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嗨，我有来自共享文件夹的图像数据集。数据集路径如下：/media/sharing_folder/data 和 data 文件夹有两个子文件夹，分别是“masked”和“unmasked”。我尝试像这样导入数据：

data = []

def create_data():
    for category in CATEGORIES:
        path =  os.path.join(DATADIR, category) #path to masked or unmasked dir
        class_num = CATEGORIES.index(category)
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
                data.append([new_array, class_num])
            except Exception as e:
                pass
            
create_data()

但是，此导入运行非常缓慢。我想用 tensorflowio 导入数据。如何使用 tensorflowio 导入？

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1 回答 1

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要正确显示您的代码，请开始新的一行。在键盘上按下左上角的键（1 键右侧的键）四次。然后创建一个新行并输入您的代码。完成输入代码后，重复按上述相同的键四次。这将关闭代码区域。这是你的代码

data = []

def create_data(): 
    for category in CATEGORIES: 
        path = os.path.join(DATADIR, category) #path to masked or unmasked dir 
        class_num = CATEGORIES.index(category) 
        for img in os.listdir(path): 
            try: img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE) 
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) 
                data.append([new_array, class_num]) 
            except Exception as e: 
                pass

create_data()

现在回答您的问题，我建议您使用 keras ImageDataGenerator。文档在这里。

split=.2 # set percentage of images to use for validation
height=254 # set to desired image height
width =254 # set to desired image width
batch_size=32 # set to desired batch size
seed = 123  # set to an arbitrary value
data_dir=r'c: /media/sharing_folder/data'
img_gen=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255,validation_split=split) 
train_gen=img_gen.flow_from_directory(directory= data_dir,target_size=(height,width),
          color_mode='grayscale', class_mode=categorical, batch_size=batch_size,
          seed=seed, shuffle=True, subset='training)
valid_gen=img_gen.flow_from_directory(directory= data_dir,target_size=(height,width),
          color_mode='grayscale', class_mode=categorical, batch_size=batch_size,
          seed=seed, shuffle=False, subset='validation')

然后编译您的模型并将损失用作 categorical_crossentropy。然后使用 model.fit 使用上面定义的生成器训练您的模型。关于验证，最好在 valid_gen 中设置 shuffle=False，以便为每个 epoch 以相同的顺序提供验证图像。

于 2020-11-27T16:20:53.290 回答