Batch and Epoch

From https://machinelearningmastery.com/difference-between-a-batch-and-an-epoch/

What Is the Difference Between Batch and Epoch?

The batch size is a number of samples processed before the model is updated.

The number of epochs is the number of complete passes through the training dataset.

The size of a batch must be more than or equal to one and less than or equal to the number of samples in the training dataset.

The number of epochs can be set to an integer value between one and infinity. You can run the algorithm for as long as you like and even stop it using other criteria besides a fixed number of epochs, such as a change (or lack of change) in model error over time.

They are both integer values and they are both hyperparameters for the learning algorithm, e.g. parameters for the learning process, not internal model parameters found by the learning process.

You must specify the batch size and number of epochs for a learning algorithm.

There are no magic rules for how to configure these parameters. You must try different values and see what works best for your problem.

Worked Example

Finally, let’s make this concrete with a small example.

Assume you have a dataset with 200 samples (rows of data) and you choose a batch size of 5 and 1,000 epochs.

This means that the dataset will be divided into 40 batches, each with five samples. The model weights will be updated after each batch of five samples.

This also means that one epoch will involve 40 batches or 40 updates to the model.

With 1,000 epochs, the model will be exposed to or pass through the whole dataset 1,000 times. That is a total of 40,000 batches during the entire training process.