How should I add a Gaussian noise to the weights of network?

3

Actually my model is some triple network like below:

class EmbeddingNet(nn.Module):
def init(self):
super(EmbeddingNet, self).init()
self.convnet = nn.Sequential(nn.Conv2d(1, 32, 5), nn.PReLU(),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(32, 64, 5), nn.PReLU(),
nn.MaxPool2d(2, stride=2))

    self.fc = nn.Sequential(nn.Linear(64 * 4 * 4, 256),
                            nn.PReLU(),
                            nn.Linear(256, 256),
                            nn.PReLU(),
                            nn.Linear(256, 2)
                            )

def forward(self, x):
    output = self.convnet(x)
    output = output.view(output.size()[0], -1)
    output = self.fc(output)
    return output

def get_embedding(self, x):
    return self.forward(x)

class TripletNet(nn.Module):
def init(self, embedding_net):
super(TripletNet, self).init()
self.embedding_net = embedding_net
embedding_net = EmbeddingNet()
model = TripletNet(embedding_net)

So, I am using define some arbitrary mean and std then use them into make Gaussian noise how can i add this noise during the training process of this network in each epoch. because in each epoch i define a new mean and std.