Initializing a Model with Customized Weights

I trying to prune a custom built Lenet() Model. I first train model for 15 epochs on mnist data. Then I try to remove filters with minimum L1 norm. I am doing this by copy the weights of filters I want to retain in a list then I create a new model of Lenet with less number of filters and then I try to copy trained weights into this new reduced model.
But I am into multiple errors like “Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling backward the first time.”
Code i am trying is given below. I have also pasted screen shot of error. weights_idx is list that contains weights matrices for filters that i copied from trained model.
Please if some one can help. Thanks in advance.
class LeNet(nn.Module):

def __init__(self,layer1_outchannels=6,layer2_outchannels=16):

    super(LeNet, self).__init__()

    # 1 input image channel, 6 output channels, 3x3 square conv kernel

    self.conv1 = nn.Conv2d(1, layer1_outchannels, 5)

    self.conv2 = nn.Conv2d(layer1_outchannels, layer2_outchannels, 5)

    self.fc1 = nn.Linear(layer2_outchannels * 5 * 5, 120)  # 5x5 image dimension

    self.fc2 = nn.Linear(120, 84)

    self.fc3 = nn.Linear(84, 10)

def forward(self, x):

    x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))

    x = F.max_pool2d(F.relu(self.conv2(x)), 2)

    x = x.view(-1, int(x.nelement() / x.shape[0]))

    x = F.relu(self.fc1(x))

    x = F.relu(self.fc2(x))

    logits = self.fc3(x)

    probs = F.softmax(logits, dim=1)

    return logits, probs

model1 = LeNet(3,16).to(DEVICE)







model1, optimizer, _ = training_loop(model1, criterion, optimizer, train_loader, valid_loader, N_EPOCHS, DEVICE)

Try[...] =

Although it is not recommended…

I would not recommend to use the .data attribute, as it might yield unwanted behavior and/or other issues.
Instead, wrap the parameter manipulation into a with torch.no_grad() block:

model1 = LeNet(3,16)
a = torch.rand_like(model1.conv1.weight[0]).requires_grad_(True)
a1 = torch.rand_like(model1.conv1.weight[1]).requires_grad_(True)
a2 = torch.rand_like(model1.conv1.weight[2]).requires_grad_(True)

with torch.no_grad():
    model1.conv1.weight[0] = a
    model1.conv1.weight[1] = a1
    model1.conv1.weight[2] = a2