tensorflow线性回归例子

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loss = tf.reduce_mean(tf.square(y - y_))optimizer = tf.train.GradientDescentOptimizer(0.5)

线性回归最常用的耗费函数就是MSE均方误差

多变量线性回归例子

from __future__ import print_functionfrom __future__ import absolute_importimport osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'import sysimport loggingimport numpy as npimport tensorflow as tfdef load_data():    datafile = 'data/ex1data2.txt'    #Read into the data file    cols = np.loadtxt(datafile,delimiter=',',usecols=(0,1,2),unpack=True)     #        [[x1 x1 x1 x1 x1],    #         [x2 x2 x2 x2 x2],    #         [y y y y y]]    X = np.transpose(np.array(cols[:-1]))    y = np.transpose(np.array(cols[-1:]))    #  X,Y:                  [[x1 x2],    #                         [x1 x2],    #                         [x1 x2]]    stored_feature_means, stored_feature_stds = [], [] #寸均值和方差    Xnorm = X.copy()    for icol in range(Xnorm.shape[1]):        stored_feature_means.append(np.mean(Xnorm[:,icol]))        stored_feature_stds.append(np.std(Xnorm[:,icol]))        #Skip the first column        #问题：为什么跳过第一行，又没加过1        #if not icol: continue        #Faster to not recompute the mean and std again, just used stored values        Xnorm[:,icol] = (Xnorm[:,icol] - stored_feature_means[-1])/stored_feature_stds[-1]    return Xnorm, ytrain_X, train_y = load_data()# placeholderX = tf.placeholder("float", [None, 2])# modelb = tf.Variable(tf.zeros([1]))W = tf.Variable(tf.zeros([2, 1]))y = tf.matmul(X, W) + b# minimize mean squared error# 线性回归最常用的耗费函数就是MSE均方误差y_ = tf.placeholder("float", [None, 1])loss = tf.reduce_mean(tf.square(y - y_))optimizer = tf.train.GradientDescentOptimizer(0.5)train = optimizer.minimize(loss)# initialize variableinit = tf.global_variables_initializer()# 启动图 (graph)sess = tf.Session()sess.run(init)#也可以sess.run(tf.global_variables_initializer())sess.run(train, feed_dict={X: train_X, y_: train_y})print(sess.run(W), sess.run(b))

不用占位符的例子

import tensorflow as tfimport numpy as npimport osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'# 使用 NumPy 生成假数据(phony data), 总共 100 个点.x_data = np.float32(np.random.rand(2, 100)) # 随机输入y_data = np.dot([0.100, 0.200], x_data) + 0.300print(x_data.shape)print(y_data.shape)# 构造一个线性模型# b = tf.Variable(tf.zeros([1]))W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))y = tf.matmul(W, x_data) + b# 最小化方差loss = tf.reduce_mean(tf.square(y - y_data))optimizer = tf.train.GradientDescentOptimizer(0.5)train = optimizer.minimize(loss)# 初始化变量init = tf.global_variables_initializer()# 启动图 (graph)sess = tf.Session()sess.run(init)# 拟合平面for step in range(0, 201):    sess.run(train)    if step % 20 == 0:        print(step, sess.run(W), sess.run(b))

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