AI探索（三）Tensorflow编程模型

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import numpy as np

num_points = 1000
data_array = []
for i in xrange(num_points):
    x1 = np.random.normal(0.0,0.5)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    data_array.append([x1, y1])

x_data = [v[0] for v in data_array]
y_data = [v[1] for v in data_array]


import matplotlib.pyplot as plt

plt.plot(x_data, y_data, 'ro', label='Original data')
plt.legend()
plt.show()


import tensorflow as tf

w = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = w * x_data + b

loss = tf.reduce_mean(tf.square(y - y_data))

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

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

for step in xrange(20):
    sess.run(train)
    print(step, sess.run(w), sess.run(b))
    print(step, sess.run(loss))

    #Graphic display
    plt.plot(x_data, y_data, 'ro', label='Original data')
    plt.plot(x_data, sess.run(w) * x_data + sess.run(b))
    plt.xlabel('x')
    plt.xlim(-2,2)
    plt.ylim(0.1,0.6)
    plt.ylabel('y')
    plt.legend()
    plt.show()