Meaning of an Epoch in Neural Networks Training

Question

while I'm reading in how to build ANN in pybrain, they say:

Train the network for some epochs. Usually you would set something like 5 here,

trainer.trainEpochs( 1 )

I looked for what is that mean , then I conclude that we use an epoch of data to update weights, If I choose to train the data with 5 epochs as pybrain advice, the dataset will be divided into 5 subsets, and the wights will update 5 times as maximum.

I'm familiar with online training where the wights are updated after each sample data or feature vector, My question is how to be sure that 5 epochs will be enough to build a model and setting the weights probably? what is the advantage of this way on online training? Also the term "epoch" is used on online training, does it mean one feature vector?

Answer 1

One epoch consists of one full training cycle on the training set. Once every sample in the set is seen, you start again - marking the beginning of the 2nd epoch.

This has nothing to do with batch or online training per se. Batch means that you update once at the end of the epoch (after every sample is seen, i.e. #epoch updates) and online that you update after each sample (#samples * #epoch updates).

You can't be sure if 5 epochs or 500 is enough for convergence since it will vary from data to data. You can stop training when the error converges or gets lower than a certain threshold. This also goes into the territory of preventing overfitting. You can read up on early stopping and cross-validation regarding that.

Answer 2

sorry for reactivating this thread. im new to neural nets and im investigating the impact of 'mini-batch' training.

so far, as i understand it, an epoch (as runDOSrun is saying) is a through use of all in the TrainingSet (not DataSet. because DataSet = TrainingSet + ValidationSet). in mini batch training, you can sub divide the TrainingSet into small Sets and update weights inside an epoch. 'hopefully' this would make the network 'converge' faster.

some definitions of neural networks are outdated and, i guess, must be redefined.