41 lines
2.2 KiB
Markdown
41 lines
2.2 KiB
Markdown
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description: List of cost functions in Neataptic
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authors: Thomas Wagenaar
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keywords: cost function, loss function, mse, cross entropy, optimize
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[Cost functions](https://en.wikipedia.org/wiki/Loss_functions_for_classification)
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play an important role in neural networks. They give neural networks an indication
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of 'how wrong' they are; a.k.a. how far they are from the desired output. But
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also in fitness functions, cost functions play an important role.
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### Methods
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At the moment, there are 7 built-in mutation methods (all for networks):
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Name | Function |
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---- | ------ |
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[methods.cost.CROSS_ENTROPY](http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function) | 
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[methods.cost.MSE](https://en.wikipedia.org/wiki/Mean_squared_error) | 
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[methods.cost.BINARY](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_884) | 
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[methods.cost.MAE](https://en.wikipedia.org/wiki/Mean_absolute_error) | 
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[methods.cost.MAPE](https://en.wikipedia.org/wiki/Mean_absolute_percentage_error) | 
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[methods.cost.MSLE](none) | none
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[methods.cost.HINGE](https://en.wikipedia.org/wiki/Hinge_loss) | 
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### Usage
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Before experimenting with any of the loss functions, note that not every loss
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function might 'work' for your network. Some networks have nodes with activation
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functions that can have negative values; this will create some weird error values
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with some cost methods. So if you don't know what you're doing: stick to any of
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the first three cost methods!
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```javascript
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myNetwork.train(trainingData, {
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log: 1,
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iterations: 500,
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error: 0.03,
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rate: 0.05,
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cost: methods.cost.METHOD
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});
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```
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