Hi,

I’m writing a function that computes the sparsity of the weight matrices of the following fully connected network:

```
class FCN(nn.Module):
def __init__(self):
super(FCN, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.relu2 = nn.ReLU()
self.fc3 = nn.Linear(hidden_dim, hidden_dim)
self.relu3 = nn.ReLU()
self.fc4 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
out = self.fc1(x)
out = self.relu1(out)
out = self.fc2(out)
out = self.relu2(out)
out = self.fc3(out)
out = self.relu3(out)
out = self.fc4(out)
return out
```

The function I have written is the following:

```
def print_layer_sparsity(model):
for name,module in model.named_modules():
if 'fc' in name:
zeros = 100. * float(torch.sum(model.name.weight == 0))
tot = float(model.name.weight.nelement())
print("Sparsity in {}.weight: {:.2f}%".format(name, zeros/tot))
```

But it gives me the following error:

torch.nn.modules.module.ModuleAttributeError: ‘FCN’ object has no attribute ‘name’

It works fine when I manually enter the name of the layers (e.g.,

(model.fc1.weight == 0)

(model.fc2.weight == 0)

(model.fc3.weight == 0) …

but I’d like to make it independent from the network. In other words, I’d like to adapt my function in a way that, given any sparse network, it prints the sparsity of every layer. Any suggestions?

Thanks!!