Bump compats and update tutorials for Optimization v4 #950
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| @@ -34,7 +34,7 @@ Boltz = "1" | |||
| ChainRulesCore = "1" | |||
| ComponentArrays = "0.15.17" | |||
| ConcreteStructs = "0.2" | |||
| DataInterpolations = "5, 6" | |||
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That causes conflict too, with Symbolics
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Just saw your comment on the compat PR in Boltz, I'll find a workaround
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This is stalled and will likely lead to more people complaining about the Optimization change can we get a decision for it? |
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@avik-pal update Boltz? |
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Some tests still fail for DataInterpolations, let me dev and patch those shouldn't be hard. |
| display(l) | ||
| global iter | ||
| iter += 1 | ||
| if doplot && iter % 1 == 0 | ||
| # plot the original data | ||
| plt = scatter(tsteps, ode_data[1, :]; label = "Data") | ||
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| # plot the different predictions for individual shoot | ||
| plot_multiple_shoot(plt, preds, group_size) |
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I don't see how this is going to work if we don't have the predictions?
| println(l) | ||
| # plot current prediction against data | ||
| if doplot | ||
| pred = predict_neuralode(state.u) |
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This is not a viable solution, it doubles the cost. We should show a better standard way to do this.
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It was the same before the gradient and loss were evaluated separately so I figured it's a fair tradeoff, the current alternative is to use a global variable.
Down the line I plan to improve OptimizationState so it can be hacked for these things, I am not sure how it should be yet but should be possible
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We should show a current best practice that does not involve recomputing since that can be quite a regression.
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