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
一，准备数据
作者：码奋
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。
字段说明：
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)
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。
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
)
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")
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-IW4fJb8k-1586137598375)(./data/1-1-AUC曲线.jpg)]
我们再看一下模型在测试集上的效果.
model.evaluate(x = x_test,y = y_test)
[0.5291367897907448, 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。
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)
[0.5291367897907448, 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)
[0.5291365896656527, 0.8122605]
作者：码奋
