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("train_data.csv", sep=',', header=0, keep_default_na=True) 12 X_train=np.array(abalone.iloc[:,:6]) 13 Y_train_=np.array(abalone.iloc[:,6:]) 14 print(X_train) 15 Y_train=[] 16 for i in range(len(X_train)): 17 18 X_train[i][0] = ord(X_train[i][0])-97 19 X_train[i][2] = ord(X_train[i][2])-97 20 X_train[i][4] = ord(X_train[i][4])-97 21 22 # for i in range (len(X_train)): 23 # for j in range(6): 24 # X_train[i][j]=X_train[i][j]-0.0 25 # 26 #X_train.astype(np.float64) 27 # print(X_train,type(X_train),X_train[0][0],type(X_train[0][0])) 28 29 #binary classifier 30 for i in range(len(Y_train_)): 31 32 if Y_train_[i][0]=="draw": 33 Y_train.append(0) 34 else: 35 Y_train.append(1) 36 37 38 # multiple classifer 39 40 return cross_validation.train_test_split(X_train,Y_train,test_size=0.25,random_state=0,stratify=Y_train) 41 42 43 def GetInputs(): 44 global flag 45 X_train, X_test, Y_train, Y_test = DataPreprocessing() 46 47 #print(type(X_train),type(X_train[0][0])) 48 #print(X_train) 49 # print(len(X_test)) 50 # print(len(Y_train)) 51 # print(len(Y_test)) 52 53 54 #X_train[X_train.isnull().any(axis=1)] 55 #X_train.fillna('',inplace=True) 56 57 # print(X_train) 58 # print(Y_test) 59 60 x_train=tf.constant(X_train) 61 y_train=tf.constant(Y_train) 62 x_test=tf.constant(X_test) 63 y_test=tf.constant(Y_test) 64 # 65 # print(x_train) 66 # print(y_train) 67 # print(x_test) 68 # print(y_test) 69 70 if flag==0: 71 return x_train,y_train 72 else: 73 return x_test,y_test 74 75 76 def Main(): 77 78 global flag 79 80 feature_columns=[tf.contrib.layers.real_valued_column("",dimension=6)] 81 82 clf=tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,hidden_units=[20,40,20],n_classes=2,model_dir="/home/jiangjing/TensorflowModel/chess") 83 84 clf.fit(input_fn=GetInputs,steps=2000) 85 86 flag=1 87 accuracy_score=clf.evaluate(input_fn=GetInputs,steps=1)["accuracy"] 88 89 print("nTest Accuracy:{0:f}".format(accuracy_score)) 90 91 if __name__ =="__main__": 92 #DataPreprocessing() 93 94 Main() 95 96 exit(0)