我正在嘗試使用自定義生成器在 keras 張量流中實(shí)現(xiàn)多輸入模型，如下所示在接受的答案中在 keras 中創(chuàng)建混合數(shù)據(jù)生成器（圖像，csv） ：import randomimport pandas as pdimport numpy as npfrom glob import globfrom keras.preprocessing import image as krs_image# Create the arguments for image preprocessingdata_gen_args = dict(? ? horizontal_flip=True,? ? brightness_range=[0.5, 1.5],? ? shear_range=10,? ? channel_shift_range=50,? ? rescale=1. / 255,)# Create an empty data generatordatagen = ImageDataGenerator()# Read the image list and csvimage_file_list = glob(f'{images_dir}/{split}/**/*.JPG', recursive=True)df = pd.read_csv(f'{csv_dir}/{split}.csv', index_col=csv_data[0])random.shuffle(image_file_list)def custom_generator(images_list, dataframe, batch_size):? ? i = 0? ? while True:? ? ? ? batch = {'images': [], 'csv': [], 'labels': []}? ? ? ? for b in range(batch_size):? ? ? ? ? ? if i == len(images_list):? ? ? ? ? ? ? ? i = 0? ? ? ? ? ? ? ? random.shuffle(images_list)? ? ? ? ? ? # Read image from list and convert to array? ? ? ? ? ? image_path = images_list[i]? ? ? ? ? ? image_name = os.path.basename(image_path).replace('.JPG', '')? ? ? ? ? ? image = krs_image.load_img(image_path, target_size=(img_height, img_width))? ? ? ? ? ? image = datagen.apply_transform(image, data_gen_args)? ? ? ? ? ? image = krs_image.img_to_array(image)? ? ? ? ? ? # Read data from csv using the name of current image? ? ? ? ? ? csv_row = dataframe.loc[image_name, :]? ? ? ? ? ? label = csv_row['class']? ? ? ? ? ? csv_features = csv_row.drop(labels='class')? ? ? ? ? ? batch['images'].append(image)? ? ? ? ? ? batch['csv'].append(csv_features)? ? ? ? ? ? batch['labels'].append(label)? ? ? ? ? ? i += 1? ? ? ? batch['images'] = np.array(batch['images'])? ? ? ? batch['csv'] = np.array(batch['csv'])? ? ? ? # Convert labels to categorical values? ? ? ? batch['labels'] = np.eye(num_classes)[batch['labels']]? ? ? ? yield [batch['images'], batch['csv']], batch['labels']但是，我收到以下索引錯誤。任何幫助是極大的贊賞。