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I can't find how Keras defines "accuracy" and "loss". I know I can specify different metrics (e.g. mse, cross entropy) - but keras prints out a standard "accuracy". How is that defined? Likewise for loss: I know I can specify different types of regularization -- are those in the loss?
Ideally, I'd like to print out the equation used to define it; if not, I'll settle for an answer here.
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Accuracy(name="accuracy", dtype=None) Calculates how often predictions equal labels. This metric creates two local variables, total and count that are used to compute the frequency with which y_pred matches y_true .
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.
Class Accuracy Defined in tensorflow/python/keras/metrics.py. Calculates how often predictions matches labels. For example, if y_true is [1, 2, 3, 4] and y_pred is [0, 2, 3, 4] then the accuracy is 3/4 or . 75.
Have a look at metrics.py, there you can find definition of all available metrics including different types of accuracy. Accuracy is not printed unless you add it to the list of desired metrics when you compile your model.
Regularizers are by definition added to the loss. For example, see add_loss method of the Layerclass.
Update
The type of accuracy is determined based on the objective function, see training.py. The default choice is categorical_accuracy. Other types like binary_accuracy and sparse_categorical_accuracy are selected when the objective function is either binary or sparse.
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