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。
代码语言：javascript复制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_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 = 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(epoch0==0):
printbar()
tf.print("epoch =",epoch,"loss =",L)
tf.print("w =",linear.kernel)
tf.print("b =",linear.bias)
tf.print("")
train(500)结果：
代码语言：javascript复制InternalError: 2 root error(s) found.
(0) Internal: No unary variant device copy function found for direction: 1 and Variant type_index: tensorflow::data::(anonymous namespace)::DatasetVariantWrapper
[[{{node while_input_5/_12}}]]
[[Func/while/body/_1/cond/then/_78/StatefulPartitionedCall/cond/then/_105/input/_133/_96]]
(1) Internal: No unary variant device copy function found for direction: 1 and Variant type_index: tensorflow::data::(anonymous namespace)::DatasetVariantWrapper
[[{{node while_input_5/_12}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_302016]
Function call stack:
train -> train这里出现了一个问题，我是在谷歌colab上使用gpu进行运行的，会报这个错误，但当我切换成cpu运行时就不报错了：
代码语言：javascript复制================================================================================15:34:47
epoch = 100 loss = 4.7718153
w = [[2.00853848]
[-1.00294471]]
b = [2.51343322]
================================================================================15:34:49
epoch = 200 loss = 3.71054626
w = [[2.01135874]
[-1.00254476]]
b = [3.019526]
================================================================================15:34:51
epoch = 300 loss = 3.84821081
w = [[2.01109028]
[-1.00210166]]
b = [3.12148571]
================================================================================15:34:53
epoch = 400 loss = 3.35442448
w = [[2.01156759]
[-1.0024389]]
b = [3.14201045]
================================================================================15:34:55
epoch = 500 loss = 3.98874116
w = [[2.00852275]
[-1.00062764]]
b = [3.14614844]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
