tensorflow生成数据集（24）---《深度学习》

生成数据集的代码

#coding=utf-8import tensorflow as tfimport numpy as npimport osfrom PIL import Imagedef to_categorial(y,n_classes):    y_std=np.zeros([len(y),n_classes])    for i in range(len(y)):        y_std[i,y[i]]=1.0    return y_stddef file_label_map(path):    map={}    f=open(path,'r')    line=f.readline()    count=0;    while(line):        label=str(line).split(":")[0].split("[")[0]        map[label]=count;        line=f.readline()        count=count+1    f.close();    return mapdef getFileArr_std(dir,map_std):    map_file_result={}    map_file_label={}    file_list=os.listdir(dir)    for file in file_list:        file_path=os.path.join(dir,file)        label=file.split(".")[0].split("_")[0]        print(label)        img=Image.open(file_path)        result=np.array([])        r,g,b=img.split()        r_arr=np.array(r).reshape(299*299)        g_arr=np.array(g).reshape(299*299)        b_arr=np.array(b).reshape(299*299)        img_arr=np.concatenate((r_arr,g_arr,b_arr))        result=np.concatenate((result,img_arr))        result=result.reshape((299,299,3))        map_file_result[file]=result        map_file_label[file]=map_std[label]    ret_arr=[]    count_0=0    for file in file_list:        each_list=[]        #112*112*3的一个三维数组，现在需要将其扩展为4维度，5954*112*112*3        result=map_file_result[file]        label=map_file_label[file]        #print(label_one_zero)        each_list.append(result)        each_list.append(label)        print(count_0)        ret_arr.append(each_list)        count_0=count_0+1    np.save('F:\\train-adversarial\\train_adversarial.npy', ret_arr)def getFileArr(dir):    result_arr=[]    label_list=[]    map={}    map_file_result={}    map_file_label={}    map_new={}    count_label=0    count=0    file_list=os.listdir(dir)    for file in file_list:        file_path=os.path.join(dir,file)        label=file.split(".")[0].split("_")[0]        img=Image.open(file_path)        result=np.array([])        if(len(img.split())!=3):            map[file]="hahaha"            #os.remove(file_path)        else:            map[file]=label            if label not in label_list:                label_list.append(label)                map_new[label]=count_label                count_label=count_label+1            r,g,b=img.split()            r_arr=np.array(r).reshape(299*299)            g_arr=np.array(g).reshape(299*299)            b_arr=np.array(b).reshape(299*299)            img_arr=np.concatenate((r_arr,g_arr,b_arr))            result=np.concatenate((result,img_arr))            #result=result.reshape((3,112,112))            result=result.reshape((299,299,3))            map_file_result[file]=result            result_arr.append(result)            count=count+1    for file in file_list:        if map[file]!="hahaha":            map_file_label[file]=map_new[map[file]]        #map[file]=map_new[map[file]]    ret_arr=[]    for file in file_list:        if map[file]!="hahaha":            each_list=[]            label_one_zero=np.zeros(count_label)            #112*112*3的一个三维数组，现在需要将其扩展为4维度，5954*112*112*3            result=map_file_result[file]            label=map_file_label[file]            #print(label_one_zero)            each_list.append(result)            each_list.append(label)            ret_arr.append(each_list)    np.save('F:\\train-adversarial\\train_adversarial.npy', ret_arr)def load_data_std(train_dir,test_dir):    train_data=np.load(train_dir)    test_data=np.load(test_dir)    X_train_non,y_train_non=train_data[:,0],train_data[:,1]    X_test_non,y_test_non=test_data[:,0],test_data[:,1]    X_train=np.zeros([len(X_train_non),299,299,3])    X_test=np.zeros([len(X_test_non),299,299,3])    for i in range(len(X_train_non)):        X_train[i,:,:,:]=X_train_non[i]    for i in range(len(X_test_non)):        X_test[i,:,:,:]=X_test_non[i]    #y_train_non=y_train_non.tolist()    #y_test_non=y_test_non.tolist()    y_train=to_categorial(y_train_non,1001)    y_test=to_categorial(y_test_non,1001)       return (X_train,y_train),(X_test,y_test)def load_data(test_dir):    test_data=np.load(test_dir)    X_test_non,y_test_non=test_data[:,0],test_data[:,1]    X_test=np.zeros([len(X_test_non),299,299,3])    for i in range(len(X_test_non)):        X_test[i,:,:,:]=X_test_non[i]    y_test=to_categorial(y_test_non,182)       return X_test,y_test