To address overfitting, we can apply weight regularization to the model. 1. In other words, our model would overfit to the training data. The training loss is very smooth. For example you could try dropout of 0.5 and so on. The test size has 250000 inputs and the validation set has 20000. 200 epochs are scheduled but learning will stops if there is no improvement on validation set for 10 epochs. As you highlight, the second issue is that there is a plateau i.e. How to Diagnose Overfitting and Underfitting of LSTM Models Also consider a decay rate of 1e-6. but the validation accuracy remains 17% and the validation loss becomes 4.5%. MixUp did not improve the accuracy or loss, the result was lower than using CutMix. One reason why your training and validation set behaves so different could be that they are indeed partitioned differently and the base distributions of the two are different. Handling overfitting in deep learning models | by Bert Carremans ... This requires the choice of an error function, conventionally called a loss function, that can be used to estimate the loss of the model so that the weights can be updated to reduce the loss on the next evaluation. This is the classic " loss decreases while accuracy increases " behavior that we expect. Vary the batch size - 16,32,64; 3. Generally speaking that's a much bigger problem than having an accuracy of 0.37 (which of course is also a problem as it implies a model that does worse than a simple coin toss). In both of the previous examples—classifying text and predicting fuel efficiency—the accuracy of models on the validation data would peak after training for a number of epochs and then stagnate or start decreasing. See an example showing validation and training cost (loss) curves: The cost (loss) function is high and doesn't decrease with the number of iterations, both for the validation and training curves; We could actually use just the training curve and check that the loss is high and that it doesn't decrease, to see that it's underfitting; 3.2.
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