transposedConv1dLayer
Syntax
Description
A transposed 1-D convolution layer upsamples one-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.
returns a 1-D transposed convolution layer and sets the layer
= transposedConv1dLayer(filterSize
,numFilters
)FilterSize
and
NumFilters
properties.
returns a 1-D transposed convolutional layer and specifies additional options using one or
more name-value arguments.layer
= transposedConv1dLayer(filterSize
,numFilters
,Name=Value
)
Examples
Create 1-D Transposed Convolutional Layer
Create a 1-D transposed convolutional layer with 96 filters of length 11 and a stride of 4.
layer = transposedConv1dLayer(11,96,Stride=4)
layer = TransposedConvolution1DLayer with properties: Name: '' Hyperparameters FilterSize: 11 NumChannels: 'auto' NumFilters: 96 Stride: 4 CroppingMode: 'manual' CroppingSize: [0 0] Learnable Parameters Weights: [] Bias: [] Show all properties
Input Arguments
filterSize
— Length of filters
positive integer
Length of the filters, specified as a positive integer. The filter size defines the size of the local regions to which the neurons connect in the input.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
numFilters
— Number of filters
positive integer
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.
Example: transposedConv1dLayer(11,96,Stride=4)
creates a 1-D
transposed convolutional layer with 96 filters of length 11 and a stride of
4.
Stride
— Upsampling factor
1
(default) | positive integer
Upsampling factor of the input, specified as a positive integer that corresponds to the horizontal stride.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Cropping
— Output size reduction
0
(default) | "same"
| nonnegative integer | vector of two nonnegative integers
Output size reduction, specified as one of the following:
"same"
— Set the cropping so that the output size equalsinputSize.*Stride
, whereinputSize
is the length of the layer input. IfCropping
is"same"
, then the software automatically sets theCroppingMode
property of the layer to'same'
.The software trims an equal amount from the left and right, when possible. If the horizontal crop amount has an odd value, then the software trims an extra column from the right.
A positive integer — Crop the specified amount of data from the left and right edges.
A vector of nonnegative integers
[l r]
— Cropl
andr
from the left and right, respectively.
If you set the Cropping
option to a numeric
value, then the software automatically sets the CroppingMode
property of the layer to 'manual'
.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| char
| string
NumChannels
— Number of input channels
"auto"
(default) | positive integer
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, thenNumChannels
must be 3. If the input is the output of a convolutional layer with 16 filters, thenNumChannels
must be 16.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| char
| string
WeightsInitializer
— Function to initialize weights
'glorot'
(default) | 'he'
| 'narrow-normal'
| 'zeros'
| 'ones'
| function handle
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 of2/(numIn + numOut)
, wherenumIn = FilterSize*NumChannels
andnumOut = FilterSize*NumFilters
.'he'
– Initialize the weights with the He initializer [2]. The He initializer samples from a normal distribution with a mean of zero and a variance of2/numIn
, wherenumIn = 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)
, wheresz
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 biases
'zeros'
(default) | 'narrow-normal'
| 'ones'
| function handle
Function to initialize the biases, specified as one of the following:
'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 be of the form
bias = func(sz)
, wheresz
is the size of the biases.
The layer only initializes the biases when the Bias
property is
empty.
Data Types: char
| string
| function_handle
Weights
— Layer weights
[]
(default) | numeric array
Layer weights for the transposed convolution operation, specified as a
FilterSize
-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
Bias
— Layer biases
[]
(default) | numeric array
Layer biases for the transposed convolutional operation, specified as a
1-by-NumFilters
numeric array or []
.
The layer biases are learnable parameters. When you train a
neural 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
WeightLearnRateFactor
— Learning rate factor for weights
1
(default) | nonnegative scalar
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
BiasLearnRateFactor
— Learning rate factor for biases
1
(default) | nonnegative scalar
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
WeightL2Factor
— L2 regularization factor for weights
1 (default) | nonnegative scalar
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
BiasL2Factor
— L2 regularization factor for biases
0
(default) | nonnegative scalar
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
Name
— Layer name
''
(default) | character vector | string scalar
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 the name ''
.
Data Types: char
| string
Output Arguments
layer
— Transposed 1-D convolution layer
TransposedConvolution1DLayer
object
Transposed 1-D convolution layer, returned as a TransposedConvolution1DLayer
object.
Algorithms
1-D Transposed Convolutional Layer
A transposed 1-D convolution layer upsamples one-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 for the convolution matrix C and bias vector 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 , where C and B denote the convolution matrix and bias vector for standard convolution derived from the layer weights and biases, respectively. This operation is equivalent to the backward function of a standard convolution layer.
A 1-D transposed convolution layer upsamples a single dimension only. The dimension that the layer upsamples 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 upsamples the time dimension.
For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer upsamples 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 upsamples the spatial dimension.
Layer Input and Output Formats
Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray
objects.
The format of a dlarray
object is a string of characters, in which each
character describes the corresponding dimension of the data. The formats consists of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, 2-D image data represented as a 4-D array, where the first two dimensions
correspond to the spatial dimensions of the images, the third dimension corresponds to the
channels of the images, and the fourth dimension corresponds to the batch dimension, can be
described as having the format "SSCB"
(spatial, spatial, channel,
batch).
You can interact with these dlarray
objects in automatic differentiation
workflows such as developing a custom layer, using a functionLayer
object, or using the forward
and predict
functions with
dlnetwork
objects.
This table shows the supported input formats of TransposedConvolution1DLayer
objects and the corresponding output format. If the output of the layer is passed to a
custom layer that does not inherit from the nnet.layer.Formattable
class,
or a FunctionLayer
object with the Formattable
property
set to 0
(false), then the layer receives an unformatted
dlarray
object with dimensions ordered corresponding to the formats in
this table.
Input Format | Output Format |
---|---|
|
|
|
|
|
|
In dlnetwork
objects, TransposedConvolution1DLayer
objects
also support these input and output format combinations.
Input Format | Output Format |
---|---|
|
|
|
|
|
|
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 Proceedings of the 2015 IEEE International Conference on Computer Vision, 1026–1034. Washington, DC: IEEE Computer Vision Society, 2015. https://doi.org/10.1109/ICCV.2015.123
Version History
Introduced in R2022a
See Also
trainingOptions
| trainNetwork
| sequenceInputLayer
| lstmLayer
| bilstmLayer
| gruLayer
| maxPooling1dLayer
| averagePooling1dLayer
| globalMaxPooling1dLayer
| globalAveragePooling1dLayer
| convolution1dLayer
Topics
- Time Series Anomaly Detection Using Deep Learning
- Sequence Classification Using 1-D Convolutions
- Sequence-to-Sequence Classification Using 1-D Convolutions
- Sequence Classification Using Deep Learning
- Sequence-to-Sequence Classification Using Deep Learning
- Sequence-to-Sequence Regression Using Deep Learning
- Time Series Forecasting Using Deep Learning
- Long Short-Term Memory Neural Networks
- List of Deep Learning Layers
- Deep Learning Tips and Tricks
Ouvrir l'exemple
Vous possédez une version modifiée de cet exemple. Souhaitez-vous ouvrir cet exemple avec vos modifications ?
Commande MATLAB
Vous avez cliqué sur un lien qui correspond à cette commande MATLAB :
Pour exécuter la commande, saisissez-la dans la fenêtre de commande de MATLAB. Les navigateurs web ne supportent pas les commandes MATLAB.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)