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Is more epochs always better?
Well, the correct answer is the number of epochs is not that significant. more important is the validation and training error. As long as these two error keeps dropping, training should continue. For instance, if the validation error starts increasing that might be an indication of overfitting.
How many epochs should I train CNN?
Therefore, the optimal number of epochs to train most dataset is 11. Observing loss values without using Early Stopping call back function: Train the model up until 25 epochs and plot the training loss values and validation loss values against number of epochs.
Why are large number of epochs good or bad?
The size of your model can be a rough proxy for the complexity that it is able to express (or learn). So a huge model can represent produce more nuanced models for datasets with higher diversity in the data, however would probably take longer to train i.e. more epochs.
Is it good to train CNN for many epochs?
That is likely to happen if you train a large CNN for many epochs, and the graph could look something like this: Assuming you track the performance with a validation set, as long as validation error is decreasing, more epochs are beneficial, model is improving on seen (training) and unseen (validation) data.
Which is the optimal number of epochs to train?
Therefore, the optimal number of epochs to train most dataset is 11. Observing loss values without using Early Stopping call back function: Train the model up until 25 epochs and plot the training loss values and validation loss values against number of epochs. The plot looks like:
What happens if there are too many epochs in a training model?
Too many epochs can cause the model to overfit i.e your model will perform quite well on the training data but will have high error rates on the test data. On the other hand, very few epochs will cause the model to underfit i.e. your model will have large errors on both the training and test data.