TensorFlow2.X学习笔记(2)--TensorFlow的层次结构介绍
时间:2022-07-25
本文章向大家介绍TensorFlow2.X学习笔记(2)--TensorFlow的层次结构介绍，主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项，具有一定的参考价值，需要的朋友可以参考一下。
TensorFlow的层次结构
TensorFlow中5个不同的层次结构：
硬件层，内核层，低阶API，中阶API，高阶API
- 最底层为硬件层，TensorFlow支持CPU、GPU或TPU加入计算资源池。
- 第二层为C++实现的内核，kernel可以跨平台分布运行。
- 第三层为Python实现的操作符，提供了封装
C++内核的低级API指令，主要包括各种张量操作算子、计算图、自动微分. 如tf.Variable,tf.constant,tf.function,tf.GradientTape,tf.nn.softmax...如果把模型比作一个房子，那么第三层API就是【模型之砖】。 - 第四层为Python实现的模型组件，对低级API进行了函数封装，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。 如tf.keras.layers,tf.keras.losses,tf.keras.metrics,tf.keras.optimizers,tf.data.DataSet,tf.feature_column… 如果把模型比作一个房子，那么第四层API就是【模型之墙】。 - 第五层为Python实现的模型成品，一般为按照OOP方式封装的高级API，主要为
tf.keras.models提供的模型的类接口。 如果把模型比作一个房子，那么第五层API就是模型本身，即【模型之屋】。
低阶API示范
低阶API主要包括张量操作，计算图和自动微分。
python
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
python
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)中阶API示范
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。
下面代码在GPU上面测试不通过，有人说可以在CPU上面跑通
python
# 要使用CPU则在代码最上面加上下面两行代码
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"python
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)
# 运行结果
================================================================================11:54:07
epoch = 100 loss = 4.13672256
w = [[1.9903785]
[-0.987648785]]
b = [2.40720725]
================================================================================11:54:10
epoch = 200 loss = 4.51364088
w = [[1.99168313]
[-0.987588942]]
b = [2.90266657]
================================================================================11:54:12
epoch = 300 loss = 4.33221674
w = [[1.99366784]
[-0.98791343]]
b = [3.00248265]
================================================================================11:54:15
epoch = 400 loss = 3.12717295
w = [[1.98944652]
[-0.986787856]]
b = [3.02270603]
================================================================================11:54:17
epoch = 500 loss = 3.830863
w = [[1.99245548]
[-0.987303793]]
b = [3.02663541]高阶API示范
TensorFlow的高阶API主要为tf.keras.models提供的模型的类接口。 使用Keras接口有以下3种方式构建模型：使用Sequential按层顺序构建模型，使用函数式API构建任意结构模型，继承Model基类构建自定义模型。
(1)、使用Sequential按层顺序构建模型
python
import tensorflow as tf
from tensorflow.keras import models,layers,optimizers
#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
tf.keras.backend.clear_session()
linear = models.Sequential()
linear.add(layers.Dense(1,input_shape =(2,)))
linear.summary()
### 使用fit方法进行训练
linear.compile(optimizer="adam",loss="mse",metrics=["mae"])
linear.fit(X,Y,batch_size = 20,epochs = 200)
tf.print("w = ",linear.layers[0].kernel)
tf.print("b = ",linear.layers[0].bias)
### 运行结果
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 1) 3
=================================================================
Total params: 3
Trainable params: 3
Non-trainable params: 0
_________________________________________________________________
Train on 800 samples
............
。。。略过。。。
............
Epoch 197/200
20/800 [..............................] - ETA: 0s - loss: 4.1910 - mae: 1.5875
360/800 [============>.................] - ETA: 0s - loss: 3.7929 - mae: 1.5485
660/800 [=======================>......] - ETA: 0s - loss: 4.1543 - mae: 1.6328
800/800 [==============================] - 0s 171us/sample - loss: 4.0170 - mae: 1.6049
Epoch 198/200
20/800 [..............................] - ETA: 0s - loss: 5.7438 - mae: 1.8000
340/800 [===========>..................] - ETA: 0s - loss: 4.1317 - mae: 1.5987
700/800 [=========================>....] - ETA: 0s - loss: 4.1257 - mae: 1.6281
800/800 [==============================] - 0s 151us/sample - loss: 4.0162 - mae: 1.6045
Epoch 199/200
20/800 [..............................] - ETA: 0s - loss: 5.3425 - mae: 1.6237
340/800 [===========>..................] - ETA: 0s - loss: 3.9504 - mae: 1.5925
660/800 [=======================>......] - ETA: 0s - loss: 4.1211 - mae: 1.6202
800/800 [==============================] - 0s 162us/sample - loss: 4.0166 - mae: 1.6045
Epoch 200/200
20/800 [..............................] - ETA: 0s - loss: 4.0931 - mae: 1.7218
380/800 [=============>................] - ETA: 0s - loss: 3.9471 - mae: 1.6048
700/800 [=========================>....] - ETA: 0s - loss: 3.8557 - mae: 1.5794
800/800 [==============================] - 0s 160us/sample - loss: 4.0185 - mae: 1.6047
w= [[1.99929214]
[-0.96500361]]
b= [3.04800749](2)、继承Model基类构建自定义模型
python
# 要使用CPU则在代码最上面加上下面两行代码
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"python
import tensorflow as tf
from tensorflow.keras import models,layers,optimizers,losses,metrics
#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
ds_train = tf.data.Dataset.from_tensor_slices((X[0:n*3//4,:],Y[0:n*3//4,:]))
.shuffle(buffer_size = 1000).batch(20)
.prefetch(tf.data.experimental.AUTOTUNE)
.cache()
ds_valid = tf.data.Dataset.from_tensor_slices((X[n*3//4:,:],Y[n*3//4:,:]))
.shuffle(buffer_size = 1000).batch(20)
.prefetch(tf.data.experimental.AUTOTUNE)
.cache()
tf.keras.backend.clear_session()
# 自定义模型
class MyModel(models.Model):
def __init__(self):
super(MyModel, self).__init__()
def build(self,input_shape):
self.dense1 = layers.Dense(1)
super(MyModel,self).build(input_shape)
def call(self, x):
y = self.dense1(x)
return(y)
model = MyModel()
model.build(input_shape =(None,2))
model.summary()
### 自定义训练循环(专家教程)
optimizer = optimizers.Adam()
loss_func = losses.MeanSquaredError()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_metric = tf.keras.metrics.MeanAbsoluteError(name='train_mae')
valid_loss = tf.keras.metrics.Mean(name='valid_loss')
valid_metric = tf.keras.metrics.MeanAbsoluteError(name='valid_mae')
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features)
loss = loss_func(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss.update_state(loss)
train_metric.update_state(labels, predictions)
@tf.function
def valid_step(model, features, labels):
predictions = model(features)
batch_loss = loss_func(labels, predictions)
valid_loss.update_state(batch_loss)
valid_metric.update_state(labels, predictions)
@tf.function
def train_model(model,ds_train,ds_valid,epochs):
for epoch in tf.range(1,epochs+1):
for features, labels in ds_train:
train_step(model,features,labels)
for features, labels in ds_valid:
valid_step(model,features,labels)
logs = 'Epoch={},Loss:{},MAE:{},Valid Loss:{},Valid MAE:{}'
if epoch%100 ==0:
printbar()
tf.print(tf.strings.format(logs,
(epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result())))
tf.print("w=",model.layers[0].kernel)
tf.print("b=",model.layers[0].bias)
tf.print("")
train_loss.reset_states()
valid_loss.reset_states()
train_metric.reset_states()
valid_metric.reset_states()
train_model(model,ds_train,ds_valid,400)
#### 运行结果
Model: "my_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) multiple 3
=================================================================
Total params: 3
Trainable params: 3
Non-trainable params: 0
_________________________________________________________________
================================================================================18:17:08
Epoch=100,Loss:67.9662247,MAE:6.36445856,Valid Loss:65.3885117,Valid MAE:6.17629
w= [[1.65662384]
[-1.01629746]]
b= [1.92026019]
================================================================================18:17:16
Epoch=200,Loss:36.144165,MAE:4.0302186,Valid Loss:35.4477425,Valid MAE:4.01481533
w= [[1.99435031]
[-1.00531375]]
b= [3.02523756]
================================================================================18:17:25
Epoch=300,Loss:25.3425236,MAE:3.20795441,Valid Loss:25.308445,Valid MAE:3.25203133
w= [[1.99592912]
[-1.00504756]]
b= [3.08958364]
================================================================================18:17:34
Epoch=400,Loss:19.9554043,MAE:2.79780984,Valid Loss:20.2524834,Valid MAE:2.87172508
w= [[1.99595356]
[-1.00504148]]
b= [3.08971953]
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