multi_logistic_regression.py

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

learning_rate = 0.1
training_epochs = 2000

def sigmoid(x):
    return 1. / (1. + np.exp(-x))

x1_label1 = np.random.normal(3, 1, 1000)
x2_label1 = np.random.normal(2, 1, 1000)
x1_label2 = np.random.normal(7, 1, 1000)
x2_label2 = np.random.normal(6, 1, 1000)
x1s = np.append(x1_label1, x1_label2)
x2s = np.append(x2_label1, x2_label2)
ys = np.asarray([0.] * len(x1_label1) + [1.] * len(x1_label2))
X1 = tf.placeholder(tf.float32, shape=(None,), name="x1")
X2 = tf.placeholder(tf.float32, shape=(None,), name="x2") 
Y = tf.placeholder(tf.float32, shape=(None,), name="y")
w = tf.Variable([0., 0., 0.], name="w", trainable=True)

y_model = tf.sigmoid(w[2] * X2 + w[1] * X1 + w[0])
cost = tf.reduce_mean(-tf.log(y_model * Y + (1 - y_model) * (1 - Y)))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    prev_err = 0
    for epoch in range(training_epochs):
        err, _ = sess.run([cost, train_op], {X1: x1s, X2: x2s, Y: ys})
        print(epoch, err)
        if abs(prev_err - err) < 0.0001:
            break
        prev_err = err
    w_val = sess.run(w, {X1: x1s, X2: x2s, Y: ys})
    
x1_boundary, x2_boundary = [], []
for x1_test in np.linspace(0, 10, 100):
    for x2_test in np.linspace(0, 10, 100):
        z = sigmoid(-x2_test*w_val[2] - x1_test*w_val[1] - w_val[0])
        if abs(z - 0.5) < 0.01:
            x1_boundary.append(x1_test)
            x2_boundary.append(x2_test)

plt.scatter(x1_boundary, x2_boundary, c='b', marker='o', s=20)
plt.scatter(x1_label1, x2_label1, c='r', marker='x', s=20)
plt.scatter(x1_label2, x2_label2, c='g', marker='1', s=20)
plt.show()