def train_epoch_side_effect(net,
train_loader,
clean_val_loader,
triggered_val_loader,
epoch,
progress_bar_disable=True,
use_amp=False):
# these variables are not consequential for the early-stopping code, so we just set them to
# constants
train_acc_noop = 1.0
train_loss_noop = 1.0
ts = EpochTrainStatistics(train_acc_noop, train_loss_noop)
val_acc_noop = 1.0
if epoch < 2:
val_loss = 10.0 - epoch # we keep the loss decreasing until the first 4 epochs
# This prevents the early-stopping code from being activated,
# since the loss is decreasing every epoch
vs = EpochValidationStatistics(val_acc_noop, val_loss,
None, None)
return ts, vs
else:
val_loss = float(
epoch) # we fix the loss from here on within eps,
# we expect it to quit in 5 epochs
vs = EpochValidationStatistics(val_acc_noop, val_loss,
None, None)
return ts, vs
def train_epoch_side_effect(net,
train_loader,
val_loader,
epoch,
progress_bar_disable=True):
# these variables are not consequential for the early-stopping code, so we just set them to
# constants
train_acc_noop = 1.0
train_loss_noop = 1.0
ts = EpochTrainStatistics(train_acc_noop, train_loss_noop)
val_acc_noop = 1.0
if epoch < 2:
val_loss = 10.0 - epoch # we keep the loss decreasing until the first 4 epochs
# This prevents the early-stopping code from being activated,
# since the loss is decreasing every epoch
vs = EpochValidationStatistics(val_acc_noop, val_loss)
return ts, vs
else:
val_loss = 9.0 - eps # decrease the loss, but only by eps, so we quit
vs = EpochValidationStatistics(val_acc_noop, val_loss)
return ts, vs