I am new to CNN. What I have learned so far about the filters is that when we are giving a training example to our model, our model updates the weights by gradient descent to minimize the loss function. So my question is how the weights are retained for a particular class label?
The question is vague as my knowledge is vague. It's my 4th hour to CNN.
For example, if I am talking about the MNIST dataset with 10 labels. Let's say I am giving 1 image to my model initially. It will have a bigger loss for the forward pass. Let's say now it came for the back pass and adjusted the weights for and minimized the loss function for that label. Now, when a new label arrives for training, how will it update the weights for filters which have already been updated according to the previous label?