# 3-3,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。

``````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

1，准备数据

``````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

``````tf.keras.backend.clear_session()

model = models.Sequential()
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

``````### 使用fit方法进行训练

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

1，准备数据

``````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"]);

``````

``````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

``````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

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)

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[:,1].numpy(),c = "r")
ax1.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
ax1.legend(["positive","negative"]);
ax1.set_title("y_true");

Xp_pred = tf.boolean_mask(X,tf.squeeze(model(X)>=0.5),axis = 0)
Xn_pred = tf.boolean_mask(X,tf.squeeze(model(X)<0.5),axis = 0)

ax2.scatter(Xp_pred[:,0].numpy(),Xp_pred[:,1].numpy(),c = "r")
ax2.scatter(Xn_pred[:,0].numpy(),Xn_pred[:,1].numpy(),c = "g")
ax2.legend(["positive","negative"]);
ax2.set_title("y_pred");
``````

``````
``````