Keras + IndexError

Question

I am very new to keras. Trying to build a binary classifier for an NLP task. (My code is motivated from imdb example - https://github.com/fchollet/keras/blob/master/examples/imdb_cnn.py)

Below is my code snippet:

max_features = 30
maxlen = 30
batch_size = 32
embedding_dims = 30
nb_filter = 250
filter_length = 3
hidden_dims = 250
nb_epoch = 3

(Train_X, Train_Y, Test_X, Test_Y) = load_and_split_data()
model = Sequential()
model.add(Embedding(max_features, embedding_dims, input_length=maxlen))
model.add(Convolution1D(nb_filter=nb_filter,filter_length=filter_length,border_mode="valid",activation="relu",subsample_length=1))
model.add(MaxPooling1D(pool_length=2))
model.add(Flatten())
model.add(Dense(hidden_dims))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', class_mode="binary")
fitlog = model.fit(Train_X, Train_Y, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2)

When I run model.fit(), I get the following error:

/.virtualenvs/nnet/lib/python2.7/site-packages/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
    857         t0_fn = time.time()
    858         try:
--> 859             outputs = self.fn()
    860         except Exception:
    861             if hasattr(self.fn, 'position_of_error'):

IndexError: One of the index value is out of bound. Error code: 65535.\n
Apply node that caused the error: GpuAdvancedSubtensor1(<CudaNdarrayType(float32, matrix)>, Elemwise{Cast{int64}}.0)
Toposort index: 47
Inputs types: [CudaNdarrayType(float32, matrix), TensorType(int64, vector)]
Inputs shapes: [(30, 30), (3840,)]
Inputs strides: [(30, 1), (8,)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[GpuReshape{3}(GpuAdvancedSubtensor1.0, MakeVector{dtype='int64'}.0)]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

Can you please help me resolve this ?

Answer 1

You need to Pad the imdb sequences you are using, add those lines:

from keras.preprocessing import sequence
Train_X = sequence.pad_sequences(Train_X, maxlen=maxlen)
Test_X = sequence.pad_sequences(Test_X, maxlen=maxlen)

Before building the actual model.