1、准备数据
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)
部分数据：
相关字段说明：
- 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】
2、探索数据
（1）标签分布
%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()
（2） 年龄分布
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()
（3） 年龄和标签之间的相关性
%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()
3、数据预处理
（1）将Pclass转换为one-hot编码
dfresult=pd.DataFrame() #将船票类型转换为one-hot编码 dfPclass=pd.get_dummies(dftrain_raw["Pclass"]) #设置列名 dfPclass.columns =[‘Pclass_‘+str(x) for x in dfPclass.columns] dfresult = pd.concat([dfresult,dfPclass],axis = 1) dfresult
（2） 将Sex转换为One-hot编码
#Sex dfSex = pd.get_dummies(dftrain_raw[‘Sex‘]) dfresult = pd.concat([dfresult,dfSex],axis = 1) dfresult
（3） 用0填充Age列缺失值，并重新定义一列Age_null用来标记缺失值的位置
#将缺失值用0填充 dfresult[‘Age‘] = dftrain_raw[‘Age‘].fillna(0) #增加一列数据为Age_null，同时将不为0的数据用0，将为0的数据用1表示，也就是标记出现0的位置 dfresult[‘Age_null‘] = pd.isna(dftrain_raw[‘Age‘]).astype(‘int32‘) dfresult
（4） 直接拼接SibSp、Parch、Fare
dfresult[‘SibSp‘] = dftrain_raw[‘SibSp‘] dfresult[‘Parch‘] = dftrain_raw[‘Parch‘] dfresult[‘Fare‘] = dftrain_raw[‘Fare‘] dfresult
（5） 标记Cabin缺失的位置
#Carbin dfresult[‘Cabin_null‘] = pd.isna(dftrain_raw[‘Cabin‘]).astype(‘int32‘) dfresult
（6）将Embarked转换成one-hot编码
#Embarked #需要注意的参数是dummy_na=True，将缺失值另外标记出来 dfEmbarked = pd.get_dummies(dftrain_raw[‘Embarked‘],dummy_na=True) dfEmbarked.columns = [‘Embarked_‘ + str(x) for x in dfEmbarked.columns] dfresult = pd.concat([dfresult,dfEmbarked],axis = 1) dfresult
最后，我们将上述操作封装成一个函数：
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)
然后进行数据预处理：
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)
3、使用tensorflow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()
4、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
)
结果：
Epoch 1/30 WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: 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. 9/9 [==============================] - 0s 30ms/step - loss: 4.3524 - auc: 0.4888 - val_loss: 3.0274 - val_auc: 0.5492 Epoch 2/30 9/9 [==============================] - 0s 6ms/step - loss: 2.7962 - auc: 0.4710 - val_loss: 1.8653 - val_auc: 0.4599 Epoch 3/30 9/9 [==============================] - 0s 6ms/step - loss: 1.6765 - auc: 0.4040 - val_loss: 1.2673 - val_auc: 0.4067 Epoch 4/30 9/9 [==============================] - 0s 7ms/step - loss: 1.1195 - auc: 0.3799 - val_loss: 0.9501 - val_auc: 0.4006 Epoch 5/30 9/9 [==============================] - 0s 6ms/step - loss: 0.8156 - auc: 0.4874 - val_loss: 0.7090 - val_auc: 0.5514 Epoch 6/30 9/9 [==============================] - 0s 5ms/step - loss: 0.6355 - auc: 0.6611 - val_loss: 0.6550 - val_auc: 0.6502 Epoch 7/30 9/9 [==============================] - 0s 6ms/step - loss: 0.6308 - auc: 0.7169 - val_loss: 0.6502 - val_auc: 0.6546 Epoch 8/30 9/9 [==============================] - 0s 6ms/step - loss: 0.6088 - auc: 0.7156 - val_loss: 0.6463 - val_auc: 0.6610 Epoch 9/30 9/9 [==============================] - 0s 6ms/step - loss: 0.6066 - auc: 0.7163 - val_loss: 0.6372 - val_auc: 0.6644 Epoch 10/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5964 - auc: 0.7253 - val_loss: 0.6283 - val_auc: 0.6646 Epoch 11/30 9/9 [==============================] - 0s 7ms/step - loss: 0.5876 - auc: 0.7326 - val_loss: 0.6253 - val_auc: 0.6717 Epoch 12/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5827 - auc: 0.7409 - val_loss: 0.6195 - val_auc: 0.6708 Epoch 13/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5769 - auc: 0.7489 - val_loss: 0.6170 - val_auc: 0.6762 Epoch 14/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5719 - auc: 0.7555 - val_loss: 0.6156 - val_auc: 0.6803 Epoch 15/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5662 - auc: 0.7629 - val_loss: 0.6119 - val_auc: 0.6826 Epoch 16/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5627 - auc: 0.7694 - val_loss: 0.6107 - val_auc: 0.6892 Epoch 17/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5586 - auc: 0.7753 - val_loss: 0.6084 - val_auc: 0.6927 Epoch 18/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5539 - auc: 0.7837 - val_loss: 0.6051 - val_auc: 0.6983 Epoch 19/30 9/9 [==============================] - 0s 7ms/step - loss: 0.5479 - auc: 0.7930 - val_loss: 0.6011 - val_auc: 0.7056 Epoch 20/30 9/9 [==============================] - 0s 9ms/step - loss: 0.5451 - auc: 0.7986 - val_loss: 0.5996 - val_auc: 0.7128 Epoch 21/30 9/9 [==============================] - 0s 7ms/step - loss: 0.5406 - auc: 0.8047 - val_loss: 0.5962 - val_auc: 0.7192 Epoch 22/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5357 - auc: 0.8123 - val_loss: 0.5948 - val_auc: 0.7212 Epoch 23/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5295 - auc: 0.8181 - val_loss: 0.5928 - val_auc: 0.7267 Epoch 24/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5275 - auc: 0.8223 - val_loss: 0.5910 - val_auc: 0.7296 Epoch 25/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5263 - auc: 0.8227 - val_loss: 0.5884 - val_auc: 0.7325 Epoch 26/30 9/9 [==============================] - 0s 7ms/step - loss: 0.5199 - auc: 0.8313 - val_loss: 0.5860 - val_auc: 0.7356 Epoch 27/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5145 - auc: 0.8356 - val_loss: 0.5835 - val_auc: 0.7386 Epoch 28/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5138 - auc: 0.8383 - val_loss: 0.5829 - val_auc: 0.7402 Epoch 29/30 9/9 [==============================] - 0s 7ms/step - loss: 0.5092 - auc: 0.8405 - val_loss: 0.5806 - val_auc: 0.7416 Epoch 30/30 9/9 [==============================] - 0s 6ms/step - loss: 0.5082 - auc: 0.8394 - val_loss: 0.5792 - val_auc: 0.7424
5、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")
然后看在在测试集上的效果：
model.evaluate(x = x_test,y = y_test)
结果：
6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc: 0.7869 [0.5286471247673035, 0.786877453327179]
6、使用模型
（1）预测概率
model.predict(x_test[0:10])
结果：
array([[0.34822357],
[0.4793241 ],
[0.43986577],
[0.7916608 ],
[0.50268507],
[0.536609 ],
[0.29079646],
[0.6085641 ],
[0.34384924],
[0.17756936]], dtype=float32)
（2）预测类别
model.predict_classes(x_test[0:10])
结果：
WARNING:tensorflow:From <ipython-input-36-a161a0a6b51e>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.
Instructions for updating:
Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).
array([[0],
[0],
[0],
[1],
[1],
[1],
[0],
[1],
[0],
[0]], dtype=int32)
7、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）使用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
（1）加载模型
# identical to the previous one model = models.load_model(‘./data/keras_model.h5‘) model.evaluate(x_test,y_test)
WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer. 6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc_1: 0.7869 [0.5286471247673035, 0.786877453327179]
（2）保存模型结构和恢复模型结构
# 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)
（3）保存模型权重
# 保存模型权重 model.save_weights(‘./data/keras_model_weight.h5‘)
（4）恢复模型结构并加载权重
# 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)
6/6 [==============================] - 0s 3ms/step - loss: 0.5217 - auc: 0.8123 [0.521678626537323, 0.8122605681419373]
2）tensorflow原生方式
# 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)
INFO:tensorflow:Assets written to: ./data/tf_model_savedmodel/assets export saved model. 6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc_1: 0.7869 [0.5286471247673035, 0.786877453327179]
参考：
开源电子书地址：https://lyhue1991.github.io/eat_tensorflow2_in_30_days/
GitHub 项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days
原文地址：https://www.cnblogs.com/xiximayou/p/12638196.html
时间： 2024-07-30 21:32:04