machine learning - What is the difference between steps and epochs in TensorFlow?

Question

In most of the models, there is a steps parameter indicating the number of steps to run over data. But yet I see in most practical usage, we also execute the fit function N epochs.

What is the difference between running 1000 steps with 1 epoch and running 100 steps with 10 epoch? Which one is better in practice? Any logic changes between consecutive epochs? Data shuffling?

Answer 1

A training step is one gradient update. In one step batch_size examples are processed.

An epoch consists of one full cycle through the training data. This is usually many steps. As an example, if you have 2,000 images and use a batch size of 10 an epoch consists of:

2,000 images / (10 images / step) = 200 steps.

If you choose your training image randomly (and independently) in each step, you normally do not call it epoch. [This is where my answer differs from the previous one. Also see my comment.]