# Multiple metrics in keras - why and when might we want to use it?

In the keras documentation an example for the usage of metrics is given when compiling the model:

model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=['mae', 'acc'])


Here, both the mean_absolute_error and accuracy are selected. It is not explained, however, why and when specifying two or more metrics might be useful. What is happening in the training phase in such case? Are all of the chosen metrics used somehow? When might I want to consider choosing more than one metric? In particular, I am training a deep neural net, is there a specific metric I should be looking at?

Edit: thanks to the answer of @Alexey Burnakov I realized that the metrics do not take part in the training, so I update my question.

In your example, $$L = (Y - Y') ^ 2 / n$$ is the loss function which is minimzed along the training phase.