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)