Comments (4)
stiff
commented on August 17, 2024
1
It seems like under certain circumstances some rounding error creeps in and damages everything. I tried network with 20 hidden layers of 20 units, and it's almost always returned same result for any input. Also after different invocations of learning the same data I get different predictions, is it expected behaviour?
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stevenmiller888
commented on August 17, 2024
I'm unable to replicate this issue. Can you show me your training data and network configuration?
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D-Nice
commented on August 17, 2024
Apologies, it seems to have been an issue with the inputs being incorrectly normalized from my end, after I just retried and took a deeper look. So the inputs were always being set to 1. Still, even if all inputs are always a constant number, is this something that should still happen, where it returns NaN? Feel free to close this issue after answering :)
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stevenmiller888
commented on August 17, 2024
That is not expected. I've been able to replicate with a constant number as the input. I'm not sure what's causing this. Will try to investigate soon.
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Related Issues (20)
- todo: clean up back propagation
- can mind take inputs that vary from 0-1? (ex: 0.2, 0.3, 0.5, etc) HOT 3
- Is that possible to call 'learn' multiple times? HOT 1
- The example code always gives ~1 HOT 2
- your website has been hacked HOT 3
- changes for output>hidden always on results[0]?
- todo: add more tests
- download() and upload() the Mind HOT 1
- Where is bias in your implementation? HOT 1
- NaN returns when first training entry is longer then second HOT 1
- Mind showing NaN by adding another feature to XOR example
- Your provided example doesn't work perfectly. HOT 2
- How can we add multiple output nodes?
- browser script please HOT 5
- character is not defined
- Division by input in tutorial
- Typescript Support
- todo: add multiple uploads HOT 1
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