I would like to know, if there is a way to reset weights for a PyTorch model.
Here is my code:
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=5)
        self.conv4 = nn.Conv2d(64, 64, kernel_size=5)

        self.pool = nn.MaxPool2d(5, stride=3)
        self.pool2 = nn.MaxPool2d(3, stride=1)
        self.activation = nn.ReLU()
        self.fc1 = nn.Linear(4096, 20)
        self.fc2 = nn.Linear(20, 1)

    def forward(self, x):
        x = self.activation( self.pool(self.conv1(x)) )
        x = self.activation( self.pool(self.conv2(x)) )
        x = self.activation( self.pool(self.conv3(x)) )
        x = self.activation( self.pool2(self.conv4(x)) )
        x = x.view(-1, 64*8*8)

        x = self.activation(self.fc1(x))
        x = self.fc2(x)
        return x

I just to want average couple runs of the model, in order to evaluate it.
Any idea how can I do that?