Comments (4)
From my understanding that sounds good.
Will there be an easy way to iterate over them? Neuron.ACTIVATION.get_all()
or what have you? May not be helpful or needed.
Also, is there a way to "choose" one for you? Neuron.ACTIVATION.find_a_good_fit(some_parameters_i_dont_understand)
from anny.
Since it is just an object you can loop over them like:
// lodash
_.each(Neuron.Activation, (activation, name) => {...})
// vanilla js
Object.keys(Neuron.Activation).forEach(key => {
Neuron.Activation[key]
})
// or
for (const key in Neuron.Activation) {
if (Neuron.Activation.hasOwnProperty(key) {
// do stuff
}
}
There are possibly heuristics we can use to try and find an activation function for you. It might require asking some questions or something. Realistically, I think knowing what they do and what they are good at is essential to good machine learning. Also, experimenting is really fun and another great way to findTheBestFunction
:)
from anny.
👍 thanks for explaining
So you'd make _.each
ignore create()
? Or does _.each
only cover objects and not functions... but I thought functions are objects... how does that work? :)
from anny.
You're right, in these cases it would totally pick up create()
. That is what I get for coding at this hour :P The implementation of this fix will be something different, but the goal is the same and remains good. Split out the activation function definition somewhere, validate it, and allow adding new ones.
Thanks for the dive in and catching that. Night night 🌝
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 Network.Error class or factory HOT 2
- Create Trainer class HOT 1
- Better weight initialization
- Trainer shuffle option
- Setup test mocks
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from anny.