BankNote

 # coding=utf-8
 import pandas as pd
 import numpy as np
 from sklearn import cross_validation
 import tensorflow as tf

 global flag
 flag=0

 def DataPreprocessing():
     abalone = pd.read_csv("ceshi.csv", sep=',', header=0, keep_default_na=True,na_values=[])
     X_train=np.array(abalone.iloc[:,:4])
     Y_train=np.array(abalone.iloc[:,4:])
     # Y_train=[]
     # for i in range(len(X_train)):
     #     if X_train[i][0] == 'M':
     #         X_train[i][0]=0
     #     elif X_train[i][0]=='F':
     #         X_train[i][0]=1
     #     else:
     #         X_train[i][0]=2
     #
     # for i in range(len(Y_train_)):
     #
     #     #print(Y_train[i][0])
     #     Y_train.append(Y_train_[i][0])

     # print(X_train)
     # print(len(X_train))
     # print(Y_train)
     # print(len(Y_train))
    # print(min(Y_train))
    # print(max(Y_train))

     return cross_validation.train_test_split(X_train,Y_train,test_size=0.25,random_state=0,stratify=Y_train)

 def GetInputs():
     global flag
     X_train, X_test, Y_train, Y_test = DataPreprocessing()

     #print(X_train)
     # print(len(X_test))
     # print(len(Y_train))
     # print(len(Y_test))

     #X_train[X_train.isnull().any(axis=1)]
     #X_train.fillna('',inplace=True)

     print(X_train)
     print(Y_test)

     x_train=tf.constant(X_train)
     y_train=tf.constant(Y_train)
     x_test=tf.constant(X_test)
     y_test=tf.constant(Y_test)

     print(x_train)
     print(y_train)
     print(x_test)
     print(y_test)

     if flag==0:
         return x_train,y_train
     else:
         return x_test,y_test

 def Main():

     global flag

     feature_columns=[tf.contrib.layers.real_valued_column("",dimension=4)]

     clf=tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,hidden_units=[10,20,10],n_classes=2,model_dir="/home/jiangjing/TensorflowModel/banknote")

     clf.fit(input_fn=GetInputs,steps=2000)

     flag=1
     accuracy_score=clf.evaluate(input_fn=GetInputs,steps=1)["accuracy"]

     print("nTest Accuracy:{0:f}".format(accuracy_score))

 if __name__ =="__main__":
     #DataPreprocessing()

     Main()

 exit(0)