Keras - 1D Convolution How it works

Question

From this example: https://github.com/fchollet/keras/blob/master/examples/imdb_cnn.py

comes this snippet below. The embedding layer outputs a 400 x 50 matrix for each example in a batch. My question is how does the 1D convolution work? How does it work across the 400 x 50 matrix?

# we start off with an efficient embedding layer which maps
# our vocab indices into embedding_dims dimensions
model.add(Embedding(max_features,
                    embedding_dims,
                    input_length=maxlen,
                    dropout=0.2))

# we add a Convolution1D, which will learn nb_filter
# word group filters of size filter_length:
model.add(Convolution1D(nb_filter=nb_filter,
                        filter_length=filter_length,
                        border_mode='valid',
                        activation='relu',
                        subsample_length=1))

Answer 1

In convolutional neural networks (CNNs), 1D and 2D filters are not really 1 and 2 dimensional. It is a convention for description.

In your example, each 1D filter is actually a Lx50 filter, where L is a parameter of filter length. The convolution is only performed in one dimension. That may be why it is called 1D. So, with proper padding, each 1D filter convolution gives a 400x1 vector. The Convolution1D layer will eventually output a matrix of 400*nb_filter.