I’ve been struggling with this patch of code where I cannot seem to get the size of my matrices right. This one is based on a batch size of 4.
https://github.com/adomakor412/NERTO_CIMSS_GOES-R/blob/a35f882fe15f68cb6df22bc5c28d503ca18a88b3/AUC_model.ipynb 1
If you have trouble rendering the code use https://nbviewer.jupyter.org/ 1
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 2)
#         super(Net, self).__init__()
#         self.conv1 = nn.Conv2d(3, 32, 5)
#         self.pool = nn.MaxPool2d(2, 2)
#         self.conv2 = nn.Conv2d(32, 64, 5)
#         self.fc1 = nn.Linear(64*9*9, 1024)
#         self.fc2 = nn.Linear(1024, 7)
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
#         x = self.pool(F.relu(self.conv1(x)))
#         x = self.pool(F.relu(self.conv2(x)))
#         x = x.view(x.size(0), -1)
#         #x = x.view(-1, 64)
#         #x = F.relu(self.fc1(x))
#         #x = F.relu(self.fc2(x))
#         #x = self.fc2(x)
        return x
net = Net()

And when try the net() this way, I get an index out of range error:
class Net(nn.Module):
    def __init__(self):
#         super().__init__()
#         self.conv1 = nn.Conv2d(3, 6, 5)
#         self.pool = nn.MaxPool2d(2, 2)
#         self.conv2 = nn.Conv2d(6, 16, 5)
#         self.fc1 = nn.Linear(16 * 5 * 5, 120)
#         self.fc2 = nn.Linear(120, 84)
#         self.fc3 = nn.Linear(84, 2)
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, 5)
        self.fc1 = nn.Linear(64*9*9, 1024)
        self.fc2 = nn.Linear(1024, 7)
    def forward(self, x):
#         x = self.pool(F.relu(self.conv1(x)))
#         x = self.pool(F.relu(self.conv2(x)))
#         x = torch.flatten(x, 1) # flatten all dimensions except batch
#         x = F.relu(self.fc1(x))
#         x = F.relu(self.fc2(x))
#         x = self.fc3(x)
#         return x
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(x.size(0), -1)
        #x = x.view(-1, 64)
        #x = F.relu(self.fc1(x))
        #x = F.relu(self.fc2(x))
        #x = self.fc2(x)
        return x