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globalMaxPooling1dLayer

1-D global max pooling layer

    Description

    A 1-D global max pooling layer performs downsampling by outputting the maximum of the time or spatial dimensions of the input.

    The dimension that the layer pools over depends on the layer input:

    • For time series and vector sequence input (data with three dimensions corresponding to the channels, observations, and time steps), the layer pools over the time dimension.

    • For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer pools over the spatial dimension.

    • For 1-D image sequence input (data with four dimensions corresponding to the spatial pixels, channels, observations, and time steps), the layer pools over the spatial dimension.

    Creation

    Description

    example

    layer = globalMaxPooling1dLayer creates a 1-D global max pooling layer.

    layer = globalMaxPooling1dLayer(Name=name) sets the optional Name property.

    Properties

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    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 1-D global max pooling layer.

    layer = globalMaxPooling1dLayer
    layer = 
      GlobalMaxPooling1DLayer with properties:
    
        Name: ''
    
    

    Include a 1-D global max pooling layer in a layer array.

    layers = [
        sequenceInputLayer(12)
        convolution1dLayer(11,96)
        reluLayer
        globalMaxPooling1dLayer
        fullyConnectedLayer(10)
        softmaxLayer
        classificationLayer]
    layers = 
      7x1 Layer array with layers:
    
         1   ''   Sequence Input           Sequence input with 12 dimensions
         2   ''   Convolution              96 11 convolutions with stride 1 and padding [0  0]
         3   ''   ReLU                     ReLU
         4   ''   1-D Global Max Pooling   1-D global max pooling
         5   ''   Fully Connected          10 fully connected layer
         6   ''   Softmax                  softmax
         7   ''   Classification Output    crossentropyex
    

    Algorithms

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    Introduced in R2021b