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functionToLayerGraph

Convert deep learning model function to a layer graph

Description

example

lgraph = functionToLayerGraph(fun,x) returns a layer graph based on the deep learning array function fun. functionToLayerGraph converts only those operations in fun that operate on dlarray objects among the inputs in x. To include extra parameters or data in fun, see the topic Parameterizing Functions or the example Create Layer Graph from Function.

functionToLayerGraph evaluates fun(x) and traces the execution to derive an equivalent layer graph, to the extent possible. The steps in fun(x) that functionToLayerGraph can trace are both based on dlarray arguments and are supported calls for dlarray. See List of Functions with dlarray Support. For unsupported functions, functionToLayerGraph creates a PlaceholderLayer.

lgraph = functionToLayerGraph(fun,x,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments in the previous syntax.

Examples

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The simplemodel function at the end of this example creates fully connected outputs followed by a softmax operation. To create a layer graph from this function based on dlarray data, create input arrays as dlarray objects, and create a function handle to the simplemodel function including the data.

rng default % For reproducibility
dlX1 = dlarray(rand(10),'CB');
dlX2 = dlarray(zeros(10,1),'CB');
fun = @(x)simplemodel(x,dlX1,dlX2);

Call functionToLayerGraph using a dlarray for the input data dlX.

dlX = dlarray(ones(10,1),'CB');
lgraph = functionToLayerGraph(fun,dlX)
lgraph = 
  LayerGraph with properties:

         Layers: [2x1 nnet.cnn.layer.Layer]
    Connections: [1x2 table]
     InputNames: {1x0 cell}
    OutputNames: {1x0 cell}

Examine the resulting layers in lgraph.

disp(lgraph.Layers)
  2x1 Layer array with layers:

     1   'fc_1'   Fully Connected   10 fully connected layer
     2   'sm_1'   Softmax           softmax
function y = simplemodel(x,w,b)
y = fullyconnect(x,w,b);
y = softmax(y);
end

Input Arguments

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Function to convert, specified as a function handle.

Example: @relu

Data Types: function_handle

Data for the function, specified as any data type. Only dlarray data is traced and converted to a layer graph.

Example: dlarray(zeros(12*50,23))

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | struct | table | cell | function_handle | categorical | datetime | duration | calendarDuration | fi

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'GenerateLayer','placeholder-layer'

Type of layer to generate for unsupported operations in fun, specified as 'custom-layer' or 'placeholder-layer'.

When an operation in fun does not correspond to a layer in Deep Learning Toolbox™, the software generates a layer to represent that functionality. The 'GenerateLayer' option specifies the type of layer as follows.

Example: 'GenerateLayer','placeholder-layer'

Prefix for generate custom layers, specified as a char vector.

This option applies only when the 'GenerateLayer' option is 'custom-layer'. The name of each generated custom layer starts with the specified prefix.

Example: 'CustomLayerPrefix','myGeneratedLayer'

Output Arguments

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Layer graph, returned as a LayerGraph object.

Introduced in R2019b