`validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'scipy.sparse.csr.csr_matrix'>

[anmolnaik7]

Transform y to 3 categories using get_dummies()

y_train = pd.get_dummies(y_train_tfidf)
y_test = pd.get_dummies(y_test_tfidf)
checkpoint = ModelCheckpoint(filepath='best_weights.hdf5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
hist = model.fit(X_train_tfidf, y_train, epochs=20, batch_size=128,validation_split=0.4, callbacks=callbacks_list, verbose=False)

model.save('sequential_model.h5')

[
~\AppData\Roaming\Python\Python37\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)
1038 (x, y, sample_weight), validation_data = (
1039 data_adapter.train_validation_split(
-> 1040 (x, y, sample_weight), validation_split=validation_split))
1041
1042 if validation_data:

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\data_adapter.py in train_validation_split(arrays, validation_split)
1374 raise ValueError(
1375 "validation_split is only supported for Tensors or NumPy "
-> 1376 "arrays, found following types in the input: {}".format(unsplitable))
1377
1378 if all(t is None for t in flat_arrays):

ValueError: validation_split is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'scipy.sparse.csr.csr_matrix'>]](url)

Any way possible?