void NeuralNetwork::RTRL_update_gradients()
{
// for every neuron
for (unsigned int k = m_num_inputs; k < m_neurons.size(); k++)
{
// for all possible connections
for (unsigned int i = m_num_inputs; i < m_neurons.size(); i++)
// to
for (unsigned int j = 0; j < m_neurons.size(); j++) // from
{
int t_idx = ConnectionExists(i, j);
if (t_idx != -1)
{
//double t_derivative = unsigned_sigmoid_derivative( m_neurons[k].m_activation );
double t_derivative = 0;
if (m_neurons[k].m_activation_function_type
== NEAT::UNSIGNED_SIGMOID)
{
t_derivative = unsigned_sigmoid_derivative(
m_neurons[k].m_activation);
}
else if (m_neurons[k].m_activation_function_type
== NEAT::TANH)
{
t_derivative = tanh_derivative(
m_neurons[k].m_activation);
}
double t_sum = 0;
// calculate the other sum
for (unsigned int l = 0; l < m_neurons.size(); l++)
{
int t_l_idx = ConnectionExists(k, l);
if (t_l_idx != -1)
{
t_sum += m_connections[t_l_idx].m_weight
* m_neurons[l].m_sensitivity_matrix[i][j];
}
}
if (i == k)
{
t_sum += m_neurons[j].m_activation;
}
m_neurons[k].m_sensitivity_matrix[i][j] = t_derivative
* t_sum;
}
else
{
m_neurons[k].m_sensitivity_matrix[i][j] = 0;
}
}
}
}
Real bptt_unit_derivative(Group *group,int unit,int tick)
{
Real d;
if (group->activationType==LOGISTIC_ACTIVATION)
d= (sigmoid_derivative(group->outputs[tick][unit],
group->temperature));
else if (group->activationType==TANH_ACTIVATION)
d= (tanh_derivative(group->outputs[tick][unit],
group->temperature));
else
{
Choke0("Group %s does not have legal activationType",group->name);
exit(-1);
return 0; /* just so stupid compilers don't complain */
}
d += group->primeOffset;
return d;
}
