NLP Course - Week 3 Exercise

[kermoonshahii]

I run this code with tf=='2.2.0-rc1' and the code raises value error.
Would you help me to solve this, please ?
ValueError Traceback (most recent call last)
in ()
11
12 num_epochs = 50
---> 13 history = model.fit(training_sequences, training_labels, epochs=num_epochs, validation_data=(test_sequences, test_labels), verbose=2)
14
15 print("Training Complete")

3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
63 def _method_wrapper(self, *args, **kwargs):
64 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 65 return method(self, *args, **kwargs)
66
67 # Running inside run_distribute_coordinator already.

/usr/local/lib/python3.6/dist-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, **kwargs)
748 workers=workers,
749 use_multiprocessing=use_multiprocessing,
--> 750 model=self)
751
752 # Container that configures and calls tf.keras.Callbacks.

/usr/local/lib/python3.6/dist-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)
1094 self._insufficient_data = False
1095
-> 1096 adapter_cls = select_data_adapter(x, y)
1097 self._adapter = adapter_cls(
1098 x,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in select_data_adapter(x, y)
958 "Failed to find data adapter that can handle "
959 "input: {}, {}".format(
--> 960 _type_name(x), _type_name(y)))
961 elif len(adapter_cls) > 1:
962 raise RuntimeError(

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

[H-rim]

I had same issue, and solved it by adding some code before 'model.fit()'
training_sequences, test_sequences = np.array(training_sequences), np.array(test_sequences) training_labels, test_labels = np.array(training_labels), np.array(test_labels)

refer to this link: https://stackoverflow.com/questions/58682026/failed-to-find-data-adapter-that-can-handle-input-class-numpy-ndarray-cl