NEToolKit/NEToolKitExamples/src/xor_experiment.cpp at master · CBenoit/NEToolKit · GitHub

This repository was archived by the owner on Jul 1, 2021. It is now read-only.

History

434 lines (354 loc) · 14.2 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

#include <iostream>

#include <ctime>

#include <random> // random_device, mt19937

#include <functional>

#include <thread> // std::this_thread::sleep_for

#include <chrono> // std::chrono::seconds

#include <netkit/network/network.h>

#include <netkit/network/network_primitive_types.h>

#include <netkit/network/activation_functions.h>

#include <netkit/neat/genome.h>

#include <netkit/neat/gene.h>

#include <netkit/neat/parameters.h>

#include <netkit/neat/neat.h>

#include <netkit/neat/rtneat.h>

#include <netkit/neat/novelbank.h>

#include "xor_experiment.h"

#include "utils.h"

void rate_xor_population(netkit::neat& neat);

double evaluate_network(netkit::network &net);

void print_xor_network_results(netkit::network& net);

bool is_a_xor_solution(const netkit::genome& geno);

exp_stats run_xor_experiment(bool display_xor_experiment_details, bool with_novelty = false);

std::vector<std::vector<netkit::neuron_value_t>> inputs_per_run = {

{0, 0},

{1, 0},

{0, 1},

{1, 1}

};

std::vector<netkit::neuron_value_t> expected_output_per_run = {0, 1, 1, 0};

void xor_network_test() {

std::cout << "Starting the (handmade) example xor network..." << std::endl;

netkit::network net;

netkit::neuron_id_t i1 = net.add_neuron(netkit::INPUT, netkit::neuron{&netkit::steepened_sigmoid});

netkit::neuron_id_t i2 = net.add_neuron(netkit::INPUT, netkit::neuron{&netkit::steepened_sigmoid});

netkit::neuron_id_t o = net.add_neuron(netkit::OUTPUT, netkit::neuron{&netkit::steepened_sigmoid});

netkit::neuron_id_t h = net.add_neuron(netkit::HIDDEN, netkit::neuron{&netkit::steepened_sigmoid});

net.add_link(netkit::network::BIAS_ID, h, -15);

net.add_link(netkit::network::BIAS_ID, o, -5);

net.add_link(i1, h, 10);

net.add_link(i2, h, 10);

net.add_link(i1, o, 10);

net.add_link(i2, o, 10);

net.add_link(h, o, -20);

std::cout << "Neural network created." << std::endl;

std::cout << net << std::endl;

print_xor_network_results(net);

wait_user();

std::cout << "\nPerforming the same with a network generated from a genome..." << std::endl;

netkit::parameters params;

params.number_of_inputs = 2;

params.number_of_outputs = 1;

netkit::neat neat(params);

netkit::genome genome(&neat);

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(),

netkit::genome::BIAS_ID, 4, -15));

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(),

netkit::genome::BIAS_ID, 3, -5));

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(), 1, 4, 10));

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(), 2, 4, 10));

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(), 1, 3, 10));

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(), 2, 3, 10));

genome.add_gene(netkit::gene(neat.innov_pool.next_innovation(), 4, 3, -20));

std::cout << genome << std::endl;

netkit::network gennet = genome.generate_network();

std::cout << gennet << std::endl;

print_xor_network_results(gennet);

wait_user();

std::cout << "\nBenchmark: how much does it takes to activate the xor network 100 000 times?" << std::endl;

std::cout << "Running the benchmark..." << std::endl;

clock_t begin = std::clock();

for (unsigned int i = 100000; i--;) {

net.activate();

}

clock_t end = std::clock();

double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;

std::cout << "Done!" << std::endl;

std::cout << "It took " << elapsed_secs << " seconds of processor time to do so." << std::endl;

}

void run_n_xor_experiments(unsigned int number_of_experiments, bool with_novelty, bool record_csv, std::string output_filename) {

std::cout << "Starting " << number_of_experiments << " xor evolution experiments..." << std::endl;

std::vector<exp_stats> all_results;

all_results.reserve(number_of_experiments);

double average_number_of_generations = 0;

double average_neurons = 0;

double average_links = 0;

double average_fitness = 0;

unsigned int number_of_success = 0;

for (unsigned int i = 0; i < number_of_experiments; ++i) {

exp_stats stats = run_xor_experiment(false, with_novelty);

std::cout << "\n======= experiment " << i;

if (stats.success) {

std::cout << " (success)";

} else {

std::cout << " (failure)";

}

std::cout << " ===========" << std::endl;

print_exp_stats(stats);

// I do not move the division outside of the loop to avoid too much float point precision loss...

average_number_of_generations += static_cast<double>(stats.number_of_generations) / static_cast<double>

(number_of_experiments);

average_neurons += static_cast<double>(stats.number_of_neurons) / static_cast<double>(number_of_experiments);

average_links += static_cast<double>(stats.number_of_links) / static_cast<double>(number_of_experiments);

average_fitness += stats.best_fitness_ever / static_cast<double>(number_of_experiments);

if (stats.success)

number_of_success += 1;

all_results.push_back(stats);

if (record_csv) {

netkit::serializer ser(output_filename, ";", true);

ser.append(stats.success);

ser.append(stats.number_of_generations);

ser.append(stats.number_of_neurons);

ser.append(stats.number_of_links);

ser.append(stats.best_fitness_ever);

ser.new_line();

ser.close();

}

double std_number_of_generations = compute_standard_deviation(number_of_experiments,

average_number_of_generations, [&](size_t i) {

return static_cast<double>(all_results[i].number_of_generations);

});

double std_neurons = compute_standard_deviation(number_of_experiments, average_neurons, [&](size_t i) {

return static_cast<double>(all_results[i].number_of_neurons);

});

double std_links = compute_standard_deviation(number_of_experiments, average_links, [&](size_t i) {

return static_cast<double>(all_results[i].number_of_links);

});

double std_fitness = compute_standard_deviation(number_of_experiments, average_fitness, [&](size_t i) {

return all_results[i].best_fitness_ever;

});

std::cout << "\n====== summary ======" << std::endl;

std::cout << "Average generations required to get a solution:\t" << average_fitness

<< " (std = " << std_number_of_generations << ")" << std::endl;

std::cout << "Average neurons in the best solutions:\t-\t" << average_neurons

<< " (std = " << std_neurons << ")" << std::endl;

std::cout << "Average links in the best solutions:\t-\t" << average_links

<< " (std = " << std_links << ")" << std::endl;

std::cout << "Average fitness of the best solutions:\t-\t" << average_fitness

<< " (std = " << std_fitness << ")" << std::endl;

std::cout << "Number of successes:\t-\t-\t-\t" << number_of_success

<< " (which means " << number_of_experiments - number_of_success << " failure(s))" << std::endl;

}

void run_one_xor_experiment(bool with_novelty) {

std::cout << "Starting one detailed xor evolution experiment..." << std::endl;

exp_stats stats = run_xor_experiment(true, with_novelty);

std::cout << "\nSummary:" << std::endl;

print_exp_stats(stats);

}

void run_one_real_time_xor_experiment(bool display_details) {

std::cout << "Starting one detailed realtime xor evolution experiment..." << std::endl;

// init rtNEAT

netkit::parameters params;

params.number_of_inputs = 2;

params.number_of_outputs = 1;

params.initial_population_size = 20;

params.minmum_alive_time_before_being_replaced = 6;

params.interspecies_crossover_prob = 0.15;

netkit::rtneat rtneat(params);

rtneat.init();

rtneat.generate_all_organisms();

std::vector<netkit::organism>& organisms = rtneat.get_all_organisms();

if (display_details) {

std::cout << "\n\n======== Here's the initial population. =========" << std::endl;

for (netkit::species& spec : rtneat.get_all_species()) {

spec.update_stats();

print_species_stats(spec);

}

wait_user();

}

// go for a simulation of 1000 ticks.

for (netkit::tick_t tick = 0; tick < 10000; ++tick) {

if (display_details)

std::cout << "\n\n======== tick " << tick << " =========" << std::endl;

for (netkit::organism& org : organisms) {

evaluate_network(org.get_network());

org.increase_time_alive(); // very important!!

if (display_details) {

std::cout << "org " << org.get_genome_id() << "'s fitness = " << org.get_fitness() << std::endl;

}

std::this_thread::sleep_for(std::chrono::milliseconds(500));

rtneat.epoch();

// you may want to perform the following less often.

rtneat.update_best_genome_ever();

if (is_a_xor_solution(*rtneat.get_best_genome_ever())) {

if (display_details)

std::cout << "\n=====> Found a solution!" << std::endl;

break;

}

if (display_details) {

auto opt_best_geno = rtneat.get_best_genome_ever();

netkit::network net = opt_best_geno->generate_network();

std::cout << "\nHere's the best genome ever produced for this run:" << std::endl;

std::cout << *opt_best_geno << std::endl;

std::cout << net << std::endl;

print_xor_network_results(net);

}

// ===== local functions =====

// --- for novelty only ---

std::optional<netkit::genome> novelty_rate_population(netkit::neat& neat, netkit::novelbank<xor_position>& novelbank) {

std::vector<netkit::novelgenome<xor_position>> all_ng;

all_ng.reserve(neat.pop()->size());

while (neat.has_more_organisms_to_process()) {

netkit::organism org = neat.generate_and_get_next_organism();

netkit::novelgenome<xor_position> ng{org.get_genome_id()};

std::vector<size_t> runs = {0, 1, 2, 3};

netkit::network &net = org.get_network();

for (auto run : runs) {

net.flush();

net.load_inputs(inputs_per_run[run]);

net.activate_until_relaxation();

ng.get_pos().values.push_back(net.get_outputs()[0]);

}

novelbank.pop_register(ng);

all_ng.push_back(std::move(ng));

}

std::optional<netkit::genome> valid_candidate;

// update fitness by evaluating novelty.

for (auto& ng : all_ng) {

neat.pop()->get_genome(ng.get_genome_id()).set_fitness(novelbank.evaluate(ng));

if (is_a_xor_solution(neat.pop()->get_genome(ng.get_genome_id()))) {

valid_candidate = {neat.pop()->get_genome(ng.get_genome_id())};

}

novelbank.bank_update(); // update the novelbank using all new element evaluated so far.

// clear the population (because no evaluation afterward)

novelbank.pop_clear();

return valid_candidate;

}

// ------------------------

exp_stats run_xor_experiment(bool display_xor_experiment_details, bool with_novelty) {

// init novelty bank

netkit::novelbank<xor_position> novelbank(999, 0.25, 10);

// init NEAT

netkit::parameters params;

params.number_of_inputs = 2;

params.number_of_outputs = 1;

params.initial_population_size = 300;

netkit::neat neat(params);

neat.init();

exp_stats stats;

stats.success = false; // will be set to true if a solution is found.

stats.number_of_generations = 150; // will be changed to the actual number of generations until a solution is found.

if (with_novelty) {

novelty_rate_population(neat, novelbank);

} else {

rate_xor_population(neat);

}

if (display_xor_experiment_details) {

std::cout << "\n\n======== Here's the initial generation's population. =========" << std::endl;

for (netkit::species& spec : neat.get_all_species()) {

spec.update_stats();

print_species_stats(spec);

}

wait_user();

}

std::optional<netkit::genome> opt_best_geno;

// go for a maximum of stats.number_of_generations generations evolution.

for (size_t gen = 1; gen <= stats.number_of_generations; ++gen) {

if (display_xor_experiment_details)

std::cout << "\n\n======== Here's the generation " << gen << "'s population. =========" << std::endl;

neat.epoch(); // go to next generation

if (with_novelty) {

opt_best_geno = novelty_rate_population(neat, novelbank);

if (display_xor_experiment_details) {

for (netkit::species& spec : neat.get_all_species()) {

spec.update_stats();

print_species_stats(spec);

}

if (opt_best_geno.has_value()) {

if (display_xor_experiment_details)

std::cout << "\n=====> Found a solution!" << std::endl;

stats.success = true;

stats.number_of_generations = gen;

auto net = opt_best_geno->generate_network();

stats.best_fitness_ever = evaluate_network(net);

break;

}

} else {

rate_xor_population(neat);

neat.update_best_genome_ever();

if (display_xor_experiment_details) {

for (netkit::species& spec : neat.get_all_species()) {

spec.update_stats();

print_species_stats(spec);

}

if (is_a_xor_solution(*neat.get_best_genome_ever())) {

if (display_xor_experiment_details)

std::cout << "\n=====> Found a solution!" << std::endl;

stats.success = true;

stats.number_of_generations = gen;

opt_best_geno = neat.get_best_genome_ever();

stats.best_fitness_ever = opt_best_geno->get_fitness();

break;

}

if (display_xor_experiment_details)

wait_user();

}

netkit::network net = opt_best_geno->generate_network();

stats.number_of_neurons = net.number_of_neurons();

stats.number_of_links = net.number_of_links();

if (display_xor_experiment_details) {

std::cout << "\nHere's the best genome ever produced for this run:" << std::endl;

std::cout << *opt_best_geno << std::endl;

std::cout << net << std::endl;

print_xor_network_results(net);

}

return stats;

}

void rate_xor_population(netkit::neat& neat) {

while (neat.has_more_organisms_to_process()) {

netkit::organism org = neat.generate_and_get_next_organism();

org.set_fitness(evaluate_network(org.get_network()));

}

double evaluate_network(netkit::network& net) {

std::vector<size_t> runs = {0, 1, 2, 3};

double error_sum = 0;

for (auto run : runs) {

net.flush();

net.load_inputs(inputs_per_run[run]);

net.activate_until_relaxation();

error_sum += std::abs(net.get_outputs()[0] - expected_output_per_run[run]);

}

return std::pow(4.0 - error_sum, 2);

}

void print_xor_network_results(netkit::network& net) {

for (auto& inputs : inputs_per_run) {

std::cout << "==================" << std::endl;

std::cout << inputs[0] << " xor " << inputs[1] << std::endl;

net.flush();

net.load_inputs(inputs);

net.activate_until_relaxation();

std::cout << "Result: " << net.get_outputs()[0] << std::endl;

}

bool is_a_xor_solution(const netkit::genome& geno) {

netkit::network net = geno.generate_network();

std::vector<size_t> runs = {0, 1, 2, 3};

for (auto run : runs) {

net.flush();

net.load_inputs(inputs_per_run[run]);

net.activate_until_relaxation();

if (std::abs(net.get_outputs()[0] - expected_output_per_run[run]) > 0.45) {

return false;

}

return true;

}

NEToolKit/NEToolKitExamples/src/xor_experiment.cpp at master · CBenoit/NEToolKit · GitHub

Expand file tree

Breadcrumbs

xor_experiment.cpp

Latest commit

History

Breadcrumbs

xor_experiment.cpp

File metadata and controls