We proceed with the recipe as follows:
- We will use the iris dataset again and set up our script the same way as before. We first load the libraries; start a session; load the data; declare the batch size; and create the placeholders, variables, and model output as follows:
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.array([x[3] for x in iris.data]) y_vals = np.array([y[0] for y in iris.data]) batch_size = 50 learning_rate = 0.001 x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) ...
