- First, we will talk about loss functions for regression, which means predicting a continuous dependent variable. To start, we will create a sequence of our predictions and a target as a tensor. We will output the results across 500 x values between -1 and 1. See the How it works... section for a plot of the outputs. Use the following code:
x_vals = tf.linspace(-1., 1., 500) target = tf.constant(0.)
- The L2 norm loss is also known as the Euclidean loss function. It is just the square of the distance to the target. Here, we will compute the loss function as if the target is zero. The L2 norm is a great loss function because it is very curved near the target and algorithms can use this fact to converge to the target more slowly ...
