tensorflow 实现线性回归

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

rng = np.random

learning_rate = 0.01
training_epochs = 1000
display_step = 50

train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,

7.042,10.791,5.313,7.997,5.654,9.27,3.1])

train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
2.827,3.465,1.65,2.904,2.42,2.94,1.3])

n_samples = train_X.shape[0]

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

W = tf.Variable(rng.randn(), name = "weight")
b = tf.Variable(rng.randn(), name = "bias")

pred = tf.add(tf.mul(X,W),b)

cost = tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init = tf.initialize_all_variables()

with tf.Session() as sess:
sess.run(init)

for epoch in range(training_epochs):
for (x,y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict = {X:x, Y:y})

if (epoch + 1) % display_step == 0:
c = sess.run(cost, feed_dict = {X:train_X, Y:train_Y})
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b)

print "Optimization Finished!"
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n'

plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()