What's the about rnn-time-series-anomaly-detection HOT 3 CLOSED

def evaluate_1step_pred(args, model, test_dataset):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    total_loss = 0
    with torch.no_grad():
        hidden = model.init_hidden(args.eval_batch_size)
        for nbatch, i in enumerate(range(0, test_dataset.size(0) - 1, args.bptt)):

            inputSeq, targetSeq = get_batch(args,test_dataset, i)
            outSeq, hidden = model.forward(inputSeq, hidden)

            loss = criterion(outSeq.view(args.batch_size,-1), targetSeq.view(args.batch_size,-1))
            hidden = model.repackage_hidden(hidden)
            total_loss+= loss.item()

    return total_loss / nbatch     **# you mean here? his is my mistake. it should be loss / ( nbatch+1)**

def preprocessing(self, path, train=True):
    """ Read, Standardize, Augment """

    with open(str(path), 'rb') as f:
        data = torch.FloatTensor(pickle.load(f))
        label = data[:,-1]
        data = data[:,:-1]
    if train:
        self.mean = data.mean(dim=0)
        self.std= data.std(dim=0)
        self.length = len(data)
        data,label = self.augmentation(data,label)
    else:
        if self.augment_test_data:
            data, label = self.augmentation(data, label)

    data = standardization(data,self.mean,self.std)

    return data,label