clearCache

Load the dlnetwork object and class names from the MAT file dlnetDigits.mat.

s = load("dlnetDigits.mat");
net = s.net;
classNames = s.classNames;

Accelerate the model loss function modelLoss listed at the end of the example.

fun = @modelLoss;
accfun = dlaccelerate(fun);

Clear any previously cached traces of the accelerated function using the clearCache function.

clearCache(accfun)

View the properties of the accelerated function. Because the cache is empty, the Occupancy property is 0.

accfun

accfun = 
  AcceleratedFunction with properties:

          Function: @modelLoss
           Enabled: 1
         CacheSize: 50
           HitRate: 0
         Occupancy: 0
         CheckMode: 'none'
    CheckTolerance: 1.0000e-04

The returned AcceleratedFunction object stores the traces of underlying function calls and reuses the cached result when the same input pattern reoccurs. To use the accelerated function in a custom training loop, replace calls to the model gradients function with calls to the accelerated function. You can invoke the accelerated function as you would invoke the underlying function. Note that the accelerated function is not a function handle.

Evaluate the accelerated model gradients function with random data using the dlfeval function.

X = rand(28,28,1,128,"single");
X = dlarray(X,"SSCB");

T = categorical(classNames(randi(10,[128 1])));
T = onehotencode(T,2)';
T = dlarray(T,"CB");

[loss,gradients,state] = dlfeval(accfun,net,X,T);

View the Occupancy property of the accelerated function. Because the function has been evaluated, the cache is nonempty.

accfun.Occupancy

ans = 
2

Clear the cache using the clearCache function.

clearCache(accfun)

View the Occupancy property of the accelerated function. Because the cache has been cleared, the cache is empty.

accfun.Occupancy

ans = 
0

function [loss,gradients,state] = modelLoss(net,X,T)

[Y,state] = forward(net,X);
loss = crossentropy(Y,T);
gradients = dlgradient(loss,net.Learnables);

end