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.
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 ...