Hi,

I am new to pyro. To try it I wanted to train a CNN module using bayesian inference using pyro.

Can Anyone help me to transform it to random_module pyro?

The network is the following:

```
class NetC(nn.Module):
def __init__(self, nc, nclass):
super(NetC, self).__init__()
self.nclass = nclass
self.convlayers = nn.Sequential(
nn.Conv2d(nc, 32, kernel_size=(2, 2), stride=1),
nn.BatchNorm2d(32),
nn.ReLU(False),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(32, 64, kernel_size=2, stride=1),
nn.BatchNorm2d(64),
nn.ReLU(False),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(64, 64, kernel_size=2, stride=1),
nn.BatchNorm2d(64),
nn.ReLU(False),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(64, 128, kernel_size=2, stride=1),
nn.BatchNorm2d(128),
nn.ReLU(False),
)
self.fc = nn.Sequential(
nn.Linear(128*6*6, 2048), # FC
nn.Dropout(),
nn.ReLU(False),
nn.Linear(2048, 2048),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(2048, self.nclass), # classifier
)
def forward(self, x):
x0 = self.convlayers(x)
x0 = x0.view(x0.size(0), -1)
return self.fc(x0)
```

(edited by @fritzo to format python code)