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3-1,低阶API示范.md

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3-1,低阶API示范

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。

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_ts%3600)//60,tf.int32)
    second = tf.cast(tf.floor(today_ts%60),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 epoch%1000 == 0:
            printbar()
            tf.print("epoch =",epoch," loss =",loss,)
            tf.print("w =",w)
            tf.print("b =",b)
            tf.print("")
            
train(5000)

##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 epoch%1000 == 0:
            printbar()
            tf.print("epoch =",epoch," loss =",loss,)
            tf.print("w =",w)
            tf.print("b =",b)
            tf.print("")
train(5000)

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