scheduler = LRSchedulerSGD()
tr = Trainer(model, loss_DANN)
tr.fit(train_gen_s,
train_gen_t,
n_epochs=dann_config.N_EPOCHS,
validation_data=[val_gen_s, val_gen_t],
metrics=[acc],
steps_per_epoch=dann_config.STEPS_PER_EPOCH,
val_freq=dann_config.VAL_FREQ,
opt='sgd',
opt_kwargs={
'lr': 0.01,
'momentum': 0.9
},
lr_scheduler=scheduler,
callbacks=[
print_callback(watch=[
"loss", "domain_loss", "val_loss", "val_domain_loss",
'trg_metrics', 'src_metrics'
]),
ModelSaver('DANN', dann_config.SAVE_MODEL_FREQ),
HistorySaver('log_with_sgd',
dann_config.VAL_FREQ,
extra_losses={
'domain_loss':
['domain_loss', 'val_domain_loss'],
'train_domain_loss':
['domain_loss_on_src', 'domain_loss_on_trg']
})
])
val_freq=dann_config.VAL_FREQ,
opt='sgd',
opt_kwargs={
'lr': dann_config.LR,
'momentum': 0.9
},
lr_scheduler=scheduler,
callbacks=[
print_callback(watch=[
"loss", "domain_loss", "val_loss", "val_domain_loss",
'trg_metrics', 'src_metrics'
]),
ModelSaver(
str(experiment_name + '_' + dann_config.SOURCE_DOMAIN +
'_' + dann_config.TARGET_DOMAIN + '_' + details_name),
dann_config.SAVE_MODEL_FREQ,
save_by_schedule=True,
save_best=True,
eval_metric='accuracy'),
WandbCallback(
config=dann_config,
name=str(dann_config.SOURCE_DOMAIN + "_" +
dann_config.TARGET_DOMAIN + "_" + details_name),
group=experiment_name),
HistorySaver(
str(experiment_name + '_' + dann_config.SOURCE_DOMAIN +
'_' + dann_config.TARGET_DOMAIN + "_" + details_name),
dann_config.VAL_FREQ,
path=str('_log/' + experiment_name + "_" + details_name),
extra_losses={
'domain_loss': ['domain_loss', 'val_domain_loss'],