Fix error in unit test of ModifiedHuberLossOp

python/paddle/v2/framework/tests/test_modified_huber_loss_op.py

@@ -5,22 +5,31 @@
 
 def modified_huber_loss_forward(val):
     if val < -1:
-        return -4 * val
+        return -4. * val
     elif val < 1:
-        return (1 - val) * (1 - val)
+        return (1. - val) * (1. - val)
     else:
-        return 0
+        return 0.
 
 
 class TestModifiedHuberLossOp(OpTest):
     def setUp(self):
         self.op_type = 'modified_huber_loss'
         samples_num = 32
-        self.inputs = {
-            'X': np.random.uniform(-1, 1., (samples_num, 1)).astype('float32'),
-            'Y': np.random.choice([0, 1], samples_num).reshape((samples_num, 1))
-        }
-        product_res = self.inputs['X'] * (2 * self.inputs['Y'] - 1)
+
+        x_np = np.random.uniform(-2., 2., (samples_num, 1)).astype('float32')
+        y_np = np.random.choice([0, 1], samples_num).reshape(
+            (samples_num, 1)).astype('float32')
+        product_res = x_np * (2. * y_np - 1.)
+        # keep away from the junction of piecewise function
+        for pos, val in np.ndenumerate(product_res):
+            while abs(val - 1.) < 0.05:
+                x_np[pos] = np.random.uniform(-2., 2.)
+                y_np[pos] = np.random.choice([0, 1])
+                product_res[pos] = x_np[pos] * (2 * y_np[pos] - 1)
+                val = product_res[pos]
+
+        self.inputs = {'X': x_np, 'Y': y_np}
         loss = np.vectorize(modified_huber_loss_forward)(product_res)
 
         self.outputs = {
@@ -32,7 +41,7 @@ def test_check_output(self):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out', max_relative_error=0.005)
+        self.check_grad(['X'], 'Out', max_relative_error=0.01)
 
 
 if __name__ == '__main__':