indexing1dLayer
Description
A 1-D indexing layer extracts the data from the specified index of the time or spatial dimensions of the input data.
Creation
Description
creates a 1-D
indexing layer that extracts the data from the first index of the time or spatial
dimensions of the input data.layer
= indexing1dLayer
creates a 1-D
indexing layer object and sets the layer
= indexing1dLayer(index)Index
property.
sets the 1-D Indexing and layer
= indexing1dLayer(___,Name=Value
)Name
properties using one or more name-value arguments.
Properties
1-D Indexing
Index
— Index of data to extract
"first"
(default) | "last"
Index of data to extract, specified as one of these values:
"first"
— Extract data from the first index of the input."last"
— Extract data from the last index of the input.
If the layer has a padding mask input, then the layer ignores padding values when it extracts data.
HasPaddingMaskInput
— Flag indicating whether layer has mask input
0
(false
) (default) | 1
(true
)
Flag indicating whether the layer has an input that represents the padding mask,
specified as 0
(false
) or 1
(true
).
If the HasPaddingMaskInput
property is 0
(false
), then the layer has one input with the name
"in"
, which corresponds to the input data. In this case, the layer
treats all elements as data.
If the HasPaddingMaskInput
property is 1
(true
), then the layer has two inputs with the names
"in"
and "mask"
, which correspond to the input
data and the mask, respectively. In this case, the padding mask is an array of ones and
zeros. The layer uses and ignores elements of the input when the corresponding element in
the mask is one or zero, respectively.
Layer
Name
— Layer name
""
(default) | character vector | string scalar
NumInputs
— Number of inputs
1
| 2
This property is read-only.
Number of inputs to the layer, returned as 1
or
2
.
If the HasPaddingMaskInput
property is 0
(false
), then the layer has one input with the name
"in"
, which corresponds to the input data. In this case, the layer
treats all elements as data.
If the HasPaddingMaskInput
property is 1
(true
), then the layer has two inputs with the names
"in"
and "mask"
, which correspond to the input
data and the mask, respectively. In this case, the padding mask is an array of ones and
zeros. The layer uses and ignores elements of the input when the corresponding element in
the mask is one or zero, respectively.
Data Types: double
InputNames
— Input names
"in"
| ["in" "mask"]
This property is read-only.
Input names of the layer, returned as a cell array of character vectors.
If the HasPaddingMaskInput
property is 0
(false
), then the layer has one input with the name
"in"
, which corresponds to the input data. In this case, the layer
treats all elements as data.
If the HasPaddingMaskInput
property is 1
(true
), then the layer has two inputs with the names
"in"
and "mask"
, which correspond to the input
data and the mask, respectively. In this case, the padding mask is an array of ones and
zeros. The layer uses and ignores elements of the input when the corresponding element in
the mask is one or zero, respectively.
The Indexing1DLayer
object stores this property as a cell array of character
vectors.
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 Indexing Layer
Create a 1-D indexing layer.
layer = indexing1dLayer
layer = Indexing1DLayer with properties: Name: '' Index: "first" HasPaddingMaskInput: 0 Learnable Parameters No properties. State Parameters No properties. Use properties method to see a list of all properties.
Include a 1-D indexing layer in a layer graph.
numChannels = 1; embeddingOutputSize = 64; numWords = 128; maxSequenceLength = 100; maxPosition = maxSequenceLength+1; numHeads = 4; numKeyChannels = 4*embeddingOutputSize; net = dlnetwork; layers = [ sequenceInputLayer(numChannels) wordEmbeddingLayer(embeddingOutputSize,numWords,Name="word-emb") embeddingConcatenationLayer(Name="emb-cat") positionEmbeddingLayer(embeddingOutputSize,maxPosition,Name="pos-emb"); additionLayer(2,Name="add") selfAttentionLayer(numHeads,numKeyChannels,AttentionMask="causal") indexing1dLayer(Name="idx-first") fullyConnectedLayer(numWords) softmaxLayer]; net = addLayers(net,layers); net = connectLayers(net,"emb-cat","add/in2");
View the neural network architecture.
plot(net) axis off box off
Algorithms
1-D Indexing Layer
A 1-D indexing layer extracts the data from the specified index of the time or spatial dimensions of the input data.
For example:
For sequence data
X
represented by anumChannels
-by-numObservations
-by-numTimeSteps
array, wherenumChannels
,numObservations
, andnumTimeSteps
are the numbers of channels, observations, and time steps of the input, respectively, the output isX(:,:,idx)
, whereidx
is the index that corresponds to theIndex
property.For 1-D image data
X
represented by aheight
-by-numChannels
-by-numObservations
, whereheight
,numChannels
, andnumObservations
are the height, number of channels, and number of observations of the input images, respectively, the output isX(idx,:,:)
, whereidx
is the index that corresponds to theIndex
property.
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 describe 2-D image data that is 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, as having the format "SSCB"
(spatial, spatial, channel, batch).
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 Indexing1DLayer
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 |
---|---|
"SCB" (spatial, channel, batch) | "CB" (channel, batch) |
"CBT" (channel, batch, time) | "CB" (channel, batch) |
"SC" (spatial, channel) | "CU" (channel, unspecified) |
"SB" (spatial, batch) | "BU" (batch, unspecified) |
"SU" (spatial, unspecified) | "UU" (unspecified, unspecified) |
In dlnetwork
objects, Indexing1DLayer
objects also support
these input and output format combinations.
Input Format | Output Format |
---|---|
"CT" (channel, time) | "CU" (channel, unspecified) |
"BT" (batch, time) | "BU" (batch, unspecified) |
"TU" (time, unspecified) | "UU" (unspecified, unspecified) |
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Version History
Introduced in R2023bR2024a: Code generation support
Generate C or C++ code using MATLAB® Coder™ or generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
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