Tensorflow Data Adapter Error: ValueError: Failed to find data adapter that can handle input
PythonTensorflowKerasLstmPython Problem Overview
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!
Python Solutions
Solution 1 - Python
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 - Python
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 - Python
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 - Python
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 - Python
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
Solution 6 - Python
For some reason I also experienced this problem when I passed my custom generator function directly to model.fit(), rather than creating an instance of it first.
I.e, given:
def batch_generator(...):
...
yield(...)
I called model.fit(batch_generator,...), rather than:
generator_instance = batch_generator(...)
model.fit(generator_instance, ...)
Solution 7 - Python
may be it will help someone. First check your data type if it is numpy array & possibly ur algo required a DF.
print(X.shape, X.dtype)
print(y.shape, y.dtype)
convert your numpy array into Pandas DF
train_x = pd.DataFrame(train_x)
train_y = pd.DataFrame(train_y)