Multi-Layer Neural Network - Training Process

Question

I'm building a multilayer neural network. I have question regarding training process, I have a set of training-data with desired outputs. I am using Backpropagation algorithm for updating the connections weights.

should the network train training-data separately? eg: the network takes 1 input, whenever it finds the proper connections weights that give actual-output equals to desired output; the network takes another training-input.

is this correct?

Answer 1

No, regardless of whether or not the actual output equals to the target output , backpropagation algorithm should move to next element of the training set. Then it will update the weights/parameters after a certain amount of training cases have passed, which is determined by the batch size specified. And for each training iteration passed, the average total error should normally be lower than previous iteration.