Comments (2)
ckcollab
commented on July 4, 2024
What different kinds of errors are there?
from anny.
levithomason
commented on July 4, 2024
Anny currently includes crossEntropy, meanSquared, rootMeanSquared, and arcTan. You can define any error function you like. An error function takes the Network's actual output (array of the output value of every Neuron in the output Layer) and the expected output and returns a number. See here.
There are different error functions for different use cases. For example, classification problems are best served by the "cross entropy" error function. Some material I've read of late suggests only to use cross entropy and soft max activations for most problems, including object recognition.
from anny.
Related Issues (20)
- Use more advanced learning algorithm HOT 1
- Implement batch training HOT 3
- Add more training tests and examples HOT 1
- Input neurons should not use activationFn HOT 1
- Support convolutional networks
- Add changelog HOT 1
- Network.train() should not log on succes/fail HOT 1
- Fail if no training progress after N epochs
- Setup docs and demo hosting
- Better way to add bias neuron HOT 1
- Validation before training
- Support normal and derivative network error functions HOT 1
- Remove mathjs dependency
- Normalize training data before training
- Create Neuron.Activation class or factory HOT 4
- Create Trainer class HOT 1
- Better weight initialization
- Trainer shuffle option
- Setup test mocks
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