Combining different filter lengths in 1D convolutional layers

[sebastianruder]

[1] show that combining filter lengths around the 'best' filter length achieves better sentence classification results than just using the 'best' filter length. Is there a possibility to specify a combination of filter lengths for 1D convolution? If not, is this a feature that should be implemented?

Zhang, Y., & Wallace, B. (2015). A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification, (1). Retrieved from http://arxiv.org/abs/1510.03820

[dbonadiman]

This is kinda of topic but thanks a lot for the paper :)
For the question itself i think that it should be up to the user to do that, on the other side the k-max pooling layer is missing from Keras and that is a big issue.

[sebastianruder]

Np. :)
If it's up to the user, how would I go about implementing combined feature lengths? Can I have separate convolution layers of different configurations that convolve on the same input and merge them?

[dbonadiman]

Using the Graph model yes. Give me 1 or two hours and i will come back i was planning to add it to my codebase :)

[dbonadiman]

It was faster then i thought.

#let's define some variables
nb_filters = 100
filter_lenghts = [3,4,5]
convs = []
maxlen = 30

model = Graph()
#adding the input as usual
model.add_input(name='sentence', input_shape=(1,), dtype=int)
#passing the sentence through the Embedding layer
model.add_node(Embedding(nb_words, emb_dim), name = 'sentence_embeddings', input = 'sentence')

#and now create the convolutions/max_pooling 
for i in filter_lenghts:
    model.add_node(Convolution1D(nb_filter=nb_filters,
                                 filter_length=i,
                                 border_mode="valid",
                                 activation="relu",
                                 subsample_length=1), name=str(i)+'_convolution', input= 'sentence_embeddings')

    #that's an approximation of 1-maxpooling that i never really checked.
    model.add_node(MaxPooling1D(pool_length= maxlen - i + 1), name=str(i)+'_maxpooling', input=str(i)+'_convolution')
    model.add_node(Flatten(), name = str(i)+' sentence_embedding', input=str(i)+'_maxpooling')
    convs.append(str(i)+' sentence_embedding')

#now concat  all the results and  do whatever you want with the new sentence_embedding :D
model.add(NEXT_LAYER, name='whatever', inputs=convs)

I rewritten that in that comment never really compiled but if some error is raised it should be easily debugged.

In order to implement it with the sequential model you should replicate your input sentence many times but i'm not a fun of this approach.

[dbonadiman]

@sebastianruder Just a reminder do not exaggerate on the nb_filters because depending on the task you are modelling it might not converge (just experienced that problem). 200 may be good if you have only one convolution but with more 50 may be enough.

[sebastianruder]

@dbonadiman: Wow, that was fast indeed! Thanks a lot for the commented implementation snippet and the tip! :D

[dbonadiman]

@sebastianruder It is fast to do these implementations if you have a working model to test them over ;)

[MdAsifKhan]

It creates an identifier issue:
File "build/bdist.linux-x86_64/egg/keras/layers/containers.py", line 366, in add_node
Exception: Unknown identifier: 7input

[thisisambros]

This graph model can still be created with keras 2.0 using sequential models?

[hsiaoyi0504]

@Ambros94 They are different containers actually. You should check the documentation for further details.