sandeep-krishnamurthy commented on a change in pull request #12697: [MXNET
-1004] Poisson NegativeLog Likelihood loss
URL: https://github.com/apache/incubator-mxnet/pull/12697#discussion_r224191528
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File path: tests/python/unittest/test_loss.py
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@@ -348,6 +348,44 @@ def test_triplet_loss():
optimizer='adam')
assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.05
+@with_seed()
+def test_poisson_nllloss():
+ pred = mx.nd.random.normal(shape=(3, 4))
+ min_pred = mx.nd.min(pred)
+ #This is necessary to ensure only positive random values are generated for
prediction,
+ # to avoid ivalid log calculation
+ pred[:] = pred + mx.nd.abs(min_pred)
+ target = mx.nd.random.normal(shape=(3, 4))
+ min_target = mx.nd.min(target)
+ #This is necessary to ensure only positive random values are generated for
prediction,
+ # to avoid ivalid log calculation
+ target[:] += mx.nd.abs(min_target)
+
+ Loss = gluon.loss.PoissonNLLLoss(from_logits=True)
+ Loss_no_logits = gluon.loss.PoissonNLLLoss(from_logits=False)
+ #Calculating by brute formula for default value of from_logits = True
+
+ # 1) Testing for flag logits = True
+ brute_loss = np.mean(np.exp(pred.asnumpy()) - target.asnumpy() *
pred.asnumpy())
+ loss_withlogits = Loss(pred, target)
+ assert_almost_equal(brute_loss, loss_withlogits.asscalar())
+
+ #2) Testing for flag logits = False
+ loss_no_logits = Loss_no_logits(pred, target)
+ np_loss_no_logits = np.mean(pred.asnumpy() - target.asnumpy() *
np.log(pred.asnumpy() + 1e-08))
+ if np.isnan(loss_no_logits.asscalar()):
+ assert_almost_equal(np.isnan(np_loss_no_logits),
np.isnan(loss_no_logits.asscalar()))
+ else:
+ assert_almost_equal(np_loss_no_logits, loss_no_logits.asscalar())
+
+ #3) Testing for Sterling approximation
+ np_pred = np.random.uniform(1, 5, (2, 3))
Review comment:
Please add tests for hybridized version as well.
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