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 err

Answer 1

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)

Answer 2

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):

    ...

Answer 3

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.

Answer 4

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)

Answer 5

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(...)