convolution1dLayer
Description
A 1-D convolutional layer applies sliding convolutional filters to 1-D input. The layer convolves the input by moving the filters along the input and computing the dot product of the weights and the input, then adding a bias term.
The dimension that the layer convolves over 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 convolves over the time dimension.
For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer convolves over 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 convolves over the spatial dimension.
Creation
Syntax
Description
creates a 1-D convolutional layer and sets the layer
= convolution1dLayer(filterSize
,numFilters
)FilterSize
and NumFilters
properties.
sets optional properties using one or more name-value arguments.layer
= convolution1dLayer(filterSize
,numFilters
,Name=Value
)
Input Arguments
filterSize
— Width of filters
positive integer
Width of the filters, specified as a positive integer.
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 layer output.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
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: convolution1dLayer(11,96,Padding=1)
creates a 1-D
convolutional layer with 96 filters of size 11, and specifies padding of size 1
on the left and right of the layer input.
Stride
— Step size for traversing input
1
(default) | positive integer
Step size for traversing the input, specified as a positive integer.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
DilationFactor
— Factor for dilated convolution
1
(default) | positive integer
Factor for dilated convolution (also known as atrous convolution), specified as a positive integer.
Use dilated convolutions to increase the receptive field (the area of the input that the layer can see) of the layer without increasing the number of parameters or computation.
The layer expands the filters by inserting zeros between each
filter element. The dilation factor determines the step size for
sampling the input, or equivalently, the upsampling factor of the
filter. It corresponds to an effective filter size of
(FilterSize – 1) .* DilationFactor + 1
. For
example, a 1-by-3 filter with a dilation factor of
2
is equivalent to a 1-by-5 filter with zeros
between the elements.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Padding
— Padding to apply to input
[0 0]
(default) |
"same"
|
"causal"
| nonnegative integer | vector of nonnegative integers
Padding to apply to the input, specified as one of the following:
"same"
— Apply padding such that the output size isceil(inputSize/stride)
, whereinputSize
is the length of the input. WhenStride
is1
, the output is the same size as the input."causal"
— Apply left padding to the input, equal to(FilterSize - 1) .* DilationFactor
. WhenStride
is1
, the output is the same size as the input.Nonnegative integer
sz
— Add padding of sizesz
to both ends of the input.Vector
[l r]
of nonnegative integers — Add padding of sizel
to the left andr
to the right of the input.
Example: Padding=[2 1]
adds padding of size 2 to
the left and size 1 to the right of the input.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| char
| string
PaddingValue
— Value to pad data
0
(default) | scalar |
"symmetric-include-edge"
|
"symmetric-exclude-edge"
|
"replicate"
Value to pad data, specified as one of the following:
PaddingValue | Description | Example |
---|---|---|
Scalar | Pad with the specified scalar value. |
|
"symmetric-include-edge" | Pad using mirrored values of the input, including the edge values. |
|
"symmetric-exclude-edge" | Pad using mirrored values of the input, excluding the edge values. |
|
"replicate" | Pad using repeated border elements of the input. |
|
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 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)
, wheresz
is the size of the biases.
The layer initializes the biases only 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-NumChannels
-by-numFilters
numeric array or []
.
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.
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 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
.
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
Properties
Convolution
FilterSize
— Width of filters
positive integer
This property is read-only.
Width of the filters, specified as a positive integer.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
NumFilters
— Number of filters
positive integer
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
Stride
— Step size for traversing input
1
(default) | positive integer
Step size for traversing the input, specified as a positive integer.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
DilationFactor
— Factor for dilated convolution
1
(default) | positive integer
Factor for dilated convolution (also known as atrous convolution), specified as a positive integer.
Use dilated convolutions to increase the receptive field (the area of the input that the layer can see) of the layer without increasing the number of parameters or computation.
The layer expands the filters by inserting zeros between each filter
element. The dilation factor determines the step size for sampling the
input, or equivalently, the upsampling factor of the filter. It
corresponds to an effective filter size of (FilterSize – 1) .*
DilationFactor + 1
. For example, a 1-by-3 filter with a
dilation factor of 2
is equivalent to a 1-by-5 filter
with zeros between the elements.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
PaddingSize
— Size of padding
[0 0]
(default) | vector of two nonnegative integers
Size of padding to apply to each side of the input, specified as a vector [l
r]
of two nonnegative integers, where l
is the padding
applied to the left and r
is the padding applied to the right.
When you create a layer, use the Padding
name-value argument to specify the padding size.
Data Types: double
PaddingMode
— Method to determine padding size
'manual'
(default) |
'same'
|
'causal'
This property is read-only.
Method to determine padding size, specified as one of the following:
'manual'
– Pad using the integer or vector specified byPadding
.'same'
– Apply padding such that the output size isceil(inputSize/Stride)
, whereinputSize
is the length of the input. WhenStride
is1
, the output is the same as the input.'causal'
– Apply causal padding. Pad the left of the input with padding size(FilterSize - 1) .* DilationFactor
.
To specify the layer padding, use the Padding
name-value argument.
Data Types: char
PaddingValue
— Value to pad data
0
(default) | scalar |
"symmetric-include-edge"
|
"symmetric-exclude-edge"
|
"replicate"
This property is read-only.
Value to pad data, specified as one of the following:
PaddingValue | Description | Example |
---|---|---|
Scalar | Pad with the specified scalar value. |
|
"symmetric-include-edge" | Pad using mirrored values of the input, including the edge values. |
|
"symmetric-exclude-edge" | Pad using mirrored values of the input, excluding the edge values. |
|
"replicate" | Pad using repeated border elements of the input. |
|
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| char
| string
NumChannels
— Number of input channels
"auto"
(default) | positive integer
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, 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
Parameters and Initialization
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 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)
, wheresz
is the size of the biases.
The layer initializes the biases only when the Bias
property is
empty.
The Convolution1DLayer
object stores this property as a character vector or a
function handle.
Data Types: char
| string
| function_handle
Weights
— Layer weights
[]
(default) | numeric array
Layer weights for the transposed convolution operation, specified as a
FilterSize
-by-NumChannels
-by-numFilters
numeric array or []
.
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
function uses 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.
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 the trainnet
function uses the Bias
property as the initial value. If
Bias
is empty, then software uses the initializer
specified by BiasInitializer
.
Data Types: single
| double
Learning Rate and Regularization
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
Layer
Name
— Layer name
""
(default) | character vector | string scalar
NumInputs
— Number of inputs
1
(default)
This property is read-only.
Number of inputs to the layer, returned as 1
. This layer accepts a
single input only.
Data Types: double
InputNames
— Input names
{'in'}
(default)
This property is read-only.
Input names, returned as {'in'}
. This layer accepts a single input
only.
Data Types: cell
NumOutputs
— Number of outputs
1
(default)
This property is read-only.
Number of outputs from the layer, returned as 1
. This layer has a
single output only.
Data Types: double
OutputNames
— Output names
{'out'}
(default)
This property is read-only.
Output names, returned as {'out'}
. This layer has a single output
only.
Data Types: cell
Examples
Create 1-D Convolutional Layer
Create a 1-D convolutional layer with 96 filters of width of 11.
layer = convolution1dLayer(11,96);
Include a 1-D convolutional layer in a Layer
array.
layers = [ sequenceInputLayer(3,MinLength=20) layer reluLayer globalMaxPooling1dLayer fullyConnectedLayer(10) softmaxLayer]
layers = 6x1 Layer array with layers: 1 '' Sequence Input Sequence input with 3 dimensions 2 '' 1-D Convolution 96 11 convolutions with stride 1 and padding [0 0] 3 '' ReLU ReLU 4 '' 1-D Global Max Pooling 1-D global max pooling 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax
Algorithms
1-D Convolutional Layer
A 1-D convolutional layer applies sliding convolutional filters to 1-D input. The layer convolves the input by moving the filters along the input and computing the dot product of the weights and the input, then adding a bias term.
The dimension that the layer convolves over 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 convolves over the time dimension.
For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer convolves over 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 convolves over 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 consist of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, you can represent vector sequence data as a 3-D array, in which the first
dimension corresponds to the channel dimension, the second dimension corresponds to the
batch dimension, and the third dimension corresponds to the time dimension. This
representation is in the format "CBT"
(channel, batch, time).
You can interact with these dlarray
objects in automatic differentiation
workflows, such as those for 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 Convolution1DLayer
objects and the
corresponding output format. If the software passes the output of the layer 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 according to the formats
in this table. The formats listed here are only a subset. The layer may support additional
formats such as formats with additional "S"
(spatial) or
"U"
(unspecified) dimensions.
Input Format | Output Format |
---|---|
|
|
|
|
|
|
In dlnetwork
objects, Convolution1DLayer
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 2015 IEEE International Conference on Computer Vision (ICCV), 1026–34. Santiago, Chile: IEEE, 2015. https://doi.org/10.1109/ICCV.2015.123
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
You can generate generic C/C++ code that does not depend on third-party libraries and deploy the generated code to hardware platforms.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
You can generate CUDA code that is independent of deep learning libraries and deploy the generated code to platforms that use NVIDIA® GPU processors.
Version History
Introduced in R2021b
See Also
trainnet
| trainingOptions
| dlnetwork
| sequenceInputLayer
| lstmLayer
| bilstmLayer
| gruLayer
| maxPooling1dLayer
| averagePooling1dLayer
| globalMaxPooling1dLayer
| globalAveragePooling1dLayer
| transposedConv1dLayer
| exportNetworkToSimulink
| Convolution 1D
Layer
Topics
- 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
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