Attribute Error: `loss.backward()` returns None-CodePudding

I'm trying to implement the Learner object and its steps and facing an issue with the loss.backward() function as it raises and AttributeError: 'NoneType' object has no attribute 'data'

The entire process works when I follow the Chapter 04 MNIST Basics. However, implementing within a class raises this error. Could anybody guide me on why this occurs and ways to fix this?

Here's the code below:

class Basic_Optim:
    
    def __init__(self, params, lr):
        self.params = list(params)
        self.lr = lr
        
    def step(self):
        for p in self.params:
            p.data -= self.lr * p.grad.data
    
    def zero(self):
        for p in self.params:
            p.grad = None


class Learner_self:
    
    def __init__(self, train, valid, model, loss, metric, params, lr):
        self.x = train
        self.y = valid
        self.model = model
        self.loss = loss
        self.metric = metric
        self.opt_func = Basic_Optim(params, lr)   
        
    def fit(self, epochs):
        for epoch in range(epochs):
            self.train_data()
            score = self.valid_data()
            print(score, end = ' | ')
            
    def train_data(self):
        for x, y in self.x:
            preds = self.model(x)
            loss = self.loss(preds, y)
            loss_b = loss.backward()
            print(f'Loss: {loss:.4f}, Loss Backward: {loss_b}')
            
            self.opt_func.step()
            self.opt_func.zero()
    
    def valid_data(self):
        accuracy = [self.metric(xb, yb) for xb, yb in self.y]
        return round(torch.stack(accuracy).mean().item(), 4)
    
    
learn = Learner_self(dl, valid_dl, simple_net, mnist_loss, metric=batch_accuracy,
                     params=linear_model.parameters(), lr = 1)
learn.fit(10)

OUTPUT from the print statement inside the train_data prints: Loss: 0.0516, Loss Backward: None and then raises the Attribute error shared above.

Please let me know if you want any more details. Every other function such as mnist_loss, batch_accuracy, simple_net are exactly the same from the book. Thank you in advance.

CodePudding user response：

It seems like your optimizer and your trainer do not work on the same model.
You have model=simple_net, while the parameters for the optimizer are those of a different model params=linear_model.parameters().

Try passing params=simple_net.parameters() -- that is, make sure the trainer's params are those of model.