dlarray Limitations for Code Generation - MATLAB & Simulink (original) (raw)

Recommended Usage

For code generation, use the dlarray (Deep Learning Toolbox) function to create deep learning arrays. For example, suppose you have a pretraineddlnetwork (Deep Learning Toolbox) network object in themynet.mat MAT-file. To predict the responses for this network, create an entry-point function in MATLAB® as shown in this code.

function a = foo(in) dlIn = dlarray(in, 'SSC');

persistent dlnet; if isempty(dlnet) dlnet = coder.loadDeepLearningNetwork('mynet.mat'); end

dlA = predict(dlnet, dlIn);

a = extractdata(dlA);

end

Using Variable-Size dlarray

You can generate code for MATLAB code that uses variable-size dlarray objects.

For example, define this MATLAB design file:

function out = fooAdd(in1,in2) %#codegen dlIn1_1 = dlarray(in1); dlIn1_2 = dlarray(in2); out = dlIn1_1 + dlIn1_2; end

Specify the two inputs in1 and in2 to be unbounded two-dimensional arrays of single type. Create the appropriate code configuration object cfg to generate generic C MEX code for fooAdd. Generate MEX code and run the generated MEX.

t_in1 = coder.typeof(single(1),[inf inf],[1 1]); t_in2 = coder.typeof(single(1),[inf inf],[1 1]);

codegen fooAdd -args {t_in1,t_in2} -report

out = fooAdd_mex(single(eye(4,4)),single(ones(4,1)));

When generating code for variable-size dlarray objects, adhere to these restrictions:

• The U dimension of a dlarray object must be of fixed size.
• If the dlarray data format fmt contains only one character, the corresponding data array X can have only one variable-size dimension. All other dimensions of X must be singleton.
• For operations between a dlarray object and a numeric array that might implicitly expand either operands, do not combine a fixed sizeU dimension of the dlarray object with a variable-size dimension of the numeric array.
• For unary operations such as max, min, and mean on a variable-sizedlarray object, specify the intended working dimension explicitly as a constant value. See Automatic dimension restriction.

Limitations

For deep learning arrays, code generation has the following limitations:

• The data format argument of the dlarray object must be a compile-time constant. For example,
  function out = foo()
  dlA = dlarray(ones(5,4),'SSC'); %fmt 'SSC' is constant
  .
  .
  .
  end
• The code generation report does not display the size of thedlarray object. The size is always displayed as1x1.
  
• In MATLAB, dlarray enforces the order of labels'SCBTU'. This enforcement eliminates ambiguous semantics in operations, which implicitly match labels between inputs. This behavior is mimicked during MEX code generation. However, for standalone code generation such as static, dynamic libraries, or executables, the data format follows the specification of the fmt argument of the dlarray object. As a result, if the input or output of an entry-point function is a dlarray object and its order of labels is not 'SCBTU', then the data layout will be different between the MATLAB environment and standalone code.
  For example, consider a function foo with adlarray object as an output.
  function dlA = foo()
  rng default
  dlA = dlarray(rand(5,4), 'BC');

end
In MATLAB, dlA is4(C)-by-5(B).
dlA =
4(C) × 5(B) dlarray
0.8147 0.9058 0.1270 0.9134 0.6324
0.0975 0.2785 0.5469 0.9575 0.9649
0.1576 0.9706 0.9572 0.4854 0.8003
0.1419 0.4218 0.9157 0.7922 0.9595
For standalone code generation, dlA is5(B)-by-4(C).

• For code generation, the dlarray input to thepredict method of the dlnetwork object must be single data type.

See Also

Objects

• dlarray (Deep Learning Toolbox) | dlnetwork (Deep Learning Toolbox)

Related Examples

• Generate Digit Images Using Variational Autoencoder on Intel CPUs

More About

• Code Generation for dlarray
• Define Custom Training Loops, Loss Functions, and Networks (Deep Learning Toolbox)
• Train Network Using Custom Training Loop (Deep Learning Toolbox)
• Make Predictions Using dlnetwork Object (Deep Learning Toolbox)