nb_blocks_per_stack=3,
backcast_length=backcast_length,
hidden_layer_units=128,
share_weights_in_stack=False,
device=device)
optimiser = optim.Adam(net.parameters())
test_losses = []
actual_class_dir = data_dir + "/" + name + "/"
for (_, dirs, files) in os.walk(actual_class_dir):
iteration = 0
for file in files:
if 'mat' in file:
continue
iteration += 1
print(iteration)
if iteration > 30:
break
data, x_test, y_test, norm_constant = naf.one_file_training_data(
actual_class_dir, file, forecast_length, backcast_length,
batch_size)
for i in range(10):
naf.eval_test(backcast_length, forecast_length, net,
norm_constant, test_losses, x_test, y_test)
naf.train_100_grad_steps(checkpoint_name, data, device, net,
optimiser, test_losses)
print("\t\t FIle loop, epoch: %d\n" % (epoch))
plot_file = True
i = 0
if 'mat' in fil:
continue
print("Reading files from: %s, file loaded: %s" %
(actual_class_dir, fil))
if epoch >= epoch_limit: #or difference < threshold:
break
data, x_train, y_train, x_test, y_test, norm_constant, diagnosis = naf.one_file_training_data(
actual_class_dir,
fil,
forecast_length,
backcast_length,
batch_size,
device,
lead=lead)
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,