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How is validation set used in neural network?
It is used for tuning the network’s hyperparameters, and comparing how changes to them affect the predictive accuracy of the model. Whereas the training set can be thought of as being used to build the neural network’s gate weights, the validation set allows fine tuning of the parameters or architecture of the neural network model.
How is loss calculated in a neural network?
The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike accuracy, loss is not a percentage. It is a summation of the errors made for each example in training or validation sets.
How is it possible that validation loss should increase?
After some time, validation loss started to increase, whereas validation accuracy is also increasing. The test loss and test accuracy continue to improve. How is this possible? It seems that if validation loss increase, accuracy should decrease.
Why is the validation loss more stable in machine learning?
The reason the validation loss is more stable is that it is a continuous function: It can distinguish that prediction 0.9 for a positive sample is more correct than a prediction 0.51. For accuracy, you round these continuous logit predictions to { 0; 1 } and simply compute the percentage of correct predictions.
How is the accuracy of a neural network determined?
Ideally, one would expect the reduction of loss after each, or several, iteration(s). The accuracy of a model is usually determined after the model parameters are learned and fixed and no learning is taking place.
Why is my validation loss lower than my training loss?
Regularization methods often sacrifice training accuracy to improve validation/testing accuracy — in some cases that can lead to your validation loss being lower than your training loss. Secondly, keep in mind that regularization methods such as dropout are not applied at validation/testing time.