def _parse_verification_results(self, task_declaration: TaskDeclaration,
finished_verification_assignments: ListVerificationAssignments):
fake_workers = {}
for va in finished_verification_assignments:
assert va.verification_result.error is None
for result in va.verification_result.result:
if result['is_fake']:
try:
fake_workers[result['worker_id']] += 1
except KeyError:
fake_workers[result['worker_id']] = 1
task_declaration.tflops += va.verification_result.tflops
# what to do if many verifiers ?
if va.verification_result.weights_ipfs:
# if weights_ipfs is None than fake workers are present
task_declaration.weights_ipfs = va.verification_result.weights_ipfs
if task_declaration.last_iteration:
task_declaration.loss = va.verification_result.loss
task_declaration.accuracy = va.verification_result.accuracy
logger.info('Copy summarization for {}, loss: {}, accuracy: {}'.format(
task_declaration, task_declaration.loss, task_declaration.accuracy))
else:
logger.info('Copy summarization for {}'.format(task_declaration))
return fake_workers
def _save_loss_and_accuracy(self, task_declaration: TaskDeclaration, finished_task_assignments):
assert task_declaration.current_iteration > 1
loss = []
accuracy = []
# collect loss and accuracy for prev iteration
iteration = str(task_declaration.current_iteration - 1)
for ta in finished_task_assignments:
if ta.train_result.eval_results is not None and ta.train_result.eval_results.get(iteration):
loss.append(ta.train_result.eval_results[iteration]['loss'])
accuracy.append(ta.train_result.eval_results[iteration]['accuracy'])
assert len(loss) and len(accuracy)
task_declaration.loss = sum(loss)/len(loss)
task_declaration.accuracy = sum(accuracy)/len(accuracy)
logger.info('Save avr iteration: {} loss: {} and accuracy: {}'.format(
iteration, task_declaration.loss, task_declaration.accuracy))