Is there a way to use the *median* as the reduction operation when computing the loss? I have this piece of code:

```
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
```

I would like to take the median of losses in a batch, instead of the mean, and get gradients based on that.

mxahan
(Zahid Hasan)
#2
In that case, you can write a custom function that does the following pseudocode

```
loss = elementwise_cross_entropy(output, target)
median_val_index = index of loss median value
return loss[median_val_index]
```

Is there more detail on how to create a new loss function using this approach? I cannot find the elementwise_cross_entropy in functional.py.

You can specify `none`

as the reduction method and get the median from the result.

```
y_hat = torch.randn(3, 5, requires_grad=True)
y = torch.empty(3, dtype=torch.long).random_(5)
print(f"Outputs: \n{y_hat}\n")
print(f"Targets: \n{y}\n")
loss = torch.nn.CrossEntropyLoss()
loss_none = torch.nn.CrossEntropyLoss(reduction="none")
l_mean = loss(y_hat, y)
l_none = loss_none(y_hat, y)
print(f"Loss with 'mean' reduction: \n{l_mean}\n")
print(f"Loss with 'none' reduction: \n{l_none}\n")
print(f"Median loss with 'none' reduction: \n{torch.median(l_none)}")
```

```
# Output:
Outputs:
tensor([[ 2.0904, 0.2319, 0.0346, 0.5896, 1.3256],
[ 0.4279, 0.6223, -0.1241, 1.8375, 0.7244],
[-0.4159, 0.0537, -0.2241, 0.4073, 0.6651]], requires_grad=True)
Targets:
tensor([1, 3, 1])
Loss with 'mean' reduction:
1.6558622121810913
Loss with 'none' reduction:
tensor([2.5377, 0.6982, 1.7317], grad_fn=<NllLossBackward0>)
Median loss with 'none' reduction:
1.7316656112670898
```

Hope this helps

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