def Data_load(num_timesteps_input, num_timesteps_output):
A, X, means, stds, X_val = load_metr_la_data()
# split_line1 = int(X.shape[0] * 0.6)
# split_line2 = int(X.shape[0] * 0.8)
# train_original_data = X[:, :, :split_line1]
train_original_data = X
# val_original_data = X[:, :, split_line1:split_line2]
val_original_data = X_val
test_original_data = X #X[split_line1:, :, :]
training_input, training_target = generate_dataset(
train_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output)
val_input, val_target = generate_dataset(
val_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output)
test_input, test_target = generate_dataset(
test_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output)
return A, means, stds, training_input, training_target, val_input, val_target, test_input, test_target
def Data_load(num_timesteps_input, num_timesteps_output):
A, X, means, stds, nodes = load_metr_la_data()
# A, X, max_X, min_X = load_metr_la_data()
# A, X, max_value, X_val = load_metr_la_data()
split_line1 = int(X.shape[2] * 0.6)
split_line2 = int(X.shape[2] * 0.8)
train_original_data = X[:, :, :split_line1]
val_original_data = X[:, :, split_line1:split_line2]
# val_original_data = X_val
test_original_data = X[:, :, split_line2:]
training_input, training_target = generate_dataset(
train_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output)
val_input, val_target = generate_dataset(
val_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output)
test_input, test_target = generate_dataset(
test_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output)
return A, means, stds, training_input, training_target, val_input, val_target, test_input, test_target, nodes
X_batch = X_batch.to(device=args.device)
y_batch = y_batch.to(device=args.device)
out = net(A_wave, X_batch)
loss = loss_criterion(out, y_batch)
print(f'{i}/{training_input.shape[0]}: step loss:{loss.item()}')
loss.backward()
optimizer.step()
epoch_training_losses.append(loss.detach().cpu().numpy())
return sum(epoch_training_losses) / len(epoch_training_losses)
if __name__ == '__main__':
torch.manual_seed(7)
print(args.device)
A, X, means, stds = load_metr_la_data()
print(A.shape)
split_line1 = int(len(X) * 0.6)
split_line2 = int(len(X) * 0.8)
#
train_original_data = X[:split_line1]
val_original_data = X[split_line1:split_line2]
# val_mean, val_std = means[split_line1:split_line2], stds[split_line1:split_line2]
test_original_data = X[split_line2:]
# test_mean, test_std = means[split_line2:], stds[split_line2:]
training_input, training_target, train_mean_t, train_std_t = generate_dataset(
train_original_data,
num_timesteps_input=num_timesteps_input,