0.7, 100.0, 0.1, turnover_rate, # gamma, epsilon, nu, turnover rate 0.10, 0.95, 0.8, 2.0, weighting_dg, # k_m, k_r, a_i, alpha. alpha is 2 in 4.1 _ASYNC_FLAG=_ASYNC_FLAG, _TURNOVER_MODE=_TURNOVER_MODE) # ============ Config. X: ============ for i in range(1): for train_set_size_ctr in range(2, 3): hpc.reset_hpc_module() tar_patts = [] for p in training_patterns_associative[:5*train_set_size_ctr]: tar_patts.append(p[1]) ann = NeocorticalNetwork(io_dim, 30, io_dim, 0.01, 0.9) print "Starting experiment; HPC chaotic recall i iterations and HPC pseudopatterns..." # This also saves the experiment results: # relative frequency as in successful 2x5 goodness of fit. Experiments_4_x.experiment_4_2_hpc_recall_every_i_iters( hpc, train_set_size_ctr, training_patterns_associative[:5 * train_set_size_ctr], train_iters=15) # For now, this is the ONLY place where the counter is incremented. Tools.increment_experiment_counter() print "Performing memory consolidation.." # This is rather hard-coded for demo-purposes. NeocorticalMemoryConsolidation.iterate_over_experiments_suite_span_output_demo_local(Tools.get_experiment_counter()-1, Tools.get_experiment_counter()) print "Please see the saved_data/ folder for the associated experiment output."
global_gs = [] local_gs = [] for i in range(40): ann_global = NeocorticalModuleTraining.global_sequential_FFBP_training(ss=set_size, training_iterations=200) ann_local = NeocorticalModuleTraining.traditional_training_with_catastrophic_interference( ss=set_size, training_iterations=200) # global_io_results = Tools.generate_recall_attempt_results_for_ann(ann_global, original_training_set) # local_io_results = Tools.generate_recall_attempt_results_for_ann(ann_local, original_training_set) # # Tools.save_aggregate_image_from_ios(global_io_results, 'global_aggregate_im', 0) # Tools.save_aggregate_image_from_ios(local_io_results, 'local_aggregate_im', 1) global_goodness = NeocorticalMemoryConsolidation.evaluate_goodness_of_fit(ann_global, original_training_set) local_goodness = NeocorticalMemoryConsolidation.evaluate_goodness_of_fit(ann_local, original_training_set) global_gs.append(global_goodness) local_gs.append(local_goodness) log_line = 'EVALUATED baseline. g\'s - ' + 'global: ' + str(global_goodness) + ', local: ' + str(local_goodness) print log_line Tools.append_line_to_log(log_line) avg_global_g = Tools.get_avg(global_gs) avg_local_g = Tools.get_avg(local_gs) avgs_global.append(avg_global_g) avgs_local.append(avg_local_g) final_result_line = 'Final results for current set size: global avg. = ' + str(avg_global_g) + ', local avg. = ' + \