下面的范例使用TensorFlow的低阶API实现线性回归模型。
import@H_403_8@ tensorflow as tf #@H_403_8@ 打印时间分割线 @H_403_8@@tf.function @H_403_8@def@H_403_8@ printbar(): ts =@H_403_8@ tf.timestamp() today_ts = ts%(24*60*60@H_403_8@) hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24@H_403_8@) minite = tf.cast((today_ts%3600)//60@H_403_8@,tf.int32) second = tf.cast(tf.floor(today_ts%60@H_403_8@),tf.int32) @H_403_8@ timeformat(m): @H_403_8@if tf.strings.length(tf.strings.format("{}",m))==1@H_403_8@: @H_403_8@return(tf.strings.format(0{}"403_8@else403_8@ tf.strings.join([timeformat(hour),timeformat(minite),timeformat(second)],separator = :) tf.@H_403_8@print(=========="*8,end = ""@H_403_8@print@H_403_8@(timestring) @H_403_8@ 样本数量 n = 400 @H_403_8@ 生成测试用数据集 X = tf.random.uniform([n,2],minval=-10,maxval=10@H_403_8@) w0 = tf.constant([[2.0],[-1.0@H_403_8@]]) b0 = tf.constant(3.0@H_403_8@) Y = X@w0 + b0 + tf.random.normal([n,1],mean = 0.0,stddev= 2.0) @H_403_8@ @表示矩阵乘法,增加正态扰动 @H_403_8@ 使用动态图调试 @H_403_8@ w =@H_403_8@ tf.Variable(tf.random.normal(w0.shape)) b = tf.Variable(0.0@H_403_8@) @H_403_8@ train(epoches): @H_403_8@for epoch @H_403_8@in tf.range(1,epoches+1@H_403_8@): with tf.GradientTape() as tape: @H_403_8@正向传播求损失 Y_hat = X@w +@H_403_8@ b loss = tf.squeeze(tf.transpose(Y-Y_hat)@(Y-Y_hat))/(2.0*@H_403_8@n) @H_403_8@ 反向传播求梯度 dloss_dw,dloss_db =@H_403_8@ tape.gradient(loss,[w,b]) @H_403_8@ 梯度下降法更新参数 w.assign(w - 0.001*@H_403_8@dloss_dw) b.assign(b - 0.001*@H_403_8@dloss_db) @H_403_8@if epoch%1000 ==@H_403_8@ 0: printbar() tf.@H_403_8@epoch = loss =403_8@w =403_8@b =403_8@) train(5000)
结果：
================================================================================15:18:17@H_403_8@ epoch = 1000 loss = 2.66289544@H_403_8@ w = [[2.0176034@H_403_8@] [-1.02091444@H_403_8@]] b = 1.92718041 ================================================================================15:18:19@H_403_8@ epoch = 2000 loss = 2.12707591@H_403_8@ w = [[2.01378@H_403_8@] [-1.01979101@H_403_8@]] b = 2.63039422 ================================================================================15:18:21@H_403_8@ epoch = 3000 loss = 2.05447602@H_403_8@ w = [[2.01237178@H_403_8@] [-1.01937926@H_403_8@]] b = 2.88924217 ================================================================================15:18:23@H_403_8@ epoch = 4000 loss = 2.04463911@H_403_8@ w = [[2.01185489@H_403_8@] [-1.01922464@H_403_8@]] b = 2.98452425 ================================================================================15:18:24@H_403_8@ epoch = 5000 loss = 2.04330635@H_403_8@ w = [[2.01166272@H_403_8@] [-1.01917028@H_403_8@]] b = 3.01959634
转换成静态图加速：
结果：
================================================================================15:19:50@H_403_8@ epoch = 1000 loss = 2.6668539@H_403_8@ w = [[2.01762223@H_403_8@] [-1.02092016@H_403_8@]] b = 1.92363214 ================================================================================15:19:51@H_403_8@ epoch = 2000 loss = 2.12761354@H_403_8@ w = [[2.01378703@H_403_8@] [-1.01979291@H_403_8@]] b = 2.6290853 ================================================================================15:19:52@H_403_8@ epoch = 3000 loss = 2.0545485@H_403_8@ w = [[2.0123744@H_403_8@] [-1.01938@H_403_8@]] b = 2.888762 ================================================================================15:19:53@H_403_8@ epoch = 4000 loss = 2.04464912@H_403_8@ w = [[2.01185584@H_403_8@] [-1.019225@H_403_8@]] b = 2.98434567 ================================================================================15:19:54@H_403_8@ epoch = 5000 loss = 2.04330778@H_403_8@ w = [[2.0116632@H_403_8@] [-1.0191704@H_403_8@]] b = 3.01952934
参考：
开源电子书地址：https://lyhue1991.github.io/eat_tensorflow2_in_30_days/
GitHub 项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days
