I’ve been playing around with Pyro for about a week now but unfortunately I can’t get it to train properly. My setup is somewhat akin to the SS-VAE example, except that my approach is supervised.

My model sets priors and samples a multinomial likelihood, where the probabilities are softmaxed from Gaussian latent variables.

My guide contains a neural net and predicts mu and sigma for my latents, and also samples those latent variables too.

Like so:

```
def model(self, input_batch, labels):
batch_size = input_batch.size(0)
# Sample from priors (generative side)
with pyro.iarange('independent'):
...
# Pre-binned histogram observed
counts = torch.sum(labels, dim=1) # Sum over target columns
likelihood = pyro.distributions.multinomial(ps=probabilities, n=counts)
def guide(self, input_batch, labels):
...
```

I have no trouble running the the model and guide. It fails after evaluating those (in `loss_and_grads`

)

```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-57-93fd337a0d5c> in <module>()
----> 1 train()
...
AttributeError: 'Variable' object has no attribute 'log_pdf'
```

Why does it somehow think the `trace`

object is a `Variable`

? Traceback attached.

Please let me know I am approaching this as intended. I’m using Pyro 0.1.2 and Pytorch 0.2.

Thanks!