PyTorch NotImplementedError in forward - deep-learning

Question

i'm making a code that classifies numbers by using pytorch
epochess =[]
train_losses = []
test_losses = []
acc_training =[]
acc_testing = []
for epoch in range (epochs):
train_acc, train_epoch_loss = train_CNN(model,loss_function, optimizer, train_load, device)
print('epoch',epoch ,'training loss',train_epoch_loss)
train_losses.append(train_epoch_loss)
print('epoch',epoch,'training accuracy',train_acc)
acc_training.append(train_acc)
test_acc, test_epoch_loss = validate_CNN(model, loss_function, test_load, device)
print('epoch',epoch,'testing loss',test_epoch_loss)
test_losses.append(test_epoch_loss)
print('epoch',epoch,'testing accuracy',test_acc)
acc_testing.append(test_acc)
epochess.append(epoch)
and I get an erreur , I was following the right path just like it said on youtube
here's the following erreur
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-17-0bcb51ebbc3d> in <module>
5 acc_testing = []
6 for epoch in range (epochs):
----> 7 train_acc, train_epoch_loss = train_CNN(model,loss_function, optimizer, train_load, device)
8 print('epoch',epoch ,'training loss',train_epoch_loss)
9 train_losses.append(train_epoch_loss)
2 frames
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _forward_unimplemented(self, *input)
242 registered hooks while the latter silently ignores them.
243 """
--> 244 raise NotImplementedError(f"Module [{type(self).__name__}] is missing the required \"forward\" function")
245
246
NotImplementedError: Module [CNN] is missing the required "forward" function

Answer 1

How did you implement your model?
Did you use a built-in model from PyTorch or did you create a custom model?
If you created a custom model, make sure you use the right components from PyTorch (e.g. torch.nn.Linear and torch.nn.Conv2d https://pytorch.org/tutorials/beginner/introyt/modelsyt_tutorial.html), otherwise PyTorch might complain about certain functions missing, like is happening in your case.