<p>Updating model for each training example means batch size of 1, aka stochastic gradient descent(SGD).</p>
<p>1 iteration is defined as forward propagate, calculate loss, backpropagate and finally update weights.</p>
<p>Since batch size is 1, running 5 epochs on 100 training examples with SGD means you will do 500 iterations, yes.</p>

CC BY-SA 4.0
2021-07-25T15:50:01.937Z
2021-07-25T15:50:01.937Z
meliksahturker
<p>Per <a href="https://developers.google.com/machine-learning/glossary#iteration" rel="nofollow noreferrer">google machine-learning glossary</a>, when I have 100 training examples and update my model for each training example, if I train my model 5 epochs without early-stop, there are 500 iterations in total, is my understanding correct?</p>

<terminology>
Is my understanding about the number of iterations correct?
JJJohn
# Is my understanding about the number of iterations correct?

(Asked by: username_0 on 2021-07-25)

Per [google machine-learning glossary](https://developers.google.com/machine-learning/glossary#iteration), when I have 100 training examples and update my model for each training example, if I train my model 5 epochs without early-stop, there are 500 iterations in total, is my understanding correct?

**Tags:** terminology


---

## 1 answers

### Answer by username_1 on 2021-07-25. Score: 1 | Accepted answer

Updating model for each training example means batch size of 1, aka stochastic gradient descent(SGD).

1 iteration is defined as forward propagate, calculate loss, backpropagate and finally update weights.

Since batch size is 1, running 5 epochs on 100 training examples with SGD means you will do 500 iterations, yes.


---

