中阶API示范
下面的范例使用TensorFlow的中阶API实现线性回归模型。
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:
with tf.GradientTape() as tape:
Y_hat = linear(X_batch)
loss = losses.mean_squared_error(tf.reshape(Y_hat,[-1]),tf.reshape(Y_batch,[-1]))
grads = tape.gradient(loss,linear.variables)
optimizer.apply_gradients(zip(grads,linear.variables))
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)
================================================================================16:22:49
epoch = 100 loss = 3.95253944
w = [[2.01676154]
[-1.01275289]]
b = [2.29978585]
================================================================================16:22:51
epoch = 200 loss = 3.60490489
w = [[2.01732683]
[-1.01249826]]
b = [2.77192116]
================================================================================16:22:53
epoch = 300 loss = 4.19013166
w = [[2.01749277]
[-1.01428568]]
b = [2.86695313]
================================================================================16:22:56
epoch = 400 loss = 4.73355436
w = [[2.01622605]
[-1.01426661]]
b = [2.88615513]
================================================================================16:22:59
epoch = 500 loss = 4.5389142
w = [[2.01557302]
[-1.01302123]]
b = [2.88996792]