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Neural Network statistics?

5 vues (au cours des 30 derniers jours)
Ali Almakhmari
Ali Almakhmari le 10 Août 2023
Modifié(e) : Walter Roberson le 22 Août 2023
While the neural network is training using the train command, at each Epoch, the Gradient and Performance change. I want to save the Gradient and Performance as an array for each Epoch in MATLAB (preferably through script/code). Is there a way to do that?

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recent works
recent works le 10 Août 2023
Modifié(e) : Walter Roberson le 22 Août 2023
Yes, you can save the gradient and performance values for each epoch during the training process in MATLAB using a custom script. To do this, you'll need to modify the training loop and add code to save the desired information to arrays or data structures. Here's a high-level example of how you can achieve this:
% Example training loop
num_epochs = 100;
gradients = zeros(num_epochs, 1);
performance = zeros(num_epochs, 1);
% Assuming you have a neural network model 'net' and training data 'data'
for epoch = 1:num_epochs
% Train the neural network for one epoch
[net, gradient, current_performance] = train_one_epoch(net, data);
% Save the gradient and performance values
gradients(epoch) = gradient;
performance(epoch) = current_performance;
% Display progress or additional metrics if needed
fprintf('Epoch %d - Gradient: %.4f - Performance: %.4f\n', epoch, gradient, current_performance);
% Now you have 'gradients' and 'performance' arrays containing the values for each epoch
% You can analyze or visualize these arrays as needed
In this example, you'll need to replace train_one_epoch with your actual training function that performs one epoch of training, computes the gradient, and evaluates the performance. Make sure your training function returns the current gradient and performance values.
You can customize the code above to suit your specific neural network architecture and training setup. Additionally, you can choose to save the gradient and performance values to a file if you want to analyze the results after the training is complete.

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