Is there a quick way to access (then plot) the l2 norm of the distance between the initial set of weights w_0 and a set of weights at iteration t, w_t ?
I’d like to access this quantity to then plot it in 2D (along a random dimension).

I believe net.parameters() where net is defined as below

class MnistNetSmall(nn.Module):
def __init__(self):
super(MnistNetSmall, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.fc1 = nn.Linear(20 * 12 * 12, 10)
#self.fc1 = nn.Linear(28 * 28, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 20 * 12 * 12)
#x = x.view(-1, 28 * 28)
x = self.fc1(x)
return F.log_softmax(x, dim=1)
net = MnistNetSmall()

is what I need. Though this quantity is a generator object.

Any ideas how to collect net.parameters() at each iteration for plotting purposes?