1 # coding=utf-8
2 import pandas as pd
3 import numpy as np
4 from sklearn import cross_validation
5 import tensorflow as tf
6
7 global flag
8 flag=0
9
10 def DataPreprocessing():
11 abalone = pd.read_csv("ceshi.csv", sep=',', header=0, keep_default_na=True,na_values=[])
12 X_train=np.array(abalone.iloc[:,:4])
13 Y_train=np.array(abalone.iloc[:,4:])
14 # Y_train=[]
15 # for i in range(len(X_train)):
16 # if X_train[i][0] == 'M':
17 # X_train[i][0]=0
18 # elif X_train[i][0]=='F':
19 # X_train[i][0]=1
20 # else:
21 # X_train[i][0]=2
22 #
23 # for i in range(len(Y_train_)):
24 #
25 # #print(Y_train[i][0])
26 # Y_train.append(Y_train_[i][0])
27
28 # print(X_train)
29 # print(len(X_train))
30 # print(Y_train)
31 # print(len(Y_train))
32 # print(min(Y_train))
33 # print(max(Y_train))
34
35 return cross_validation.train_test_split(X_train,Y_train,test_size=0.25,random_state=0,stratify=Y_train)
36
37
38 def GetInputs():
39 global flag
40 X_train, X_test, Y_train, Y_test = DataPreprocessing()
41
42 #print(X_train)
43 # print(len(X_test))
44 # print(len(Y_train))
45 # print(len(Y_test))
46
47
48 #X_train[X_train.isnull().any(axis=1)]
49 #X_train.fillna('',inplace=True)
50
51 print(X_train)
52 print(Y_test)
53
54 x_train=tf.constant(X_train)
55 y_train=tf.constant(Y_train)
56 x_test=tf.constant(X_test)
57 y_test=tf.constant(Y_test)
58
59 print(x_train)
60 print(y_train)
61 print(x_test)
62 print(y_test)
63
64 if flag==0:
65 return x_train,y_train
66 else:
67 return x_test,y_test
68
69
70 def Main():
71
72 global flag
73
74 feature_columns=[tf.contrib.layers.real_valued_column("",dimension=4)]
75
76 clf=tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,hidden_units=[10,20,10],n_classes=2,model_dir="/home/jiangjing/TensorflowModel/banknote")
77
78 clf.fit(input_fn=GetInputs,steps=2000)
79
80 flag=1
81 accuracy_score=clf.evaluate(input_fn=GetInputs,steps=1)["accuracy"]
82
83 print("nTest Accuracy:{0:f}".format(accuracy_score))
84
85 if __name__ =="__main__":
86 #DataPreprocessing()
87
88 Main()
89
90 exit(0)
