How to train a sequence to classification network on GPU

Prashant Kumar answered . 2025-03-23 04:35:47

Training on a GPU should indeed be faster, but the issue with categorical arrays can be tricky. Here's a solution:

Convert Categorical Array to Numeric Array

You can convert the categorical array to a numeric array using the grp2idx function. This way, you can move the data to the GPU without errors1.

Here's an example:

% Assuming 'labels' is your categorical array
numericLabels = grp2idx(labels);
numericLabelsGPU = gpuArray(numericLabels);

Train Network on GPU

After converting the labels, you can proceed to train your network on the GPU:

% Define training options
options = trainingOptions('adam', ...
    'ExecutionEnvironment', 'auto', ... % Automatically handle data movement
    'MiniBatchSize', 64, ...
    'MaxEpochs', 10, ...
    'InitialLearnRate', 0.001);

% Train the network
net = trainNetwork(X, numericLabelsGPU, layers, options);

By setting the ExecutionEnvironment to 'auto', MATLAB will handle the data movement and computations on the GPU for you.

Summary

1. Convert Categorical to Numeric: Use grp2idx to convert categorical labels to numeric labels.

2. Set ExecutionEnvironment to 'auto': Let MATLAB handle data movement.

3. Train Network: Use trainNetwork with the converted data.

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