《30天吃掉那只 TensorFlow2.0》 3-3 高阶API示范
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3-3 高阶API示范
文章目录
下面的范例使用TensorFlow的高阶API实现线性回归模型和DNN二分类模型。
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。
此处分别演示使用Sequential按层顺序构建模型以及继承Model基类构建自定义模型。
这里还是一开始定义一个打印时间分割线的函数
import tensorflow as tf
#打印时间分割线
@tf.function
def printbar():
today_ts = tf.timestamp()%(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+timestring)
一，线性回归模型
此范例我们使用Sequential按层顺序构建模型，并使用内置model.fit方法训练模型【面向新手】。
1，准备数据
首先我们得先准备数据，我们可以用tensorflow来生成随机数，由于我们是线性回归，我们就可以利用矩阵乘法生成，并且加入一些正态扰动即可
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import models,layers,losses,metrics,optimizers
#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],[-3.0]])
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
# 数据可视化
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
plt.figure(figsize = (12,5))
ax1 = plt.subplot(121)
ax1.scatter(X[:,0],Y[:,0], c = "b")
plt.xlabel("x1")
plt.ylabel("y",rotation = 0)
ax2 = plt.subplot(122)
ax2.scatter(X[:,1],Y[:,0], c = "g")
plt.xlabel("x2")
plt.ylabel("y",rotation = 0)
plt.show()
2，定义模型
这里的定义模型，我们可以用tf.keras的高阶API，简单就可以定义完成，只需要通过add
tf.keras.backend.clear_session()
model = models.Sequential()
model.add(layers.Dense(1,input_shape =(2,)))
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 1) 3
=================================================================
Total params: 3
Trainable params: 3
Non-trainable params: 0
3，训练模型
这里可以直接用fit方法训练，不需要写过多的东西
### 使用fit方法进行训练
model.compile(optimizer="adam",loss="mse",metrics=["mae"])
model.fit(X,Y,batch_size = 10,epochs = 200)
tf.print("w = ",model.layers[0].kernel)
tf.print("b = ",model.layers[0].bias)
Epoch 197/200
400/400 [==============================] - 0s 190us/sample - loss: 4.3977 - mae: 1.7129
Epoch 198/200
400/400 [==============================] - 0s 172us/sample - loss: 4.3918 - mae: 1.7117
Epoch 199/200
400/400 [==============================] - 0s 134us/sample - loss: 4.3861 - mae: 1.7106
Epoch 200/200
400/400 [==============================] - 0s 166us/sample - loss: 4.3786 - mae: 1.7092
w = [[1.99339032]
[-3.00866461]]
b = [2.67018795]
# 结果可视化
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
w,b = model.variables
plt.figure(figsize = (12,5))
ax1 = plt.subplot(121)
ax1.scatter(X[:,0],Y[:,0], c = "b",label = "samples")
ax1.plot(X[:,0],w[0]*X[:,0]+b[0],"-r",linewidth = 5.0,label = "model")
ax1.legend()
plt.xlabel("x1")
plt.ylabel("y",rotation = 0)
ax2 = plt.subplot(122)
ax2.scatter(X[:,1],Y[:,0], c = "g",label = "samples")
ax2.plot(X[:,1],w[1]*X[:,1]+b[0],"-r",linewidth = 5.0,label = "model")
ax2.legend()
plt.xlabel("x2")
plt.ylabel("y",rotation = 0)
plt.show()
二，DNN二分类模型
此范例我们使用继承Model基类构建自定义模型，并构建自定义训练循环【面向专家】
1，准备数据
这里和前面有些类似，首先我们利用tensorflow的random函数来得到我们的数据
-
生成正样本, 小圆环分布
-
生成负样本, 大圆环分布
生成后也可以可视化，对数据的分布有一个更好的理解
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
#正负样本数量
n_positive,n_negative = 2000,2000
#生成正样本, 小圆环分布
r_p = 5.0 + tf.random.truncated_normal([n_positive,1],0.0,1.0)
theta_p = tf.random.uniform([n_positive,1],0.0,2*np.pi)
Xp = tf.concat([r_p*tf.cos(theta_p),r_p*tf.sin(theta_p)],axis = 1)
Yp = tf.ones_like(r_p)
#生成负样本, 大圆环分布
r_n = 8.0 + tf.random.truncated_normal([n_negative,1],0.0,1.0)
theta_n = tf.random.uniform([n_negative,1],0.0,2*np.pi)
Xn = tf.concat([r_n*tf.cos(theta_n),r_n*tf.sin(theta_n)],axis = 1)
Yn = tf.zeros_like(r_n)
#汇总样本
X = tf.concat([Xp,Xn],axis = 0)
Y = tf.concat([Yp,Yn],axis = 0)
#样本洗牌
data = tf.concat([X,Y],axis = 1)
data = tf.random.shuffle(data)
X = data[:,:2]
Y = data[:,2:]
#可视化
plt.figure(figsize = (6,6))
plt.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
plt.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
plt.legend(["positive","negative"]);
这里还是构建数据管道
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:,:])) \\
.batch(20) \\
.prefetch(tf.data.experimental.AUTOTUNE) \\
.cache()
2，定义模型
这里的定义模型是和中阶API的一样的，定义正向传播和模型的结构
tf.keras.backend.clear_session()
class DNNModel(models.Model):
def __init__(self):
super(DNNModel, self).__init__()
def build(self,input_shape):
self.dense1 = layers.Dense(4,activation = "relu",name = "dense1")
self.dense2 = layers.Dense(8,activation = "relu",name = "dense2")
self.dense3 = layers.Dense(1,activation = "sigmoid",name = "dense3")
super(DNNModel,self).build(input_shape)
# 正向传播
@tf.function(input_signature=[tf.TensorSpec(shape = [None,2], dtype = tf.float32)])
def call(self,x):
x = self.dense1(x)
x = self.dense2(x)
y = self.dense3(x)
return y
model = DNNModel()
model.build(input_shape =(None,2))
model.summary()
Model: "dnn_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense1 (Dense) multiple 12
_________________________________________________________________
dense2 (Dense) multiple 40
_________________________________________________________________
dense3 (Dense) multiple 9
=================================================================
Total params: 61
Trainable params: 61
Non-trainable params: 0
_________________________________________________________________
3，训练模型
### 自定义训练循环
optimizer = optimizers.Adam(learning_rate=0.01)
loss_func = tf.keras.losses.BinaryCrossentropy()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_metric = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
valid_loss = tf.keras.metrics.Mean(name='valid_loss')
valid_metric = tf.keras.metrics.BinaryAccuracy(name='valid_accuracy')
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features)
loss = loss_func(labels, predictions)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, 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)
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:,Accuracy:,Valid Loss:,Valid Accuracy:'
if epoch%100 ==0:
printbar()
tf.print(tf.strings.format(logs,
(epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result())))
train_loss.reset_states()
valid_loss.reset_states()
train_metric.reset_states()
valid_metric.reset_states()
train_model(model,ds_train,ds_valid,1000)
================================================================================17:35:02
Epoch=100,Loss:0.194088802,Accuracy:0.923064,Valid Loss:0.215538561,Valid Accuracy:0.904368
================================================================================17:35:22
Epoch=200,Loss:0.151239693,Accuracy:0.93768847,Valid Loss:0.181166962,Valid Accuracy:0.920664132
================================================================================17:35:43
Epoch=300,Loss:0.134556711,Accuracy:0.944247484,Valid Loss:0.171530813,Valid Accuracy:0.926396072
================================================================================17:36:04
Epoch=400,Loss:0.125722557,Accuracy:0.949172914,Valid Loss:0.16731061,Valid Accuracy:0.929318547
================================================================================17:36:24
Epoch=500,Loss:0.120216407,Accuracy:0.952525079,Valid Loss:0.164817035,Valid Accuracy:0.931044817
================================================================================17:36:44
Epoch=600,Loss:0.116434008,Accuracy:0.954830289,Valid Loss:0.163089141,Valid Accuracy:0.932202339
================================================================================17:37:05
Epoch=700,Loss:0.113658346,Accuracy:0.956433,Valid Loss:0.161804497,Valid Accuracy:0.933092058
================================================================================17:37:25
Epoch=800,Loss:0.111522928,Accuracy:0.957467675,Valid Loss:0.160796657,Valid Accuracy:0.93379426
================================================================================17:37:46
Epoch=900,Loss:0.109816991,Accuracy:0.958205402,Valid Loss:0.159987748,Valid Accuracy:0.934343576
================================================================================17:38:06
Epoch=1000,Loss:0.10841465,Accuracy:0.958805501,Valid Loss:0.159325734,Valid Accuracy:0.934785843
# 结果可视化
fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))
ax1.scatter(Xp[:,0].numpy(),Xp[以上是关于《30天吃掉那只 TensorFlow2.0》 3-3 高阶API示范的主要内容，如果未能解决你的问题，请参考以下文章
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