Well. Network weights can be large and hence the matrix, so cut and paste is not going to be great, though doable with lots of patience :-)
Training a large neural network, and hence weight matrix, is not feasible within the time limits of quantopian live/backtest, but can be done in the research, since I haven't heard of any time limits.
The research platform doesn't allow writing out a CSV both locally or an external server, so there isnt a mechanism to push it out to a file or to an external server.
I think I can "theoretically open(,'w').write() the weights, but where does it go. Its local to the server my research instance is running on. There is no gaurantees that even my research platform instance can find it once reloaded.
The regular quantopian backtest/live cannot either.
May be I am completely misunderstanding what you are suggesting
Was just looking for a simpler route to go from research to a live algorithm. The more I try, I find quantopian live/backtest not so great for coming up with and fine tuning a model/algorithm.
But Quantopian research is, if only we could move back and forth easily.