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TransposedConvolution3dLayer

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 is the transpose of convolution and does not perform deconvolution.

Creation

Create a transposed convolution 3-D layer using transposedConv3dLayer.

Properties

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Transposed Convolution

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.

When creating the layer, you can specify FilterSize as a scalar to use the same value for the height, width, and depth.

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

This property is read-only.

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 layer output.

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

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

Method to determine cropping size, specified as 'manual' or 'same'.

The software automatically sets the value of CroppingMode based on the 'Cropping' value you specify when creating the layer.

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

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

To specify the cropping size, use the 'Cropping' option of transposedConv3dLayer.

Output size reduction, specified as a matrix of nonnegative integers [t l f; b r bk], t, l, f, b, r, bk are the amounts to crop from the top, left, front, bottom, right, and back of the input, respectively.

To specify the cropping size manually, use the 'Cropping' option of transposedConv2dLayer.

Example: [0 1 0 1 0 1]

This property is read-only.

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

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 biases, specified as one of these values:

  • "zeros" — Initialize the biases with zeros.

  • "ones" — Initialize the biases with ones.

  • "narrow-normal" — Initialize the biases by independently sampling from a normal distribution with a mean of zero and a standard deviation of 0.01.

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

The layer initializes the biases only 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 of 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 the trainnet and trainNetwork functions use the Weights property as the initial value. If the Weights property is empty, then the software 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 you train a neural network, if Bias is nonempty, then the trainnet and trainNetwork functions use the Bias property as the initial value. If Bias is empty, then software uses the initializer specified by BiasInitializer.

At training time, Bias is a 1-by-1-by-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. The software determines the global L2 regularization factor based on the settings you specify 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 trainnet and dlnetwork functions automatically assign names to layers with the name "".

The TransposedConvolution3dLayer object stores this property as a character vector.

Data Types: char | string

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

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: []

Use properties method to see a list of all properties.

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. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf

[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In 2015 IEEE International Conference on Computer Vision (ICCV), 1026–34. Santiago, Chile: IEEE, 2015. https://doi.org/10.1109/ICCV.2015.123

Version History

Introduced in R2019a