3 篇文章

中阶API示范

TensorFlow有5个不同的层次结构：即硬件层内核层低阶API中阶API高阶API。本章我们将以线性回归为例，直观对比展示在低阶API，中阶API，高阶API这三个层级实现模型的特点。

TensorFlow的层次结构从低到高可以分成如下五层。

TensorFlow的中阶API主要包括各种模型层，损失函数，优化器，数据管道，特征列等等。

``````import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers

#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 = 800

# 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

#构建输入数据管道
ds = tf.data.Dataset.from_tensor_slices((X,Y)) \
.shuffle(buffer_size = 1000).batch(100) \
.prefetch(tf.data.experimental.AUTOTUNE)

#定义优化器
optimizer = optimizers.SGD(learning_rate=0.001)
``````
``````linear = layers.Dense(units = 1)
linear.build(input_shape = (2,))

@tf.function
def train(epoches):
for epoch in tf.range(1,epoches+1):
L = tf.constant(0.0) #使用L记录loss值
for X_batch,Y_batch in ds:
Y_hat = linear(X_batch)
loss = losses.mean_squared_error(tf.reshape(Y_hat,[-1]),tf.reshape(Y_batch,[-1]))
L = loss

if(epoch%100==0):
printbar()
tf.print("epoch =",epoch,"loss =",L)
tf.print("w =",linear.kernel)
tf.print("b =",linear.bias)
tf.print("")

train(500)
``````