title: Runtime Error: mat1 and mat2 shapes cannot be multiplied in pytorch
body: <p>I'm new to deep learning and I have created a model using the code below for the prediction of plant disease</p>
  <pre><code>class CNN_Model(nn.Module):
    def __init__(self):
      super(CNN_Model, self).__init__()
      self.cnn_model = nn.Sequential(
          nn.Conv2d(3, 16, 3),    
          nn.ReLU(),
          nn.MaxPool2d(2, 2),     
          nn.Conv2d(16, 32, 5),   
          nn.ReLU(),
          nn.MaxPool2d(2, 2),     
      )
  
      self.fc_model = nn.Sequential(
          nn.Flatten(),           
          nn.Linear(800, 300),    
          nn.ReLU(),
          nn.Linear(300, 38),     
          nn.Softmax(dim=1)
      )
  
    def forward(self, x):
        x = self.cnn_model(x)
        x = self.fc_model(x)
  
        return x
  </code></pre>
  <pre><code>model = CNN_Model()
  
  out = model(imgs)
  out
  </code></pre>
  <p>When I'm trying to run the above code, I'm getting the error mat1 and mat2 cannot be multiplied. I have tried the answers posted for questions similar to this, but still, my issue is not solved.</p>
  <pre><code>RuntimeError                              Traceback (most recent call last)
  /tmp/ipykernel_66/1768380315.py in &lt;module&gt;
  ----&gt; 1 out = model(imgs)
        2 out
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
     1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
     1050                 or _global_forward_hooks or _global_forward_pre_hooks):
  -&gt; 1051             return forward_call(*input, **kwargs)
     1052         # Do not call functions when jit is used
     1053         full_backward_hooks, non_full_backward_hooks = [], []
  
  /tmp/ipykernel_66/1577403502.py in forward(self, x)
       26   def forward(self, x):
       27       x = self.cnn_model(x)
  ---&gt; 28       x = self.fc_model(x)
       29 
       30       return x
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
     1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
     1050                 or _global_forward_hooks or _global_forward_pre_hooks):
  -&gt; 1051             return forward_call(*input, **kwargs)
     1052         # Do not call functions when jit is used
     1053         full_backward_hooks, non_full_backward_hooks = [], []
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
      137     def forward(self, input):
      138         for module in self:
  --&gt; 139             input = module(input)
      140         return input
      141 
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
     1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
     1050                 or _global_forward_hooks or _global_forward_pre_hooks):
  -&gt; 1051             return forward_call(*input, **kwargs)
     1052         # Do not call functions when jit is used
     1053         full_backward_hooks, non_full_backward_hooks = [], []
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
       94 
       95     def forward(self, input: Tensor) -&gt; Tensor:
  ---&gt; 96         return F.linear(input, self.weight, self.bias)
       97 
       98     def extra_repr(self) -&gt; str:
  
  /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
     1845     if has_torch_function_variadic(input, weight):
     1846         return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
  -&gt; 1847     return torch._C._nn.linear(input, weight, bias)
     1848 
     1849 
  
  RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x119072 and 800x300)
  </code></pre>
  <p>Please someone help me to solve these errors.</p>