Hi !

I am kind of new to owl, and I have a bit of a problem with neural networks :

Basically I am trying to implement a network that takes non-matricial data.

More specifically I am trying to code a neural network that takes trees as input (a variant of tbcnn)

The problem I am encountering is that, the input being binary trees, it does not always have the same structure

this is theoretically not a problem (since the differentiation is normally done on the parameters, but only on the optimization parameters), i tried to write the layers that operate on trees (the convolution layers) as Lambda nodes in owl.

But, if I got owl requires all input for a NN node to be Algodiff.t . (which my trees are not)

The solution I have found is to encode my binary trees as matrices (using a list of nodes ordered depth-first). It should work (i have not finished), but is not very elegant.

Do you know if there is a more elegant way ?