Comments (4)
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?
from mind.
I'm unable to replicate this issue. Can you show me your training data and network configuration?
from mind.
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 :)
from mind.
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.
from mind.
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
- Why its better? HOT 4
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from mind.