```
class BayesianDynResNet(PyroModule):
def __init__(self):
super().__init__()
HIDDEN_DIM = 50
self.linear1 = PyroModule[nn.Linear](2, HIDDEN_DIM)
self.linear1.weight = PyroSample(dist.Normal(0., 1.)
.expand([HIDDEN_DIM, 2]).to_event(2))
self.linear1.bias = PyroSample(dist.Normal(
0., 10.).expand([HIDDEN_DIM]).to_event(1))
self.linear2 = PyroModule[nn.Linear](HIDDEN_DIM, 2)
self.linear2.weight = PyroSample(dist.Normal(
0., 1.).expand([2, HIDDEN_DIM]).to_event(2))
self.linear2.bias = PyroSample(
dist.Normal(0., 10.).expand([1]).to_event(1))
def res_forward(self, x, w1, b1, w2, b2):
res = x@w1.T + b1
res = torch.tanh(res)
res = res@w2.T + b2
return res
def propagate(self, t, y0):
w1 = self.linear1.weight
b1 = self.linear1.bias
w2 = self.linear2.weight
b2 = self.linear2.bias
y = []
y.append(y0[None, ...])
# the first step is already the initial condition
for i in range(t.shape[0]-1):
y0 = y0 + self.res_forward(y0, w1, b1, w2, b2)
y.append(y0[None, ...])
y = torch.cat(y, axis=0)
return y
def forward(self, t, y0, y=None):
sigma = pyro.sample("sigma", dist.Uniform(0., 10.))
mean = self.propagate(t, y0)
obs = pyro.sample("obs", dist.Normal(mean, sigma), obs=y)
return mean
# defining the model for inference
model = BayesianDynResNet3()
guide = AutoDiagonalNormal(model)
# define optimizera and variational inference
adam = pyro.optim.Adam({"lr": 1e-3})
svi = SVI(model, guide, adam, loss=Trace_ELBO())
```

Hi so I’m trying to construct a residual neural network that can learn dynamic systems. The idea is to sample from the linear layers before we use our res net block and then use variational inference to learn to distribution of the weights. Sadly the model doesn’t converge at all. (Althoug when using droput the model learns the dynamics). Does anyone see some implementation failures?