Python <；keras.preprocessing.image.DirectoryIterator>；对象返回TypeError:+；的操作数类型不受支持：'；int'；和'；str'；

我使用以下代码片段为培训和验证生成器创建了

对象

class DataLoader:

    @staticmethod
    def load_data(data_config, prefix = "blond"):
      
      train_datagen =  tf.keras.preprocessing.image.ImageDataGenerator(
            preprocessing_function=tf.keras.applications.inception_v3.preprocess_input,
            rotation_range=30,
            width_shift_range=0.2,
            height_shift_range=0.2,
            shear_range=0.2,
            zoom_range=0.2,
            horizontal_flip=True
        )

      valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
            preprocessing_function=tf.keras.applications.inception_v3.preprocess_input,
            rotation_range=30,
            width_shift_range=0.2,
            height_shift_range=0.2,
            shear_range=0.2,
            zoom_range=0.2,
            horizontal_flip=True
        )
      train_generator = train_datagen.flow_from_directory(
            'data/celeba-dataset/{}-train'.format(prefix),
                target_size=(data_config.data.IMG_HEIGHT, data_config.data.IMG_WIDTH),
                batch_size=data_config.train.BATCH_SIZE)

然后我创建另一个模型类，其中包含以下

load\u data

和

train

函数

class Model(BaseModel):

    def __init__(self, config):
        super().__init__(config)
        self.img_height = int(self.config.data.IMG_HEIGHT)
        self.img_width = int(self.config.data.IMG_WIDTH)
        self.base_model = tf.keras.applications.InceptionV3(weights='imagenet',
                        include_top=False,
                        input_shape=(self.img_height, self.img_width, 3))

        self.model = None
        self.training_samples = int(self.config.data.TRAINING_SAMPLES)
        self.batch_size = int(self.config.train.BATCH_SIZE)
        self.steps_per_epoch = int(self.training_samples) // int(self.batch_size)
        self.num_epochs = int(self.config.train.EPOCHS)

        self.train_generator = None
        self.validation_generator = None
    
    def load_data(self):

        self.train_generator, self.validation_generator = DataLoader().load_data(self.config)

    def train(self):
        self.model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.0001, momentum=0.9), 
               loss='categorical_crossentropy', 
               metrics=[tf.keras.metrics.TopKCategoricalAccuracy(k = 1)])

        checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath='weights.best.inc.blond.hdf5', 
                               verbose=1, save_best_only=True)

        model_history = self.model.fit(self.train_generator,
                                        validation_data = self.validation_generator,
                                        steps_per_epoch= self.steps_per_epoch,
                                        epochs = self.num_epochs,
                                        class_weight='auto',
                                        callbacks=[checkpointer])
        return model_history.history['loss'], model_history.history['val_loss']

在运行以下代码时

model = Model(CFG)
model.load_data()
model.build()
model.train()

我得到了下面的回溯

Traceback (most recent call last):
  File "/Users/sauravmaheshkar/github/compression/train.py", line 14, in <module>
    model.train()
  File "/Users/sauravmaheshkar/github/compression/model/ForgetModel.py", line 70, in train
    callbacks=[checkpointer, ],
  File "/Users/sauravmaheshkar/opt/anaconda3/envs/compression/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1064, in fit
    steps_per_execution=self._steps_per_execution)
  File "/Users/sauravmaheshkar/opt/anaconda3/envs/compression/lib/python3.6/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 1112, in __init__
    model=model)
  File "/Users/sauravmaheshkar/opt/anaconda3/envs/compression/lib/python3.6/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 898, in __init__
    self._size = len(x)
  File "/Users/sauravmaheshkar/opt/anaconda3/envs/compression/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py", line 68, in __len__
    return (self.n + self.batch_size - 1) // self.batch_size  # round up
TypeError: unsupported operand type(s) for +: 'int' and 'str'

软件包版本

• tensorflow

  ==2.4.1

• Keras预处理

  ==1.1.2

预期产量

---

运行正常的tensorflow训练循环

我认为问题出在DataLoader类的load_data函数中。具体而言，在这方面：

batch\u size=data\u config.train.batch\u size

这一行和您的数据配置文件告诉我，您只需要添加int，您的问题就会得到解决。因此，将该行替换为：

batch\u size=int（data\u config.train.batch\u size）

我建议您也检查其他参数

或者，我认为从Json文件中的int值中删除引号也会起作用

---

正如您所看到的，数字/整数不需要引号。

堆栈跟踪中引用代码的第一行是：File“/Users/sauravmaheshkar/github/compression/model/ForgetModel.py”，第70行，in-train callbacks=[checkpointer，]，您省略了

检查点的定义。请提供由
…
表示的代码。最终，
self.batch\u size
是一个
str
，但是如果没有缺失的代码，就很难确定这是哪里出了问题。我刚刚添加了整个代码@MichaelRuthThank，我已经在
模型
类中明确键入了对
batch\u size
的所有引用，但是忘记了
DataLoader
类。后来出现了一些错误，但已修复。
"""Project Config in JSON"""

CFG = {
    "data": {
        "data_folder" : "data/CelebA/",
        "images_folder" : "data/CelebA/img_align_celeba/img_align_celeba/",
        "IMG_HEIGHT": "218",
        "IMG_WIDTH": "178",
        "TRAINING_SAMPLES": "10000",
        "VALIDATION_SAMPLES": "2000",
        "TEST_SAMPLES": "2000",
    },
    "train": {
        "BATCH_SIZE": "64",
        "EPOCHS": "10",
    }   
}