Epoch vs Iteration when training neural networks [closed]

What is the difference between epoch and iteration when training a multi-layer perceptron?

In the neural network terminology:

• one epoch = one forward pass and one backward pass of all the training examples
• batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
• number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).

For example: if you have 1000 training examples, and your batch size is 500, then it will take 2 iterations to complete 1 epoch.

FYI: Tradeoff batch size vs. number of iterations to train a neural network

The term "batch" is ambiguous: some people use it to designate the entire training set, and some people use it to refer to the number of training examples in one forward/backward pass (as I did in this answer). To avoid that ambiguity and make clear that batch corresponds to the number of training examples in one forward/backward pass, one can use the term mini-batch.

Epoch and iteration describe different things.

Epoch

An epoch describes the number of times the algorithm sees the entire data set. So, each time the algorithm has seen all samples in the dataset, an epoch has been completed.

Iteration

An iteration describes the number of times a batch of data passed through the algorithm. In the case of neural networks, that means the forward pass and backward pass. So, every time you pass a batch of data through the NN, you completed an iteration.

Example

An example might make it clearer.

Say you have a dataset of 10 examples (or samples). You have a batch size of 2, and you've specified you want the algorithm to run for 3 epochs.

Therefore, in each epoch, you have 5 batches (10/2 = 5). Each batch gets passed through the algorithm, therefore you have 5 iterations per epoch. Since you've specified 3 epochs, you have a total of 15 iterations (5*3 = 15) for training.

Many neural network training algorithms involve making multiple presentations of the entire data set to the neural network. Often, a single presentation of the entire data set is referred to as an "epoch". In contrast, some algorithms present data to the neural network a single case at a time.

"Iteration" is a much more general term, but since you asked about it together with "epoch", I assume that your source is referring to the presentation of a single case to a neural network.