# Implementation of Squared Earth movers distance loss function for ordinal scale

Hi everyone, I recently came across the paper on “Squared earth mover’s distance-based loss for training deep neural networks.” ([1611.05916] Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks). I want to use the squared EMD loss function for an ordinal classification problem .

However, I could not find a single implementation for the same. Though there is a tensorflow implementation which is as follows:

``````def earth_mover_distance(
**kwargs
) -> Callable:
"""
Wrapper for earth_mover distance for unified interface with self-guided earth mover distance loss.
"""
import tensorflow as tf
from tensorflow.keras import backend as K

def _earth_mover_distance(
y_true: K.placeholder,
y_pred: K.placeholder
) -> K.placeholder:
return tf.reduce_mean(tf.square(tf.cumsum(y_true, axis=-1) - tf.cumsum(y_pred, axis=-1)), axis=-1)

return _earth_mover_distance
``````

1 Like

You could try to translate this code by using the `torch` namespace.
E.g this might work (untested):

``````def earth_mover_distance(y_true, y_pred):
``````
1 Like

Hello sir ,i tired the above code but i got the following problem
TypeError: cumsum() received an invalid combination of arguments - got (list, dim=int), but expected one of:

• (Tensor input, int dim, *, torch.dtype dtype, Tensor out)
• (Tensor input, name dim, *, torch.dtype dtype, Tensor out)
As the error message explains: pass a `tensor` to `torch.cumsum` instead of a `list`:
``````torch.cumsum([1., 2.], dim=0)