TensorFlow经典案例3:实现线性回归

#实现线性回归
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng = np.random

learn_rate = 0.01
training_epochs = 1000
display_step = 50

#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])
n_samples = train_X.shape[0]

#创建图
X = tf.placeholder("float")
Y = tf.placeholder("float")

W = tf.Variable(rng.randn(),name="weigth")
b = tf.Variable(rng.randn(),name="bias")

prediction = tf.add(tf.multiply(X,W),b)

cost = tf.reduce_sum(tf.pow(prediction-Y,2) / (2*n_samples))

train_step = tf.train.GradientDescentOptimizer(learn_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(training_epochs):
        for(x,y) in zip(train_X,train_Y):
            sess.run(train_step,feed_dict={X:x,Y:y})
        if (i + 1) % display_step == 0:
            c = sess.run(cost,feed_dict={X:train_X,Y:train_Y})
            print("Epoch:", ‘%04d‘ % (i + 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‘)

    plt.plot(train_X,train_Y,‘ro‘,label="origal data")
    plt.plot(train_X,sess.run(W) * train_X + sess.run(b),label="fit line")
    plt.legend()
    plt.show()

    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])

    print("Testing... (Mean square loss Comparison)")

    testing_cost = sess.run(

        tf.reduce_sum(tf.pow(prediction - Y, 2)) / (2 * test_X.shape[0]),

        feed_dict={X: test_X, Y: test_Y})  

    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()