当我在 python 中使用自定义数据生成器将数据与 CNN model 拟合时，如何解决“ValueError: 0 is not in range”？(tf.Keras.utils.Sequence))

Question

我想即时对语音样本应用增强。 我测试了我的自定义语音增强数据生成器，它似乎运行良好。 但是当我适应 CNN model 时，出现以下错误：

由于上传代码的限制，我稍后会添加代码的rest。

UnknownError                              Traceback (most recent call last)
Cell In [31], line 1
----> 1 history = model.fit(train_aug_gen,
      2           #steps_per_epoch=5,
      3           #validation_steps=5,
      4           validation_data = val_aug_gen,
      5           epochs=num_epochs,verbose=2,          
      6           callbacks = [earlystopping, scheduler, checkpointer])

File ~/miniforge3/envs/env_tf/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File ~/miniforge3/envs/env_tf/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     52 try:
     53   ctx.ensure_initialized()
---> 54   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     55                                       inputs, attrs, num_outputs)
     56 except core._NotOkStatusException as e:
     57   if name is not None:

UnknownError: Graph execution error:

2 root error(s) found.
  (0) UNKNOWN:  KeyError: 0
Traceback (most recent call last):

  File "/Users/local/miniforge3/envs/env_tf/lib/python3.9/site-packages/pandas/core/indexes/range.py", line 391, in get_loc
    return self._range.index(new_key)

ValueError: 0 is not in range

我不知道如何解决这个问题。

    class CustomVoiceAugmentation(tf.keras.utils.Sequence):
        def __init__(self, dir_data, data_df, aug_level, num_classes, shuffle):
            .......
            .......
            print(f"Found {self.data_frame.shape[0]} voices belonging to {self.num_classes} classes") 
           
        def __len__(self):
            return (self.data_len // self.batch_size)
    
        
        def __voice_augmentation(self,signalvoice):
                .....
                .....
                .....
                return augmented_voice

    def __get_voice(self,index, batch_x, batch_y, dir_data):
        X = []
        Y = []
        for i, name in enumerate(batch_x):
            label = batch_y[i]
            pathvoice = os.path.join(self.dir_data + '/'+ label+ '/'+ name)
            .....
            .....
            .....
        
        return X, Y
        
    def __to_melspectrogram (self,augmented_voice_signal):
        ......     
        return image

        def __getitem__(self, index):
            
            batch_x = self.data_frame["VoiceName"][index * self.batch_size:(index + 1) * self.batch_size]
            batch_y = self.data_frame["label"][index * self.batch_size:(index + 1) * self.batch_size]
            X ,Y = self.__get_voice(index, batch_x, batch_y, self.dir_data)
            ......
            ......
            ......
            return tf.convert_to_tensor(image), new_Y
        
        
        def on_epoch_end(self):
            if self.shuffle == True:
                self.data_frame = self.data_frame.sample(frac=1).reset_index(drop=True)
                

Answer 1

添加“iloc”，label=batch_y.iloc[i] 解决了这个问题。