# 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!")