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Looking at core/matmodlab.py, it seems that J0 is initially evaluated during startup but never updated (with a numerical jacobian or with the returned jacobian). Also curious, the returned DDSDDE seem…
sswan updated
7 years ago
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I think we could do this by numerically approximating the Jacobian via finite difference. Alternatively we could `stack` a bunch of `grad()`s, computing the determinant as in [this suggestion](https:/…
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To be able to use the jacobian every activation function derivative other than softmax has to return a diagonal matrix and the backrpopagation algorithm has to use matrix multiplication instead of had…
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Hi there, I added the following lines to the original code to print the value of the input-output jacobian, and found that only the input-output jacobian of the first image in a batch has value during…
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Thank you for your grate work,I have two questions about your paper and codes.
1. In the prediction stage of EKF, the calculation formula (11) of the covariance matrix is correct, but in the code, yo…
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There are many places in the two modules where we use jacobian() and thus diff() to extract the linear coefficients of expressions. In general, we know that the expressions are linear in certain varia…
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```
Hi,
First of all, I would like to thank you for your open source.
While I was looking at your code, I got some questions regarding A matrix and
df_dx in implicit solver.
In you code, number…
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After #1666 is merged, there will be 214 warnings left, and 1370 node are not subnodes, which would be good to improve.
- [ ] missing nodes:
- [x] CC'
- [x] GCstats
- [x] RR'
- [x] Symb…
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I want to provide a sparse matrix to `jacobian!`, see this [thread](https://discourse.julialang.org/t/non-sorted-sparsematrixcsc/37133/17) on Discourse.
However, setting zero-valued elements in a …
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This works fine
```julia
using Symbolics
@variables x y
Symbolics.jacobian([x + x*y, x^2 + y],[x, y])
```
But adding this throws an error
```julia
using ReversePropagation
Symbolics.jacob…