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Slow learning time solved with equal number of positive and negative training samples.

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davidjacnogueira
2016-11-03 02:59:32 +00:00
parent ff7bfe1fa2
commit 9ff33f7b65
6 changed files with 124 additions and 119 deletions
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16
src/Layer.h
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@@ -34,23 +34,19 @@ public:
use_constant_weight_init,
constant_weight_init)));
};
~Layer() {
m_num_nodes = 0;
m_num_inputs_per_node = 0;
m_nodes.clear();
};
//std::vector<Node> & GetNodes() {
// return m_nodes;
//}
const std::vector<Node> & GetNodes() const {
return m_nodes;
}
void GetOutputAfterSigmoid(const std::vector<double> &input, std::vector<double> * output) const {
void GetOutputAfterSigmoid(const std::vector<double> &input,
std::vector<double> * output) const {
assert(input.size() == m_num_inputs_per_node);
output->resize(m_num_nodes);
@@ -71,7 +67,8 @@ public:
for (size_t i = 0; i < m_nodes.size(); i++) {
double net_sum;
m_nodes[i].GetInputInnerProdWithWeights(input_layer_activation, &net_sum);
m_nodes[i].GetInputInnerProdWithWeights(input_layer_activation,
&net_sum);
//dE/dwij = dE/doj . doj/dnetj . dnetj/dwij
double dE_doj = 0.0;
@@ -80,8 +77,7 @@ public:
dE_doj = deriv_error[i];
doj_dnetj = utils::deriv_sigmoid(net_sum);
for (int j = 0; j < m_num_inputs_per_node; j++) {
(*deltas)[j] += dE_doj * doj_dnetj * m_nodes[i].GetWeights()[j];