Activations¶
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namespace paddle¶
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class ActivationFunction¶
- Subclassed by paddle::IdentityActivation - Public Functions - 
ActivationFunction()¶
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virtual ~ActivationFunction()¶
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virtual const std::string &getName() const = 0¶
 - Public Static Functions - 
ActivationFunction *create(const std::string &type)¶
 
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class 
Defines
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ACTIVATION_CLASS_NAME(ACTIVATION_NAME)¶
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BEGIN_DEFINE_ACTIVATION(ACTIVATION_NAME)¶
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END_DEFINE_ACTIVATION(ACTIVATION_NAME)¶
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namespace paddle¶
- Functions - 
void forward(Argument &act)¶
- SigmoidActivation - f(z) = {1}{1+exp(-z)} - Relu Activation. - forward. y = max(0, z) - derivative of relu is: - 1 if z > 0 - 0 otherwise. - BRelu Activation. - forward. y = min(24, max(0, z)) - derivative of brelu is: - 1 if 0 < z < 24 - 0 otherwise. - TODO(yuyang18): Remove magic number 24 or make it configuable. - tanh activation. - f(z) = tanh(z)={e^z-e^{-z}}{e^z+e^{-z}} - Soft relu activation. - f(z) = ln(1+e^z) - Abs Activation. - Forward: f(z) = abs(z) - Derivative: - 1 if z>0 - -1 if z<0 - 0 if z=0 - Square Activation. - f(z) = z^2. 
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ACTIVATION_CLASS_NAME(softmax)¶
- Softmax on all frames of one sequence. Width of frame must be one. 
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ACTIVATION_CLASS_NAME() paddle::stanh()
 - Variables - 
ClassRegistrar<ActivationFunction> gActivationRegistrar¶
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InitFunction paddle::__reg_activation__identity([]{gActivationRegistrar.registerClass< IdentityActivation >("");gActivationRegistrar.registerClass< IdentityActivation >("linear");})
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MatrixPtr sftMaxSum_¶
- Do Softmax activation for all sample. P(y=j|x) = {e^{x^Tw_j}}{^K_{k=1}e^{x^Tw_k}} 
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real a¶
- Scaled Tanh Activation - f(z) = 1.7159 * tanh(2/3*z) 
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real b¶
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class IdentityActivation¶
- The IdentityActivation class. - Do nothing when forward/backward. - Inherits from paddle::ActivationFunction - Public Functions - 
virtual const std::string &getName() const¶
 - Public Static Attributes - 
const std::string name¶
 
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virtual const std::string &
 
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void