I’ve started to work with pytorch a few weeks ago.(No prior knowledge of ML)

I want to biuld a image classifier that detects wheather an image is a cat or a dog.

I have data in the form of:

```
tensor([[1.6886e-06, 4.2819e-06, 1.3871e-06, ..., 1.4353e-05, 1.4173e-05,
1.3931e-05],
[1.5680e-06, 4.9453e-06, 1.2062e-06, ..., 1.5017e-05, 1.4896e-05,
1.4173e-05],
[1.3871e-06, 5.3072e-06, 2.5330e-06, ..., 1.4052e-05, 1.3992e-05,
1.3992e-05],
...,
[5.8499e-06, 5.9706e-06, 5.2469e-06, ..., 2.4727e-06, 1.8093e-06,
2.5933e-06],
[5.0056e-06, 5.1262e-06, 5.3072e-06, ..., 3.6185e-06, 1.4474e-06,
1.8696e-06],
[4.8247e-06, 5.1865e-06, 5.2469e-06, ..., 4.1613e-06, 3.2567e-06,
1.8093e-06]])
tensor([[0., 1.],
[0., 1.],
[1., 0.],
...,
[0., 1.],
[0., 1.],
[0., 1.]])
```

The image is a 50 by 50 gray scale and the output is a 1D tensor.

This is my Network Class:

```
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(50*50, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 64)
self.fc4 = nn.Linear(64, 2)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return F.log_softmax(x, dim=1)
net = Net()
```

This is my training loop using `optim.Adam`

:

```
EPOCHS=6
for epoch in range(EPOCHS):
for key in range(24946):
X = data[key]
y =out[key]
net.zero_grad()
output = net(X)
loss = F.nll_loss(output, y)
loss.backward()
optimizer.step()
print(loss)
```

However i get the following error:

`RuntimeError: size mismatch, m1: [1 x 50], m2: [2500 x 64]`

I believe this is due to the input to my first layer.

Can someone please explain how I would input each image to my Network?