generator=torch.Generator().manual_seed(42))
valid_loader = DataLoader(valid_dataset, batch_size=batch_size)
model = BeatTrackingNet()
parameters = model.parameters()
if GPU:
model = model.cuda()
if optimizer == 'Adam':
optimizer = Adam(parameters, lr=learning_rate)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.2)
criterion = BCELoss()
params = list(model.parameters()) + list(criterion.parameters())
total_params = sum(x.size()[0] *
x.size()[1] if len(x.size()) > 1 else x.size()[0]
for x in params if x.size())
print(f'Total parameters: {total_params}')
# train and valid
for i in range(1, num_epoch + 1):
# training
print(f"Epoch {i}: Training Start.")
model.train()
running_loss = 0.0
batch_loss = list()
batch_step = 0
train_loader = DataLoader(train_dataset, batch_size=batch_size)
for input, label in train_loader:
optimizer.zero_grad()
