# Ensure all learners starts with exactly same weights set_equal_weights(all_learner_models) # print a summary of the model architecture summary(all_learner_models[0].model, input_size=(channels, width, height), device=str(device)) # Now we're ready to start collective learning # Get initial accuracy results = Results() results.data.append(initial_result(all_learner_models)) plot = ColearnPlot(score_name=score_name) # Do the training for round_index in range(n_rounds): results.data.append( collective_learning_round(all_learner_models, vote_threshold, round_index)) print_results(results) plot.plot_results(results) plot.plot_votes(results) # Plot the final result with votes plot.plot_results(results) plot.plot_votes(results, block=True) print("Colearn Example Finished!")
# "class_weight": {0: 1, 1: 0.27} }, model_evaluate_kwargs={"steps": vote_batches}, criterion="auc", minimise_criterion=False )) set_equal_weights(all_learner_models) results = Results() # Get initial score results.data.append(initial_result(all_learner_models)) plot = ColearnPlot(score_name=all_learner_models[0].criterion) for round_index in range(n_rounds): results.data.append( collective_learning_round(all_learner_models, vote_threshold, round_index) ) print_results(results) # then make an updating graph plot.plot_results(results) plot.plot_votes(results) plot.plot_results(results, n_learners) plot.plot_votes(results, block=True) print("Colearn Example Finished!")