Need some help figuring out the in and out features of my model

[Ronnylel]

[Ronnylel]

Hey so I'm pretty new to ML and I've just been following the video with a little bit of experimentation on my side and in the 2nd module however I've come across an error.

The video(25 hour long form version)- https://youtu.be/V_xro1bcAuA?si=9npkagnenW25kpSR

So I've followed through with the video and partitioned my data and built the model but I am running into the error- "mat1 and mat2 shapes cannot be multiplied (200x8 and 5x1)"

The following is the initializationdefinition of the model-

class BinaryClassificationModel(nn.Module):
def init(self):
super().init()
self.l1=nn.Linear(in_features=2,out_features=8)
self.l2=nn.Linear(in_features=5,out_features=1)

def forward(self, x:torch.Tensor) -> torch.Tensor:
return self.l2(self.l1(x))

And the initialization of the model

binary_model_0=BinaryClassificationModel()

In my understanding I don't even know where it got the 200X8 from when the shape of X_test itself is 200X2

Now as far as I've understood when I call the binary_model_0 with the X_test data, the classification is supposed to be all over the place but i get the matrix multiplication error. Any ideas on how to resolve this would be much appreciated, Thank You.

P.S. The dataset was generated through the sklearn.dataset.make_circles() method

[Ronnylel]

Figured it out, turns out I didn't assign my layers properly