I'm new to Keras and im trying to do Binary MLP on a dataset, and keep getting indices out of bounds with no idea why.
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(64, input_dim=20, init='uniform', activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop')
model.fit(trainx, trainy, nb_epoch=20, batch_size=16) # THROWS INDICES ERROR
Error:
model.fit(trainx, trainy, nb_epoch=20, batch_size=16)
Epoch 1/20
Traceback (most recent call last):
File "<ipython-input-6-c81bd7606eb0>", line 1, in <module>
model.fit(trainx, trainy, nb_epoch=20, batch_size=16)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\keras\models.py", line 646, in fit
shuffle=shuffle, metrics=metrics)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\keras\models.py", line 271, in _fit
ins_batch = slice_X(ins, batch_ids)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\keras\models.py", line 65, in slice_X
return [x[start] for x in X]
File "C:\Users\Thiru\Anaconda3\lib\site-packages\keras\models.py", line 65, in <listcomp>
return [x[start] for x in X]
File "C:\Users\Thiru\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1963, in __getitem__
return self._getitem_array(key)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\pandas\core\frame.py", line 2008, in _getitem_array
return self.take(indexer, axis=1, convert=True)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1371, in take
convert=True, verify=True)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\pandas\core\internals.py", line 3619, in take
indexer = maybe_convert_indices(indexer, n)
File "C:\Users\Thiru\Anaconda3\lib\site-packages\pandas\core\indexing.py", line 1750, in maybe_convert_indices
raise IndexError("indices are out-of-bounds")
IndexError: indices are out-of-bounds
Does anyone have any idea why this is happening? Im able to run other models just fine
I came here looking for the same issue resolution for the auto-sklearn and pandas dataframe. The solution is to pass the X dataframe as X.values. I.e. fit(X.values,y)
Answer from the comment - trainx and trainy should be numpy arrays. You can convert the data frame to numpy array using as_matrix()
method. I also faced this issue. It's weird that Keras does not give meaningful error message.
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