diff --git a/src/Network/ACSFNeuralNetwork.cpp b/src/Network/ACSFNeuralNetwork.cpp
index 07cae17ebb143cd26fa0d331462bb733bc098fae..87fa47590e2fb0f83bac3e00186efb4c40fdfbf7 100644
--- a/src/Network/ACSFNeuralNetwork.cpp
+++ b/src/Network/ACSFNeuralNetwork.cpp
@@ -12,48 +12,59 @@ lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBO
std::unordered_map<ELEMENT_SYMBOL, std::vector<NEURON_TYPE>> type_hidden_neurons) {
/* Construct the neural network */
std::vector<size_t> inputs;
- std::vector<size_t> subnet_outputs;
- size_t neuron_idx;
- std::unordered_map<ELEMENT_SYMBOL, std::vector<size_t>> subnet_idxs;
- std::unordered_map<ELEMENT_SYMBOL, std::vector<size_t>> subnet_neuron_idxs;
- std::unordered_map<ELEMENT_SYMBOL, std::vector<size_t>> subnet_connection_idxs;
+ std::unordered_map<ELEMENT_SYMBOL, size_t> subnet_neuron_shifts;
+ std::unordered_map<ELEMENT_SYMBOL, size_t> subnet_connection_shifts;
+ std::unordered_map<ELEMENT_SYMBOL, bool> subnet_constructed;
+ size_t last_neuron_bias_idx = 0;
+ size_t last_connection_weight_idx = 0;
+
+ std::shared_ptr<Neuron> output_neuron = std::make_shared<NeuronLinear>();
+ size_t last_neuron_idx = this->add_neuron(output_neuron, BIAS_TYPE::NO_BIAS);
+ std::vector<size_t> outputs = {last_neuron_idx};
+
for(size_t i = 0; i < elements_list.size(); i++) {
std::vector<size_t> previous_layer;
std::vector<size_t> new_layer;
+ size_t first_input_neuron_index = last_neuron_idx + 1;
+
/* Create input neurons for sub-net */
std::shared_ptr<NeuronLinear> inp_n;
for(size_t j = 0; j < elements[elements_list.at(i)]->getSymmetryFunctions().size(); j++) {
inp_n = std::make_shared<NeuronLinear>();
- neuron_idx = this->add_neuron(inp_n, BIAS_TYPE::NO_BIAS);
- previous_layer.emplace_back(neuron_idx);
- inputs.emplace_back(neuron_idx);
+ last_neuron_idx = this->add_neuron(inp_n, BIAS_TYPE::NO_BIAS);
+ previous_layer.emplace_back(last_neuron_idx);
+ inputs.emplace_back(last_neuron_idx);
}
/* Add an additional input neuron for charge, if provided */
if(with_charge) {
inp_n = std::make_shared<NeuronLinear>();
- neuron_idx = this->add_neuron(inp_n, BIAS_TYPE::NO_BIAS);
- previous_layer.emplace_back(neuron_idx);
- inputs.emplace_back(neuron_idx);
+ last_neuron_idx = this->add_neuron(inp_n, BIAS_TYPE::NO_BIAS);
+ previous_layer.emplace_back(last_neuron_idx);
+ inputs.emplace_back(last_neuron_idx);
}
/* Create subnet for the current element */
bool new_subnet = false;
- if(subnet_neuron_idxs.find(elements_list.at(i)) == subnet_neuron_idxs.end()) {
+ if(subnet_constructed.find(elements_list.at(i)) == subnet_constructed.end()) {
new_subnet = true;
- subnet_neuron_idxs[elements_list.at(i)] = std::vector<size_t>();
- subnet_connection_idxs[elements_list.at(i)] = std::vector<size_t>();
+ subnet_constructed[elements_list.at(i)] = true;
+ subnet_neuron_shifts[elements_list.at(i)] = last_neuron_bias_idx;
+ subnet_connection_shifts[elements_list.at(i)] = last_connection_weight_idx;
}
+
+ // std::cout << "Particle " << i << ", input neuron indices: " << first_input_neuron_index << " - " << last_neuron_idx << std::endl;
/* Create hidden layers in sub-net */
std::vector<unsigned int> n_neurons = n_hidden_neurons[elements_list.at(i)];
std::vector<NEURON_TYPE> types = type_hidden_neurons[elements_list.at(i)];
- size_t local_neuron_idx = 0;
- size_t local_connection_idx = 0;
+
+ size_t local_neuron_idx = subnet_neuron_shifts[elements_list.at(i)];
+ size_t local_connection_idx = subnet_connection_shifts[elements_list.at(i)];
for(size_t j = 0; j < n_neurons.size(); j++) { /* Iterate over hidden layers */
/* Create hidden neurons */
@@ -79,28 +90,31 @@ lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBO
}
if(new_subnet) {
- neuron_idx = this->add_neuron(hid_n,
+ last_neuron_idx = this->add_neuron(hid_n,
BIAS_TYPE::NEXT_BIAS);
- subnet_neuron_idxs[elements_list.at(i)].emplace_back(neuron_idx);
+ // std::cout << " new subnet, neuron index: " << last_neuron_idx << ", neuron bias: " << last_neuron_bias_idx << std::endl;
+ last_neuron_bias_idx++;
} else {
- std::cout << subnet_neuron_idxs[elements_list.at(i)].at(0)+local_neuron_idx << std::endl;
- neuron_idx = this->add_neuron(hid_n,
+ last_neuron_idx = this->add_neuron(hid_n,
BIAS_TYPE::EXISTING_BIAS, local_neuron_idx);
+ // std::cout << " old subnet, neuron index: " << last_neuron_idx << ", neuron bias: " << local_neuron_idx << std::endl;
}
local_neuron_idx++;
- new_layer.emplace_back(neuron_idx);
+ new_layer.emplace_back(last_neuron_idx);
/* Connect hidden neuron to the previous layer */
for(auto prev_n : previous_layer) {
if(new_subnet) {
- subnet_connection_idxs[elements_list.at(i)].emplace_back(this->add_connection_simple(prev_n,
- neuron_idx,
- SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT));
-
+ this->add_connection_simple(prev_n,
+ last_neuron_idx,
+ SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);
+ // std::cout << " new subnet, connection weight bias: " << last_connection_weight_idx << std::endl;
+ last_connection_weight_idx++;
} else {
this->add_connection_simple(prev_n,
- neuron_idx,
- SIMPLE_CONNECTION_TYPE::EXISTING_WEIGHT, subnet_connection_idxs[elements_list.at(i)].at(0)+local_connection_idx);
+ last_neuron_idx,
+ SIMPLE_CONNECTION_TYPE::EXISTING_WEIGHT, local_connection_idx);
+ // std::cout << " old subnet, connection weight bias: " << local_connection_idx << std::endl;
}
local_connection_idx++;
}
@@ -148,23 +162,13 @@ lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBO
// }
/* Create output neurons for sub-net */
- std::shared_ptr<NeuronLinear> sub_out_n = std::make_shared<NeuronLinear>();
- neuron_idx = this->add_neuron(sub_out_n, BIAS_TYPE::NO_BIAS);
- subnet_outputs.emplace_back(neuron_idx);
for(auto prev_n : previous_layer) {
- this->add_connection_simple(prev_n, neuron_idx);
+ this->add_connection_constant(prev_n, outputs[ 0 ], 1.0);
}
+
}
- /* Specify network inputs */
+ /* Specify network inputs and outputs */
this->specify_input_neurons(inputs);
-
- /* Create final output layer */
- std::shared_ptr<NeuronLinear> final_out_n = std::make_shared<NeuronLinear>();
- neuron_idx = this->add_neuron(final_out_n, BIAS_TYPE::NO_BIAS);
- for(auto subnet_output : subnet_outputs) {
- this->add_connection_constant(subnet_output, neuron_idx, 1);
- }
- std::vector<size_t> outputs = {neuron_idx};
this->specify_output_neurons(outputs);
}
diff --git a/src/examples/acsf2.cpp b/src/examples/acsf2.cpp
index f896a108e8fa8212022e7c59e0ee4bb9b5a04e69..4782805279ce74fc2c8f8f02cb50f3459e400962 100644
--- a/src/examples/acsf2.cpp
+++ b/src/examples/acsf2.cpp
@@ -133,7 +133,7 @@ int main() {
elements[l4n::ELEMENT_SYMBOL::He] = &helium;
/* Read data */
- l4n::XYZReader reader("/home/martin/lib4neuro/data/HE21+T1.xyz");
+ l4n::XYZReader reader("../../data/HE21+T1.xyz");
reader.read();
std::cout << "Finished reading data" << std::endl;
@@ -142,10 +142,10 @@ int main() {
/* Create a neural network */
std::unordered_map<l4n::ELEMENT_SYMBOL, std::vector<unsigned int>> n_hidden_neurons;
- n_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {2};
+ n_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {2, 1};
std::unordered_map<l4n::ELEMENT_SYMBOL, std::vector<l4n::NEURON_TYPE>> type_hidden_neurons;
- type_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {l4n::NEURON_TYPE::LOGISTIC};
+ type_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LINEAR};
l4n::ACSFNeuralNetwork net(elements, *reader.get_element_list(), reader.contains_charge(), n_hidden_neurons, type_hidden_neurons);
// l4n::NeuralNetwork net;