Hi, i’m trying to create a linear regression neural network. It’s my first time using pytorch, and i’m usinge multiple inputs. However i keep stubling into a problem where my target size is different to input size at the criterion function. My output has the size of [1] and the target one [], which is where i got stuck, as i don’t understand how it can be that size - it contains a number (i’m using this dataset https://www.kaggle.com/uciml/pima-indians-diabetes-database ). I tried browsing for this problem but nothing really helped me. Sorry if this is a stupid question or i’m doing something wrong, i never worked with pytorch before.

Here is the code:

```
pdTrain = pd.read_csv("train.csv", header=None)
pdTest = pd.read_csv("test.csv", header=None)
tmpTrain = pdTrain.values
tmpTest = pdTest.values
trainDataset = torch.from_numpy(tmpTrain).float()
testDataset = torch.from_numpy(tmpTest).float()
batch_size = 100
n_iters = 30
epochs = n_iters / (len(trainDataset) / batch_size)
epochs = int(epochs)
train_loader = torch.utils.data.DataLoader(dataset=trainDataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=testDataset,
batch_size=batch_size,
shuffle=False)
class LinearRegression(nn.Module):
def __init__(self,input_size,output_size):
super(LinearRegression,self).__init__()
self.linear = nn.Linear(input_size,output_size)
def forward(self,x):
out = self.linear(x)
return out
input_size = 8
output_size = 1
learning_rate = 0.002
model = LinearRegression(input_size,output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)
for epoch in range(epochs):
epoch += 1
for i in enumerate(train_loader):
for j in i[1]:
inputs = j[0:8]
result = j[8]
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, result) #problem
loss.backward()
optimizer.step()
print('epoch {}, loss {}'.format(epoch, loss))
```