TensorFlow自学笔记-05 基于官方文档 多元线性回归

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


# 定义归一化函数
def normalize(X):
    mean = np.mean(X)
    std = np.std(X)
    X = (X - mean)/std
    return X


def append_bias_reshape(features, labels):
    m = features.shape[0]
    n = features.shape[1]
    x = np.reshape(np.c_[np.ones(m), features], [m, n + 1])
    y = np.reshape(labels, [m, 1])
    return x, y


boston = tf.contrib.learn.datasets.load_dataset('boston')
X_train, Y_train = boston.data, boston.target
X_train = normalize(X_train)
X_train, Y_train = append_bias_reshape(X_train, Y_train)
m = len(X_train)
n = 13 + 1

X = tf.placeholder(tf.float32, name='X', shape=[m, n])
Y = tf.placeholder(tf.float32, name='Y')

w = tf.Variable(tf.random_normal([n, 1]))
b = tf.Variable(0.0)

Y_hat = tf.matmul(X, w) + b

loss = tf.reduce_mean(tf.square(Y - Y_hat, name='loss'))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)

init_op = tf.global_variables_initializer()
total = []

with tf.Session() as sess:
    sess.run(init_op)
    writer = tf.summary.FileWriter('graphs', sess.graph)
    for i in range(100):
        _, l = sess.run([optimizer, loss], feed_dict={X: X_train, Y: Y_train})
        total.append(l)
        print('Epoch {0}: Loss {1}'.format(i, l))
    writer.close()
    w_value, b_value = sess.run([w, b])

plt.plot(total)
plt.show()

N = 500
X_new = X_train[N, :]
Y_pred = (np.matmul(X_new, w_value) + b_value).round(1)
print('Predicted value: ${0} Actual value: / ${1}'.format(Y_pred[0]*1000, Y_train[N]*1000), '\nDone')

运行结果：

Predicted value: $23800.0 Actual value: / $[16800.]