TensorFlow简单实例(一):linear_regression

 http://www.longxyun.com/blog.html [原文地址]

  很简单的线性回归例子，用的是Python2.7，Tensorflow1.0.

# -*- coding: utf-8 -*-'''A linear regression learning algorithm example using TensorFlow library.Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''from __future__ import print_functionimport tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.random# 参数，分别是学习率，迭代次数，以及每50次迭代就打印一些东西learning_rate = 0.01training_epochs = 1000display_step = 50# Training Data（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 Graph InputX = tf.placeholder("float")Y = tf.placeholder("float")# Set model weights（设置模型权重）# 定义两个需要求出的w和b变量W = tf.Variable(rng.randn(), name="weight")b = tf.Variable(rng.randn(), name="bias")# Construct a linear model(构建线性模型)# 拟合 X * W + b# 预测值pred = tf.add(tf.multiply(X, W), b)#代价损失和优化方法#cost为真实值y与拟合值h<hypothesis>之间的距离#reduce_sum为求和cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)# Gradient descent（梯度下降）optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# 以上整个图的定义完成# Initializing the variablesinit = tf.global_variables_initializer()# Launch the graph（启动图）with tf.Session() as sess:    sess.run(init)    # Fit all training data    #training_epochs是迭代次数    for epoch in range(training_epochs):        for (x, y) in zip(train_X, train_Y):            sess.run(optimizer, feed_dict={X: x, Y: y})        # Display logs per epoch step（每轮打印一些内容）        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（画图）    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()    # 以下为测试    # 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()

结果为：

图像是:

测试结果：

其loss值为 0.0140917

图像：