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transposedConv3dLayer

Transposed 3-D convolution layer

A transposed 3-D convolution layer upsamples three-dimensional feature maps.

This layer is sometimes incorrectly known as a "deconvolution" or "deconv" layer. This layer is the transpose of convolution and does not perform deconvolution.

Syntax

layer = transposedConv3dLayer(filterSize,numFilters)
layer = transposedConv3dLayer(filterSize,numFilters,Name,Value)

Description

layer = transposedConv3dLayer(filterSize,numFilters) returns a transposed 3-D convolution layer and sets the FilterSize and NumFilters properties.

example

layer = transposedConv3dLayer(filterSize,numFilters,Name,Value) returns a transposed 3-D convolutional layer and specifies additional options using one or more name-value pair arguments.

Examples

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Create a transposed 3-D convolutional layer with 32 filters, each with a height, width, and depth of 11. Use a stride of 4 in the horizontal and vertical directions and 2 along the depth.

layer = convolution3dLayer(11,32,'Stride',[4 4 2])
layer = 
  Convolution3DLayer with properties:

              Name: ''

   Hyperparameters
        FilterSize: [11 11 11]
       NumChannels: 'auto'
        NumFilters: 32
            Stride: [4 4 2]
    DilationFactor: [1 1 1]
       PaddingMode: 'manual'
       PaddingSize: [2x3 double]

   Learnable Parameters
           Weights: []
              Bias: []

  Show all properties

Input Arguments

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Height, width, and depth of the filters, specified as a vector [h w d] of three positive integers, where h is the height, w is the width, and d is the depth. FilterSize defines the size of the local regions to which the neurons connect in the input.

If you set FilterSize using an input argument, then you can specify FilterSize as scalar to use the same value for all three dimensions.

Example: [5 5 5] specifies filters with a height, width, and depth of 5.

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.

Example: 96

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'Cropping',1

Transposed Convolution

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Step size for traversing the input in three dimensions, specified as a vector [a b c] of three positive integers, where a is the vertical step size, b is the horizontal step size, and c is the step size along the depth. When creating the layer, you can specify Stride as a scalar to use the same value for step sizes in all three directions.

Example: [2 3 1] specifies a vertical step size of 2, a horizontal step size of 3, and a step size along the depth of 1.

Output size reduction, specified as one of the following:

  • 'same' – Set the cropping so that the output size equals inputSize .* Stride, where inputSize is the height, width, and depth of the layer input. If you set the 'Cropping' option to 'same', then the software automatically sets the CroppingMode property of the layer to 'same'.

    The software trims an equal amount from the top and bottom, the left and right, and the front and back, if possible. If the vertical crop amount has an odd value, then the software trims an extra row from the bottom. If the horizontal crop amount has an odd value, then the software trims an extra column from the right. If the depth crop amount has an odd value, then the software trims an extra plane from the back.

  • A positive integer – Crop the specified amount of data from all the edges.

  • A vector of nonnegative integers [a b c] – Crop a from the top and bottom, crop b from the left and right, and crop c from the front and back.

  • a matrix of nonnegative integers [t l f; b r bk] of nonnegative integers — Crop t, l, f, b, r, bk from the top, left, front, bottom, right, and back of the input, respectively.

Example: [1 2 2]

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

This parameter must be equal to the number of channels of the input to this convolutional layer. For example, if the input is a color image, then the number of channels for the input must be 3. If the number of filters for the convolutional layer prior to the current layer is 16, then the number of channels for this layer must be 16.

Parameters and Initialization

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

  • 'glorot' – Initialize the weights with the Glorot initializer [1] (also known as Xavier initializer). The Glorot initializer independently samples from a uniform distribution with zero mean and variance 2/(numIn + numOut), where numIn = filterSize(1)*filterSize(2)*filterSize(3)*NumChannels, numOut = filterSize(1)*filterSize(2)*filterSize(3)*numFilters, and NumChannels is the number of input channels.

  • '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(1)*filterSize(2)*filterSize(3)*NumChannels and NumChannels is the number of input channels.

  • 'narrow-normal' – Initialize the weights by independently sampling from a normal distribution with zero mean and standard deviation 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 transposed 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 training 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(1)-by-FilterSize(2)-by-FilterSize(3)-by-numFilters-by-NumChannels array.

Data Types: single | double

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

The layer biases are learnable parameters. When training 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 1-by-1-by-1-by-numFilters array.

Data Types: single | double

Learn Rate and Regularization

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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 specified with the trainingOptions function.

Example: 2

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 specified with the trainingOptions function.

Example: 2

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.

Example: 2

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.

Example: 2

Layer

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Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

Output Arguments

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Transposed 3-D convolution layer, returned as a TransposedConvolution3dLayer object.

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, pp. 249-256. 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 IEEE international conference on computer vision, pp. 1026-1034. 2015.

Introduced in R2019a