Implement dropout#4554
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Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
Uses inverted dropout: during training, retained activations are scaled by 1/(1-dropout_rate) so that no rescaling is needed at inference time. The dropout mask is stored during Forward and reused in Backward. The base-class randomizer_ (TRand*) is used for random number generation. Signed-off-by: Stefan Weil <sw@weilnetz.de>
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 55 |
| Duplication | 10 |
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Signed-off-by: Stefan Weil <sw@weilnetz.de>
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Signed-off-by: Stefan Weil <sw@weilnetz.de>
Signed-off-by: Stefan Weil <sw@weilnetz.de>
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This implementation for dropout was largely created with qwen3-coder and Claude Sonnet 4.6.
It is still completely untested.