We proceed with the recipe as follows:
- First, we load the necessary libraries, start a graph, and load the iris dataset. After that, we will split the dataset into train and test sets to visualize the loss on both. 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]) train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False) test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices))) x_vals_train = x_vals[train_indices] x_vals_test = x_vals[test_indices] y_vals_train = y_vals[train_indices] ...
