IndexError

[NanditaDamaraju]

After upgrading to the latest version of Keras I get the following error when I try to use graph.fit on the model:

nb_epoch = 10
max_features = 20
maxlen = 9
batch_size = 32
embedding_dims = 64
nb_filters = 10
filter_length = 3
hidden_dims = 25

graph = Graph()
graph.add_input(name='sequence', ndim=2)
graph.inputs['sequence'].input = T.imatrix()


graph.add_node(Embedding(max_features, embedding_dims),
               name = 'sequence_embedding',
               input = 'sequence')
graph.add_node(Convolution1D(input_dim=embedding_dims,
                    nb_filter=nb_filters,
                    filter_length=filter_length,
                    border_mode="valid",
                    activation="relu",
                    subsample_length=1),

                    name = 'sequence_conv',
                    input = 'sequence_embedding')


graph.add_node(Flatten(), name = 'sequence_flatten', input = 'sequence_conv')
output_size = nb_filters * (((maxlen - filter_length) / 1) + 1)
print output_size
graph.add_node(Dense(output_size, hidden_dims,activation = "relu" ),
               name = 'sequence_dense',
               input = 'sequence_flatten')


graph.add_node(
            Dense(hidden_dims, 32,
                activation = "relu"),
            name='merged_output',
            input='sequence_dense',
            merge_mode='concat')
graph.add_node(Dense(32,1, activation='sigmoid'),
                name= 'merged_final',
              input = 'merged_output')
graph.add_output(
            name='output',
            input='merged_final')


graph.compile('rmsprop',{'output':'mse'})

Fitting the input:

graph.fit(
                    {'sequence':X_train, 'output': Y_train},
                    batch_size=32,
                    nb_epoch=nb_epoch,
                    verbose = 1
    )

On keras-0.1.2, works fine

X_train_shape: (87680, 9)
Y_train_shape: (87680,)
Epoch 0
 9568/87680 [==>...........................] - ETA: 28s - output: 0.0847

On keras-0.1.3
IndexError: index 62887 is out of bounds for axis 0 with size 1

Full error:

X_train_shape: (87680, 9)
Y_train_shape: (87680,)
Epoch 0
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-6-c3fc7e82caf1> in <module>()
      6                     batch_size=32,
      7                     nb_epoch=nb_epoch,
----> 8                     verbose = 1
      9     )

/Library/Python/2.7/site-packages/keras/models.pyc in fit(self, data, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)
    676                             verbose=verbose, callbacks=callbacks,
    677                             val_f=val_f, val_ins=val_ins,
--> 678                             shuffle=shuffle, metrics=metrics)
    679         return history
    680 

/Library/Python/2.7/site-packages/keras/models.pyc in _fit(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, metrics)
    199                 batch_ids = index_array[batch_start:batch_end]
    200                 try:
--> 201                     ins_batch = slice_X(ins, batch_ids)
    202                 except TypeError as err:
    203                     print('TypeError while preparing batch. \

/Library/Python/2.7/site-packages/keras/models.pyc in slice_X(X, start, stop)
     53     if type(X) == list:
     54         if hasattr(start, '__len__'):
---> 55             return [x[start] for x in X]
     56         else:
     57             return [x[start:stop] for x in X]

IndexError: index 62887 is out of bounds for axis 0 with size 1

Any idea what's going wrong?

[fchollet]

Any idea what's going wrong?

With this little info provided, not a chance...

[NanditaDamaraju]

Updated info @fchollet

[fchollet]

graph.add_input(name='sequence', ndim=2)
graph.inputs['sequence'].input = T.imatrix()

That is unnecessary, you can simply do:

graph.add_input(name='sequence', ndim=2, dtype='int')

As for your error: an Embedding layer now requires an input that is a Numpy integer matrix. What does your input look like?

[NanditaDamaraju]

@fchollet changed X_train to numpy.matrix, same error still persists.

X_train:
Shape: (87680, 9)
Type : <class 'numpy.matrixlib.defmatrix.matrix'>
matrix([[  0.,   0.,   0., ...,   0.,  10.,  18.],
        [  0.,   0.,   0., ...,   0.,   0.,  18.],
        [  0.,   0.,   0., ...,  18.,   6.,  18.],
        ..., 
        [ 19.,  15.,  16., ...,   4.,  10.,  19.],
        [ 19.,  15.,  19., ...,   0.,  11.,   5.],
        [ 19.,  15.,  19., ...,  18.,   9.,   2.]])

Error:
IndexError: index 8478 is out of bounds for axis 0 with size 1

[fchollet]

Ok, I have enough info now, I'll look at it when I get some time.

[lemuriandezapada]

hm, try increasing max_features by 1.
I often get errors like this when I have more words to go into the embedding layer than the size of the layer.

[loyeamen]

I have got the same error:

C:\WinPython-64bit-2.7.9.4\python-2.7.9.amd64\lib\site-packages\keras-0.1.2-py2.7.egg\keras\models.pyc in fit(self, data, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)
676 verbose=verbose, callbacks=callbacks,
677 val_f=val_f, val_ins=val_ins,
--> 678 shuffle=shuffle, metrics=metrics)
679 return history
680

C:\WinPython-64bit-2.7.9.4\python-2.7.9.amd64\lib\site-packages\keras-0.1.2-py2.7.egg\keras\models.pyc in _fit(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, metrics)
199 batch_ids = index_array[batch_start:batch_end]
200 try:
--> 201 ins_batch = slice_X(ins, batch_ids)
202 except TypeError as err:
203 print('TypeError while preparing batch. \

C:\WinPython-64bit-2.7.9.4\python-2.7.9.amd64\lib\site-packages\keras-0.1.2-py2.7.egg\keras\models.pyc in slice_X(X, start, stop)
53 if type(X) == list:
54 if hasattr(start, 'len'):
---> 55 return [x[start] for x in X]
56 else:
57 return [x[start:stop] for x in X]

IndexError: index 3595 is out of bounds for axis 0 with size 1

[matsuyamax]

I've seen this issue before. Sample weights which should default to an array of ones are not properly normalized with the Graph models. I have a simple fix.