Hi Syed,
To address the problem of weights and biases not updating during training, we need to ensure that the gradients are computed accurately and that the parameter updates are applied correctly. Let's make the necessary adjustments to the code:
% Initialize Adam optimizer parameters trailingAvgG = []; trailingAvgSqG = []; trailingAvgR = []; trailingAvgSqR = [];
% Define learning parameters miniBatchSize = 600; learnRate = 0.01;
% Define the neural network layers layers = [ % Your network layers here ];
net = dlnetwork(layers); numEpochs = 200;
% Assuming 'dataMat' is your input data dldata = arrayDatastore(dataMat, 'IterationDimension', 3); mbq = minibatchqueue(dldata, 'MiniBatchSize', miniBatchSize, 'OutputEnvironment', 'cpu');
iteration = 0; for epoch = 1:numEpochs shuffle(mbq);
while hasdata(mbq)
iteration = iteration + 1;
[XTrain] = next(mbq);
XTrain = dlarray(XTrain, 'TBC'); % Call the RNN model function
[datafromRNN, lossR] = RNN_model(XTrain, net); % Compute gradients and update parameters
[gradientsR] = dlfeval(@gradientFunction, mean(lossR), net);
[net, trailingAvgR, trailingAvgSqR] = adamupdate(net, gradientsR, trailingAvgR, trailingAvgSqR, iteration, learnRate); disp(['Iteration ', num2str(iteration), ', Loss: ', num2str(extractdata(lossR))]);
end
endfunction [gradientsR] = gradientFunction(lossR, net) gradientsR = dlgradient(lossR, net.Learnables); end
function [datafromRNN, loss] = RNN_model(data, net) z = data; [coder, last] = forward(net, z, 'Outputs', {'maxpool1d_2', 'fc_3'}); loss = mse(last, z); end
By ensuring that the gradients are correctly computed and the Adam optimizer updates the parameters accordingly, the weights and biases of the network should now change between iterations, leading to effective training progress.
Now let’s answer your questions.
Why are the weights and biases not updating, and why do the biases remain zero? If biases are not updating and remain zero, it could indicate a problem with the initialization or the learning rate being too low. Ensure that biases are initialized correctly, preferably with small random values to break symmetry. Additionally, consider adjusting the learning rate to a more suitable value that allows biases to update effectively.
How can I ensure that the gradients computed are correct and being applied effectively?
To verify the correctness of computed gradients and their effective application, you can employ various techniques:
Gradient Checking: Implement numerical gradient checking to compare computed gradients with numerical approximations. Discrepancies may indicate issues in gradient computation. Visualizing Gradients: Plot and analyze the gradients to ensure they follow expected patterns and magnitudes. Debugging Gradient Functions: Review the gradient computation function (gradientFunction) to ensure it correctly calculates gradients with respect to the loss.
Are there any specific settings or modifications I should consider to resolve this issue?
To enhance the training process and address the issues at hand, consider the following settings and modifications:
Learning Rate Adjustment: Experiment with different learning rates to find an optimal value that facilitates weight and bias updates without causing instability. Regularization Techniques: Introduce regularization methods like L1 or L2 regularization to prevent overfitting and aid in smoother weight updates. Batch Normalization: Verify the implementation of batch normalization layers to stabilize training and improve gradient flow. Network Architecture: Evaluate the complexity and design of your neural network architecture to ensure it is suitable for the task at hand and facilitates effective weight updates.
I hope this will help resolve your issues.
