Tensorflow | ValueError：未能找到可以处理输入的数据适配器：<class 'numpy.ndarray'></class>

Question

I'm doing some code with Tensorflow and I'm facing this issue, as input I have two lists per sample and as output I have an encoded list of classes.

For example:

(raw) Values[0]: [[11.42, 10.09, 6.7], [7.52, 6.18, 2.11]]
(encoded) Targets[0]: [2, 3, 4, 8, 1, 5, 16, 6, 1, 7]

As in the example:

input_shape = 6000 samples x 2 lists/sample x 3 items/list
output_shape = 6000 samples x 10 items/sample

This is my first time dealing with multiple lists as input, and the problem here is that both lists contain important information for the model and that I can't just merge them, because they represent different states of the input data.

What would you do in this case? Maybe there is a way of merging the lists while keeping the information that both lists were initialy two different lists.

Here is the full traceback:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-79-d859259762d2> in <module>
----> 1 model.fit(x=values, y=target_tokenized)

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~/.local/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)
   1061           use_multiprocessing=use_multiprocessing,
   1062           model=self,
-> 1063           steps_per_execution=self._steps_per_execution)
   1064 
   1065       # Container that configures and calls `tf.keras.Callback`s.

~/.local/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, steps_per_execution)
   1102       self._steps_per_execution_value = steps_per_execution.numpy().item()
   1103 
-> 1104     adapter_cls = select_data_adapter(x, y)
   1105     self._adapter = adapter_cls(
   1106         x,

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in select_data_adapter(x, y)
    969         "Failed to find data adapter that can handle "
    970         "input: {}, {}".format(
--> 971             _type_name(x), _type_name(y)))
    972   elif len(adapter_cls) > 1:
    973     raise RuntimeError(

ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {'(<class \'list\'> containing values of types {"<class \'int\'>"})'})

Answer 1

You need to convert the list to numpy arrays

import numpy as np
lists = np.asarray(reg_lists)