break
data, x_train, y_train, x_test, y_test, norm_constant = naf.one_file_training_data(
actual_class_dir, file, forecast_length, backcast_length,
batch_size, device)
while i < limit: #difference > threshold and
i += 1
global_step = naf.train_full_grad_steps(
data, device, net, optimiser, test_losses,
training_models + training_checkpoint, x_train.shape[0])
train_eval = naf.evaluate_training(backcast_length,
forecast_length, net,
norm_constant, test_losses,
x_train, y_train,
the_lowest_error, device)
experiment.log_metric('train_loss', train_eval)
new_eval = naf.evaluate_training(backcast_length,
forecast_length,
net,
norm_constant,
test_losses,
x_test,
y_test,
the_lowest_error,
device,
plot_eval=False,
class_dir=name,
while i < 2: #old was 5 #difference > threshold and
i += 1
epoch += 1
print("Actual epoch: ", epoch,
"\nActual inside file loop: ", i)
global_step = train_full_grad_steps(
data, device, net, optimiser, test_losses,
training_models + training_checkpoint,
x_train.shape[0])
train_eval = naf.evaluate_training(backcast_length,
forecast_length,
net,
norm_constant,
test_losses,
x_train,
y_train,
the_lowest_error,
device,
experiment=experiment)
experiment.log_metric('train_loss', train_eval)
new_eval = naf.evaluate_training(backcast_length,
forecast_length,
net,
norm_constant,
test_losses,
x_test,
y_test,
the_lowest_error,
device,
iteration += 1
if iteration > 30 or difference < threshold:
break
data, x_train, y_train, x_test, y_test, norm_constant = naf.one_file_training_data(
actual_class_dir, file, forecast_length, backcast_length,
batch_size)
while difference > threshold and i < limit:
i += 1
global_step = naf.train_full_grad_steps(
data, device, net, optimiser, test_losses,
training_checkpoint, x_train.shape[0])
new_eval = naf.evaluate_training(backcast_length,
forecast_length, net,
norm_constant, test_losses,
x_test, y_test,
the_lowest_error, device)
print(
f"GlobalStep: {global_step}, New evaluation sccore: {new_eval}"
)
if new_eval < old_eval:
difference = old_eval - new_eval
old_eval = new_eval
with torch.no_grad():
print("New evaluation value:", new_eval,
" iteration:", i)
print("Saving...")
new_checkpoint_name = str(checkpoint_name[:-3] +
str(len(test_losses)) +
".th")