def validate(self): self.model.is_good_mode = True good_val_loss, good_val_acc = validate(self.model, self.train_dataloader, self.criterion) self.model.is_good_mode = False bad_val_loss, bad_val_acc = validate(self.model, self.train_dataloader, self.criterion) self.model.is_good_mode = True self.writer.add_scalar('good/val/loss', good_val_loss, self.num_epochs_done) self.writer.add_scalar('good/val/acc', good_val_acc, self.num_epochs_done) self.writer.add_scalar('bad/val/loss', bad_val_loss, self.num_epochs_done) self.writer.add_scalar('bad/val/acc', bad_val_acc, self.num_epochs_done) self.writer.add_scalar('diff/val/loss', good_val_loss - bad_val_loss, self.num_epochs_done) self.writer.add_scalar('diff/val/acc', good_val_acc - bad_val_acc, self.num_epochs_done) self.plot_all_weights_histograms() if self.num_epochs_done > self.config.get('val_acc_stop_threshold_num_warmup_epochs', -1): if good_val_acc < self.config.get('good_val_acc_stop_threshold', 0.): self.stop(f'Good val accuracy is too low (epoch #{self.num_epochs_done}): {good_val_acc}') elif bad_val_acc > self.config.get('bad_val_acc_stop_threshold', 1.): self.stop(f'Bad val accuracy is too high (epoch #{self.num_epochs_done}): {bad_val_acc}') else: pass if self.num_epochs_done > self.config.get('diff_threshold_num_warmup_epochs', -1): if good_val_acc - bad_val_acc < self.config.get('good_and_bad_val_acc_diff_threshold', float('-inf')): self.stop(f'Difference between good and val accuracies is too small '\ f'(epoch #{self.num_epochs_done}): {good_val_acc} - {bad_val_acc} = {good_val_acc - bad_val_acc}')
def test_already_published_response_means_aok(self): "bot-lax coerces already-published error responses to successful 'published' responses" lax_resp = { 'status': conf.ERROR, 'message': 'mock', 'token': 'a', 'id': 'b', 'code': 'already-published' } # coercion happens resp = adapt.mkresponse(**lax_resp) self.assertEqual(resp['status'], conf.PUBLISHED) # coercion doesn't result in an invalid response utils.validate(resp, conf.RESPONSE_SCHEMA)
def test_already_published_response_means_aok(self): "bot-lax coerces already-published error responses to successful 'published' responses" lax_resp = { 'status': conf.ERROR, 'message': 'mock', 'token': 'a', 'id': 'b', 'code': 'already-published' } # coercion happens resp = adapt.mkresponse(**lax_resp) self.assertEqual(resp['status'], conf.PUBLISHED) # coercion doesn't result in an invalid response utils.validate(resp, conf.RESPONSE_SCHEMA)
def from_file(self, lines): """Creating treasure map as list 5 by 5 from file""" for i in range(5): line = lines[i].split() row = list(map(int, line)) if validate(row): self.grid[i] = [Cell(value) for value in row] else: input("Error: your file has incorrect values. For exit press Enter")
def from_keyboard(self): """Creating treasure map as list 5 by 5 from keyboard""" counter = 0 while counter < 5: line = input() line = line.strip().split() row = list(map(int, line)) if validate(row): self.grid[counter] = [Cell(value) for value in row] counter += 1
def treasure_map_from_keyboard(): """Creating treasure map as list 5 by 5 from keyboard""" treasure_map = [] while len(treasure_map) < 5: line = input() line = line.strip().split() row = list(map(int, line)) if validate(row): treasure_map.append(row) return treasure_map
def treasure_map_from_file(lines): """Creating treasure map as list 5 by 5 from file""" treasure_map = [] for line in lines: line = line.split() row = list(map(int, line)) if validate(row): treasure_map.append(row) else: return False return treasure_map
def main(ix): args = utils.initialize() tr_dl, vd_dl, ts_dl, e_ix_ln, r_ix_ln, t_ix_ln, tp_ix, tp_rix = utils.data( args) mdl, opt, lr_sc, ls_f, st_e, bst_ls = utils.prepare( args, e_ix_ln, r_ix_ln, t_ix_ln) tb_sw = SummaryWriter() if utils.is_master(args) else None if not args.test: ls_mtr = utils.BestMetric() if utils.is_master(args) else None for e in range(st_e, args.epochs + 1): utils.train(args, e, mdl, opt, ls_f, tr_dl, tb_sw) if e % args.validation_frequency == 0 or e == args.epochs: utils.validate(args, e, mdl, opt, ls_f, vd_dl, tp_ix, tp_rix, ls_mtr, tb_sw) lr_sc.step() else: utils.test(args, mdl, ts_dl, tp_ix, tp_rix, tb_sw) if utils.is_master(args): tb_sw.flush() tb_sw.close()
for yhat, y, emb in zip(outputs, labels, embedds) ] for l in losses: ag.backward(l) trainer.step(batch_size) acc_mtc.update(labels, outputs) loss_mtc.update(0, losses) if (it % save_period) == 0 and it != 0: _, train_loss = loss_mtc.get() _, train_acc = acc_mtc.get() toc = time.time() logger.info( '\n[epoch % 2d] [it % 3d] train loss: %.6f, train_acc: %.6f | time: %.6f' % (epoch, it, train_loss, train_acc, toc - tic)) logger.info("Radius {}".format( loss.R.data(ctx=mx.gpu(0)).asscalar())) results = validate(net, ctx, val_datas, targets) for result in results: logger.info('{}'.format(result)) loss_mtc.reset() acc_mtc.reset() tic = time.time() net.save_parameters("./models/mobilefacenet-ring-it-%d.params" % it) it += 1 epoch += 1
print(f'Start epoch {epoch}') model.train() epoch_train_loss = train_epoch(model=model, optimizer=optimizer, loader=train_loader, device=device, criterion=criterion, epoch_num=epoch, log_step=args.log_step, use_batches=-1) train_loss.extend(epoch_train_loss) model.eval() print('Start validation') val_loss, val_ppl = validate(model=model, loader=valid_loader, device=device, criterion=criterion, use_batches=-1) best_checkpoint = make_checkpoint(model, val_ppl, checkpoint_dir=args.checkpoint_dir, current_checkpoint=best_checkpoint, prefix=args.prefix, metric_name=args.metric_name) valid_loss.append(val_loss) valid_ppl.append(val_ppl) fig, axs = plt.subplots(3, figsize=(10, 10)) axs[0].plot(train_loss, '-*') axs[0].grid()
def validate(self): self.model.eval() loss, acc = validate(self.model, self.val_dataloader, self.criterion) self.writer.add_scalar('Val/loss', loss.item(), self.num_iters_done) self.writer.add_scalar('Val/acc', acc.item(), self.num_iters_done)
def test_validate_negative(capsys, test_input, expected): validate(test_input) captured = capsys.readouterr() assert captured.out == ERROR_MESSAGE
def test_validate(test_input, expected): assert validate(test_input) == expected
def apply_validation(ctx, new, prev): validate(prev, new, ctx.nlp) return (ctx, new)
def test_incoming_messages(self): "each message generated is a valid request" inst = fs_adaptor.IncomingQueue(self.ingest_dir, 'ingest') list(map(lambda req: utils.validate(req, conf.REQUEST_SCHEMA), list(inst))) # raises ValidationException if invalid
def test_incoming_messages(self): "each message generated is a valid request" inst = fs_adaptor.IncomingQueue(self.ingest_dir, 'ingest') list( map(lambda req: utils.validate(req, conf.REQUEST_SCHEMA), list(inst))) # raises ValidationException if invalid