Hi! I wanted make the full MNIST digit classifier, I got some advises, so I made this digit classifier, I did everything excactly like in the book(naturally, I’ve read the book many times, revised Calculus,Slope intercept form, Limit, Matrix multiplication, spent days understandig how neuralt networks work, asked many questions, so in theory I understand the basic concepts… )

However, I made some adjustments, and it doesn’t work as expected, first of all, my accuracy is alway s 0.9, I have no idea why, and secondly I can’t use my model… so the prediction method keeps throwing an AttributError (list object has no attribute decode_batch)

error, and I have no idea why. Here’s my code(As you can see I have difficulties using preformatted text, I’m sorry) It’s everything but efficient, good, fast, working but I’m trying.:

```
`from fastai.vision.all import *`
` from fastbook import *`
` path = untar_data(URLs.MNIST) `
` path_train = path/'training'`
` path_valid = path/'testing'`
` train_x = get_image_files(path_train).sorted()
train_y = [int(element.parent.name) for element in train_x]`
` train_x = [tensor(Image.open(element)) for element in train_x]`
` train_x = torch.stack(train_x).float() / 255`
` train_x = train_x.view(-1,28*28)`
` for i in range(len(train_y)):`
` sample = [0] * 10`
` sample[train_y[i]] = 1`
`train_y[i] = sample`
` train_y = tensor(train_y)`
` dset = list(zip(train_x,train_y))`
` valid_x = get_image_files(path_valid).sorted()`
` valid_y = [int(element.parent.name) for element in valid_x]`
` valid_x = [tensor(Image.open(element)) for element in valid_x]`
` valid_x = torch.stack(valid_x).float() / 255`
```

` valid_x = valid_x.view(-1,28*28)`

` for i in range(len(valid_y)):`

` sample = [0] * 10`

` sample[valid_y[i]] = 1`

` valid_y[i] = sample`

` valid_y = tensor(valid_y)`

` valid_dset = list(zip(valid_x,valid_y))`

` def mnist_loss(predictions, targets):`

`predictions = predictions.sigmoid()`

` return torch.where(targets==1, 1-predictions, predictions).mean()`

` dl = DataLoader(dset,batch_size=256)`

` valid_dl = DataLoader(valid_dset,batch_size=256)`

` dls = DataLoaders(dl,valid_dl)`

` list(dls.train)[0][0].shape`

` simple_net = nn.Sequential(`

` nn.Linear(28*28,100),`

` nn.ReLU(),`

` nn.Linear(100,50),`

` nn.ReLU(),`

` nn.Linear(50,30),`

` nn.ReLU(),`

` nn.Linear(30,10),`

` nn.ReLU()`

)

` def batch_accuracy(xb, yb):`

` preds = xb.sigmoid()`

` correct = (preds>0.5) == yb`

` return correct.float().mean()`

` learner=Learner(dls,simple_net,opt_func=SGD,loss_func=mnist_loss,metrics=batch_accuracy)`