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convolution1dLayer

1-D convolutional layer

    Description

    A 1-D convolutional layer applies sliding convolutional filters to 1-D input. The layer convolves the input by moving the filters along the input and computing the dot product of the weights and the input, then adding a bias term.

    The dimension that the layer convolves 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 convolves over the time dimension.

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

    Creation

    Description

    example

    layer = convolution1dLayer(filterSize,numFilters) creates a 1-D convolutional layer and sets the FilterSize and NumFilters properties.

    layer = convolution1dLayer(filterSize,numFilters,Name=Value) also sets the optional Stride, DilationFactor, NumChannels, Parameters and Initialization, Learning Rate and Regularization, and Name properties using one or more name-value arguments. To specify input padding, use the Padding name-value argument. For example, convolution1dLayer(11,96,Padding=1) creates a 1-D convolutional layer with 96 filters of size 11, and specifies padding of size 1 on the left and right of the layer input.

    Input Arguments

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    Name-Value Pair Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: convolution1dLayer(11,96,Padding=1) creates a 1-D convolutional layer with 96 filters of size 11, and specifies padding of size 1 on the left and right of the layer input.

    Padding to apply to the input, specified as one of the following:

    • "same" — Apply padding such that the output size is ceil(inputSize/stride), where inputSize is the length of the input. When Stride is 1, the output is the same size as the input.

    • "causal" — Apply left padding to the input, equal to (FilterSize - 1) .* DilationFactor. When Stride is 1, the output is the same size as the input.

    • Nonnegative integer sz — Add padding of size sz to both ends of the input.

    • Vector [l r] of nonnegative integers — Add padding of size l to the left and r to the right of the input.

    Example: Padding=[2 1] adds padding of size 2 to the left and size 1 to the right of the input.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    Properties

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    Convolution

    This property is read-only.

    Width of the filters, specified as a positive integer.

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

    This property is read-only.

    Number of filters, specified as a positive integer. This number corresponds to the number of neurons in the convolutional layer that connect to the same region in the input. This parameter determines the number of channels (feature maps) in the output of the convolutional layer.

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

    Step size for traversing the input, specified as a positive integer.

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

    Factor for dilated convolution (also known as atrous convolution), specified as a positive integer.

    Use dilated convolutions to increase the receptive field (the area of the input that the layer can see) of the layer without increasing the number of parameters or computation.

    The layer expands the filters by inserting zeros between each filter element. The dilation factor determines the step size for sampling the input, or equivalently, the upsampling factor of the filter. It corresponds to an effective filter size of (FilterSize – 1) .* DilationFactor + 1. For example, a 1-by-3 filter with a dilation factor of 2 is equivalent to a 1-by-5 filter with zeros between the elements.

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

    Size of padding to apply to each side of the input, specified as a vector [l r] of two nonnegative integers, where l is the padding applied to the left and r is the padding applied to the right.

    When you create a layer, use the Padding name-value argument to specify the padding size.

    Data Types: double

    This property is read-only.

    Method to determine padding size, specified as one of the following:

    • 'manual' – Pad using the integer or vector specified by Padding.

    • 'same' – Apply padding such that the output size is ceil(inputSize/Stride), where inputSize is the length of the input. When Stride is 1, the output is the same as the input.

    • 'causal' – Apply causal padding. Pad the left of the input with padding size (FilterSize - 1) .* DilationFactor.

    To specify the layer padding, use the Padding name-value argument.

    Data Types: char

    This property is read-only.

    Value to pad data, specified as one of the following:

    PaddingValueDescriptionExample
    ScalarPad with the specified scalar value.

    [314][0031400]

    'symmetric-include-edge'Pad using mirrored values of the input, including the edge values.

    [313][1331441]

    'symmetric-exclude-edge'Pad using mirrored values of the input, excluding the edge values.

    [314][4131413]

    'replicate'Pad using repeated border elements of the input.

    [313][3331444]

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    This property is read-only.

    Number of channels for each filter, specified as 'auto' or a positive integer.

    If NumChannels is 'auto', then the software automatically determines the number of channels at training time.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    Parameters and Initialization

    Function to initialize the weights, specified as one of the following:

    • 'glorot' – Initialize the weights with the Glorot initializer [1] (also known as the Xavier initializer). The Glorot initializer independently samples from a uniform distribution with a mean of zero and a variance of 2/(numIn + numOut), where numIn = FilterSize*NumChannels and numOut = FilterSize*NumFilters.

    • 'he' – Initialize the weights with the He initializer [2]. The He initializer samples from a normal distribution with zero mean and variance 2/numIn, where numIn = FilterSize*NumChannels.

    • 'narrow-normal' – Initialize the weights by independently sampling from a normal distribution with a mean of zero and a standard deviation of 0.01.

    • 'zeros' – Initialize the weights with zeros.

    • 'ones' – Initialize the weights with ones.

    • Function handle – Initialize the weights with a custom function. If you specify a function handle, then the function must be of the form weights = func(sz), where sz is the size of the weights. For an example, see Specify Custom Weight Initialization Function.

    The layer only initializes the weights when the Weights property is empty.

    Data Types: char | string | function_handle

    Function to initialize the bias, specified as one of the following:

    • 'zeros' – Initialize the bias with zeros.

    • 'ones' – Initialize the bias with ones.

    • 'narrow-normal' – Initialize the bias by independently sampling from a normal distribution with zero mean and standard deviation 0.01.

    • Function handle – Initialize the bias with a custom function. If you specify a function handle, then the function must be of the form bias = func(sz), where sz is the size of the bias.

    The layer only initializes the bias when the Bias property is empty.

    Data Types: char | string | function_handle

    Layer weights for the convolutional layer, specified as a numeric array.

    The layer weights are learnable parameters. You can specify the initial value for the weights directly using the Weights property of the layer. When you train a network, if the Weights property of the layer is nonempty, then trainNetwork uses the Weights property as the initial value. If the Weights property is empty, then trainNetwork uses the initializer specified by the WeightsInitializer property of the layer.

    At training time, Weights is a FilterSize-by-NumChannels-by-NumFilters array.

    Data Types: single | double

    Layer biases for the convolutional layer, specified as a numeric array.

    The layer biases are learnable parameters. When you train a network, if Bias is nonempty, then trainNetwork uses the Bias property as the initial value. If Bias is empty, then trainNetwork uses the initializer specified by BiasInitializer.

    At training time, Bias is a 1-by-NumFilters array.

    Data Types: single | double

    Learning Rate and Regularization

    Learning rate factor for the weights, specified as a nonnegative scalar.

    The software multiplies this factor by the global learning rate to determine the learning rate for the weights in this layer. For example, if WeightLearnRateFactor is 2, then the learning rate for the weights in this layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions function.

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

    Learning rate factor for the biases, specified as a nonnegative scalar.

    The software multiplies this factor by the global learning rate to determine the learning rate for the biases in this layer. For example, if BiasLearnRateFactor is 2, then the learning rate for the biases in the layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions function.

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

    L2 regularization factor for the weights, specified as a nonnegative scalar.

    The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization for the weights in this layer. For example, if WeightL2Factor is 2, then the L2 regularization for the weights in this layer is twice the global L2 regularization factor. You can specify the global L2 regularization factor using the trainingOptions function.

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

    L2 regularization factor for the biases, specified as a nonnegative scalar.

    The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization for the biases in this layer. For example, if BiasL2Factor is 2, then the L2 regularization for the biases in this layer is twice the global L2 regularization factor. You can specify the global L2 regularization factor using the trainingOptions function.

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

    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 1-D convolutional layer with 96 filters of width of 11.

    layer = convolution1dLayer(11,96)
    layer = 
      Convolution1DLayer with properties:
    
                  Name: ''
    
       Hyperparameters
            FilterSize: 11
           NumChannels: 'auto'
            NumFilters: 96
                Stride: 1
        DilationFactor: 1
           PaddingMode: 'manual'
           PaddingSize: [0 0]
          PaddingValue: 0
    
       Learnable Parameters
               Weights: []
                  Bias: []
    
      Show all properties
    
    

    Include a 1-D convolutional 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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    References

    [1] Glorot, Xavier, and Yoshua Bengio. "Understanding the Difficulty of Training Deep Feedforward Neural Networks." In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–356. Sardinia, Italy: AISTATS, 2010.

    [2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In Proceedings of the 2015 IEEE International Conference on Computer Vision, 1026–1034. Washington, DC: IEEE Computer Vision Society, 2015.

    Introduced in R2021b