From 424990dbed189a341a60548c0ac13a2533399a1b Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Sun, 6 Jan 2019 09:42:14 +0100
Subject: [PATCH] ENH: Several improvements made in Simulator example.
---
src/examples/simulator.cpp | 75 ++++++++++++++++++++++++++------------
1 file changed, 51 insertions(+), 24 deletions(-)
diff --git a/src/examples/simulator.cpp b/src/examples/simulator.cpp
index cfae4031..b4aa0abe 100644
--- a/src/examples/simulator.cpp
+++ b/src/examples/simulator.cpp
@@ -29,6 +29,13 @@ int main(int argc, char** argv){
l4n::CSVReader reader("/home/martin/Desktop/data_Heaviside.txt", "\t", true); // File, separator, skip 1st line
reader.read(); // Read from the file
+ /* Open file for writing */
+ std::string filename = "simulator_output.txt";
+ std::ofstream output_file(filename);
+ if(!output_file.is_open()) {
+ throw std::runtime_error("File '" + filename + "' can't be opened!");
+ }
+
/* Create data set for both the training and testing of the neural network */
std::vector<unsigned int> inputs = { 3 }; // Possible multiple inputs, e.g. {0,3}
std::vector<unsigned int> outputs = { 1 }; // Possible multiple outputs, e.g. {1,2}
@@ -83,7 +90,7 @@ int main(int argc, char** argv){
// 1) Threshold for the successful ending of the optimization - deviation from minima
// 2) Number of iterations to reset step size to tolerance/10.0
// 3) Maximal number of iterations - optimization will stop after that, even if not converged
- l4n::GradientDescent gs(1e-3, 100, 10);
+ l4n::GradientDescent gs(1e-3, 100, 200);
// Weight and bias randomization in the network according to the uniform distribution
// Calling methods nn.randomize_weights() and nn.randomize_biases()
@@ -97,21 +104,37 @@ int main(int argc, char** argv){
/* Cross - validation */
l4n::CrossValidator cv(&gs, &mse);
- // Parameters: 1) Number of data-set parts used for CV, 2) Number of tests performed
- cv.run_k_fold_test(10, 1);
+ // Parameters:
+ // 1) Number of data-set parts used for CV
+ // 2) Number of tests performed
+ // git 3) File-path to the files with data from cross-validation (one CV run - one file)
+ cv.run_k_fold_test(10, 3, &output_file);
/* Save network to the text file */
nn.save_text("test_net.4n");
- /* Check of the saved network */
+ /* Check of the saved network - print to STDOUT */
std::cout << std::endl << "The original network info:" << std::endl;
- nn.print_stats();
- nn.print_weights();
+ nn.write_stats();
+ nn.write_weights();
+ nn.write_biases();
l4n::NeuralNetwork nn_loaded("test_net.4n");
std::cout << std::endl << "The loaded network info:" << std::endl;
- nn_loaded.print_stats();
- nn_loaded.print_weights();
+ nn_loaded.write_stats();
+ nn.write_weights();
+ nn.write_biases();
+
+ /* Check of the saved network - write to the file */
+ output_file << std::endl << "The original network info:" << std::endl;
+ nn.write_stats(&output_file);
+ nn.write_weights(&output_file);
+ nn.write_biases(&output_file);
+
+ output_file << std::endl << "The loaded network info:" << std::endl;
+ nn_loaded.write_stats(&output_file);
+ nn.write_weights(&output_file);
+ nn.write_biases(&output_file);
/* Example of evaluation of a single input, normalized input, de-normalized output */
std::vector<double> input_norm(ds.get_input_dim()),
@@ -131,25 +154,29 @@ int main(int argc, char** argv){
ds.de_normalize_single(input_norm, input);
ds.de_normalize_single(expected_output_norm, expected_output);
- std::cout << std::endl << "input: ";
- for (auto el: input_norm) { std::cout << el << ", "; }
- std::cout << std::endl;
- std::cout << "output: ";
- for (auto el: output) { std::cout << el << ", "; }
- std::cout << std::endl;
- std::cout << "expected output: ";
- for (auto el: expected_output) { std::cout << el << ", "; }
- std::cout << std::endl;
+ /* Evaluate network on an arbitrary data-set and save results into the file */
+ l4n::DataSet ds2;
+
+ std::vector<double> inp, out;
+ for(double i = 0; i < 5; i++) {
+ inp = {i};
+ out = {i+2};
+
+ ds2.add_data_pair(inp, out);
+ }
+
+ output_file << std::endl << "Evaluating network on the dataset: " << std::endl;
+ ds2.store_data_text(&output_file);
+
+ output_file << "Output and the error:" << std::endl;
+ mse.eval_on_data_set(&ds2, &output_file);
+
+ /* Close the output file for writing */
+ output_file.close();
return 0;
- } catch(const std::runtime_error& e) {
- std::cerr << e.what() << std::endl;
- exit(EXIT_FAILURE);
- } catch(const std::out_of_range& e) {
- std::cerr << e.what() << std::endl;
- exit(EXIT_FAILURE);
- } catch(const std::invalid_argument& e) {
+ } catch(const std::exception& e) {
std::cerr << e.what() << std::endl;
exit(EXIT_FAILURE);
}
--
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