void mlp::set_activation_function(int activation_function, mlp_layer layer)
{
if (grt_mlp.validateActivationFunction(activation_function) == false)
{
flext::error("activation function %d is invalid, hint should be between 0-%d", activation_function, GRT::Neuron::NUMBER_OF_ACTIVATION_FUNCTIONS - 1);
return;
}
GRT::Neuron::ActivationFunctions activation_function_ = (GRT::Neuron::ActivationFunctions)activation_function;
switch (layer)
{
case LAYER_INPUT:
input_activation_function = activation_function_;
break;
case LAYER_HIDDEN:
hidden_activation_function = activation_function_;
break;
case LAYER_OUTPUT:
output_activation_function = activation_function_;
break;
default:
ml::error("no activation function for layer: " + std::to_string(layer));
return;
}
post("activation function set to " + grt_mlp.activationFunctionToString(activation_function_));
}
void ann::set_activation_function(int activation_function, ann_layer layer)
{
GRT::Neuron::Type activation_function_ = GRT::Neuron::Type::LINEAR;
try
{
activation_function_ = get_grt_neuron_type(activation_function);
}
catch (std::exception& e)
{
flext::error(e.what());
return;
}
if (grt_ann.validateActivationFunction(activation_function_) == false)
{
flext::error("activation function %d is invalid, hint should be between 0-%d", activation_function, GRT::Neuron::NUMBER_OF_ACTIVATION_FUNCTIONS - 1);
return;
}
switch (layer)
{
case LAYER_INPUT:
input_activation_function = activation_function_;
break;
case LAYER_HIDDEN:
hidden_activation_function = activation_function_;
break;
case LAYER_OUTPUT:
output_activation_function = activation_function_;
break;
default:
ml::error("no activation function for layer: " + std::to_string(layer));
return;
}
post("activation function set to " + grt_ann.activationFunctionToString(activation_function_));
}
