diff --git a/python/paddle/fluid/contrib/mixed_precision/bf16/amp_utils.py b/python/paddle/fluid/contrib/mixed_precision/bf16/amp_utils.py
index 4551947e0fad2..0fb86593b2d62 100644
--- a/python/paddle/fluid/contrib/mixed_precision/bf16/amp_utils.py
+++ b/python/paddle/fluid/contrib/mixed_precision/bf16/amp_utils.py
@@ -235,7 +235,7 @@ def bf16_guard():
 def are_post_ops_bf16(post_ops, keep_fp32_ops):
     for post_op in post_ops:
         for op in post_op:
-            if op.type in keep_fp32_ops:
+            if op in keep_fp32_ops:
                 return False
     return True
 
diff --git a/python/paddle/fluid/tests/book/test_fit_a_line.py b/python/paddle/fluid/tests/book/test_fit_a_line.py
index 65542e2096cc2..a533d1b40cf8d 100644
--- a/python/paddle/fluid/tests/book/test_fit_a_line.py
+++ b/python/paddle/fluid/tests/book/test_fit_a_line.py
@@ -48,7 +48,8 @@ def train(use_cuda, save_dirname, is_local, use_bf16, pure_bf16):
         cost = fluid.layers.square_error_cost(input=y_predict, label=y)
         avg_cost = fluid.layers.mean(cost)
 
-    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    lr = 5e-3 if use_bf16 else 1e-3
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=lr)
 
     if use_bf16:
         sgd_optimizer = amp.bf16.decorate_bf16(
@@ -83,7 +84,7 @@ def train_loop(main_program):
                 avg_loss_value, = exe.run(main_program,
                                           feed=feeder.feed(data),
                                           fetch_list=[avg_cost])
-                if avg_loss_value[0] < 10.0 or pure_bf16:
+                if avg_loss_value[0] < 10.0:
                     if save_dirname is not None:
                         paddle.static.save_inference_model(save_dirname, [x],
                                                            [y_predict], exe)