What is the difference between steps and epochs in TensorFlow?

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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.]

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