Hello,

I am trying to make predictions with a Pyro Bayesian neural network, and while the validation errors from the Stochastic Variational Inference during the training phase are very small (meaning the Bayesian network is very well trained), when I try to make predictions based on this Pyro neural network model, I am getting the accuracy rate (% of correct predictions from the test set) of 0%. I don’t know why this is happening.

My likelihood function is a multinomial likelihood, which I specified in my model statement as below:

```
# define likelihood function for our Bayesian layer.
class MyModel(PyroModule):
def __init__(self, model, name=""):
super( ).__init__( )
self.model = model
def forward(self, input, true_answer = None, y=None):
if true_answer != None:
classification_scores = model(input)
softmax_tensor = nn.Softmax(dim=-1)(classification_scores)
if true_answer == torch.tensor([0]):
true_answer_tensor = torch.tensor([[1.,0.,0.,0.]])
elif true_answer == torch.tensor([1]):
true_answer_tensor = torch.tensor([[0.,1.,0.,0.]])
elif true_answer == torch.tensor([2]):
true_answer_tensor = torch.tensor([[0.,0.,1.,0.]])
elif true_answer == torch.tensor([3]):
true_answer_tensor = torch.tensor([[0.,0.,0.,1.]])
else:
classification_scores = model(input)
softmax_tensor = nn.Softmax(dim=-1)(classification_scores)
true_answer_tensor = None
# specify the multinomial likelihood for my `y`
return pyro.sample("y",
dist.Multinomial(1, probs = softmax_tensor),
obs = true_answer_tensor)
```

and then, I am trying to make predictions like below:

```
# turn on the evaluation mode.
model = MyModel(original_model)
model.eval()
pred_obj = Predictive(model, guide=my_guide, num_samples = 100)
prediction = pred_obj.forward(input)
prediction_tensor = prediction["y"].squeeze(1)
prediction_tensor = torch.sum(prediction_tensor, dim=0)
my_prediction = torch.argmax(prediction_tensor).tolist()
```

I am thinking either my model definition and/or the way I use the `Predictive`

function is wrong.

Is there any error in my code? It might have to do with the fact that I am not explicitly returning `my_guide`

in my training function. Do I need to explicitly return the trained `my_guide`

at the end of my training function, in order to make my `Predictive()`

statement to properly refer to the trained `my_guide`

(as opposed to `my_guide`

before the training)?

Thank you,