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Using NumPy APIs to improve performance and the code quality,  #325

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Using NumPy APIs to improve performance and the code quality,  #325

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15 changes: 7 additions & 8 deletions tensorflow_ranking/python/losses_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,8 @@
from __future__ import print_function

import math

import numpy as np
import tensorflow as tf

from tensorflow_ranking.python import losses as ranking_losses
Expand Down Expand Up @@ -151,14 +153,11 @@ def _loss(si, sj, label_diff, delta):

def _batch_aggregation(batch_loss_list, reduction=None):
"""Returns the aggregated loss."""
loss_sum = 0.
weight_sum = 0.
for loss, weight, count in batch_loss_list:
loss_sum += loss
if reduction == 'mean':
weight_sum += weight
else:
weight_sum += count
loss_sum = np.sum([loss for loss, weight, count in batch_loss_list])
if reduction == 'mean':
weight_sum = np.sum([weight for loss, weight, count in batch_loss_list])
else:
weight_sum = np.sum([count for loss, weight, count in batch_loss_list])
return loss_sum / weight_sum


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