I’m trying to control the steering of a car by getting an output between -1 ,1. Currently, I trained my network with this model.

```
import torch.nn as nn
import torch.nn.functional as F
# define the CNN architecture
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# convolutional layer (sees 32x32x3 image tensor)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
# convolutional layer (sees 16x16x16 tensor)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(32 * 4 * 4, 256)
self.fc2 = nn.Linear(256, 1)
self.dropout = nn.Dropout(0.25)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
# flatten image input
x = x.view(-1, 32 * 4 * 4)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
# create a complete CNN
model = Net()
print(model)
# move tensors to GPU if CUDA is available
if train_on_gpu:
model.cuda()
```

Also you can see the jupyter notebook here The problem is i’m not sure what to use to get the output in real time such as np.argmax, np.max, ect…

```
def autopilot(self):
img = self.preprocess(self.cam.value)
count = self.cam.count
if count!= self.temp:
print('RUN!')
self.model.eval()
with torch.no_grad():
output = self.model(img)
_, angle_tensor = torch.max(output,1)
self.angle_out = angle_tensor.cpu().data.numpy()
#self.angle_out = np.argmax(output.cpu().data.numpy())#angle[0].cpu().numpy()
self.temp = count
print(self.angle_out)
else:
pass
```

I’m new to pytorch and i’m stuck trying to get the correct output like I did in jupyter notebook