From a47613d0f4f6f01d274f0e0a5c23d5ce644a0480 Mon Sep 17 00:00:00 2001
From: bes0030 <bes0030@login3.smc.salomon.it4i.cz>
Date: Mon, 2 Sep 2019 16:27:08 +0200
Subject: [PATCH] [WIP] trying to find an optimal configuration of network to
fit He4+ cluster
---
src/examples/dev_sandbox.cpp | 38 +++++++++++++++++-------------------
1 file changed, 18 insertions(+), 20 deletions(-)
diff --git a/src/examples/dev_sandbox.cpp b/src/examples/dev_sandbox.cpp
index 97255099..624aab95 100644
--- a/src/examples/dev_sandbox.cpp
+++ b/src/examples/dev_sandbox.cpp
@@ -12,15 +12,15 @@ void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
std::vector<double> domain_bounds(2 * (net.get_n_weights() + net.get_n_biases()));
for (size_t i = 0; i < domain_bounds.size() / 2; ++i) {
- domain_bounds[2 * i] = -10;
- domain_bounds[2 * i + 1] = 10;
+ domain_bounds[2 * i] = -150;
+ domain_bounds[2 * i + 1] = 150;
}
double c1 = 1.7;
double c2 = 1.7;
double w = 0.7;
- size_t n_particles = 100;
- size_t iter_max = 30;
+ size_t n_particles = 300;
+ size_t iter_max = 500;
/* if the maximal velocity from the previous step is less than 'gamma' times the current maximal velocity, then one
* terminating criterion is met */
@@ -60,7 +60,8 @@ double optimize_via_gradient_descent(l4n::NeuralNetwork& net,
<< "***********************************************************************************************************************"
<< std::endl;
l4n::GradientDescentBB gd(1e-6,
- 1000);
+ 1000,
+ 60000);
gd.optimize(ef);
@@ -113,18 +114,18 @@ int main() {
/* Specify cutoff functions */
// l4n::CutoffFunction1 cutoff1(10.1);
l4n::CutoffFunction2 cutoff1(8);
- l4n::CutoffFunction2 cutoff2(25);
+ //l4n::CutoffFunction2 cutoff2(25);
// 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::G1 sym_f1(&cutoff1);
l4n::G2 sym_f2(&cutoff1, 2.09, 0.8);
l4n::G2 sym_f3(&cutoff1, 0.01, 0.04);
- l4n::G2 sym_f4(&cutoff2, 0.02, 0.04);
- l4n::G2 sym_f5(&cutoff2, 2.09, 0.04);
+// l4n::G2 sym_f4(&cutoff2, 0.02, 0.04);
+// l4n::G2 sym_f5(&cutoff2, 2.09, 0.04);
// l4n::G3 sym_f4(&cutoff4, 0.3);
@@ -133,7 +134,7 @@ int main() {
// 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, &sym_f7, &sym_f8};
+ std::vector<l4n::SymmetryFunction*> helium_sym_funcs = {&sym_f2, &sym_f3}; //, &sym_f4, &sym_f5}; //, &sym_f6, &sym_f7, &sym_f8};
l4n::Element helium = l4n::Element("He",
helium_sym_funcs);
@@ -141,7 +142,7 @@ int main() {
elements[l4n::ELEMENT_SYMBOL::He] = &helium;
/* Read data */
- l4n::XYZReader reader("/home/martin/Desktop/HE4+T0.xyz", true);
+ l4n::XYZReader reader("/home/bes0030/HE4+T0.xyz", true);
reader.read();
std::cout << "Finished reading data" << std::endl;
@@ -150,7 +151,7 @@ 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, 1};
+ n_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {20, 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, l4n::NEURON_TYPE::LINEAR};
@@ -159,16 +160,13 @@ int main() {
l4n::MSE mse(&net, ds.get());
- std::cout << net.get_min_max_weight().first << " " << net.get_min_max_weight().second << std::endl;
net.randomize_parameters();
- // optimize_via_particle_swarm(net, mse);
- double err1 = optimize_via_LBMQ(net, mse);
+ optimize_via_particle_swarm(net, mse);
+// double err1 = optimize_via_LBMQ(net, mse);
double err2 = optimize_via_gradient_descent(net, mse);
- if(err2 > 0.00001) {
- throw std::runtime_error("Training was incorrect!");
- }
+ std::cout << "Weights: " << net.get_min_max_weight().first << " " << net.get_min_max_weight().second << std::endl;
/* Print fit comparison with real data */
std::vector<double> output;
@@ -177,11 +175,11 @@ int main() {
for(auto e : *ds->get_data()) {
for(unsigned int i = 0; i < e.first.size(); i++) {
std::cout << e.first.at(i) << " ";
- if(i % 5 == 4) {
+ if(i % 2 == 1) {
std::cout << std::endl;
}
}
- std::cout << e.second.at(0) << " ";
+ std::cout << "OUTS (DS, predict): " << e.second.at(0) << " ";
net.eval_single(e.first, output);
std::cout << output.at(0) << std::endl;
}
--
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