Python Deep Learning Cookbook

How to do it...

1. Import the libraries and dataset as follows:

import numpy as np from sklearn.model_selection import train_test_splitimport matplotlib.pyplot as plt# We will be using the Iris Plants Databasefrom sklearn.datasets import load_irisSEED = 2017

2. First, we subset the imported data as shown here:

# The first two classes (Iris-Setosa and Iris-Versicolour) are linear separableiris = load_iris()idxs = np.where(iris.target<2)X = iris.data[idxs]y = iris.target[idxs]

3. Let's plot the data for two of the four variables with the following code snippet:

plt.scatter(X[Y==0][:,0],X[Y==0][:,2], color='green', label='Iris-Setosa')plt.scatter(X[Y==1][:,0],X[Y==1][:,2], color='red', label='Iris-Versicolour')plt.title('Iris Plants Database') ...