Understanding loss functions in linear regression

It is important to know the effect of loss functions in algorithm convergence. Here, we will illustrate how the L1 and L2 loss functions affect convergence in linear regression.

Getting ready

We will use the same iris data set as in the prior recipe, but we will change our loss functions and learning rates to see how convergence changes.

How to do it...

We proceed with the recipe as follows:

1. The start of the program is the same as the last recipe, until we get to our loss function. We load the necessary libraries, start a session, load the data, create placeholders, and define our variables and model. One thing to note is that we are pulling out our learning rate and model iterations. We are doing this because we want to show the effect of quickly changing these parameters. Use the following code:

import matplotlib.pyplot as plt 
import numpy as np 
import tensorflow as tf 
from sklearn import datasets 
sess = tf.Session() 
iris = datasets.load_iris...