Evaluating Parzen window log-likelihood for GAN

Question

I am trying to reimplement the original GAN paper by Ian Goodfellow et al. And I need to show that my implementation achieves same or similar results as the authors achieved. But I am not sure how to evaluate this metric. I took a look at their implementation but I got some funny results. In the paper they report 225 +- 2 on the MNIST for this metric, while the results I get are bellow -400000000. I thought that maybe the model is bad, but it generates really good images of MNIST digits.

Can someone tell me what am I doing wrong?

Bellow is the code which I used. I copied the part of the code from the official implementation.

Note: valid variable are images taken from the MNIST dataset.

 def get_nll(x, parzen, batch_size=10):
  """
  Credit: Yann N. Dauphin
  """

  inds = range(x.shape[0])
  n_batches = int(numpy.ceil(float(len(inds)) / batch_size))
  print("N batches:", n_batches)

  times = []
  nlls = []
  for i in range(n_batches):
      begin = time.time()
      nll = parzen(x[inds[i::n_batches]])
      end = time.time()
      times.append(end-begin)
      nlls.extend(nll)

      if i % 10 == 0:
          print(i, numpy.mean(times), numpy.mean(nlls))

  return numpy.array(nlls)

 def log_mean_exp(a):
     """
     Credit: Yann N. Dauphin
     """

     max_ = a.max(1)

     return max_ + T.log(T.exp(a - max_.dimshuffle(0, 'x')).mean(1))



  def cross_validate_sigma(samples, data, sigmas, batch_size):
      lls = []
      for sigma in sigmas:
          print("Sigma:", sigma)
          parzen = theano_parzen(samples, sigma)
          tmp = get_nll(data, parzen, batch_size = batch_size)
          lls.append(numpy.asarray(tmp).mean())
          del parzen
          gc.collect()

      ind = numpy.argmax(lls)
      print(max(lls))
      return sigmas[ind]

 noise = torch.randn((10000, 100), device=device)
 gen_model.eval()
 gan_out = gen_model(noise)

 sigma_range = numpy.logspace(-1., 0., num=10)
 sigma = cross_validate_sigma(gan_out.reshape(10000,-1), valid[0:10000], sigma_range, 100)