[GitHub] sandeep-krishnamurthy commented on a change in pull request #12697: [MXNET -1004] Poisson NegativeLog Likelihood loss

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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
 
 

 ##########
 File path: tests/python/unittest/test_loss.py
 ##########
 @@ -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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