Hello guys, you can find my code below. It throws the following error “Expected more than 1 value per channel when training, got input size torch.Size([1, 256])”

This is my data generator code:

```
class datagen(Dataset):
def __init__(self, df, tfms):
super(datagen, self).__init__()
self.x = df['x']
self.y = df['y']
self.tfms = tfms
def __len__(self):
return len(self.x)
def __getitem__(self, i):
img = imageio.imread(self.x[i])
stacked_img = np.repeat(img[..., np.newaxis], 3, -1)
x = self.tfms(image = stacked_img)
y = self.y[i]
return (x, y)
train_set = datagen(train_df, ttfms)
train = iter(DataLoader(train_set, batch_size = 64, shuffle = False, drop_last = True, num_workers = 16, pin_memory = True))
val_set = datagen(val_df, vtfms)
val = iter(DataLoader(val_set, batch_size = 64, shuffle = False, drop_last = True, num_workers = 16, pin_memory = True))
```

This is my code for network and training:

```
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv = models.resnet101(pretrained = True)
for param in self.conv.parameters():
param.requires_grad = False
self.fc1 = nn.Sequential(nn.Linear(64000, 2048),
nn.Linear(2048, 1024),
nn.Linear(1024, 256))
self.bn1 = nn.BatchNorm1d(256)
self.fc2 = nn.Sequential(nn.Linear(256, 64),
nn.Linear(64, 16))
self.bn2 = nn.BatchNorm1d(16)
self.out = nn.Linear(16, 1)
def forward(self, x):
x = self.conv(x)
x = x.view(1, 64000)
x = F.relu(self.bn1(self.fc1(x)))
x = self.bn2(self.fc2(x))
x = F.sigmoid(self.out(x))
return x
model = Network()
model = model.to(device)
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr = 0.07)
for e in tqdm(range(10)):
x, y = train.next()
x, y = x['image'].float().to(device), y.float().to(device)
optimizer.zero_grad()
ps = model.forward(x)
train_loss = criterion(ps, y.reshape(-1, 1))
train_loss.backward()
optimizer.step()
```