Hello Everyone,

Since we have learned more about what’s under the hood of fastai I was interested in building a model from scratch instead of using one of the preset like models.resnet34.

This has been harder then I expected.

I’m trying to recreate this model and then use it with the fastai cnn_learner - https://github.com/pytorch/examples/blob/master/mnist/main.py

```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4*4*50, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 4*4*50)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
```

But I keep getting different errors depending on how I try and add it. Here is my Kaggle example. https://www.kaggle.com/pascalnoble/minst-fastai-3ab05b

`learn = cnn_learner(data, Net,pretrained=False, metrics=error_rate)`

Here is the error

`TypeError: __init__() takes 1 positional argument but 2 were given`

If added like this

`learn = cnn_learner(data, Net(),pretrained=False, metrics=error_rate)`

then the error is

`TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not bool`

If anyone has any ideas or solutions let me know.

It looks like someone else had a similar question that went unanswered -Customize Densenet model