Tensorflow study notes: simple classification using linear regression

#coding:utf-8
'''
使用线性回归进行分类
'''

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

def model(X,w):
    return tf.add(tf.multiply(w[1], tf.pow(X, 1)), 
            tf.multiply(w[0], tf.pow(X, 0))) 

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(tf.float32)
Y = tf.placeholder(tf.float32)

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_op = tf.global_variables_initializer()

sess.run(init_op)

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,',cost:',current_cost)

w_val = sess.run(w)
print('learned parameters:',w_val)
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}))
sess.close()

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