I assume by custom training loops, you are referring to custom training loops in the Deep Learning Toolbox. If so, here is a good resource to start with: Define Model Loss Function for Custom Training Loop.
There are two different methods to access the weight arrays, depending on how you defined the neural network in the custom training loop. If you used a dlnetwork, then you can access the weights of any layer that has learnable parameters.
Example for dlnetwork:
% Create feedforward neural network
layers = [
featureInputLayer(1,"Name","featureinput")
fullyConnectedLayer(10,"Name","fc_1")
sigmoidLayer("Name","sigmoid_1")
fullyConnectedLayer(10,"Name","fc_2")
sigmoidLayer("Name","sigmoid_2")
fullyConnectedLayer(10,"Name","fc_3")
sigmoidLayer("Name","sigmoid_3")];
dlnet = dlnetwork(layers);
% Access weights of the first FC layer
dlnet.Layers(2).Weights
If you used a model function in the custom training loop instead, then you would need to define a parameters array that contains the weights and biases of the model upfront. You can access these parameters at any time using the names you already defined.
