Choosing loss functions

As discussed earlier, in regression we define the loss function or objective function and the aim is to find the coefficients such that the loss is minimized. In this recipe, you will learn how to define loss functions in TensorFlow and choose a proper loss function depending on the problem at hand.

Getting ready

Declaring a loss function requires defining the coefficients as variables and the dataset as placeholders. One can have a constant learning rate or changing learning rate and regularization constant. In the following code, let m be the number of samples, n the number of features, and P the number of classes. We should define these global parameters before the code:

m = 1000
n = 15
P = 2

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