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DAG container #7
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Agreed, and with the current design it should not even be hard to do. And then we or someone else can build higher level functions to read cuda-convnet/caffe model files and instantiate such a model, but who'd want such a thing, hehe. |
We need to discuss how to implement this DAG container. I suggest to keep all symbolic variables hidden and simply work with some unique ids that is generated by the "add" function. Also I think that we need to handle multiple inputs and multiple outputs for this container. |
GeneralAgreed on hiding all symbolic variables. But I think instead of using automatic unique IDs, we should let the user give unique names to all layers:
or, with keyword arguments:
Multiple inputsA nice example of using this would be a depth-from-stereo algorithm which needs two pictures as input. With the name giving, this should be relatively easy, something like this would be nice:
Multiple OutputsA use-case for this (vision again) would be human attributes, e.g. for a picture say whether the person is male/female and also has glasses or not. For this, we would also need a kind of weighted-sum-of-costs cost, which I think both of us had in our toolboxes. Then, it could simply be that the user calls Thoughts? |
Addendum: and if we make two additional things:
Then it is also possible to use it exactly as in your first example. If you agree and want, I can write up a proposal implementation when I get time (maybe this evening). |
I agree with your suggestions and with addendum as well. |
Add a direct acyclic graph container so we could builld a network as in Caffe and Lasagne.
Example:
model = bb8.DAG()
fc1 = model.add('input', bb8.Linear(32, 64))
relu1 = model.add(fc1, bb8.ReLU())
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