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In PyTorch you have full control on the graph and weight, everything feels like Python. So feeding some of the learning between “sub-graph” is easy. Not sure if
by punnerud 7y ago
In PyTorch you have full control on the graph and weight, everything feels like Python.
So feeding some of the learning between “sub-graph” is easy. Not sure if this is possible on Tensorflow/Keras?
He describes the sub-graph training in the context that they they have all the predictors in one big model, and with control of the network can feedforward and train sub-graph (read sub-parts) of the model.
- eanzenberg 7y agoThis is possible in keras, just drive new models that are functions of a monolith model and train independently. I still don’t understand the point though. If you train a “subgraph”, the other tasks dependent on the part of the graph will have to get retrained anyways, since those edits will affect the other tasks.