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.
Create a transposed convolution 3-D output layer using transposedConv3dLayer.
FilterSize — Height, width, and depth of filtersHeight, 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.
NumFilters — Number of filtersNumber 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
Stride — Step size for traversing input[1 1 1] (default) | vector of three positive integersStep 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.
CroppingMode — Method to determine cropping size'manual' (default) | 'same'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.
CroppingSize — Output size reduction[0 0 0;0 0 0] (default) | matrix of nonnegative integersOutput 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]
NumChannels — Number of channels for each filter'auto' (default) | integerNumber of channels for each filter, specified 'auto' or an
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.
WeightsInitializer — Function to initialize weights'glorot' (default) | 'he' | 'narrow-normal' | 'zeros' | 'ones' | function handleFunction 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
BiasInitializer — Function to initialize bias'zeros' (default) | 'narrow-normal' | 'ones' | function handleFunction 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
Weights — Layer weights[] (default) | numeric arrayLayer 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
Bias — Layer biases[] (default) | numeric arrayLayer 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 is a
1-by-1-by-1-by-NumFilters array.
Data Types: single | double
WeightLearnRateFactor — Learning rate factor for weightsLearning 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
BiasLearnRateFactor — Learning rate factor for biasesLearning 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
WeightL2Factor — L2 regularization factor for weightsL2 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
BiasL2Factor — L2 regularization factor for biasesL2 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
Name — Layer name'' (default) | character vector | string scalar
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
NumInputs — Number of inputsNumber of inputs of the layer. This layer accepts a single input only.
Data Types: double
InputNames — Input names{'in'} (default)Input names of the layer. This layer accepts a single input only.
Data Types: cell
NumOutputs — Number of outputsNumber of outputs of the layer. This layer has a single output only.
Data Types: double
OutputNames — Output names{'out'} (default)Output names of the layer. This layer has a single output only.
Data Types: cell
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
[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.
averagePooling3dLayer | convolution3dLayer | maxPooling3dLayer | transposedConv2dLayer | transposedConv3dLayer
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