How Keras really fits the models via epochs

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I am a bit confused on how Keras fits the models. In general, Keras models are fitted by simply using model.fit(...) something like the following:

model.fit(X_train, y_train, epochs=300, batch_size=64, validation_data=(X_test, y_test))

My question is: Because I stated the testing data by the argument validation_data=(X_test, y_test), does it mean that each epoch is independent? In other words, I understand that at each epoch, Keras train the model using the training data (after getting shuffled) followed by testing the trained model using the provided validation_data. If that's the case, then no matter how many epochs I choose, I only take the results of the last epoch!!

If this scenario is correct, so we do we need multiple epoches? Unless these epoches are dependent somwhow where each epoch uses the same NN weights from the previous epoch, correct?

Thank you

data-science keras neural-network
1 Answers

When Keras fit your model it pass throught all the dataset at each epoch by a step corresponding to your batch_size.
For exemple if you have a dataset of 1000 items and a batch_size of 8, the weight of your model will be updated by using 8 items and this until it have seen all your data set.

At the end of that epoch, the model will try to do a prediction on your validation set.
If we have made only one epoch, it would mean that the weight of the model is updated only once per element (because it only "saw" one time the complete dataset).
But in order to minimize the loss function and by backpropagation, we need to update those weights multiple times in order to reach the optimum loss, so pass throught all the dataset multiple times, in other word, multiple epochs.

I hope i'm clear, ask if you need more informations.

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