コード例 #1
0
ファイル: NeuralNetwork.cpp プロジェクト: noskill/MultiNEAT
void NeuralNetwork::CalculateNeuronActivation(size_t i){
    double x = m_neurons[i].m_activesum;
    m_neurons[i].m_activesum = 0;
    // Apply the activation function
    double y = 0.0;
    switch (m_neurons[i].m_activation_function_type)
    {
    case SIGNED_SIGMOID:
        y = af_sigmoid_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case UNSIGNED_SIGMOID:
        y = af_sigmoid_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case TANH:
        y = af_tanh(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case TANH_CUBIC:
        y = af_tanh_cubic(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case SIGNED_STEP:
        y = af_step_signed(x, m_neurons[i].m_b);
        break;
    case UNSIGNED_STEP:
        y = af_step_unsigned(x, m_neurons[i].m_b);
        break;
    case SIGNED_GAUSS:
        y = af_gauss_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case UNSIGNED_GAUSS:
        y = af_gauss_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case ABS:
        y = af_abs(x, m_neurons[i].m_b);
        break;
    case SIGNED_SINE:
        y = af_sine_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case UNSIGNED_SINE:
        y = af_sine_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case SIGNED_SQUARE:
        y = af_square_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case UNSIGNED_SQUARE:
        y = af_square_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    case LINEAR:
        y = af_linear(x, m_neurons[i].m_b);
        break;
    default:
        y = af_sigmoid_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
        break;
    }
    m_neurons[i].m_activation = y;
}
コード例 #2
0
void NeuralNetwork::ActivateLeaky(double a_dtime)
{
	// Loop connections. Calculate each connection's output signal.
	for (unsigned int i = 0; i < m_connections.size(); i++)
	{
		m_connections[i].m_signal =
				m_neurons[m_connections[i].m_source_neuron_idx].m_activation
						* m_connections[i].m_weight;
	}
	// Loop the connections again. This time add the signals to the target neurons.
	// This will largely require out of order memory writes. This is the one loop where
	// this will happen.
	for (unsigned int i = 0; i < m_connections.size(); i++)
	{
		m_neurons[m_connections[i].m_target_neuron_idx].m_activesum +=
				m_connections[i].m_signal;
	}
	// Now we have the leaky integrator step for the neurons
	for (unsigned int i = m_num_inputs; i < m_neurons.size(); i++)
	{
		double t_const = a_dtime / m_neurons[i].m_timeconst;
		m_neurons[i].m_membrane_potential = (1.0 - t_const)
				* m_neurons[i].m_membrane_potential
				+ t_const * m_neurons[i].m_activesum;
	}
	// Now loop nodes_activesums, pass the signals through the activation function
	// and store the result back to nodes_activations
	// also skip inputs since they do not get an activation
	for (unsigned int i = m_num_inputs; i < m_neurons.size(); i++)
	{
		double x = m_neurons[i].m_membrane_potential + m_neurons[i].m_bias;
		m_neurons[i].m_activesum = 0;
		// Apply the activation function
		double y = 0.0;
		switch (m_neurons[i].m_activation_function_type)
		{
		case SIGNED_SIGMOID:
			y = af_sigmoid_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case UNSIGNED_SIGMOID:
			y = af_sigmoid_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case TANH:
			y = af_tanh(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case TANH_CUBIC:
			y = af_tanh_cubic(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case SIGNED_STEP:
			y = af_step_signed(x, m_neurons[i].m_b);
			break;
		case UNSIGNED_STEP:
			y = af_step_unsigned(x, m_neurons[i].m_b);
			break;
		case SIGNED_GAUSS:
			y = af_gauss_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case UNSIGNED_GAUSS:
			y = af_gauss_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case ABS:
			y = af_abs(x, m_neurons[i].m_b);
			break;
		case SIGNED_SINE:
			y = af_sine_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case UNSIGNED_SINE:
			y = af_sine_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case SIGNED_SQUARE:
			y = af_square_signed(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case UNSIGNED_SQUARE:
			y = af_square_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		case LINEAR:
			y = af_linear(x, m_neurons[i].m_b);
			break;
		default:
			y = af_sigmoid_unsigned(x, m_neurons[i].m_a, m_neurons[i].m_b);
			break;
		}
		m_neurons[i].m_activation = y;
	}

}