Fix unit tests of softmax_cross_entropy for soft target

chainer · Oct 9, 2019 · 620b55d · 620b55d
1 parent f847214
commit 620b55d
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Showing 2 changed files with 29 additions and 4 deletions.
diff --git a/chainer/functions/loss/softmax_cross_entropy.py b/chainer/functions/loss/softmax_cross_entropy.py
@@ -447,8 +447,7 @@ def _double_backward_softmax_cross_entropy(x, t, normalize, class_weight,
 def softmax_cross_entropy(
         x, t, normalize=True, cache_score=True, class_weight=None,
         ignore_label=-1, reduce='mean', enable_double_backprop=False,
-        soft_target_loss='kl-divergence'):
-        # soft_target_loss='cross-entropy'):
+        soft_target_loss='cross-entropy'):
     """Computes cross entropy loss for pre-softmax activations.
 
     Args:

diff --git a/tests/chainer_tests/functions_tests/loss_tests/test_softmax_cross_entropy.py b/tests/chainer_tests/functions_tests/loss_tests/test_softmax_cross_entropy.py
@@ -616,9 +616,9 @@ def check_forward(self, xp):
     'shape': [(3,), (3, 2), (3, 2, 2)],
     'dtype': [numpy.float16, numpy.float32, numpy.float64],
     'reduce': ['mean', 'no'],
-    'soft_target_loss': ['cross-entropy', 'kl-divergence'],
+    'soft_target_loss': ['kl-divergence'],
 })))
-class TestSoftTargetExpectNearZero(BaseSoftTarget, unittest.TestCase):
+class TestSoftTargetKLDivergence(BaseSoftTarget, unittest.TestCase):
 
     def setUp(self):
         BaseSoftTarget.setUp(self)
@@ -637,4 +637,30 @@ def check_forward(self, xp):
                                 **self.check_forward_options)
 
 
+@testing.parameterize(*(testing.product({
+    'nb': [1, 2, 4],
+    'shape': [(3,), (3, 2), (3, 2, 2)],
+    'dtype': [numpy.float16, numpy.float32, numpy.float64],
+    'reduce': ['mean', 'no'],
+    'soft_target_loss': ['cross-entropy'],
+})))
+class TestSoftTargetCrossEntropy(BaseSoftTarget, unittest.TestCase):
+
+    def setUp(self):
+        BaseSoftTarget.setUp(self)
+        self.t = functions.softmax(self.x).array
+        self.expect = numpy.sum(-self.t * functions.log_softmax(self.x).array,
+                                axis=1)
+        if self.reduce == 'mean':
+            self.expect = numpy.average(self.expect)
+
+    def check_forward(self, xp):
+        x = xp.asarray(self.x)
+        t = xp.asarray(self.t)
+        loss = functions.softmax_cross_entropy(
+            x, t, reduce=self.reduce, soft_target_loss=self.soft_target_loss)
+        testing.assert_allclose(loss.data, self.expect,
+                                **self.check_forward_options)
+
+
 testing.run_module(__name__, __file__)