Package deepnetts.net.layers.activation
Interface ActivationFunction
- All Superinterfaces:
 Consumer<TensorBase>
Common base interface for all activation functions used in layers.
 Classes implementing this interface should provide methods for calculating
 value and first derivative of the activation function.
 Activation function performs non-linear transformation of its input
 before its sent to layer output.
 First derivative of a function shows how fast and in what direction function
 is changing if its input changes, and it is used by training algorithm.
 For more see https://en.wikipedia.org/wiki/Activation_function
- See Also:
 
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Method Summary
Modifier and TypeMethodDescriptiondefault voidaccept(TensorBase tensor) voidvoidapply(TensorBase tensor, int from, int to) static ActivationFunctioncreate(ActivationType type) Creates and returns specified type of activation function.floatgetPrime(float y) Returns the first derivative of activation function for specified output yfloatgetValue(float x) Returns the value of activation function for specified input x 
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Method Details
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getValue
float getValue(float x) Returns the value of activation function for specified input x- Parameters:
 x- input for activation- Returns:
 - value of activation function
 
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apply
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apply
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accept
- Specified by:
 acceptin interfaceConsumer<TensorBase>
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getPrime
float getPrime(float y) Returns the first derivative of activation function for specified output y- Parameters:
 y- output of activation function- Returns:
 - first derivative of activation function
 
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create
Creates and returns specified type of activation function. A factory method for creating activation functions;- Parameters:
 type- type of the activation function- Returns:
 - returns instance of specified activation function type
 
 
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