move layer accuracy to metric.py

python/paddle/v2/fluid/layers/__init__.py

@@ -28,6 +28,8 @@ import math_op_patch
 from math_op_patch import *
 import detection
 from detection import *
+import metric
+from metric import *
 
 __all__ = []
 __all__ += math_op_patch.__all__
@@ -38,3 +40,4 @@ __all__ += control_flow.__all__
 __all__ += ops.__all__
 __all__ += device.__all__
 __all__ += detection.__all__
+__all__ += metric.__all__

python/paddle/v2/fluid/layers/metric.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+All layers just related to metric.
+"""
+
+from ..layer_helper import LayerHelper
+from ..initializer import Normal, Constant
+from ..framework import Variable
+from ..param_attr import ParamAttr
+
+__all__ = ['accuracy']
+
+
+def accuracy(input, label, k=1, correct=None, total=None):
+    """
+    This function computes the accuracy using the input and label.
+    The output is the top_k inputs and their indices.
+    """
+    helper = LayerHelper("accuracy", **locals())
+    topk_out = helper.create_tmp_variable(dtype=input.dtype)
+    topk_indices = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="top_k",
+        inputs={"X": [input]},
+        outputs={"Out": [topk_out],
+                 "Indices": [topk_indices]},
+        attrs={"k": k})
+    acc_out = helper.create_tmp_variable(dtype="float32")
+    if correct is None:
+        correct = helper.create_tmp_variable(dtype="int64")
+    if total is None:
+        total = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="accuracy",
+        inputs={
+            "Out": [topk_out],
+            "Indices": [topk_indices],
+            "Label": [label]
+        },
+        outputs={
+            "Accuracy": [acc_out],
+            "Correct": [correct],
+            "Total": [total],
+        })
+    return acc_out

python/paddle/v2/fluid/layers/nn.py

@@ -34,7 +34,6 @@ __all__ = [
     'cos_sim',
     'cross_entropy',
     'square_error_cost',
-    'accuracy',
     'chunk_eval',
     'sequence_conv',
     'conv2d',
@@ -1020,40 +1019,6 @@ def square_error_cost(input, label):
     return square_out
 
 
-def accuracy(input, label, k=1, correct=None, total=None):
-    """
-    This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
-    """
-    helper = LayerHelper("accuracy", **locals())
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
-    topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": k})
-    acc_out = helper.create_tmp_variable(dtype="float32")
-    if correct is None:
-        correct = helper.create_tmp_variable(dtype="int64")
-    if total is None:
-        total = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="accuracy",
-        inputs={
-            "Out": [topk_out],
-            "Indices": [topk_indices],
-            "Label": [label]
-        },
-        outputs={
-            "Accuracy": [acc_out],
-            "Correct": [correct],
-            "Total": [total],
-        })
-    return acc_out
-
-
 def chunk_eval(input,
                label,
                chunk_scheme,