TensorFlow跑线性回归例子

原创 楚盟  2018-05-16 07:02  阅读 242 views 次

这是github上面一个TensorFlow实现线性回归例子,线性回归可以用来预测商品价格,比如国外房价、期货等等;


'''
基于TensorFlow实现线性回归学习算法示例
作者: Aymeric Damien
项目地址: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
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 图形输入
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# 构建一个线性模型
pred = tf.add(tf.multiply(X, W), b)

# 均方差
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# 梯度下降
# 注意,最小化()知道修改W和b,因为变量对象可训练=默认为true
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# 初始化变量(即分配它们的默认值)
init = tf.global_variables_initializer()

# 开始训练
with tf.Session() as sess:

    # 运行初始化程序
    sess.run(init)

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

        # 显示日志每个纪元的步骤
        if (epoch+1) {63fd7216c5ae0f9cf7c97f142287f2ba18f6b0ab629f69cbe4e60fec2a876a4e} display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '{63fd7216c5ae0f9cf7c97f142287f2ba18f6b0ab629f69cbe4e60fec2a876a4e}04d' {63fd7216c5ae0f9cf7c97f142287f2ba18f6b0ab629f69cbe4e60fec2a876a4e} (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')

    # 图形显示
    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()

    # 测试示例,按照要求(问题#2)
    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})  # same function as cost above
    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()

连续运行两次代码,就会出现线性回归训练图
TensorFlow实现线性回归例子

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