What function defines accuracy in Keras when the loss is mean squared error (MSE)?

Question

How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error?

The model I use has output activation linear and is comp

Answer 1

The loss function (Mean Square Error in this case) is used to indicate how far your predictions deviate from the target values. In the training phase, the weights are updated based on this quantity. If you are dealing with a classification problem, it is quite common to define an additional metric called accuracy. It monitors in how many cases the correct class was predicted. This is expressed as a percentage value. Consequently, a value of 0.0 means no correct decision and 1.0 only correct decisons. While your network is training, the loss is decreasing and usually the accuracy increases.

Note, that in contrast to loss, the accuracy is usally not used to update the parameters of your network. It helps to monitor the learning progress and the current performane of the network.