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]=018 # elif X_train[i][0]=='F':19 # X_train[i][0]=120 # else:21 # X_train[i][0]=222 #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 flag40 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_train66 else:67 return x_test,y_test68 69 70 def Main():71 72 global flag73 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=181 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)