Implementing Loss Functions

Loss functions are very important to machine learning algorithms. They measure the distance between the model outputs and the target (truth) values. In this recipe, we show various loss function implementations in TensorFlow.

Getting ready

In order to optimize our machine learning algorithms, we will need to evaluate the outcomes. Evaluating outcomes in TensorFlow depends on specifying a loss function. A loss function tells TensorFlow how good or bad the predictions are compared to the desired result. In most cases, we will have a set of data and a target on which to train our algorithm. The loss function compares the target to the prediction and gives a numerical distance between the two.

For this recipe, we will cover the main loss functions that we can implement in TensorFlow.

To see how the different loss functions operate, we will plot them in this recipe. We will first start a computational graph and load matplotlib, a python plotting library, as follows:

import...