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  • Nov 28 2021 14:16
    oprypin closed #12
  • Nov 28 2021 14:16
    oprypin commented #12
  • Nov 28 2021 14:13
    bararchy closed #10
  • Nov 28 2021 14:13
    bararchy commented #12
  • Nov 28 2021 14:12

    bararchy on v1.2.0

    (compare)

  • Nov 28 2021 14:11

    bararchy on master

    sync version (compare)

  • Nov 28 2021 14:10

    bararchy on master

    Fix for Crystal 1.2.2 fix format (compare)

  • Nov 27 2021 12:04
    oprypin commented #12
  • Jan 26 2021 19:49
    oprypin opened #12
  • Oct 24 2019 09:26
    fab1an2 commented #11
  • Oct 23 2019 10:25
    bararchy closed #11
  • Oct 23 2019 09:27
    bararchy commented #11
  • Oct 23 2019 09:23
    bararchy commented #11
  • Oct 23 2019 09:19
    fab1an2 opened #11
  • Sep 21 2019 10:36
    bararchy commented #10
  • Sep 21 2019 10:35
    bararchy commented #10
  • Sep 21 2019 10:34
    bararchy commented #10
  • Sep 21 2019 10:13
    xor256 edited #10
  • Sep 21 2019 10:12
    xor256 opened #10
  • Dec 17 2017 11:41

    bararchy on v1.1.1

    (compare)

Martyn Jago
@mjago
Specs work
Finished in 71.3 milliseconds
14 examples, 0 failures, 0 errors, 0 pending
I compiled github/libfann head
Bar Hofesh
@bararchy

hm... running specs on master shows

Fann::Network
  initializes
works
Fann::Network
  initializes standard network
  initializes cascade network
  free memory
  trains on single data
  Shows MSE
  trains and evaluate single data
  train on batchMax epochs     8000. Desired error: 0.0010000000.
Epochs            1. Current error: 0.2745950818. Bit fail 2.
Epochs           91. Current error: 0.0009867755. Bit fail 0.
  train on batch
  train on cascadeMax neurons 500. Desired error: 0.001000
Neurons       0. Current error: 0.250000. Total error:  1.0000. Epochs    51. Bit fail   4
Neurons       1. Current error: 0.000746. Total error:  0.0030. Epochs   117. Bit fail   0. candidate steepness 1.00. function FANN_GAUSSIAN_SYMMETRIC
Train outputs    Current error: 0.000000. Epochs    123
  train on cascade

Finished in 3.2 milliseconds
10 examples, 0 failures, 0 errors, 0 pending

So only 10 examples

how did you get 14?
:)
are you using latest version of Crystal-fann?
Martyn Jago
@mjago
:smile:
Ok so I was using a shard to get the error, and cloned your repo to run the specs
Bar Hofesh
@bararchy
Ok, so I'll just make sure again, are you using latest crystal-fann ?
maybe the shard isn't using latest crystal-fann version?
Martyn Jago
@mjago
NeuraLegion/crystal-fann
shard is using that with branch: master

I get this .......Max epochs 8000. Desired error: 0.0010000000.
Epochs 1. Current error: 0.3286948800. Bit fail 2.
Epochs 63. Current error: 0.0009598084. Bit fail 0.
.Max neurons 500. Desired error: 0.001000
Neurons 0. Current error: 0.250000. Total error: 1.0000. Epochs 51. Bit fail 4
Neurons 1. Current error: 0.000618. Total error: 0.0025. Epochs 116. Bit fail 0. candidate steepness 0.25. function FANN_GAUSSIAN_SYMMETRIC
Train outputs Current error: 0.000000. Epochs 120
......

Finished in 79.43 milliseconds
14 examples, 0 failures, 0 errors, 0 pending
Sun Dec 17: crystal-fann/ >
```

I hate gitter sometimes
Bar Hofesh
@bararchy
@mjago You're right, it was my bad, I wans't on latest
can you tell me which of the README examples wont work for you?
basiclly all of them are in the specs, so it's wierd they work there but not in the code
Martyn Jago
@mjago
# Work on array of test data (batch)
Hmm strange - one of the specs failed once (train_data_spec.cr I think) but now all pass - I’ll go and try the example again
Bar Hofesh
@bararchy
Sure, let me know :)
Martyn Jago
@mjago
Still fails yet the first and last example work fine
Perhaps its the crystal version ?
Crystal 0.24.1+5 [a1e90f0bd] (2017-12-16)

LLVM: 4.0.0
Default target: x86_64-apple-macosx
Bar Hofesh
@bararchy
I'm also on Head
I'm on Linux hough
should not matter, but let me do another test
Martyn Jago
@mjago
libFann version (head)
d71d547 * master origin/master ensure that cmake is not dependent on directory it's run in
Bar Hofesh
@bararchy
@mjago Looking at it more deeply, we didn't update the README to showcase latest specs
@mjago Updated the README now
can you try the changed examples and let me know if you get into any issue?
Martyn Jago
@mjago
@bararchy yay 🎉 all good now
Bar Hofesh
@bararchy
Cool, sorry for that XD
@mjago
enjoy the lib
Martyn Jago
@mjago
@barachy note however that without branch: master crystal deps installs an old version (with typos)
Bar Hofesh
@bararchy
I'll create a release
Martyn Jago
@mjago
@bararchy yes I know nothing about ANN etc so have some learning to do :+1:
@barachy thanks for use of the library !
Bar Hofesh
@bararchy
no problem, enjoy :)
Martyn Jago
@mjago
@bararchy I noticed that fann_set_learning_rate() expects to pass a float (doesn’t work with a double as currently defined)
Bar Hofesh
@bararchy
Hm..... interesting, let me take a look, I might missed something
Martyn Jago
@mjago
Do you want a PR for #learning_rate and #learning_rate = ?
Bar Hofesh
@bararchy
Sure, would love it :)
also, you should really take a look at SHAInet, it's much faster, and give better results, it also allows for more precision in network architecture
Martyn Jago
@mjago
Sure, I definitely will do
Bar Hofesh
@bararchy
:+1:
Martyn Jago
@mjago
My pet project is decoding morse from a noisy audio source - already works from a clean ‘ideal source'
Bar Hofesh
@bararchy
really cool! is that an opensource project?
Martyn Jago
@mjago
It will be. It will join an open-source morse (CW) tutor I wrote in Ruby hopefully