AI探索（三）Tensorflow编程模型

Tensorflow编程模型

。。。。后续完善

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = ''

import numpy as np

num_points =

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[] for v in data_array]

y_data = [v[] 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([], -1.0, 1.0))

b = tf.Variable(tf.zeros([]))

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():

    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(-,)

    plt.ylim(0.1,0.6)

    plt.ylabel('y')

    plt.legend()

    plt.show()