# tensorflow基础模型之线性回归

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from __future__ import print_function

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

rng = numpy.random

# 参数
learning_rate = 0.01
training_epochs = 1000
display_step = 50

# 训练数据
train_X = numpy.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 = numpy.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]

# tf图输入
X = tf.placeholder("float")
Y = tf.placeholder("float")

# 设置模型权重
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# 构造线性模型

# 均方差（Mean squared error）
cost = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples)
# 梯度下降
# Variable对象的trainable属性值默认为True，minimize()函数会调整W和b

# 初始化全局的变量
init = tf.global_variables_initializer()

# 开始训练
with tf.Session() as sess:
# 执行初始化
sess.run(init)

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

# 每轮训练输出日志
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')

# 展示
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()

# 测试样本
test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

print("Testing... (Mean square loss Comparison)")
testing_cost = sess.run(
tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
feed_dict={X: test_X, Y: test_Y})  # 同训练损失函数
print("Testing cost=", testing_cost)
print("Absolute mean square loss difference:", abs(training_cost - testing_cost))

plt.plot(test_X, test_Y, 'bo', label='Testing data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()