class Neural(nn.Module):
def init(self):
    super(Neural, self).__init__()
    
    self.ConvNet = nn.Sequential((nn.Conv2d(in_channels=1,
                                            out_channels=5,
                                            kernel_size=3,
                                            stride=1,
                                            padding=1), 
                                  nn.ReLU(inplace=True),
                                  nn.Conv2d(in_channels=5,
                                            out_channels=10,
                                            kernel_size=3,
                                            stride=1,
                                            padding=1), 
                                  nn.MaxPool2d(2, 2),
                                  nn.ReLU(inplace=True),
                                  
                                  ))


List item


Declare all the layers for classification
    self.classifier = nn.Sequential(
        nn.Linear(14 * 14 * 10, 128),
        nn.ReLU(inplace=True),
        nn.Linear(128, 128),
        nn.ReLU(inplace=True),
        nn.Linear(128, 10))
    
def forward(self, x):
  
    # Apply the feature extractor in the input
    x = self.ConvNet(x)
    
    # Squeeze the three spatial dimensions in one
    x = x.view(-1, 14* 14* 10)
    
    # Classify the images
    x = self.classifier(x)
    return x

criterion = nn.CrossEntropyLoss()
model= Neural()
optimizer = optim.SGD(model.parameters(), lr=0.03, momentum=0.1)
TypeError                                 Traceback (most recent call last)
 in 
1 criterion = nn.CrossEntropyLoss()
----> 2 model= Neural()
3 optimizer = optim.SGD(model.parameters(), lr=0.03, momentum=0.1)
 in init(self)
4         super(Neural, self).init()
5
----> 6         self.ConvNet = nn.Sequential((nn.Conv2d(in_channels=1,
7                                                 out_channels=5,
8                                                 kernel_size=3,
~\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\container.py in init(self, *args)
67         else:
68             for idx, module in enumerate(args):
—> 69                 self.add_module(str(idx), module)
70
71     def _get_item_by_idx(self, iterator, idx) → T:
~\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py in add_module(self, name, module)
370         “”"
371         if not isinstance(module, Module) and module is not None:
 → 372             raise TypeError("{} is not a Module subclass".format(
373                 torch.typename(module)))
374         elif not isinstance(name, torch._six.string_classes):
TypeError: tuple is not a Module subclass