Steps: 1
Output tensor([[-0.6720, -0.7148],
        [-0.6492, -0.7391],
        [-0.8532, -0.5552],
        [-0.5556, -0.8527],
        [-0.7415, -0.6470],
        [-0.5553, -0.8531],
        [-0.6171, -0.7755],
        [-0.7120, -0.6746],
        [-0.7319, -0.6558],
        [-0.8165, -0.5834]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
Loss: tensor(0.6867, device='cuda:0', grad_fn=<NllLossBackward>)
Running loss: 0.6866830587387085
Epoch: 1/5..  Training Loss: 0.687.. 
Steps: 2
Output tensor([[-0.6337, -0.7563],
        [-0.5863, -0.8128],
        [-0.6071, -0.7873],
        [-0.4860, -0.9547],
        [-0.5577, -0.8499],
        [-0.5574, -0.8503],
        [-0.4319, -1.0478],
        [-0.5193, -0.9036],
        [-0.4108, -1.0880],
        [-0.4097, -1.0902]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
Loss: tensor(0.5200, device='cuda:0', grad_fn=<NllLossBackward>)
Running loss: 0.5199903845787048
Epoch: 1/5..  Training Loss: 0.520.. 
Steps: 3
Output tensor([[-0.3847, -1.1416],
        [-0.5190, -0.9042],
        [-0.2826, -1.4018],
        [-0.4019, -1.1059],
        [-0.4460, -1.0222],
        [-0.2860, -1.3915],
        [-0.3044, -1.3378],
        [-0.3103, -1.3215],
        [-0.3084, -1.3265],
        [-0.4343, -1.0432]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
Loss: tensor(0.3677, device='cuda:0', grad_fn=<NllLossBackward>)
Running loss: 0.3677431643009186
Epoch: 1/5..  Training Loss: 0.368.. 
Steps: 4
Output tensor([[-0.1690, -1.8613],
        [-0.3174, -1.3020],
        [-0.2780, -1.4160],
        [-0.3510, -1.2173],
        [-0.3469, -1.2272],
        [-0.2468, -1.5200],
        [-0.3050, -1.3361],
        [-0.3087, -1.3258],
        [-0.2869, -1.3886],
        [-0.2056, -1.6830]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
Loss: tensor(0.2815, device='cuda:0', grad_fn=<NllLossBackward>)
Running loss: 0.2815292477607727
Epoch: 1/5..  Training Loss: 0.282.. 

Sample output without the validation function included.