pytorch error in multiplying matrices in neural network - pytorch

Question

I was trying to make a Neural Network in PyTorch, however I ran into the error below. I'm still new to this topic so I am not able to understand how I should go about solving this.
Code:
class ANN_Model(nn.Module):
def __init__(self,input_features=8,hidden1=8,hidden2=200,hidden3=200,hidden4=300,hidden5=300,hidden6=400,hidden7=400,hidden8=300,hidden9=300,out_features=2):
super().__init__()
self.f_connected1=nn.Linear(input_features,hidden1)
self.f_connected2=nn.Linear(hidden1,hidden2)
self.f_connected2=nn.Linear(hidden2,hidden3)
self.f_connected2=nn.Linear(hidden3,hidden4)
self.f_connected2=nn.Linear(hidden4,hidden5)
self.f_connected2=nn.Linear(hidden5,hidden6)
self.f_connected2=nn.Linear(hidden6,hidden7)
self.f_connected2=nn.Linear(hidden7,hidden8)
self.f_connected2=nn.Linear(hidden8,hidden9)
self.out=nn.Linear(hidden9,out_features)
def forward(self,x):
x=F.relu(self.f_connected1(x))
x=F.relu(self.f_connected2(x))
x=F.relu(self.f_connected3(x))
x=F.relu(self.f_connected4(x))
x=F.relu(self.f_connected5(x))
x=F.relu(self.f_connected6(x))
x=F.relu(self.f_connected7(x))
x=F.relu(self.f_connected8(x))
x=F.relu(self.f_connected9(x))
x=self.out(x)
return x
loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr = 0.01)
epochs = 500
final_losses = []
for i in range(epochs):
i = i + 1
y_pred = model.forward(X_train)
loss=loss_function(y_pred, y_train)
final_losses.append(loss.item())
if i%10==1:
print("Epoch number: {} and the loss: {}".format(i, loss.item()))
optimizer.zero_grad()
loss.backward()
optimizer.step()
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Input In [13], in <cell line: 3>()
3 for i in range(epochs):
4 i = i + 1
----> 5 y_pred = model.forward(X_train)
6 loss=loss_function(y_pred, y_train)
7 final_losses.append(loss.item())
Input In [8], in ANN_Model.forward(self, x)
14 def forward(self,x):
15 x=F.relu(self.f_connected1(x))
---> 16 x=F.relu(self.f_connected2(x))
17 x=F.relu(self.f_connected3(x))
18 x=F.relu(self.f_connected4(x))
File ~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/linear.py:114, in Linear.forward(self, input)
113 def forward(self, input: Tensor) -> Tensor:
--> 114 return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (691x8 and 300x300)
I was trying to make a Neural Network in PyTorch, however I ran into the error below. I'm still new to this topic so I am not able to understand how I should go about solving this.

Answer 1

I found it, in your model's constructor __init__ every layer is named self.f_connected2 and because of that it expects a shape of (batch_size,300).