Learning The TensorFlow Way of Linear Regression

Getting ready

In this recipe, we will loop through batches of data points and let TensorFlow update the slope and y-intercept. Instead of generated data, we will us the iris dataset that is built in to the Scikit Learn. Specifically, we will find an optimal line through data points where the x-value is the petal width and the y-value is the sepal length. We choose these two because there appears to be a linear relationship between them, as we will see in the graphs at the end. We will also talk more about the effects of different loss functions in the next section, but for this recipe we will use the L2 loss function.

How to do it…

1. We start by loading the necessary libraries, creating a graph, and loading the data:

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

   x_vals = np...