networkDataLayout - Deep learning network data layout for learnable parameter

        initialization - MATLAB ([original](https://in.mathworks.com/help/deeplearning/ref/networkdatalayout.html)) ([raw](?raw))

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Deep learning network data layout for learnable parameter initialization

Since R2022b

Description

Network data layout objects represent the data size anddlarray format information of input data for parameter initialization.

You can use networkDataLayout objects to initializedlnetwork objects and custom layer learnable parameters using its size and format information as an alternative to using example data.

Creation

Syntax

Description

`layout` = networkDataLayout(sz) creates an unformatted network data layout object and sets theSize property.

example

`layout` = networkDataLayout(sz,fmt) creates a formatted network data layout object and sets theSize and Format properties.

example

Properties

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Size, specified as a row vector of two or more nonnegative integers orNaN values, where sz(i) denotes the size of dimension i and NaN values correspond to unknown dimension sizes.

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

Data format, specified as a string scalar or a character vector. Each character in the string must be one of the following dimension labels:

• "S" — Spatial
• "C" — Channel
• "B" — Batch
• "T" — Time
• "U" — Unspecified

You can specify multiple dimensions labeled "S" or"U". You can use the labels "C","B", and "T" at most once.

Data Types: string | char

Object Functions

Examples

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Create an unformatted networkDataLayout object representing data of size [28 28 1].

layout = networkDataLayout([28 28 1])

layout = networkDataLayout with properties:

  Size: [28 28 1]
Format: ''

Create a formatted networkDataLayout object representing a batch of 2-D RGB images of size [227 227], where the batch size is unknown.

layout = networkDataLayout([227 227 3 NaN],"SSCB")

layout = networkDataLayout with properties:

  Size: [227 227 3 NaN]
Format: 'SSCB'

Create an uninitialized dlnetwork object that has two unconnected inputs.

layers = [ convolution2dLayer(5,16,Name="conv") batchNormalizationLayer reluLayer fullyConnectedLayer(50) flattenLayer concatenationLayer(1,2,Name="cat") fullyConnectedLayer(10) softmaxLayer];

net = dlnetwork(layers,Initialize=false);

View the input names of the network.

ans = 1×2 cell {'conv'} {'cat/in2'}

Create network data layout objects that represent input data for the inputs. For the first input, specify a batch of 28-by-28 grayscale images. For the second input specify a batch of single-channel feature data.

layout1 = networkDataLayout([28 28 1 NaN],"SSCB"); layout2 = networkDataLayout([1 NaN],"CB");

Initialize the network using the network data layout objects.

net = initialize(net,layout1,layout2)

net = dlnetwork with properties:

     Layers: [8×1 nnet.cnn.layer.Layer]
Connections: [7×2 table]
 Learnables: [8×3 table]
      State: [2×3 table]
 InputNames: {'conv'  'cat/in2'}
OutputNames: {'softmax'}
Initialized: 1

View summary with summary.

Create a formatted network data layout object representing 2-D image sequences. Specify the format "SSCBT" (spatial, spatial, channel, batch, time).

layout = networkDataLayout([227 227 3 NaN 100],"SSCBT");

Find the dimensions with the label "S".

dim = finddim(layout,"S")

Version History

Introduced in R2022b