利用tensorflow模拟线性回归

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

#指定参数
learning_rate = 0.01  #学习率
training_epochs = 1000  #训练代数
display_step = 50    #隔50代打印一次日志


# 生成训练数据
train_X = np.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 = np.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])

#保存特征数据的长度
n_samples = train_X.shape[0]

# 设置占位符
X = tf.placeholder(dtype="float32")
Y = tf.placeholder(dtype="float32")


# 设置模型的权重和偏置
W = tf.Variable(np.random.randn(), name="weight")   #均从正态分布中随机取值
b = tf.Variable(np.random.randn(), name="bias")

# 设置线性回归的方程
pred = tf.add(tf.multiply(X, W), b)

# 设置cost为均方误差
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# 梯度下降
# 注意，minimize() 可以自动修正w和b，因为默认设置Variables的trainable=True
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# 初始化所有variables 
init = tf.global_variables_initializer()

# 开始训练
with tf.Session() as sess:
    sess.run(init)

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

        # 打印出每次迭代的log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')

    # 作图(真实值和与预测出的线性函数图线)
    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()
    
    # 生成测试集数据
    test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    #打印测试集上的损失值和训练集与测试集损失值差异
    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))

    #绘制测试集真实值和预测出的线性函数图线
    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()

运行结果如下: