We proceed with the recipe as follows:
- 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() x_vals = np.array([x[3] for x in iris.data]) y_vals = np.array([y[0] for y in iris.data]) batch_size = 25 learning_rate = 0.1 ...
