# How can I get a conv layer weight In the course of training?

I am a new pytorch user. when I run the mnist example of pytorch, I find the conv weight has not changed! how can I get the changed weight?

`````` class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)

def forward(self, x,index):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)

# which I added to test whether the weight has changed
if (self.conv1.weight.data==self.keep).all(): print('same!')

return F.log_softmax(x)
``````

I find the print are all â€śsame!â€ť, how can I get the changed weight?

Which optimizer do you use?
I guess you do not apply optim.step(), which updates your weight while training

`````` class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)

self.keep=self.conv1.weight.data.clone() # modify here

def forward(self, x,index):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)

# which I added to test whether the weight has changed
if (self.conv1.weight.data==self.keep).all(): print('same!')