roywei commented on a change in pull request #12750: [MXNET -1030] Cosine
Embedding Loss
URL: https://github.com/apache/incubator-mxnet/pull/12750#discussion_r224233205
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File path: tests/python/unittest/test_loss.py
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@@ -348,7 +348,35 @@ def test_triplet_loss():
optimizer='adam')
assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.05
-
+@with_seed()
+def test_cosine_loss():
+ #For similarity check
+ label = mx.nd.array([1])
+ pred = mx.nd.array([[1, 1, 1, 1],
+ [1, 2, 3, 4]])
+ target = mx.nd.array([[1, 1, 1, 1],
+ [1, 2, 3, 4]])
+ Loss = gluon.loss.CosineEmbeddingLoss()
+ loss = Loss(pred, target, label)
+
+ #computing numpy way
+ numerator = mx.nd.sum(pred * target, keepdims=True, axis=1)
+ denominator = mx.nd.sqrt(mx.nd.sum(pred**2, axis=1, keepdims=True)) \
+ * mx.nd.sqrt(mx.nd.sum(target**2, axis=1, keepdims=True))
+ assert_almost_equal(loss.asnumpy(), (1-numerator/denominator).asnumpy())
+
+ # Label == -1
Review comment:
Please refer to pytorch unit test:
https://github.com/pytorch/pytorch/blob/master/test/test_nn.py#L4986
Create random input1 input2 and labels, labels can be randomly 1 or -1, so
you can test them at once. No need to repeat with just label = -1 on fixed
inputs.
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