Hi guys. I have:

```
result = model(tensor)
score_max_index = result.argmax()
score_max = result[score_max_index]
score_max.backward()
data = tensor.grad.data.abs()
```

Data is

```
tensor([0.4954, 0.6926, 0.2542, 1.0316, 0.3206, 0.0126, 0.5587, 0.0943, 2.8202,
1.4996, 0.6181, 1.6888, 0.4801, 2.1043, 0.5233, 1.4717, 0.5883, 1.8086,
1.2205, 0.5946, 1.1455, 0.4592, 1.6773, 1.2097, 1.0795, 1.1406, 0.8416,
1.0722, 1.1076, 0.1615, 0.9181, 1.2613, 1.4448, 1.4703, 0.4576, 1.1672,
0.5309, 3.9590, 0.5291, 7.3970, 0.4947, 0.4488, 0.8801, 0.1289, 0.3571,
0.1130, 0.9194, 0.4625, 0.2476, 1.4286, 0.3808])
```

My network is:

```
self.linear1 = torch.nn.Linear(_input_dimension, _hidden_dimension)
self.activation = torch.nn.Tanh()
self.dropout = torch.nn.Dropout()
self.linear2 = torch.nn.Linear(_hidden_dimension, _hidden_dimension2)
self.activation2 = torch.nn.Tanh()
self.linear3 = torch.nn.Linear(_hidden_dimension2, _hidden_dimension3)
self.activation3 = torch.nn.Tanh()
self.linear4 = torch.nn.Linear(_hidden_dimension3, _output_dimension)
```

How can I determine which member of tensor.grad.data “belongs” to which node or edge in that network?

Thanks,

Ian