基于Tensorflow实现基本的线性回归(Linear regression)

线性回归(Linear_regression)

本文基于Tensorflow实现基本的线性回归

代码参考GitHub [Tensorflow学习 ]

代码参考GitHub [Tensorflow-Examples ]

1.numpy导入数据

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])#导入17个 train_x和train_y 数据                         n_samples = train_X.shape[0]     #得到数据train_x 的个数

当set 表示二维数组 [[1,2],[3,4],[5,6],[7,8]]
set.shape[0] 求得数组的行数
set.shape[1] 求得数组的列数
set.shape 求得数组形状

2.设置学习率和设置权重 偏差的占位符

learning_rate = 0.01     #设置学习率training_epochs = 1000   #设置训练步数display_step = 50        #设置结果显示步数# X Y的占位符,设置成32位浮点数X = tf.placeholder(tf.float32)Y = tf.placeholder(tf.float32)# 设置随机权重(weight),设置偏差(bias)为零W = tf.Variable(tf.random_uniform([1]))b = tf.Variable(tf.zeros([1]))

3.最小化误差

# 构造线性模型  y = x*w + bpred = tf.add(tf.multiply(X, W), b)# 计算均方误差  cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)# 梯度下降  Gradient descent#  Note, minimize() knows to modify W and b because Variable objects are trainable=True by defaultoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# 初始化全部变量init = tf.global_variables_initializer()

4.开始训练

# Start trainingwith tf.Session() as sess:    # Run the initializer    sess.run(init)    # Fit all training data    for epoch in range(training_epochs):        sess.run(optimizer, feed_dict={X:train_X, Y: train_Y})        # Display logs per epoch step        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')

训练结果显示

5.显示图案

显示前要在代码上加入 import matplotlib.pyplot as plt

 # Graphic display    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()

6.测试训练出的方程,在测试集上的准确率

# Testing example, as requested (Issue #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()

7.最后的代码

import tensorflow as tfimport numpyimport matplotlib.pyplot as plt# Parameterslearning_rate = 0.01training_epochs = 1000display_step = 50# Training Datatrain_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 Graph InputX = tf.placeholder(tf.float32)Y = tf.placeholder(tf.float32)# Set model weightsW = tf.Variable(tf.random_uniform([1]))b = tf.Variable(tf.zeros([1]))# Construct a linear modelpred = tf.add(tf.multiply(X, W), b)# Mean squared errorcost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)# Gradient descent#  Note, minimize() knows to modify W and b because Variable objects are trainable=True by defaultoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# Initialize the variables (i.e. assign their default value)init = tf.global_variables_initializer()# Start trainingwith tf.Session() as sess:    # Run the initializer    sess.run(init)    # Fit all training data    for epoch in range(training_epochs):        sess.run(optimizer, feed_dict={X:train_X, Y: train_Y})        # Display logs per epoch step        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')    # Graphic display    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()    # Testing example, as requested (Issue #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()