Contents
What is train loss and test loss?
Training Loss Vs Testing Loss (Machine and Deep Learning wise)? 1- Underfits, when the training loss is way more significant than the testing loss. 2- Overfits, when the training loss is way smaller than the testing loss. 3- Performs very well when the training loss and the testing loss are very close.
What does train loss mean?
Training loss is the error on the training set of data. Validation loss is the error after running the validation set of data through the trained network. Train/valid is the ratio between the two. Unexpectedly, as the epochs increase both validation and training error drop.
What is training loss in CNN?
The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. This is how a Neural Net is trained.
What loss function does CNN use?
Thus, out of the whole sum only one term will actually be added: the one with yc=1. As Jan says in a comment, AlexNet uses cross entropy as the loss function. It’s important to note, though, that a Convolutional Neural Network describes the architecture of the network, not the goal of the network.
Why is the training loss much higher than the Val loss?
As it is stated in https://keras.io/getting-started/faq/#why-is-the-training-loss-much-higher-than-the-testing-loss, during training (loss) dropout is on while for validation (val_loss) dropout is off. Also, the training loss is computed as an average for all the minibatches and the validation loss is computed on the whole set.
What to call validation loss and training loss?
If validation loss > training loss you can call it some overfitting. If validation loss < training loss you can call it some underfitting. If validation loss << training loss you can call it underfitting. Your aim is to make the validation loss as low as possible.
Why are training set losses different from training set loss?
The other reason that the results are different is because the model is being trained while the “loss” is being computed, whereas the model is fixed while “val_loss” is being computed. Since the model is training, “loss” is typically going to be larger than the true training set loss at the end of the epoch.
What to do about validation loss in deep learning?
If validation loss > training loss you can call it some overfitting. If validation loss < training loss you can call it some underfitting. If validation loss << training loss you can call it underfitting. Your aim is to make the validation loss as low as possible. Some overfitting is nearly always a good thing.