. Sutskever, and G. E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks." Advances in Neural Information Processing Systems. Vol. 25, 2012.

Extended Capabilities

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

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Introduced in R2016a