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transposedConv3dLayer

Transposed 3-D convolution layer

Description

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 performs the transpose of convolution and does not perform deconvolution.

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

example

layer = transposedConv3dLayer(filterSize,numFilters,Name,Value) returns a 3-D transposed 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 = transposedConv3dLayer(11,32,'Stride',[4 4 2])
layer = 
  TransposedConvolution3DLayer with properties:

            Name: ''

   Hyperparameters
      FilterSize: [11 11 11]
     NumChannels: 'auto'
      NumFilters: 32
          Stride: [4 4 2]
    CroppingMode: 'manual'
    CroppingSize: [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 positive integer or a vector of three positive integers [h w d], where h is the height, w is the width, and d is the depth. The filter size defines the size of the local regions to which the neurons connect in the input.

If filterSize is a scalar, then the software uses the same value for all three dimensions.

Example: [5 6 7] specifies filters with a height, width, and depth of 5, 6, and 7 respectively.

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

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

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

Name-Value 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.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: transposedConv3dLayer(11,96,'Stride',4) creates a 3-D transposed convolutional layer with 96 filters of size 11 and a stride of 4.

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.

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

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.

If you set the Cropping option to a numeric value, then the software automatically sets the CroppingMode property of the layer to 'manual'.

Example: [1 2 2]

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

Number of input channels, specified as one of the following:

  • "auto" — Automatically determine the number of input channels at training time.

  • Positive integer — Configure the layer for the specified number of input channels. NumChannels and the number of channels in the layer input data must match. For example, if the input is an RGB image, then NumChannels must be 3. If the input is the output of a convolutional layer with 16 filters, then NumChannels must be 16.

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

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 and numOut = FilterSize(1)*FilterSize(2)*FilterSize(3)*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(1)*FilterSize(2)*FilterSize(3)*NumChannels.

  • '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 a mean of zero and a standard deviation of 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 convolution operation, specified as a FilterSize(1)-by-FilterSize(2)-by-FilterSize(3)-by-numFilters-by-NumChannels numeric array or [].

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.

Data Types: single | double

Layer biases for the transposed convolutional operation, specified as a 1-by-1-by-1-by-numFilters numeric array or [].

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.

Data Types: single | double

Learning 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 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

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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 ''.

Data Types: char | string

Output Arguments

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

Algorithms

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3-D Transposed Convolutional Layer

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

The standard convolution operation downsamples the input by applying sliding convolutional filters to the input. By flattening the input and output, you can express the convolution operation as Y=CX+B for the convolution matrix C and bias B that can be derived from the layer weights and biases.

Similarly, the transposed convolution operation upsamples the input by applying sliding convolutional filters to the input. To upsample the input instead of downsampling using sliding filters, the layer zero-pads each edge of the input with padding that has the size of the corresponding filter edge size minus 1.

By flattening the input and output, the transposed convolution operation is equivalent to Y=CX+B, where C and B denote the convolution and bias matrices for standard convolution derived from the layer weights and biases, respectively. This operation is equivalent to the backward function of a standard convolution layer.

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.

Version History

Introduced in R2019a