diff --git a/python/paddle/nn/functional/norm.py b/python/paddle/nn/functional/norm.py
index 54824233f70762a932c81d4c07814e004affd3cb..e6971b3781c3ba386edef2fcfe05385d5fc0ae47 100644
--- a/python/paddle/nn/functional/norm.py
+++ b/python/paddle/nn/functional/norm.py
@@ -223,24 +223,27 @@ def batch_norm(x,
 
     helper = LayerHelper('batch_norm', **locals())
 
-    dtype = x.dtype if x.dtype is not 'float16' else 'float32'
+    param_dtype = x.dtype if x.dtype is not 'float16' else 'float32'
     saved_mean = helper.create_variable_for_type_inference(
-        dtype=dtype, stop_gradient=True)
+        dtype=param_dtype, stop_gradient=True)
     saved_variance = helper.create_variable_for_type_inference(
-        dtype=dtype, stop_gradient=True)
-    batch_norm_out = helper.create_variable_for_type_inference(dtype)
-    reserve_space = helper.create_variable_for_type_inference(
-        dtype=x.dtype, stop_gradient=True)
+        dtype=param_dtype, stop_gradient=True)
+    batch_norm_out = helper.create_variable_for_type_inference(x.dtype)
 
     outputs = {
         "Y": [batch_norm_out],
         "MeanOut": [running_mean],
         "VarianceOut": [running_var],
         "SavedMean": [saved_mean],
-        "SavedVariance": [saved_variance],
-        "ReserveSpace": [reserve_space]
+        "SavedVariance": [saved_variance]
     }
 
+    if training or trainable_statistics:
+        # reserve_space is only used for training.
+        reserve_space = helper.create_variable_for_type_inference(
+            dtype=x.dtype, stop_gradient=True)
+        outputs["ReserveSpace"] = [reserve_space]
+
     helper.append_op(
         type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs)