30天干掉tensorflow2.0-day01

0 TensorFlow的建模流程

尽管TensorFlow设计上足够灵活，可以用于进行各种复杂的数值计算。

但通常人们使用TensorFlow来实现机器学习模型，尤其常用于实现神经网络模型。

从原理上说可以使用张量构建计算图来定义神经网络，并通过自动微分机制训练模型。

但为简洁起见，一般推荐使用TensorFlow的高层次keras接口来实现神经网络网模型。

使用TensorFlow实现神经网络模型的一般流程包括：

1，准备数据

2，定义模型

3，训练模型

4，评估模型

5，使用模型

6，保存模型。

对新手来说，其中最困难的部分实际上是准备数据过程。

我们在实践中通常会遇到的数据类型包括结构化数据，图片数据，文本数据，时间序列数据。

我们将分别以titanic生存预测问题，cifar2图片分类问题，imdb电影评论分类问题，国内新冠疫情结束时间预测问题为例，演示应用tensorflow对这四类数据的建模方法。

1 准备数据

titanic数据集的目标是根据乘客信息预测他们在Titanic号撞击冰山沉没后能否生存。

结构化数据一般会使用Pandas中的DataFrame进行预处理。

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.keras import models, layers

dftrain_raw = pd.read_csv('./data/titanic/train.csv')
dftest_raw = pd.read_csv('./data/titanic/test.csv')
dftrain_raw.head(10)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	493	0	1	Molson, Mr. Harry Markland	male	55.0	0	0	113787	30.5000	C30	S
1	53	1	1	Harper, Mrs. Henry Sleeper (Myna Haxtun)	female	49.0	1	0	PC 17572	76.7292	D33	C
2	388	1	2	Buss, Miss. Kate	female	36.0	0	0	27849	13.0000	NaN	S
3	192	0	2	Carbines, Mr. William	male	19.0	0	0	28424	13.0000	NaN	S
4	687	0	3	Panula, Mr. Jaako Arnold	male	14.0	4	1	3101295	39.6875	NaN	S
5	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
6	228	0	3	Lovell, Mr. John Hall ("Henry")	male	20.5	0	0	A/5 21173	7.2500	NaN	S
7	884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
8	168	0	3	Skoog, Mrs. William (Anna Bernhardina Karlsson)	female	45.0	1	4	347088	27.9000	NaN	S
9	752	1	3	Moor, Master. Meier	male	6.0	0	1	392096	12.4750	E121	S

字段说明：

• Survived:0代表死亡，1代表存活【y标签】
• Pclass:乘客所持票类，有三种值(1,2,3) 【转换成onehot编码】
• Name:乘客姓名 【舍去】
• Sex:乘客性别 【转换成bool特征】
• Age:乘客年龄(有缺失) 【数值特征，添加“年龄是否缺失”作为辅助特征】
• SibSp:乘客兄弟姐妹/配偶的个数(整数值) 【数值特征】
• Parch:乘客父母/孩子的个数(整数值)【数值特征】
• Ticket:票号(字符串)【舍去】
• Fare:乘客所持票的价格(浮点数，0-500不等) 【数值特征】
• Cabin:乘客所在船舱(有缺失) 【添加“所在船舱是否缺失”作为辅助特征】
• Embarked:乘客登船港口:S、C、Q(有缺失)【转换成onehot编码，四维度 S,C,Q,nan】

利用Pandas的数据可视化功能我们可以简单地进行探索性数据分析EDA（Exploratory Data Analysis）。

label(Survived)分布情况

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw['Survived'].value_counts().plot(kind = 'bar',
     figsize = (12,8),fontsize=15,rot = 0)
ax.set_ylabel('Counts',fontsize = 15)
ax.set_xlabel('Survived',fontsize = 15)
plt.show()

年龄分布情况

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw['Age'].plot(kind = 'hist',bins = 20,color= 'purple',
                    figsize = (12,8),fontsize=15)

ax.set_ylabel('Frequency',fontsize = 15)
ax.set_xlabel('Age',fontsize = 15)
plt.show()

年龄和label的相关性

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw.query('Survived == 0')['Age'].plot(kind = 'density',
                      figsize = (12,8),fontsize=15)
dftrain_raw.query('Survived == 1')['Age'].plot(kind = 'density',
                      figsize = (12,8),fontsize=15)
ax.legend(['Survived==0','Survived==1'],fontsize = 12)
ax.set_ylabel('Density',fontsize = 15)
ax.set_xlabel('Age',fontsize = 15)
plt.show()

下面为正式的数据预处理

def preprocessing(dfdata):

    dfresult= pd.DataFrame()

    #Pclass乘客所持票类进行one_hot编码
    dfPclass = pd.get_dummies(dfdata['Pclass'])
    dfPclass.columns = ['Pclass_' +str(x) for x in dfPclass.columns ]
    dfresult = pd.concat([dfresult,dfPclass],axis = 1)

    #Sex性别进行one_hot编码
    dfSex = pd.get_dummies(dfdata['Sex'])
    dfresult = pd.concat([dfresult,dfSex],axis = 1)

    #Age 对age缺失的进行0填充
    dfresult['Age'] = dfdata['Age'].fillna(0)
    dfresult['Age_null'] = pd.isna(dfdata['Age']).astype('int32')

    #SibSp,Parch,Fare 数值特征不处理
    dfresult['SibSp'] = dfdata['SibSp']
    dfresult['Parch'] = dfdata['Parch']
    dfresult['Fare'] = dfdata['Fare']

    #Carbin 缺失为1，没缺失为0
    dfresult['Cabin_null'] =  pd.isna(dfdata['Cabin']).astype('int32')

    #Embarked one_hot编码，把nan考虑进去了
    dfEmbarked = pd.get_dummies(dfdata['Embarked'],dummy_na=True)
    dfEmbarked.columns = ['Embarked_' + str(x) for x in dfEmbarked.columns]
    dfresult = pd.concat([dfresult,dfEmbarked],axis = 1)

    return(dfresult)

x_train = preprocessing(dftrain_raw)
y_train = dftrain_raw['Survived'].values

x_test = preprocessing(dftest_raw)
y_test = dftest_raw['Survived'].values

print("x_train.shape =", x_train.shape )
print("x_test.shape =", x_test.shape )

x_train.shape = (712, 15)
x_test.shape = (179, 15)

x_train.head(10)

	Pclass_1	Pclass_2	Pclass_3	female	male	Age	Age_null	SibSp	Parch	Fare	Cabin_null	Embarked_C	Embarked_Q	Embarked_S	Embarked_nan
0	1	0	0	0	1	55.0	0	0	0	30.5000	0	0	0	1	0
1	1	0	0	1	0	49.0	0	1	0	76.7292	0	1	0	0	0
2	0	1	0	1	0	36.0	0	0	0	13.0000	1	0	0	1	0
3	0	1	0	0	1	19.0	0	0	0	13.0000	1	0	0	1	0
4	0	0	1	0	1	14.0	0	4	1	39.6875	1	0	0	1	0
5	0	1	0	1	0	55.0	0	0	0	16.0000	1	0	0	1	0
6	0	0	1	0	1	20.5	0	0	0	7.2500	1	0	0	1	0
7	0	1	0	0	1	28.0	0	0	0	10.5000	1	0	0	1	0
8	0	0	1	1	0	45.0	0	1	4	27.9000	1	0	0	1	0
9	0	0	1	0	1	6.0	0	0	1	12.4750	0	0	0	1	0

x_train[x_train['Embarked_nan']==1]

	Pclass_1	Pclass_2	Pclass_3	female	male	Age	Age_null	SibSp	Parch	Fare	Cabin_null	Embarked_C	Embarked_Q	Embarked_S	Embarked_nan
232	1	0	0	1	0	38.0	0	0	0	80.0	0	0	0	0	1
292	1	0	0	1	0	62.0	0	0	0	80.0	0	0	0	0	1

只有两列是缺失的

2 定义模型

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。

此处选择使用最简单的Sequential，按层顺序模型。

tf.keras.backend.clear_session()

model = models.Sequential()
model.add(layers.Dense(20,activation = 'relu',input_shape=(15,)))
model.add(layers.Dense(10,activation = 'relu' ))
model.add(layers.Dense(1,activation = 'sigmoid' ))

model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 20)                320       
_________________________________________________________________
dense_1 (Dense)              (None, 10)                210       
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 11        
=================================================================
Total params: 541
Trainable params: 541
Non-trainable params: 0
_________________________________________________________________

3 训练模型

训练模型通常有3种方法，内置fit方法，内置train_on_batch方法，以及自定义训练循环。此处我们选择最常用也最简单的内置fit方法。

# 二分类问题选择二元交叉熵损失函数
model.compile(optimizer='adam',
            loss='binary_crossentropy',
            metrics=['AUC'])

history = model.fit(x_train,y_train,
                    batch_size= 64,
                    epochs= 30,
                    validation_split=0.2 #分割一部分训练数据用于验证
                   )

Train on 569 samples, validate on 143 samples
Epoch 1/30
569/569 [==============================] - 1s 2ms/sample - loss: 1.1756 - AUC: 0.6290 - val_loss: 0.7064 - val_AUC: 0.6590
Epoch 2/30
569/569 [==============================] - 0s 65us/sample - loss: 0.7333 - AUC: 0.4361 - val_loss: 0.7154 - val_AUC: 0.4920
Epoch 3/30
569/569 [==============================] - 0s 78us/sample - loss: 0.6462 - AUC: 0.6062 - val_loss: 0.6499 - val_AUC: 0.6625
Epoch 4/30
569/569 [==============================] - 0s 65us/sample - loss: 0.6439 - AUC: 0.7018 - val_loss: 0.6540 - val_AUC: 0.6553
Epoch 5/30
569/569 [==============================] - 0s 76us/sample - loss: 0.6200 - AUC: 0.6988 - val_loss: 0.6369 - val_AUC: 0.6577
Epoch 6/30
569/569 [==============================] - 0s 63us/sample - loss: 0.6194 - AUC: 0.6903 - val_loss: 0.6283 - val_AUC: 0.6667
Epoch 7/30
569/569 [==============================] - 0s 64us/sample - loss: 0.6070 - AUC: 0.7183 - val_loss: 0.6237 - val_AUC: 0.6891
Epoch 8/30
569/569 [==============================] - 0s 65us/sample - loss: 0.6075 - AUC: 0.7341 - val_loss: 0.6139 - val_AUC: 0.6973
Epoch 9/30
569/569 [==============================] - 0s 67us/sample - loss: 0.6012 - AUC: 0.7386 - val_loss: 0.6119 - val_AUC: 0.7012
Epoch 10/30
569/569 [==============================] - 0s 66us/sample - loss: 0.5961 - AUC: 0.7458 - val_loss: 0.6119 - val_AUC: 0.7061
Epoch 11/30
569/569 [==============================] - 0s 64us/sample - loss: 0.5908 - AUC: 0.7472 - val_loss: 0.6091 - val_AUC: 0.7108
Epoch 12/30
569/569 [==============================] - 0s 67us/sample - loss: 0.5870 - AUC: 0.7567 - val_loss: 0.6084 - val_AUC: 0.7181
Epoch 13/30
569/569 [==============================] - 0s 65us/sample - loss: 0.5792 - AUC: 0.7672 - val_loss: 0.6095 - val_AUC: 0.7161
Epoch 14/30
569/569 [==============================] - 0s 63us/sample - loss: 0.5778 - AUC: 0.7697 - val_loss: 0.6105 - val_AUC: 0.7225
Epoch 15/30
569/569 [==============================] - 0s 70us/sample - loss: 0.5742 - AUC: 0.7784 - val_loss: 0.6144 - val_AUC: 0.7258
Epoch 16/30
569/569 [==============================] - 0s 67us/sample - loss: 0.5728 - AUC: 0.7739 - val_loss: 0.6171 - val_AUC: 0.7169
Epoch 17/30
569/569 [==============================] - 0s 65us/sample - loss: 0.5826 - AUC: 0.7698 - val_loss: 0.6181 - val_AUC: 0.7460
Epoch 18/30
569/569 [==============================] - 0s 68us/sample - loss: 0.5555 - AUC: 0.7895 - val_loss: 0.6053 - val_AUC: 0.7272
Epoch 19/30
569/569 [==============================] - 0s 62us/sample - loss: 0.5619 - AUC: 0.7890 - val_loss: 0.6040 - val_AUC: 0.7399
Epoch 20/30
569/569 [==============================] - 0s 66us/sample - loss: 0.5530 - AUC: 0.7972 - val_loss: 0.5990 - val_AUC: 0.7405
Epoch 21/30
569/569 [==============================] - 0s 75us/sample - loss: 0.5513 - AUC: 0.7976 - val_loss: 0.5981 - val_AUC: 0.7434
Epoch 22/30
569/569 [==============================] - 0s 76us/sample - loss: 0.5456 - AUC: 0.8040 - val_loss: 0.5955 - val_AUC: 0.7538
Epoch 23/30
569/569 [==============================] - 0s 79us/sample - loss: 0.5406 - AUC: 0.8061 - val_loss: 0.5908 - val_AUC: 0.7469
Epoch 24/30
569/569 [==============================] - 0s 77us/sample - loss: 0.5395 - AUC: 0.8105 - val_loss: 0.5900 - val_AUC: 0.7528
Epoch 25/30
569/569 [==============================] - 0s 93us/sample - loss: 0.5315 - AUC: 0.8118 - val_loss: 0.5856 - val_AUC: 0.7490
Epoch 26/30
569/569 [==============================] - 0s 70us/sample - loss: 0.5311 - AUC: 0.8107 - val_loss: 0.5891 - val_AUC: 0.7562
Epoch 27/30
569/569 [==============================] - 0s 74us/sample - loss: 0.5247 - AUC: 0.8214 - val_loss: 0.5812 - val_AUC: 0.7598
Epoch 28/30
569/569 [==============================] - 0s 65us/sample - loss: 0.5203 - AUC: 0.8243 - val_loss: 0.5787 - val_AUC: 0.7609
Epoch 29/30
569/569 [==============================] - 0s 69us/sample - loss: 0.5176 - AUC: 0.8244 - val_loss: 0.5759 - val_AUC: 0.7610
Epoch 30/30
569/569 [==============================] - 0s 59us/sample - loss: 0.5142 - AUC: 0.8317 - val_loss: 0.5836 - val_AUC: 0.7702

4 评估模型

我们首先评估一下模型在训练集和验证集上的效果。

%matplotlib inline
%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt

def plot_metric(history, metric):
    train_metrics = history.history[metric]
    val_metrics = history.history['val_'+metric]
    epochs = range(1, len(train_metrics) + 1)
    plt.plot(epochs, train_metrics, 'bo--')
    plt.plot(epochs, val_metrics, 'ro-')
    plt.title('Training and validation '+ metric)
    plt.xlabel("Epochs")
    plt.ylabel(metric)
    plt.legend(["train_"+metric, 'val_'+metric])
    plt.show()

plot_metric(history,"loss")

plot_metric(history,"AUC")

我们再看一下模型在测试集上的效果.

model.evaluate(x = x_test,y = y_test)

179/179 [==============================] - 0s 67us/sample - loss: 0.5062 - AUC: 0.8309





[0.5062098619658187, 0.8309387]

5 使用模型

#预测概率
model.predict(x_test[0:10])
#model(tf.constant(x_test[0:10].values,dtype = tf.float32)) #等价写法

array([[0.15356979],
       [0.36305907],
       [0.3694313 ],
       [0.6562717 ],
       [0.41980737],
       [0.502912  ],
       [0.1906718 ],
       [0.5757565 ],
       [0.43784416],
       [0.15257531]], dtype=float32)

#预测类别
model.predict_classes(x_test[0:10])

array([[0],
       [0],
       [0],
       [1],
       [0],
       [1],
       [0],
       [1],
       [0],
       [0]])

6 保存模型

可以使用Keras方式保存模型，也可以使用TensorFlow原生方式保存。前者仅仅适合使用Python环境恢复模型，后者则可以跨平台进行模型部署。

推荐使用后一种方式进行保存。

1，Keras方式保存

# 保存模型结构及权重

model.save('./data/keras_model.h5')  

del model  #删除现有模型

# identical to the previous one
model = models.load_model('./data/keras_model.h5')
model.evaluate(x_test,y_test)

179/179 [==============================] - 0s 920us/sample - loss: 0.5062 - AUC: 0.8309





[0.5062098619658187, 0.8309387]

# 保存模型结构
json_str = model.to_json()

# 恢复模型结构
model_json = models.model_from_json(json_str)

#保存模型权重
model.save_weights('./data/keras_model_weight.h5')

# 恢复模型结构
model_json = models.model_from_json(json_str)
model_json.compile(
        optimizer='adam',
        loss='binary_crossentropy',
        metrics=['AUC']
    )

# 加载权重
model_json.load_weights('./data/keras_model_weight.h5')
model_json.evaluate(x_test,y_test)

179/179 [==============================] - 0s 920us/sample - loss: 0.5062 - AUC: 0.8309





[0.5062098619658187, 0.8309387]

2，TensorFlow原生方式保存

# 保存权重，该方式仅仅保存权重张量
model.save_weights('./data/tf_model_weights.ckpt',save_format = "tf")

# 保存模型结构与模型参数到文件,该方式保存的模型具有跨平台性便于部署

model.save('./data/tf_model_savedmodel', save_format="tf")
print('export saved model.')

model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel')
model_loaded.evaluate(x_test,y_test)

WARNING:tensorflow:From D:\anaconda3\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: ./data/tf_model_savedmodel\assets
export saved model.
179/179 [==============================] - 0s 1ms/sample - loss: 0.5062 - AUC: 0.8309





[0.5062096395306082, 0.8309387]

具体项目见我的github