Difference between revisions of "Thread:Talk:Rolling Averages/Rolling Average vs Softmax & Cross Entropy"
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Which is essentially rolling average, where η (learning rate) equals to the α (decay rate) in exponential moving average. | Which is essentially rolling average, where η (learning rate) equals to the α (decay rate) in exponential moving average. | ||
| − | Anyway this analog isn't how rolling average works, as logits don't equal to q<sub>i</sub> | + | Anyway this analog isn't how rolling average works, as logits don't equal to q<sub>i</sub> at all. But what if we replace rolling average with gradient descent? I suppose it could learn even faster, as the outputs farther from real value get higher decay rate... |
Revision as of 06:07, 27 July 2021
If each Guess Factor bin is considered an output unit before Softmax (logit), and loss is Cross Entropy, then the gradient of each logit is then:
- qi - 1, if bin is hit
- qi, otherwise
Where qi is the output of the ith unit after Softmax (estimated probability)
If gradients are not applied on logits as normal, but instead applied on qi itself, then:
- qi := qi - η * (qi - 1) = (1 - η) * qi + η * 1, if bin i hit
- qi := qi - η * qi = (1 - η) * qi + η * 0, otherwise
Which is essentially rolling average, where η (learning rate) equals to the α (decay rate) in exponential moving average.
Anyway this analog isn't how rolling average works, as logits don't equal to qi at all. But what if we replace rolling average with gradient descent? I suppose it could learn even faster, as the outputs farther from real value get higher decay rate...
