[GitHub] eric-haibin-lin closed pull request #10980: Sampling negative samples other classes only

[GitBox <gi...@apache.org>]

eric-haibin-lin closed pull request #10980: Sampling negative samples other classes only
URL: https://github.com/apache/incubator-mxnet/pull/10980
 
 
   

This is a PR merged from a forked repository.
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diff --git a/example/gluon/embedding_learning/model.py b/example/gluon/embedding_learning/model.py
index 91f7735497d..f82240e2cd5 100644
--- a/example/gluon/embedding_learning/model.py
+++ b/example/gluon/embedding_learning/model.py
@@ -108,6 +108,7 @@ def hybrid_forward(self, F, x):
         mask = np.ones(weights.shape)
         for i in range(0, n, k):
             mask[i:i+k, i:i+k] = 0
+        mask_uniform_probs = mask * (1.0/(n-k))
 
         weights = weights * F.array(mask) * (distance < self.nonzero_loss_cutoff)
         weights_sum = F.sum(weights, axis=1, keepdims=True)
@@ -125,7 +126,7 @@ def hybrid_forward(self, F, x):
                 n_indices += np.random.choice(n, k-1, p=np_weights[i]).tolist()
             else:
                 # all samples are above the cutoff so we sample uniformly
-                n_indices += np.random.choice(n, k-1).tolist()
+                n_indices += np.random.choice(n, k-1, p=mask_uniform_probs[i]).tolist()
             for j in range(block_idx * k, (block_idx + 1) * k):
                 if j != i:
                     a_indices.append(i)


 

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