"highlight code"

source/user_guides/howto/evaluation/metrics.rst

@@ -21,20 +21,20 @@ Fluid中包含了常用分类指标，例如Precision, Recall, Accuracy等,更
 
 .. code-block:: python
 
-    labels = fluid.layers.data(name="data", shape=[1], dtype="int32")
-    data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32")
-    pred = fluid.layers.fc(input=data, size=1000, act="tanh")
-    comp = fluid.metrics.CompositeMetric()
-    acc = fluid.metrics.Precision()
-    recall = fluid.metrics.Recall()
-    comp.add_metric(acc)
-    comp.add_metric(recall)
-    for pass in range(PASSES):
-      comp.reset()
-      for data in train_reader():
-          loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
-      comp.update(preds=preds, labels=labels)
-      numpy_acc, numpy_recall = comp.eval()
+   >>> labels = fluid.layers.data(name="data", shape=[1], dtype="int32")
+   >>> data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32")
+   >>> pred = fluid.layers.fc(input=data, size=1000, act="tanh")
+   >>> comp = fluid.metrics.CompositeMetric()
+   >>> acc = fluid.metrics.Precision()
+   >>> recall = fluid.metrics.Recall()
+   >>> comp.add_metric(acc)
+   >>> comp.add_metric(recall)
+   >>> for pass in range(PASSES):
+   >>>   comp.reset()
+   >>>   for data in train_reader():
+   >>>       loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
+   >>>   comp.update(preds=preds, labels=labels)
+   >>>   numpy_acc, numpy_recall = comp.eval()
       
 
 其他任务例如MultiTask Learning，Metric Learning，Learning To Rank各种指标构造方法请参考API文档。
@@ -46,20 +46,20 @@ Fluid支持自定义指标，灵活支持各类计算任务。下文通过一个
 
 .. code-block:: python
 
-  class MyMetric(MetricBase):
-      def __init__(self, name=None):
-          super(MyMetric, self).__init__(name)
-          self.counter = 0  # simple counter
+   >>> class MyMetric(MetricBase):
+   >>>     def __init__(self, name=None):
+   >>>         super(MyMetric, self).__init__(name)
+   >>>         self.counter = 0  # simple counter
 
-      def reset(self):
-          self.counter = 0
+   >>>     def reset(self):
+   >>>         self.counter = 0
 
-      def update(self, preds, labels):
-          if not _is_numpy_(preds):
-              raise ValueError("The 'preds' must be a numpy ndarray.")
-          if not _is_numpy_(labels):
-              raise ValueError("The 'labels' must be a numpy ndarray.")
-          self.counter += sum(preds == labels)
+   >>>     def update(self, preds, labels):
+   >>>         if not _is_numpy_(preds):
+   >>>             raise ValueError("The 'preds' must be a numpy ndarray.")
+   >>>         if not _is_numpy_(labels):
+   >>>             raise ValueError("The 'labels' must be a numpy ndarray.")
+   >>>         self.counter += sum(preds == labels)
 
-      def eval(self):
-          return self.counter
+   >>>     def eval(self):
+   >>>         return self.counter