"add accuracy "

doc/design/evaluator.md

@@ -7,9 +7,9 @@ During training or serving, we provide the evaluation function to measure the mo
 ### Evaluator Design
 Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
 
-1. Initialize the metric state necessary and add it into the block.
+1. Initialize the metric state and add it into the block.
 
-2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.\
+2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
 
 
 3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
@@ -20,38 +20,30 @@ This design is shown in python API. There would be an abstract python interface
 ```python
 class Evaluator(object):
     """
-    Evalutor Base class.
+    Evaluator Base class.
     """
     def __init__(self):
        """
-       create metric states and append to block
-       """ 
-       pass
-
-    def _clear_state(self):
-      """
-      clear metric states at the begin of each pass
-      """
-      pass
+       Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.
+       Auc need four variables, `true_positives`,
+         `true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append the related mini-batch operator to main_program
 
-    def _append_evalutor_op(self):
-      """
-      add mini-batch caculate operators to block
-      add increment operator to accumulate the metric state
+       The initialization of Evaluator should be responsible for:
+       create metric states and append to the main_program
+       add mini-batch evaluator caculate operators to the main_program
+       add increment operator to accumulate the metric states
        """ 
        pass
 
-    def _merge(self):
+    def clear(self):
       """
-      Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
+      clear metric states at the begin of each pass/user specified batch
       """
-      pass
+      return init_program
 
     def evaluate(self):
       """
-      only one exported interface
-      user calculate the result
+      Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
       """
-      pass
-
+      return eval_program
 ```

python/paddle/v2/framework/evaluator.py

-import paddle.v2.framework.op as op
-import numpy as np
+from paddle.v2.framework.framework import Program, g_program, g_init_program
 import paddle.v2.framework.core as core
 
 
 class Evaluator(object):
     """
     Evalutor Base class.
-    """
 
-    def __init__(self):
-        """
-       create metric states and append to block
+    create metric states
+    add mini-batch evaluator caculate operator
+    add increment operator to accumulate the metric states
     """
-        pass
 
-    def _clear_state(self):
-        """
-      clear metric states at the begin of each pass
-      """
-        pass
+    def __init__(self, input=None, **kwargs):
+        if "program" in kwargs:
+            self._program = kwargs.get("program")
+        else:
+            self._program = input.program
+        self._states = []
 
-    def _append_evalutor_op(self):
-        """
-      add mini-batch caculate operators to block
-      add increment operator to accumulate the metric state
-      """
-        pass
+    def _create_tmp_variable(self, name, dtype):
+        return self.program.current_block().create_var(
+            name=unique_name(".".join([self.name, 'tmp'])),
+            dtype=dtype,
+            persistable=False)
 
-    def _merge(self):
+    @staticmethod
+    def clear(self):
         """
-      Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
+      clear metric states at the begin of each pass/user specified batch
+      return a clear 
       """
-        pass
+        raise NotImplementedError()
 
     def evaluate(self):
         """
-      only one exported interface
-      user calculate the result
+      Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
       """
-        pass
+        raise NotImplementedError()
+
+
+class Accuracy(Evaluator):
+    def __init__(self, input, label, k=1, **kwargs):
+        super(Accuracy, self).__init__(input=input, **kwargs)
+        topk_out = helper.create_tmp_variable(dtype=input.data_type)
+        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_dtype = kwargs.get("out_dtype", "float32")
+        acc_out = helper.create_tmp_variable(dtype=acc_out_dtype)
+        helper.append_op(
+            type="accuracy",
+            inputs={
+                "Out": [topk_out],
+                "Indices": [topk_indices],
+                "Label": [label]
+            },
+            outputs={"Accuracy": [acc_out]})

python/paddle/v2/framework/framework.py

@@ -530,7 +530,7 @@ class Parameter(Variable):
                 raise ValueError("Parameter shape should not be related with "
                                  "batch-size")
 
-        Variable.__init__(
+        super(Parameter, self).__init__(
             self, block, persistable=True, shape=shape, dtype=dtype, **kwargs)
         self.trainable = kwargs.get('trainable', True)