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Is large number of epochs good or bad idea in CNN?
In my CNN model, by using large number of epochs like 400 or above, the validations accuracy and some times test accuracy gets better, but I think this large number of epochs is not good idea? I am right or not? why? If your model is still improving (according to the validation loss ), then more epochs are better.
Which is better more epochs or fewer epochs?
If your model is still improving (according to the validation loss ), then more epochs are better. You can confirm this by using a hold-out test set to compare model checkpoints e.g. at epoch 100, 200, 400, 500.
How to improve validation loss and accuracy for CNN?
If the size of the images is too big, consider the possiblity of rescaling them before training the CNN. If possible, remove one Max-Pool layer. Lower dropout, that looks too high IMHO (but other people might disagree with me on this).
How many epochs do you need to train ResNet?
the ResNet model can be trained in 35 epoch fully-conneted DenseNet model trained in 300 epochs The number of epochs you require will depend on the size of your model and the variation in your dataset. The size of your model can be a rough proxy for the complexity that it is able to express (or learn).
When does validation accuracy decrease as epoch increases?
I have found that as the number of epochs increases, there are times where the validation accuracy actually decreases.
What happens when the number of epochs increases?
You can add regularizers and/or dropout to decrease the learning capacity of your model and/or stop the training using an EarlyStopping callback as mentionned in the faq. Intresting. So your saying increasing the number of epochs can potentially give a worse result.
How to preprocesse a batch of CNN images?
Finally, the method __data_generation returns the batch of images as the pair X, y where X is of shape (batch_size, height, width, channels) and y is of shape (batch size, ). Note that __data_generation also does some preprocessing – it normalises the images (divides by 255) and crops the center 100 x 100 portion of the image.