下面的范例使用TensorFlow的低阶API实现线性回归模型。
低阶API主要包括张量操作，计算图和自动微分。
代码语言：javascript复制import tensorflow as tf
# 打印时间分割线
@tf.function
def printbar():
ts = tf.timestamp()
today_ts = ts%(24*60*60)
hour = tf.cast(today_ts//3600 8,tf.int32)%tf.constant(24)
minite = tf.cast((today_ts600)//60,tf.int32)
second = tf.cast(tf.floor(today_ts`),tf.int32)
def timeformat(m):
if tf.strings.length(tf.strings.format("{}",m))==1:
return(tf.strings.format("0{}",m))
else:
return(tf.strings.format("{}",m))
timestring = tf.strings.join([timeformat(hour),timeformat(minite),
timeformat(second)],separator = ":")
tf.print("=========="*8,end = "")
tf.print(timestring)
# 样本数量
n = 400
# 生成测试用数据集
X = tf.random.uniform([n,2],minval=-10,maxval=10)
w0 = tf.constant([[2.0],[-1.0]])
b0 = tf.constant(3.0)
Y = X@w0 b0 tf.random.normal([n,1],mean = 0.0,stddev= 2.0) # @表示矩阵乘法,增加正态扰动
# 使用动态图调试
w = tf.Variable(tf.random.normal(w0.shape))
b = tf.Variable(0.0)
def train(epoches):
for epoch in tf.range(1,epoches 1):
with tf.GradientTape() as tape:
#正向传播求损失
Y_hat = X@w b
loss = tf.squeeze(tf.transpose(Y-Y_hat)@(Y-Y_hat))/(2.0*n)
# 反向传播求梯度
dloss_dw,dloss_db = tape.gradient(loss,[w,b])
# 梯度下降法更新参数
w.assign(w - 0.001*dloss_dw)
b.assign(b - 0.001*dloss_db)
if epoch00 == 0:
printbar()
tf.print("epoch =",epoch," loss =",loss,)
tf.print("w =",w)
tf.print("b =",b)
tf.print("")
train(5000)结果：
代码语言：javascript复制================================================================================15:18:17
epoch = 1000 loss = 2.66289544
w = [[2.0176034]
[-1.02091444]]
b = 1.92718041
================================================================================15:18:19
epoch = 2000 loss = 2.12707591
w = [[2.01378]
[-1.01979101]]
b = 2.63039422
================================================================================15:18:21
epoch = 3000 loss = 2.05447602
w = [[2.01237178]
[-1.01937926]]
b = 2.88924217
================================================================================15:18:23
epoch = 4000 loss = 2.04463911
w = [[2.01185489]
[-1.01922464]]
b = 2.98452425
================================================================================15:18:24
epoch = 5000 loss = 2.04330635
w = [[2.01166272]
[-1.01917028]]
b = 3.01959634转换成静态图加速：
代码语言：javascript复制## 使用autograph机制转换成静态图加速
w = tf.Variable(tf.random.normal(w0.shape))
b = tf.Variable(0.0)
@tf.function
def train(epoches):
for epoch in tf.range(1,epoches 1):
with tf.GradientTape() as tape:
#正向传播求损失
Y_hat = X@w b
loss = tf.squeeze(tf.transpose(Y-Y_hat)@(Y-Y_hat))/(2.0*n)
# 反向传播求梯度
dloss_dw,dloss_db = tape.gradient(loss,[w,b])
# 梯度下降法更新参数
w.assign(w - 0.001*dloss_dw)
b.assign(b - 0.001*dloss_db)
if epoch00 == 0:
printbar()
tf.print("epoch =",epoch," loss =",loss,)
tf.print("w =",w)
tf.print("b =",b)
tf.print("")
train(5000)结果：
代码语言：javascript复制================================================================================15:19:50
epoch = 1000 loss = 2.6668539
w = [[2.01762223]
[-1.02092016]]
b = 1.92363214
================================================================================15:19:51
epoch = 2000 loss = 2.12761354
w = [[2.01378703]
[-1.01979291]]
b = 2.6290853
================================================================================15:19:52
epoch = 3000 loss = 2.0545485
w = [[2.0123744]
[-1.01938]]
b = 2.888762
================================================================================15:19:53
epoch = 4000 loss = 2.04464912
w = [[2.01185584]
[-1.019225]]
b = 2.98434567
================================================================================15:19:54
epoch = 5000 loss = 2.04330778
w = [[2.0116632]
[-1.0191704]]
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
