Linear Regression with PyTorch

criterion = torch.nn.MSELoss() 
optimizer = torch.optim.SGD(model.parameters(), lr=learningRate)

for epoch in range(epochs):
    # Converting inputs and labels to Variable
    if torch.cuda.is_available():
        inputs = Variable(torch.from_numpy(x_train).cuda())
        labels = Variable(torch.from_numpy(y_train).cuda())
    else:
        inputs = Variable(torch.from_numpy(x_train))
        labels = Variable(torch.from_numpy(y_train))

    # Clear gradient buffers 
    optimizer.zero_grad()

    # get output from the model, given the inputs
    outputs = model(inputs)

    # get loss for the predicted output
    loss = criterion(outputs, labels)
    print(loss)
    # get gradients w.r.t to parameters
    loss.backward()

    # update parameters
    optimizer.step()

    print('epoch {}, loss {}'.format(epoch, loss.item()))