From 45bb931c63ea43f8fb8ea41cd6d34b02943eb409 Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Thu, 29 Aug 2019 15:05:42 +0200
Subject: [PATCH] [WIP] Looking for an optimal set of symmetry functions.
---
src/Network/ACSFNeuralNetwork.cpp | 1 -
src/examples/dev_sandbox.cpp | 123 +++++++++++++++++-------------
2 files changed, 72 insertions(+), 52 deletions(-)
diff --git a/src/Network/ACSFNeuralNetwork.cpp b/src/Network/ACSFNeuralNetwork.cpp
index 87fa4759..08d8b42f 100644
--- a/src/Network/ACSFNeuralNetwork.cpp
+++ b/src/Network/ACSFNeuralNetwork.cpp
@@ -20,7 +20,6 @@ lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBO
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};
diff --git a/src/examples/dev_sandbox.cpp b/src/examples/dev_sandbox.cpp
index 2fef174b..3955a359 100644
--- a/src/examples/dev_sandbox.cpp
+++ b/src/examples/dev_sandbox.cpp
@@ -1,3 +1,6 @@
+//
+// Created by martin on 20.08.19.
+//
#include <exception>
#include <4neuro.h>
@@ -103,45 +106,29 @@ double optimize_via_LBMQ(l4n::NeuralNetwork& net,
return err;
}
-void print_into_file(const char * fn, std::shared_ptr<l4n::DataSet> &ds, l4n::NeuralNetwork &net){
- std::ofstream outfile;
- outfile.open(fn, std::ios::out );
-
- std::vector<double> output;
- output.resize(1);
-
- for(auto e : *ds->get_data()) {
- for(auto inp_e : e.first) {
- outfile << inp_e << " ";
- }
- outfile << e.second.at(0) << " ";
- net.eval_single(e.first, output);
- outfile << output.at(0) << std::endl;
- }
-
- outfile.close();
-}
-
int main() {
try{
/* Specify cutoff functions */
- l4n::CutoffFunction1 cutoff1(10.1);
- l4n::CutoffFunction2 cutoff2(12.5);
- l4n::CutoffFunction2 cutoff3(15.2);
- l4n::CutoffFunction2 cutoff4(10.3);
- l4n::CutoffFunction2 cutoff5(12.9);
+// l4n::CutoffFunction1 cutoff1(10.1);
+ l4n::CutoffFunction2 cutoff1(8);
+// l4n::CutoffFunction2 cutoff2(15.2);
+// l4n::CutoffFunction2 cutoff4(10.3);
+// l4n::CutoffFunction2 cutoff5(12.9);
+// l4n::CutoffFunction2 cutoff6(11);
/* Specify symmetry functions */
l4n::G1 sym_f1(&cutoff1);
- l4n::G2 sym_f2(&cutoff2, 0.15, 0.75);
- l4n::G2 sym_f3(&cutoff3, 0.1, 0.2);
- l4n::G3 sym_f4(&cutoff4, 0.3);
- l4n::G4 sym_f5(&cutoff5, 0.05, true, 0.05);
- l4n::G4 sym_f6(&cutoff5, 0.05, false, 0.05);
+ l4n::G2 sym_f2(&cutoff1, 15, 8);
+ l4n::G2 sym_f3(&cutoff1, 10, 4);
+// l4n::G3 sym_f4(&cutoff4, 0.3);
+// l4n::G4 sym_f5(&cutoff5, 0.05, true, 0.05);
+// l4n::G4 sym_f6(&cutoff5, 0.05, false, 0.05);
+// l4n::G4 sym_f7(&cutoff6, 0.5, true, 0.05);
+// l4n::G4 sym_f8(&cutoff6, 0.5, false, 0.05);
- std::vector<l4n::SymmetryFunction*> helium_sym_funcs = {&sym_f1, &sym_f2, &sym_f3, &sym_f4, &sym_f5, &sym_f6};
+ std::vector<l4n::SymmetryFunction*> helium_sym_funcs = {&sym_f1, &sym_f2, &sym_f3}; //, &sym_f4, &sym_f5, &sym_f6, &sym_f7, &sym_f8};
l4n::Element helium = l4n::Element("He",
helium_sym_funcs);
@@ -149,7 +136,7 @@ int main() {
elements[l4n::ELEMENT_SYMBOL::He] = &helium;
/* Read data */
- l4n::XYZReader reader("../../data/HE21+T4.xyz");
+ l4n::XYZReader reader("/home/martin/Desktop/HE21+T2.xyz");
reader.read();
std::cout << "Finished reading data" << std::endl;
@@ -158,39 +145,73 @@ 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] = {10};
+ 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;
+// std::vector<std::shared_ptr<l4n::NeuronLinear>> inps;
+// std::vector<size_t> inps_inds;
+// for(unsigned int i = 0; i < 126; i++) {
+// std::shared_ptr<l4n::NeuronLinear> inp = std::make_shared<l4n::NeuronLinear>();
+// inps.emplace_back(inp);
+// inps_inds.emplace_back(net.add_neuron(inp, l4n::BIAS_TYPE::NO_BIAS));
+// }
+//
+// net.specify_input_neurons(inps_inds);
+//
+// std::vector<std::shared_ptr<l4n::NeuronLogistic>> hids;
+//
+// std::vector<unsigned int> hids_idxs;
+// size_t idx;
+// unsigned int n_hidden = 5;
+// for(unsigned int i = 0; i < n_hidden; i++) {
+// std::shared_ptr<l4n::NeuronLogistic> hid = std::make_shared<l4n::NeuronLogistic>();
+// hids.emplace_back(hid);
+// idx = net.add_neuron(hid, l4n::BIAS_TYPE::NEXT_BIAS);
+// hids_idxs.emplace_back(idx);
+//
+// for(unsigned int j = 0; j < 126; j++) {
+// net.add_connection_simple(j, idx);
+// }
+// }
+//
+// std::shared_ptr<l4n::NeuronLinear> out = std::make_shared<l4n::NeuronLinear>();
+// idx = net.add_neuron(out, l4n::BIAS_TYPE::NO_BIAS);
+// std::vector<size_t> out_inds = {idx};
+// for(unsigned int i = 0; i < n_hidden; i++) {
+// net.add_connection_simple(hids_idxs.at(i), idx);
+// }
+// net.specify_output_neurons(out_inds);
l4n::MSE mse(&net, ds.get());
net.randomize_parameters();
// optimize_via_particle_swarm(net, mse);
- // double err1 = optimize_via_LBMQ(net, mse);
+ double err1 = optimize_via_LBMQ(net, mse);
double err2 = optimize_via_gradient_descent(net, mse);
- print_into_file("test_results_2k_BB.txt", ds, net);
-
- /* Print fit comparison with real data */
- // std::vector<double> output;
- // output.resize(1);
-
- // for(auto e : *ds->get_data()) {
- // for(auto inp_e : e.first) {
- // std::cout << inp_e << " ";
- // }
- // std::cout << e.second.at(0) << " ";
- // net.eval_single(e.first, output);
- // std::cout << output.at(0) << std::endl;
- // }
-
- if(err2 > 0.00001) {
+ if(err2 > 0.00001) {
throw std::runtime_error("Training was incorrect!");
}
+ /* Print fit comparison with real data */
+ std::vector<double> output;
+ output.resize(1);
+
+ for(auto e : *ds->get_data()) {
+ for(unsigned int i = 0; i < e.first.size(); i++) {
+ std::cout << e.first.at(i) << " ";
+ if(i % 3 == 2) {
+ std::cout << std::endl;
+ }
+ }
+ std::cout << e.second.at(0) << " ";
+ net.eval_single(e.first, output);
+ std::cout << output.at(0) << std::endl;
+ }
} catch (const std::exception& e) {
std::cerr << e.what() << std::endl;
@@ -198,4 +219,4 @@ int main() {
}
return 0;
-}
\ No newline at end of file
+}
--
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