Turbot-DL入门教程篇-TensorFlow应用-线性回归计算

Turbot-DL入门教程篇-TensorFlow应用-线性回归计算

说明：

• 介绍如何使用tensorflow解决线性回归的问题

环境：

• Python 3.5.2

步骤：

• 创建数据集：

$ vim linear_regression_data.py 


import numpy as np
import matplotlib.pyplot as plt

trX = np.linspace(-1, 1, 101)
trY = 2 * trX + \
        np.ones(*trX.shape) * 4 + \
        np.random.randn(*trX.shape) * 0.03


plt.figure(1)
plt.plot(trX, trY, 'o')

plt.xlabel('trX')
plt.ylabel('trY')

plt.show()

• 训练数据集线性回归模型

$ vim linear_regression.py


#!/usr/bin/env python

import tensorflow as tf
import numpy as np

trX = np.linspace(-1, 1, 101)
trY = 2 * trX + \
        np.ones(*trX.shape) * 4 + \
        np.random.randn(*trX.shape) * 0.03

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

w = tf.Variable(0.0, name="weights")
b = tf.Variable(0.0, name="biases")
y_model = tf.multiply(X, w) + b

cost = tf.square(Y - y_model)

train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost)

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

for i in range(100):
        for (x, y) in zip(trX, trY):
                sess.run(train_op, feed_dict={X: x, Y: y})
w_ = sess.run(w)
b_ = sess.run(b)

print("Result : trY = " + str(w_) + "*trX + " + str(b_))

• 运行

python3 linear_regression.py