val_loss_history= [] val_correct_history= [] val_loss_history= [] val_correct_history= [] Step 4: In the next step, we will validate the model. This is a case of overfitting. . Therefore, the optimal number of epochs to train most dataset is 11. Solutions to this are to decrease your network size, or to increase dropout. Share Getting the validation loss while training - PyTorch Forums In neural network training should validation loss be lower than ... - Quora Increase the size of your model (either number of layers or the raw number of neurons per layer) Approximate number of parameters As part of the optimization algorithm, the error for the current state of the model must be estimated repeatedly. But the question is after 80 epochs, both training and validation loss stop changing, not decrease and increase. The objective here is to reduce the size of the image being passed to the CNN while maintaining the important features. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). In general, putting 80% of the data in the training set, 10% in the validation set, and 10% in the test set is a good split to start with. The first step when dealing with overfitting is to decrease the complexity of the model. 1. Reducing the learning rate reduces the variability. MixUp did not improve the accuracy or loss, the result was lower than using CutMix. How to increase CNN accuracy? - MATLAB & Simulink dealing with overfitting in the same manner as above. As a result, you get a simpler model that will be forced to learn only the . neural networks - How do I interpret my validation and training loss ... 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. You should try to get more data, use more complex features or use a d. 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.