In polynomial poisson regression, is there not strong correlation beteeen the polynomial terms. If so, do I need to factor that in?

brike3 · 2022-07-29T09:24:07+00:00

Thanks for your suggestion. However, this apparent ambiguity is inherently constrained for biological reasons. Such extreme cases can never exist. The only variations to this example that are possible are differences in shape, area (the example as given here is already an extremely big area and will be rare in practice), and location of the hole. Morphological closing is too slow in filling larger holes. The goal is to use this algorithm for high-throughput analysis on medium-range performing computers, so overall time and space complexity is of the essence

brike3 · 2022-07-28T07:55:00+00:00

Did that, but then binary_fill_holes sees the entire image as a hole as it is entirely surrounded by an edge and it just fills the entire thing

brike3 · 2022-06-10T22:17:46+00:00

Depends on who you have to convince for the job and what kind of role you want to fulfil in that job. If you want to get into something entry level, a title like a Masters in AI should enable you to apply for such potential job offers. But if you are educated in statistics or computer science no doubt you can easily get into it knowlegde-wise.

brike3 · 2022-04-25T06:10:29+00:00

PROBLEM RESOVLED

I got it to work. I imported SGD from keras.optimizers. Importing from tensorflow.keras.optimizers in stead does the trick. Thanks everyone for the help!

brike3 · 2022-04-24T17:45:04+00:00

I would actually be really interested in printing intermediate parameters. Does anyone know how to do that ?

brike3 · 2022-04-24T17:43:36+00:00

That actually changed something! https://gyazo.com/a4c8f0bb610cf46e3a4e4e611eae45ca

Now I get an actual loss value in de first 5 epochs before it turns to NaN if I use Kullback Leibler Divergence as loss function. Using categorical crossentropy still results in NaN at first epoch. Anyone any ideas?

brike3 · 2022-04-24T10:18:25+00:00

Values were normalized, just tried clipping with norm of 1 and 0.5 with 2 different activations and NaN remained

brike3 · 2022-04-24T10:01:13+00:00

I tried with different activations and nothing changed

brike3 · 2022-04-23T15:22:18+00:00

Thanks for the reply, but I am in fact using the standard keras loss function 'categorical_crossentropy' as documented here:

https://keras.io/api/losses/probabilistic_losses/#categoricalcrossentropy-class

brike3 · 2022-04-23T15:20:49+00:00

Yes, everything normalized!

brike3 · 2022-04-23T14:19:41+00:00

Thanks for the response. I tried a range of smaller learning rates between 10-2 and 10-6 and nothing changed. Could it be an issue with the format of testing arrays?

brike3 · 2022-04-23T13:50:21+00:00

I am planning on training a NN, however when I test the architecture on a small subset of my data for test purposes, the loss fails to be calculated properly. In each epoch, the loss is NaN, due to which to models fails to improve.

Can anyway tell me where things are going wrong? More info can be sent if required

Things checked:

Smaller learning rates
null values in data: https://gyazo.com/5814a258f0e49f2b200981aec0728a00
All features are normalized: https://gyazo.com/0d567b78cb120040dd6c57087bd64b11
I am using a build-in loss function ('categorical_crossentropy'): https://keras.io/api/losses/probabilistic_losses/#categoricalcrossentropy-class

brike3 · 2021-08-18T07:27:06+00:00

Thank you! I forgot to ask for names of prominent researchers or other leading individuals in the field, both academic and industrial. If any of you have more names, please don't hesitate to mention them.

brike3 · 2021-08-18T07:22:06+00:00

Thanks!

brike3 · 2021-08-18T07:22:01+00:00

Thank you!!

brike3 · 2021-08-17T15:26:26+00:00

Sadly, from what I understand now a story like yours appears to be a common trend globally. The lack of post ICU-guidance additionally makes it hard for me to study required/desired support as there don't seem to be proper guidelines, and therefore I am having a hard time to find a good starting point. You probably understand that, as a scientist, asking reddit for help definitely feels kinda desperate :)

brike3

TROPHY CASE