基于keras，使用imagedatagenerator.flow函数读入数据，训练集ACC极低

Question

在做字符识别的神经网络，数据集是用序号标好名称的图片，标签取图片的文件名。想用Imagedatagenrator
函数和flow函数，增加样本的泛化性，然后生成数据传入网络，可是这样acc=1/类别数，基本为零。请问哪里出了问题

datagen = ImageDataGenerator(
       width_shift_range=0.1,
       height_shift_range=0.1
       )
def read_train_image(self, name):
       myimg = Image.open(name).convert('RGB')
       return np.array(myimg)

def train(self):
       #训练集
       train_img_list = []
       train_label_list = []
       #测试集
       test_img_list = []
       test_label_list = []
       for file in os.listdir('train'):
           files_img_in_array = self.read_train_image(name='train/' + file)
           train_img_list.append(files_img_in_array)  # Image list add up
           train_label_list.append(int(file.split('_')[0]))  # lable list addup
       for file in os.listdir('test'):
            files_img_in_array = self.read_train_image(name='test/' + file)
            test_img_list.append(files_img_in_array)  # Image list add up
            test_label_list.append(int(file.split('_')[0]))  # lable list addup

        train_img_list = np.array(train_img_list)
        train_label_list = np.array(train_label_list)
        test_img_list = np.array(train_img_list)
        test_label_list = np.array(train_label_list)
        train_label_list = np_utils.to_categorical(train_label_list, 5788)
        test_label_list = np_utils.to_categorical(test_label_list, 5788)
        train_img_list = train_img_list.astype('float32')
        test_img_list = test_img_list.astype('float32')
        test_img_list /= 255.0
        train_img_list /= 255.0

这是图片数据的处理，图片和标签都存到list里。下面是用fit_genrator训练

model.fit_generator(
            self.datagen.flow(x=train_img_list, y=train_label_list, batch_size=2),
            samples_per_epoch=len(train_img_list),
            epochs=10,
            validation_data=(test_img_list,test_label_list),
            )

Answer 1

模型的定义没有看到，另外你epochs=10是不是太小了，还没有效果。