Hello everyone!

I am trying to obtain samples from the following neural network posterior, and use it to get uncertainties in predictions:

```
class NN(nn.Module):
def __init__(self, input_size, output_size, weights=None):
super(NN, self).__init__()
self.fc1 = nn.Linear(input_size, output_size)
if weights: # I will describe below why do I need this
self.fc1.weight.data = nn.Parameter(weights['fc1.weight'])
self.fc1.bias.data = nn.Parameter(weights['fc1.bias'])
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
output = self.fc1(x)
output = self.softmax(output)
return output
```

To do so, I am doing the following:

```
def model(x_data, y_data):
fc1w_prior = Normal(loc=torch.zeros_like(model_bayes.fc1.weight), scale=torch.eye(n=num_classes, m=image_size))
fc1b_prior = Normal(loc=torch.zeros_like(model_bayes.fc1.bias), scale=torch.ones_like(model_bayes.fc1.bias))
priors = {'fc1.weight': fc1w_prior, 'fc1.bias': fc1b_prior}, # 'out.weight': outw_prior, 'out.bias': outb_prior}
lifted_module = pyro.random_module("module", model_bayes, priors)
lifted_reg_model = lifted_module()
lhat = lifted_reg_model(x_data)
pyro.sample("obs", Categorical(logits=lhat), obs=y_data)
num_samples = 500
warmup_steps = 2000
kernel = NUTS(model)
posterior = MCMC(kernel,
num_samples=num_samples,
warmup_steps=warmup_steps,
num_chains=1).run(X_train_b.view(-1, image_size), y_train_b)
```

But for a reason, NUTS almost in a second goes through the warmup part (but there are 2000 points supposed to be obtained), and then rather fast completes the sampling part. The acceptance rate was always equal to 1.

But obtained results are weak; I sample different random point from posterior via the following code (I sample weights and biases of one point in the posterior space and put them into my network):

```
NUMBER_OF_ESTIMATIONS = 500
pred = []
for i in range(NUMBER_OF_ESTIMATIONS):
node = np.random.randint(num_samples)
weights = {}
weights['fc1.weight'] = nn.Parameter(posterior.exec_traces[node].nodes['module$$$fc1.weight']['value'])
weights['fc1.bias'] = nn.Parameter(posterior.exec_traces[node].nodes['module$$$fc1.bias']['value'])
model_with_weights = NN(28 * 28, 10, weights)
model_with_weights.eval()
for data in test_loader:
X_test, y_test = data
X_test = X_test.view(-1, 28 * 28)
predictions = model_with_weights(X_test).detach().numpy()
pred.append((np.argmax(predictions, axis=-1) == y_test.numpy()).astype(float).mean())
break
```

But results I obtained are just like a random decisionâ€¦ (mode of accuracy is approximately 0.1)

So my question is what I am doing wrong?

Is there a built-in method to obtain a prediction for weights sampled from the posterior and add them to a NN just in one line of code?

I have almost no experience with Pyro and ppl at all so that I would appreciate any insights.

Thank you!