属性错误：'；numpy.float32'；对象没有属性'__指数'；

我尝试使用

Tensorflow.keras.utils.Sequence

使用一个简单的数据生成器来训练一个模型，但是有一个numpy属性的错误

from tensorflow.keras.utils import Sequence
class DataGenerator(Sequence):
    def __init__(self, dataset, batch_size=16, dim=(1), shuffle=True):
        'Initialization'
        self.dim = dim
        self.batch_size = batch_size
        self.dataset = dataset
        self.shuffle = shuffle
        self.on_epoch_end()

    def __len__(self):
        'Denotes the number of batches per epoch'
        return tf.math.ceil(len(self.dataset) / self.batch_size)

    def __getitem__(self, index):
        'Generate one batch of data'
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]

        # Find list of IDs
        list_IDs_temp = [self.dataset.index[k] for k in indexes]

        # Generate data
        y = dataset.loc[list_IDs_temp,['rating']].to_numpy()
        X = dataset.loc[list_IDs_temp,['user_id', 'item_id']].to_numpy()
        return (X, y)

    def on_epoch_end(self):
        'Updates indexes after each epoch'
        self.indexes = np.arange(len(dataset))
        if self.shuffle == True:
            np.random.shuffle(self.indexes)

和安装：

history = model.fit(train_generator,use_multiprocessing=True, steps_per_epoch=1, epochs=10, verbose=0)

错误消息：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-23-c17d5c8aa46c> in <module>()
----> 1 history = model.fit(train_generator,use_multiprocessing=True, steps_per_epoch=1, epochs=10, verbose=0)

5 frames
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    817         max_queue_size=max_queue_size,
    818         workers=workers,
--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,

/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    233           max_queue_size=max_queue_size,
    234           workers=workers,
--> 235           use_multiprocessing=use_multiprocessing)
    236 
    237       total_samples = _get_total_number_of_samples(training_data_adapter)

/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    591         max_queue_size=max_queue_size,
    592         workers=workers,
--> 593         use_multiprocessing=use_multiprocessing)
    594     val_adapter = None
    595     if validation_data:

/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    704       max_queue_size=max_queue_size,
    705       workers=workers,
--> 706       use_multiprocessing=use_multiprocessing)
    707 
    708   return adapter

/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, standardize_function, shuffle, workers, use_multiprocessing, max_queue_size, **kwargs)
    941       raise ValueError("`sample_weight` argument is not supported when using "
    942                        "`keras.utils.Sequence` as input.")
--> 943     self._size = len(x)
    944     self._shuffle_sequence = shuffle
    945     super(KerasSequenceAdapter, self).__init__(

/tensorflow-2.1.0/python3.6/tensorflow_core/python/framework/ops.py in __index__(self)
    860 
    861   def __index__(self):
--> 862     return self._numpy().__index__()
    863 
    864   def __bool__(self):

AttributeError: 'numpy.float32' object has no attribute '__index__'

---------------------------------------------------------------------------
AttributeError回溯（最近一次呼叫上次）
在（）
---->1历史=model.fit（训练生成器，使用多处理=True，每个历元的步骤=1，历元=10，详细=0）
5帧
/tensorflow-2.1.0/python3.6/tensorflow\u core/python/keras/engine/training.py in fit（self、x、y、批处理大小、历元、冗余、回调、验证拆分、验证数据、洗牌、类权重、样本权重、初始历元、每个历元的步数、验证步骤、验证频率、最大队列大小、工作者、使用多处理、**kwargs）
817最大队列大小=最大队列大小，
818名工人=工人，
-->819使用\多处理=使用\多处理）
820
821 def评估（自我，
/tensorflow-2.1.0/python3.6/tensorflow\u core/python/keras/engine/training\u v2.py适合（self、model、x、y、批大小、epoch、verbose、回调、验证拆分、验证数据、混洗、类权重、样本权重、初始历元、每个历元的步骤、验证步骤、验证频率、最大队列大小、工作人员、使用多处理、**kwargs）
233最大队列大小=最大队列大小，
234名工人=工人，
-->235使用多处理=使用多处理）
236
237总样本数=\u获取\u总样本数\u（训练\u数据\u适配器）
/tensorflow-2.1.0/python3.6/tensorflow\u core/python/keras/engine/training\u v2.py进程内\u训练\u输入（模型、x、y、批量大小、历元、样本权重、类权重、每个历元的步数、验证拆分、验证数据、验证步骤、洗牌、分布策略、最大队列大小、工人、使用多处理）
591最大队列大小=最大队列大小，
592名工人=工人，
-->593使用多处理=使用多处理）
594 val_适配器=无
595如果验证数据：
/tensorflow-2.1.0/python3.6/tensorflow\u core/python/keras/engine/training\u v2.py进程内输入（模型、模式、x、y、批量大小、时代、样本权重、类权重、无序排列、步骤、分布策略、最大队列大小、工人、使用多进程）
704最大队列大小=最大队列大小，
705名工人=工人，
-->706使用多处理=使用多处理）
707
708返回适配器
/tensorflow-2.1.0/python3.6/tensorflow\u core/python/keras/engine/data\u adapter.py in\uuuuuuuuu init\uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu
941 raise VALUE ERROR（“'sample\u weight'参数在使用时不受支持”
942“`keras.utils.Sequence`作为输入。”）
-->943自身尺寸=透镜（x）
944 self.\u shuffle\u sequence=shuffle
945超级（Kerasequenceadapter，self）。\u初始化__(
/tensorflow-2.1.0/python3.6/tensorflow\u core/python/framework/ops.py in\uuuuuuuuuuuuuuu索引（self）
860
861定义索引（自）：
-->862返回self.\u numpy（）.\u索引
863
864 def_uuubool_uuu（自我）：
AttributeError:'numpy.float32'对象没有属性'\uu索引''

---

有什么建议吗？谢谢

正如Monica建议的那样，我用

math.ceil

而不是

中的
tf.math.ceil
方法修复了错误。
请包含完整的回溯，而不是它的精简版本。添加了完整的。谢谢！那
看起来很可疑。
tf.math.ceil
返回一个张量，甚至不是整数张量。它与示例类似。但您可能是对的，我在导入数学后看到了差异