I get the following error when i try to run this model.
RuntimeError: Given groups=1, weight of size [128, 64, 5, 5], expected input[2048, 1, 8, 8] to have 64 channels, but got 1 channels instead
I am using cifar100 dataset. Batch size is 128.
if name == ‘main’:
# functions to show an image

def imshow(img):

    img = img / 2 + 0.5  # unnormalize

    npimg = img.numpy()

    plt.imshow(np.transpose(npimg, (1, 2, 0)))

    plt.show()



class Net(nn.Module):

    def __init__(self):

        super().__init__()

        self.conv1 = nn.Conv2d(3, 64,5,1,2)

        self.conv_resh = nn.Conv2d(1, 1, 2, 2, 1)

        self.filt1 = nn.Conv2d(64, 64, 2, 2, bias=False, padding=1) 

        self.pool = nn.MaxPool2d(2, 2)

        self.conv2 = nn.Conv2d(64, 128,5,1,2)

        self.conv3 = nn.Conv2d(128,128,5,1,2)

        self.conv4 = nn.Conv2d(128, 128,5,1,2)

        self.conv5 = nn.Conv2d(128, 128,5,1,2)

        self.fc1 = nn.Linear(128, 120)

        self.fc2 = nn.Linear(120, 64)

        self.fc3 = nn.Linear(64,32)

        self.fc4 = nn.Linear(32,16)

        self.fc5 = nn.Linear(16, 100)

        h,w=32,32

    def forward(self, x):

        x = self.pool(F.relu(self.conv1(x)))

        x = x.view(-1,1,32,32)

        x = self.pool(F.relu(self.filt1(x)))

        print(x.shape)

        x = self.pool(F.relu(self.conv2(x)))

        x = self.pool(F.relu(self.conv3(x)))

        x = self.pool(F.relu(self.conv4(x)))

        x = self.pool(F.relu(self.conv5(x)))

        x = torch.flatten(x, 1)  # flatten all dimensions except batch

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

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

        x = F.relu(self.fc3(x))

        x = F.relu(self.fc4(x))

        x = self.fc5(x)

        return x

net = Net()

with torch.no_grad():

            net.filt1.weight = nn.Parameter(torch.tensor([[[[1., 0.],[0., 1.]]]]))

print(net)

print(net.filt1.weight)