Tensorflow Data Adapter Error: ValueError: Failed to find data adapter that can handle input

Question

While running a sentdex tutorial script of a cryptocurrency RNN, link here

YouTube Tutorial: Cryptocurrency-predicting RNN Model,

but have encountered an error when attempting to train the model. My tensorflow version is 2.0.0 and I'm running python 3.6. When attempting to train the model I receive the following error:

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 734, in fit
    use_multiprocessing=use_multiprocessing)

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 224, in fit
    distribution_strategy=strategy)

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 497, in _process_training_inputs
    adapter_cls = data_adapter.select_data_adapter(x, y)

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 628, in select_data_adapter
    _type_name(x), _type_name(y)))

ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {"<class 'numpy.float64'>"})

Any advice would be greatly appreciated!

Solution 1

Have you checked whether your training/testing data and training/testing labels are all numpy arrays? It might be that you're mixing numpy arrays with lists.

Solution 2

You can avoid this error by converting your labels to arrays before calling model.fit():

train_x = np.asarray(train_x)
train_y = np.asarray(train_y)
validation_x = np.asarray(validation_x)
validation_y = np.asarray(validation_y)

Solution 3

If you encounter this problem while dealing with a custom generator inheriting from the keras.utils.Sequence class, you might have to make sure that you do not mix a Keras or a tensorflow - Keras-import.
This might especially happen when you have to switch to a previous tensorflow version for compatibility (like with cuDNN).

If you for example use this with a tensorflow-version > 2...

from keras.utils import Sequence

class generatorClass(Sequence):

    def __init__(self, x_set, y_set, batch_size):
        ...

    def __len__(self):
        ...

    def __getitem__(self, idx):
        return ...

... but you actually try to fit this generator in a tensorflow-version < 2, you have to make sure to import the Sequence-class from this version like:

keras = tf.compat.v1.keras
Sequence = keras.utils.Sequence

class generatorClass(Sequence):

    ...

Solution 4

I had a similar problem. In my case it was a problem that I was using a tf.keras.Sequential model but a keras generator.

Wrong:

from keras.preprocessing.sequence import TimeseriesGenerator
gen = TimeseriesGenerator(...)

Correct:

gen = tf.keras.preprocessing.sequence.TimeseriesGenerator(...)

Solution 5

This error occured when I updated tensorflow from 1.x to 2.x It was solved after changing my import from

import keras 

to

import tensorflow.keras as keras

View more solutions

Comments

[EliadL]

What's the difference here?

[Alexandre Huat]

I don’t see how Keras would accept pandas.DataFrame but not numpy.ndarray

[John Smith]

One uses plain keras, while the other uses tf.keras.

[Aelius]

This was also my case, keras was imported both directly through statements like from keras import layers and via import tensorflow.keras. Ensure to stay consistent with the imports adding tensorflow. before each keras import or removing it.