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In pybrain documentation I found the following documentation for trainUntilConvergence as follow,

trainUntilConvergence(dataset=None, maxEpochs=None, verbose=None, continueEpochs=10, validationProportion=0.25)

Train the module on the dataset until it converges.

Return the module with the parameters that gave the minimal validation error.

If no dataset is given, the dataset passed during Trainer initialization is used. validationProportion is the ratio of the

dataset that is used for the validation dataset.

If maxEpochs is given, at most that many epochs are trained. Each time validation error hits a minimum, try for continueEpochs epochs to find a better one.

But they didn't tell what is continueEpochs and verbose parameters do or define? Does one have an idea?

Community
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2 Answers2

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verbose is almost self-explanatory - it simply prints to stdout current loss after each iteration.

continueEpochs is explained in the excerpt you provide

Each time validation error hits a minimum, try for continueEpochs epochs to find a better one

mjaskowski
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If verbose is set to True, it prints out the loss of each epoch during the training.

The trainUntilConvergence method trains on your data set until the error on the validation set is no longer decreasing for a certain number of epochs. You can vary the number of epochs that the trainer considers before stopping the training by changing the continueEpochs parameter. It defaults to 10. In other words, if the error on the validation set does not improve in 10 consecutive epochs, the training is terminated. This is also known as the early stopping method and its widely used in training neural nets.

dnth
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