1 # 1、加载数据---特征值与目标值
  2 # 2、随机初始化权重与偏置---变量op
  3 # 3、预测--->矩阵相乘
  4 # 4、预测值与真实值 ---损失--均方误差损失
  5 # 5、构建优化算法进行优化损失---设置学习率
  6 # 6、不断优化op
  7 import tensorflow as tf
  8 
  9 
 10 class MyLinearRegression(object):
 11     def __init__(self):
 12         self.learning_rate = 0.1
 13         # self.learning_rate = 0.01
 14         # self.learning_rate = 0.00000000001 # 学习率不能过小,会造成梯度消失
 15         # self.learning_rate = 100  # 学习率过大会造成梯度爆炸
 16 
 17     def build_data(self):
 18         """
 19         构建特征与目标值
 20         :return: x,y
 21         """
 22         # 给构建数据 套个命名空间
 23         with tf.variable_scope("build_data"):
 24             # 随机初始化特征值[100,1]
 25             x = tf.random_normal(
 26                 shape=[100, 1],
 27                 mean=0.0,
 28                 stddev=1.0,
 29                 name="x"
 30             )
 31 
 32             # x [100,1]  * w  + b =  y[100,1]
 33             # w [1,1]
 34             # b []
 35             # 矩阵与 数的相加----与矩阵的每一个元素相加
 36             y = tf.matmul(x, [[0.7]]) + 0.8
 37 
 38         return x, y
 39 
 40     def get_weight(self, shape):
 41         """
 42         生成权重--变量op
 43         :param shape:生成权重的形状
 44         :return: weight
 45         """
 46         with tf.variable_scope("get_weight"):
 47             weight = tf.Variable(
 48                 initial_value=tf.random_normal(
 49                     shape=shape,
 50                     mean=0.0,
 51                     stddev=1.0
 52                 ),
 53                 name="w"
 54             )
 55         return weight
 56 
 57     def get_bias(self, shape):
 58         """
 59         生成偏置--变量op
 60         :param shape:生成偏置的形状
 61         :return: bias
 62         """
 63         with tf.variable_scope("get_bias"):
 64             bias = tf.Variable(
 65                 initial_value=tf.random_normal(
 66                     shape=shape,
 67                     mean=0.0,
 68                     stddev=1.0
 69                 ),
 70                 name="b"
 71             )
 72         return bias
 73 
 74     def linear_model(self, x):
 75         """
 76         构建线性关系
 77         :param x: 特征值
 78         :return: 预测值
 79         """
 80         with tf.variable_scope("linear_model"):
 81             # x [100,1] * w[1,1] + b[]  =  y_predict[100,1]
 82             # w [1,1]
 83             # y[100,1]
 84             # b []
 85             # （1）初始化权重
 86             self.weight = self.get_weight(shape=[1, 1])
 87             # （2）初始化偏置
 88             self.bias = self.get_bias(shape=[])
 89             # （3）求取预测值
 90             y_predict = tf.matmul(x, self.weight) + self.bias
 91 
 92         return y_predict
 93 
 94     def losses(self, y_true, y_predict):
 95         """
 96         计算均方误差损失
 97         :param y_true: 真实值[100,1]
 98         :param y_predict: 预测值[100,1]
 99         :return: 均方误差损失
100         """
101         with tf.variable_scope("losses"):
102             loss = tf.reduce_mean(tf.square(y_true - y_predict))
103         return loss
104 
105     def sgd(self, loss):
106         """
107         使用sgd进行优化损失
108         :param loss: 均方误差损失
109         :return: 优化op
110         """
111         with tf.variable_scope("sgd"):
112             # GradientDescentOptimizer sgd随机梯度下降优化算法
113             # 返回sgd算法对象
114             sgd = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate)
115             # 优化均方误差损失-- 损失减小的方向
116             train_op = sgd.minimize(loss)
117 
118         return train_op
119 
120     def train(self):
121         """
122         进行训练
123         :return:None
124         """
125         with tf.variable_scope("train"):
126             # 1、加载数据
127             x, y = self.build_data()
128 
129             # 2、构建线性模型
130             y_predict = self.linear_model(x)
131 
132             # 3、计算均方误差损失
133             loss = self.losses(y, y_predict)
134 
135             # 4、指定sgd优化算法来优化loss
136             train_op = self.sgd(loss)
137 
138             # 开启会话--执行run--train_op
139             with tf.Session() as ss:
140                 # 显式的初始化w,b
141                 ss.run(tf.global_variables_initializer())
142 
143                 # 序列化events
144                 tf.summary.FileWriter("./tmp/",graph=ss.graph)
145 
146                 # 不断的run --train_op--循环
147                 for i in range(500):
148                     ss.run(train_op)
149 
150                     print("第%d次的损失为%f,权重为%f,偏置为%f" % (
151                         i + 1,
152                         loss.eval(),
153                         self.weight.eval(),
154                         self.bias.eval()
155                     ))
156 
157 
158 # 1、实例化对象
159 lr = MyLinearRegression()
160 # 2、调用方法
161 lr.train()