def test_config(self):
k_obj = metrics.KullbackLeiblerDivergence(name='kld', dtype=dtypes.int32)
self.assertEqual(k_obj.name, 'kld')
self.assertEqual(k_obj._dtype, dtypes.int32)
k_obj2 = metrics.KullbackLeiblerDivergence.from_config(k_obj.get_config())
self.assertEqual(k_obj2.name, 'kld')
self.assertEqual(k_obj2._dtype, dtypes.int32)
def test_unweighted(self):
self.setup()
k_obj = metrics.KullbackLeiblerDivergence()
self.evaluate(variables.variables_initializer(k_obj.variables))
update_op = k_obj.update_state(self.y_true, self.y_pred)
self.evaluate(update_op)
result = k_obj.result()
expected_result = np.sum(self.expected_results) / self.batch_size
self.assertAllClose(result, expected_result, atol=1e-3)
def test_weighted(self):
self.setup()
k_obj = metrics.KullbackLeiblerDivergence()
self.evaluate(variables.variables_initializer(k_obj.variables))
sample_weight = constant_op.constant([1.2, 3.4], shape=(2, 1))
result = k_obj(self.y_true, self.y_pred, sample_weight=sample_weight)
sample_weight = np.asarray([1.2, 1.2, 1.2, 3.4, 3.4, 3.4]).reshape((2, 3))
expected_result = np.multiply(self.expected_results, sample_weight)
expected_result = np.sum(expected_result) / (1.2 + 3.4)
self.assertAllClose(self.evaluate(result), expected_result, atol=1e-3)