本文是泰坦尼克号上的生存概率预测,这是基于Kaggle上的一个经典比赛项目。
数据集:
1.Kaggle泰坦尼克号项目页面下载数据:https://www.kaggle.com/c/titanic
2.网盘地址:https://pan.baidu.com/s/1BfRZdCz6Z1XR6aDXxiHmHA 提取码:jzb3
数据读取:
#%%
import tensorflow as tf
import keras
import pandas as pd
import numpy as np
data = pd.read_csv("titanic/train.csv")
print(data.head())
print(data.describe())
数据处理:
#%%
strs = "Survived Pclass Sex Age SibSp Parch Fare Embarked"
clos = strs.split(" ")
print(clos)
#%%
x_datas = data[clos]
print(x_datas.head())
#%%
print(x_datas.isnull().sum())
#%%
x_datas["Age"] = x_datas["Age"].fillna(x_datas["Age"].mean())
x_datas["Embarked"] = x_datas["Embarked"].fillna(x_datas["Embarked"].mode()[0])
#x_datas["Sex"] = pd.get_dummies(x_datas["Sex"])
x_datas = pd.get_dummies(x_datas,columns=["Pclass","Sex","Embarked"])
x_datas["Age"]/=100
x_datas["Fare"]/=100
print(x_datas.isnull().sum())
print(x_datas.head())
#%%
seq = int(0.75*(len(x_datas)))
X ,Y = x_datas.iloc[:,1:],x_datas.iloc[:,0]
X_train,Y_train,X_test,Y_test = X[:seq],Y[:seq],X[seq:],Y[seq:]
模型搭建:
#%%
model = keras.models.Sequential()
model.add(keras.layers.Dense(64,input_dim = 12,activation="relu"))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(16,activation="relu"))
model.add(keras.layers.Dense(2,activation="softmax"))
model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=["accuracy"])
print(model.summary())
模型训练与评估:
#%%
model.fit(X_train,Y_train,validation_split=0.2,epochs=100,batch_size=50)
#%%
y = model.evaluate(X_test,Y_test)
print("test loss is %f, acc %f"%(y[0],y[1]))
model.save("model_100_1.h5")
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 64) 832
_________________________________________________________________
dropout_1 (Dropout) (None, 64) 0
_________________________________________________________________
dense_2 (Dense) (None, 16) 1040
_________________________________________________________________
dense_3 (Dense) (None, 2) 34
=================================================================
Total params: 1,906
Trainable params: 1,906
Non-trainable params: 0
_________________________________________________________________
...
Epoch 96/100
534/534 [==============================] - 0s 80us/step - loss: 0.3870 - acc: 0.8277 - val_loss: 0.5083 - val_acc: 0.7612
Epoch 97/100
534/534 [==============================] - 0s 80us/step - loss: 0.3921 - acc: 0.8352 - val_loss: 0.5070 - val_acc: 0.7687
Epoch 98/100
534/534 [==============================] - 0s 82us/step - loss: 0.3940 - acc: 0.8371 - val_loss: 0.5102 - val_acc: 0.7687
Epoch 99/100
534/534 [==============================] - 0s 78us/step - loss: 0.3996 - acc: 0.8277 - val_loss: 0.5106 - val_acc: 0.7687
Epoch 100/100
534/534 [==============================] - 0s 80us/step - loss: 0.3892 - acc: 0.8352 - val_loss: 0.5082 - val_acc: 0.7612
223/223 [==============================] - 0s 63us/step
test loss is 0.389338, acc 0.829596
#%%
import tensorflow as tf
import keras
import pandas as pd
import numpy as np
data = pd.read_csv("titanic/train.csv")
print(data.head())
print(data.describe())
#%%
strs = "Survived Pclass Sex Age SibSp Parch Fare Embarked"
clos = strs.split(" ")
print(clos)
#%%
x_datas = data[clos]
print(x_datas.head())
#%%
print(x_datas.isnull().sum())
#%%
x_datas["Age"] = x_datas["Age"].fillna(x_datas["Age"].mean())
x_datas["Embarked"] = x_datas["Embarked"].fillna(x_datas["Embarked"].mode()[0])
#x_datas["Sex"] = pd.get_dummies(x_datas["Sex"])
x_datas = pd.get_dummies(x_datas,columns=["Pclass","Sex","Embarked"])
x_datas["Age"]/=100
x_datas["Fare"]/=100
print(x_datas.isnull().sum())
print(x_datas.head())
#%%
seq = int(0.75*(len(x_datas)))
X ,Y = x_datas.iloc[:,1:],x_datas.iloc[:,0]
X_train,Y_train,X_test,Y_test = X[:seq],Y[:seq],X[seq:],Y[seq:]
#%%
model = keras.models.Sequential()
model.add(keras.layers.Dense(64,input_dim = 12,activation="relu"))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(16,activation="relu"))
model.add(keras.layers.Dense(2,activation="softmax"))
model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=["accuracy"])
print(model.summary())
#%%
model.fit(X_train,Y_train,validation_split=0.2,epochs=100,batch_size=50)
#%%
y = model.evaluate(X_test,Y_test)
print("test loss is %f, acc %f"%(y[0],y[1]))
model.save("model_100_1.h5")