How to calculate perplexity in PyTorch?

Question

I am wondering the calculation of perplexity of a language model which is based on character level LSTM model. I got the code from kaggle and edited a bit for my problem but not the training way. I have added some other stuff to graph and save logs. However, as I am working on a language model, I want to use perplexity measuare to compare different results. In tensorflow, I have done it via this answer and it was easy. I have looked for a way doing it in PyTorch and literally no related result on Google. I need some help, and it is really appreciated.

Here is the related code, I believe:

criterion = nn.CrossEntropyLoss()

# create training and validation data
val_idx = int(len(data)*(1-val_frac))
data, val_data = data[:val_idx], data[val_idx:]

if(train_on_gpu):
    net.cuda()

counter = 0
n_chars = len(net.chars)
for e in range(epochs):
    # initialize hidden state
    h = net.init_hidden(batch_size)

    for x, y in get_batches(data, batch_size, seq_length):
        counter += 1

        # One-hot encode our data and make them Torch tensors
        x = one_hot_encode(x, n_chars)
        inputs, targets = torch.from_numpy(x), torch.from_numpy(y)

        if(train_on_gpu):
            inputs, targets = inputs.cuda(), targets.cuda()

        # Creating new variables for the hidden state, otherwise
        # we'd backprop through the entire training history
        h = tuple([each.data for each in h])

        # zero accumulated gradients
        net.zero_grad()

        # get the output from the model
        output, h = net(inputs, h)

        # calculate the loss and perform backprop
        loss = criterion(output, targets.view(batch_size*seq_length))

Answer 1

I was surfing around at PyTorch's website and found a calculation of perplexity. You can examine how they calculated it as ppl as follows:

criterion = nn.CrossEntropyLoss()
total_loss = 0.
...
for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
    ...
    loss = criterion(output.view(-1, ntokens), targets)
    loss.backward()
    total_loss += loss.item()
    log_interval = 200
    if batch % log_interval == 0 and batch > 0:
        cur_loss = total_loss / log_interval
        ...
        print('ppl {:8.2f}'.format(math.exp(cur_loss)))
        ...

As @SpiderRico reminded, I got it from this link