diff --git a/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py b/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
index ec10b634091fc521062457b780b0c4cafcbacec0..ae1883f1f7e44e06e378ff6d16dbc3c5060027e4 100644
--- a/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
+++ b/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
@@ -149,98 +149,5 @@ class TestSigmoidCrossEntropyWithNorm(OpTest):
         self.check_grad(['X'], 'Out')
 
 
-class TestSigmoidCrossEntropyWithLogitsOp5(OpTest):
-    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label
-    """
-
-    def setUp(self):
-        self.op_type = "sigmoid_cross_entropy_with_logits"
-        batch_size = [10, 10]
-        num_classes = 20
-        self.inputs = {
-            'X': logit(
-                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
-                .astype("float32")),
-            'Label': np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
-            .astype("float32")
-        }
-
-        # Fw Pass is implemented as elementwise sigmoid followed by
-        # elementwise logistic loss
-        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
-        sigmoid_X = expit(self.inputs['X'])
-        term1 = self.inputs['Label'] * np.log(sigmoid_X)
-        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
-        self.outputs = {'Out': -term1 - term2}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
-class TestSigmoidCrossEntropyWithNorm2(OpTest):
-    def setUp(self):
-        self.op_type = "sigmoid_cross_entropy_with_logits"
-        batch_size = [10, 10]
-        num_classes = 20
-        ignore_index = -1
-        self.inputs = {
-            'X': logit(
-                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
-                .astype("float32")),
-            'Label': np.random.randint(-1, 2, tuple(batch_size + [num_classes]))
-            .astype("float32")
-        }
-        self.attrs = {'ignore_index': ignore_index, 'normalize': True}
-        sigmoid_X = expit(self.inputs['X'])
-        term1 = self.inputs['Label'] * np.log(sigmoid_X)
-        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
-        out = -term1 - term2
-        out[np.where(self.inputs['Label'] == ignore_index)] = 0
-        if self.attrs['normalize']:
-            out = out / float(
-                np.where(self.inputs['Label'] != ignore_index)[0].size)
-        self.outputs = {'Out': out}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
-class TestSigmoidCrossEntropyWithLogitsOp6(OpTest):
-    """Test sigmoid_cross_entropy_with_logit_op with binary label
-    """
-
-    def setUp(self):
-        self.op_type = "sigmoid_cross_entropy_with_logits"
-        batch_size = [10, 10]
-        num_classes = 20
-        self.inputs = {
-            'X': logit(
-                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
-                .astype("float32")),
-            'Label': np.random.randint(0, 2, tuple(batch_size + [num_classes]))
-            .astype("float32")
-        }
-
-        # Fw Pass is implemented as elementwise sigmoid followed by
-        # elementwise logistic loss
-        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
-        sigmoid_X = expit(self.inputs['X'])
-        term1 = self.inputs['Label'] * np.log(sigmoid_X)
-        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
-        self.outputs = {'Out': -term1 - term2}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
 if __name__ == '__main__':
     unittest.main()