linear_regression_classification.py

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

x_label0 = np.random.normal(5,1,10)
x_label1 = np.random.normal(2,1,10)
xs = np.append(x_label0, x_label1)
labels = [0.] * len(x_label0) + [1.] * len(x_label1)

plt.scatter(xs, labels)

learning_rate = 0.001
training_epochs = 1000

X = tf.placeholder("float")
Y = tf.placeholder("float")

def model(X, w):
  return tf.add(tf.multiply(w[1], tf.pow(X, 1)),
                tf.multiply(w[0], tf.pow(X, 0)))
w = tf.Variable([0., 0.], name="parameters")
y_model = model(X,w)
cost = tf.reduce_sum(tf.square(Y-y_model))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

for epoch in range(training_epochs):
  sess.run(train_op, feed_dict={X:xs, Y:labels})
  current_cost = sess.run(cost, feed_dict={X:xs, Y:labels})
  if epoch % 100 == 0:
    print(epoch, current_cost)
    
w_val = sess.run(w)
print('learned parameters', w_val)
sess.close()

all_xs = np.linspace(0, 10, 100)
plt.plot(all_xs, all_xs*w_val[1] + w_val[0])
plt.show()

correct_prediction = tf.equal(Y, tf.to_float(tf.greater(y_model, 0.5)))
accuracy = tf.reduce_mean(tf.to_float(correct_prediction))
print('accuracy', sess.run(accuracy, feed_dict={X:xs, Y: labels}))