How does Keras evaluate loss on test set?

Question

I am implementing a neural network classifier, to print loss and accuracy of this NN I'm using:

score = model.evaluate(x_test, y_test, verbose=False) 
model.metrics_names
print('Test score: ', score[0])    #Loss on test
print('Test accuracy: ', score[1])

I would to know how Keras calculate the loss of a model. Specially whether it is evaluated on the first (and only) step of the test set. I have search on keras.io, but I don't have find anything about it.

Answer 1

From the documentation:

evaluate

Computes the loss on some input data, batch by batch.

Returns

Scalar test loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

So it returns you either a single value that represents a loss, or a list of values that correspond to different metrics that were added to your model. These values are calculated based on the whole test set, i. e. all values in x_test and y_test.