tensorflow 1-1,结构化数据建模流程范例

Posted 2021-06-12 ————————————————————Carrawayan

1-1,结构化数据建模流程范例

一，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 numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import tensorflow as tf 
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)

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
    dfPclass = pd.get_dummies(dfdata[\'Pclass\'])
    dfPclass.columns = [\'Pclass_\' +str(x) for x in dfPclass.columns ]
    dfresult = pd.concat([dfresult,dfPclass],axis = 1)

    #Sex
    dfSex = pd.get_dummies(dfdata[\'Sex\'])
    dfresult = pd.concat([dfresult,dfSex],axis = 1)

    #Age
    dfresult[\'Age\'] = dfdata[\'Age\'].fillna(0)
    dfresult[\'Age_null\'] = pd.isna(dfdata[\'Age\']).astype(\'int32\')

    #SibSp,Parch,Fare
    dfresult[\'SibSp\'] = dfdata[\'SibSp\']
    dfresult[\'Parch\'] = dfdata[\'Parch\']
    dfresult[\'Fare\'] = dfdata[\'Fare\']

    #Carbin
    dfresult[\'Cabin_null\'] =  pd.isna(dfdata[\'Cabin\']).astype(\'int32\')

    #Embarked
    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)

二，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
_________________________________________________________________

三，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: 3.5841 - AUC: 0.4079 - val_loss: 3.4429 - val_AUC: 0.4129
Epoch 2/30
569/569 [==============================] - 0s 102us/sample - loss: 2.6093 - AUC: 0.3967 - val_loss: 2.4886 - val_AUC: 0.4139
Epoch 3/30
569/569 [==============================] - 0s 68us/sample - loss: 1.8375 - AUC: 0.4003 - val_loss: 1.7383 - val_AUC: 0.4223
Epoch 4/30
569/569 [==============================] - 0s 83us/sample - loss: 1.2545 - AUC: 0.4390 - val_loss: 1.1936 - val_AUC: 0.4765
Epoch 5/30
569/569 [==============================] - ETA: 0s - loss: 1.4435 - AUC: 0.375 - 0s 90us/sample - loss: 0.9141 - AUC: 0.5192 - val_loss: 0.8274 - val_AUC: 0.5584
Epoch 6/30
569/569 [==============================] - 0s 110us/sample - loss: 0.7052 - AUC: 0.6290 - val_loss: 0.6596 - val_AUC: 0.6880
Epoch 7/30
569/569 [==============================] - 0s 90us/sample - loss: 0.6410 - AUC: 0.7086 - val_loss: 0.6519 - val_AUC: 0.6845
Epoch 8/30
569/569 [==============================] - 0s 93us/sample - loss: 0.6246 - AUC: 0.7080 - val_loss: 0.6480 - val_AUC: 0.6846
Epoch 9/30
569/569 [==============================] - 0s 73us/sample - loss: 0.6088 - AUC: 0.7113 - val_loss: 0.6497 - val_AUC: 0.6838
Epoch 10/30
569/569 [==============================] - 0s 79us/sample - loss: 0.6051 - AUC: 0.7117 - val_loss: 0.6454 - val_AUC: 0.6873
Epoch 11/30
569/569 [==============================] - 0s 96us/sample - loss: 0.5972 - AUC: 0.7218 - val_loss: 0.6369 - val_AUC: 0.6888
Epoch 12/30
569/569 [==============================] - 0s 92us/sample - loss: 0.5918 - AUC: 0.7294 - val_loss: 0.6330 - val_AUC: 0.6908
Epoch 13/30
569/569 [==============================] - 0s 75us/sample - loss: 0.5864 - AUC: 0.7363 - val_loss: 0.6281 - val_AUC: 0.6948
Epoch 14/30
569/569 [==============================] - 0s 104us/sample - loss: 0.5832 - AUC: 0.7426 - val_loss: 0.6240 - val_AUC: 0.7030
Epoch 15/30
569/569 [==============================] - 0s 74us/sample - loss: 0.5777 - AUC: 0.7507 - val_loss: 0.6200 - val_AUC: 0.7066
Epoch 16/30
569/569 [==============================] - 0s 79us/sample - loss: 0.5726 - AUC: 0.7569 - val_loss: 0.6155 - val_AUC: 0.7132
Epoch 17/30
569/569 [==============================] - 0s 99us/sample - loss: 0.5674 - AUC: 0.7643 - val_loss: 0.6070 - val_AUC: 0.7255
Epoch 18/30
569/569 [==============================] - 0s 97us/sample - loss: 0.5631 - AUC: 0.7721 - val_loss: 0.6061 - val_AUC: 0.7305
Epoch 19/30
569/569 [==============================] - 0s 73us/sample - loss: 0.5580 - AUC: 0.7792 - val_loss: 0.6027 - val_AUC: 0.7332
Epoch 20/30
569/569 [==============================] - 0s 85us/sample - loss: 0.5533 - AUC: 0.7861 - val_loss: 0.5997 - val_AUC: 0.7366
Epoch 21/30
569/569 [==============================] - 0s 87us/sample - loss: 0.5497 - AUC: 0.7926 - val_loss: 0.5961 - val_AUC: 0.7433
Epoch 22/30
569/569 [==============================] - 0s 101us/sample - loss: 0.5454 - AUC: 0.7987 - val_loss: 0.5943 - val_AUC: 0.7438
Epoch 23/30
569/569 [==============================] - 0s 100us/sample - loss: 0.5398 - AUC: 0.8057 - val_loss: 0.5926 - val_AUC: 0.7492
Epoch 24/30
569/569 [==============================] - 0s 79us/sample - loss: 0.5328 - AUC: 0.8122 - val_loss: 0.5912 - val_AUC: 0.7493
Epoch 25/30
569/569 [==============================] - 0s 86us/sample - loss: 0.5283 - AUC: 0.8147 - val_loss: 0.5902 - val_AUC: 0.7509
Epoch 26/30
569/569 [==============================] - 0s 67us/sample - loss: 0.5246 - AUC: 0.8196 - val_loss: 0.5845 - val_AUC: 0.7552
Epoch 27/30
569/569 [==============================] - 0s 72us/sample - loss: 0.5205 - AUC: 0.8271 - val_loss: 0.5837 - val_AUC: 0.7584
Epoch 28/30
569/569 [==============================] - 0s 74us/sample - loss: 0.5144 - AUC: 0.8302 - val_loss: 0.5848 - val_AUC: 0.7561
Epoch 29/30
569/569 [==============================] - 0s 77us/sample - loss: 0.5099 - AUC: 0.8326 - val_loss: 0.5809 - val_AUC: 0.7583
Epoch 30/30
569/569 [==============================] - 0s 80us/sample - loss: 0.5071 - AUC: 0.8349 - val_loss: 0.5816 - val_AUC: 0.7605

四，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)

[0.5191367897907448, 0.8122605]

五，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([[0.26501188],
       [0.40970832],
       [0.44285864],
       [0.78408605],
       [0.47650957],
       [0.43849158],
       [0.27426785],
       [0.5962582 ],
       [0.59476686],
       [0.17882936]], dtype=float32)

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

array([[0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0]], dtype=int32)

六，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)

[0.5191367897907448, 0.8122605]

# 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)

[0.5191367897907448, 0.8122605]

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)

[0.5191365896656527, 0.8122605]