python - 如何同时使用 DataFrameIterator 和 TensorSliceDataset 进行混合数据的训练？

Question

我正在尝试为黑色素瘤比赛的 kaggle 笔记本上的混合数据输入拟合一个多分支神经网络。我正在关注本教程以制作多分支神经网络。我现在无法展示网络架构，因为竞争正在进行中。我的模型在编译时没有显示错误。但是，在使用以下代码片段拟合模型时，代码单元会引发ValueError：

opt = Adam(lr=1e-3, decay=1e-3 / 200)
model.compile(loss=tensorflow.keras.losses.BinaryCrossentropy(from_logits=True), optimizer=opt)
# train the model
print("[INFO] training model...")
model.fit(
    x=[train_dataset, train_generator],
    epochs=2, batch_size=32)

这里，train_dataset 是从方法获取的 BatchDataset 对象，train_generator 是从from_tensor_slices().shuffle().batch()方法获取的 DataFrameIterator flow_from_dataframe()。

回溯如下所示：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-77-2e58e85574a4> in <module>
      5 model.fit(
      6         x=[train_dataset, train_generator],
----> 7     epochs=2, batch_size=32)
      8 # make predictions on the testing data

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

/opt/conda/lib/python3.7/site-packages/tensorflow/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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
    813           workers=workers,
    814           use_multiprocessing=use_multiprocessing,
--> 815           model=self)
    816 
    817       # Container that configures and calls `tf.keras.Callback`s.

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model)
   1097     self._insufficient_data = False
   1098 
-> 1099     adapter_cls = select_data_adapter(x, y)
   1100     self._adapter = adapter_cls(
   1101         x,

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in select_data_adapter(x, y)
    961         "Failed to find data adapter that can handle "
    962         "input: {}, {}".format(
--> 963             _type_name(x), _type_name(y)))
    964   elif len(adapter_cls) > 1:
    965     raise RuntimeError(

ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'>", "<class 'keras_preprocessing.image.dataframe_iterator.DataFrameIterator'>"}), <class 'NoneType'>

有人可以帮我调试这个错误吗？

标签： pythontensorflowdeep-learning

Answer 1

没有答案。应该有针对 Keras 的功能请求。它们不支持数据集和数据框迭代器的混合输入类型。以下是 model.fit 的 TF 2.2 文档中的内容：“输入数据。它可能是：

A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. "