Take a simple neural network that takes in data of dimension NxF, and output NxC where the N, F, and C represent number of samples, features, and C output neurons respectively. Needless to say, softmax function with cross-entropy is used given we are dealing with multi-class classification problem. I have some problem with my understanding on how gradients are calculated for backpropagation. I have given below the gradient calculation steps. Could someone please clarify where I am going wrong.
