Polish several API

paddle/fluid/operators/activation_op.cc

@@ -271,7 +271,8 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "Input of HardShrink operator");
     AddOutput("Out", "Output of HardShrink operator");
-    AddAttr<float>("threshold", "The value of threshold for HardShrink")
+    AddAttr<float>("threshold",
+                   "The value of threshold for HardShrink. [default: 0.5]")
         .SetDefault(0.5f);
     AddComment(R"DOC(
 HardShrink Activation Operator.

python/paddle/fluid/layers/detection.py

@@ -403,25 +403,6 @@ def ssd_loss(location,
 
       5.3 Compute the overall weighted loss.
 
-    >>> import paddle.fluid.layers as layers
-    >>> pb = layers.data(
-    >>>            name='prior_box',
-    >>>            shape=[10, 4],
-    >>>            append_batch_size=False,
-    >>>            dtype='float32')
-    >>> pbv = layers.data(
-    >>>            name='prior_box_var',
-    >>>            shape=[10, 4],
-    >>>            append_batch_size=False,
-    >>>            dtype='float32')
-    >>> loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
-    >>> scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
-    >>> gt_box = layers.data(
-    >>>         name='gt_box', shape=[4], lod_level=1, dtype='float32')
-    >>> gt_label = layers.data(
-    >>>         name='gt_label', shape=[1], lod_level=1, dtype='float32')
-    >>>     loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
-
     Args:
         location (Variable): The location predictions are a 3D Tensor with
             shape [N, Np, 4], N is the batch size, Np is total number of
@@ -465,6 +446,25 @@ def ssd_loss(location,
     Raises:
         ValueError: If mining_type is 'hard_example', now only support mining \
         type of `max_negative`.
+
+    Examples:
+        >>> pb = fluid.layers.data(
+        >>>                   name='prior_box',
+        >>>                   shape=[10, 4],
+        >>>                   append_batch_size=False,
+        >>>                   dtype='float32')
+        >>> pbv = fluid.layers.data(
+        >>>                   name='prior_box_var',
+        >>>                   shape=[10, 4],
+        >>>                   append_batch_size=False,
+        >>>                   dtype='float32')
+        >>> loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
+        >>> scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
+        >>> gt_box = fluid.layers.data(
+        >>>         name='gt_box', shape=[4], lod_level=1, dtype='float32')
+        >>> gt_label = fluid.layers.data(
+        >>>         name='gt_label', shape=[1], lod_level=1, dtype='float32')
+        >>> loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
     """
 
     helper = LayerHelper('ssd_loss', **locals())

python/paddle/fluid/layers/ops.py

@@ -40,7 +40,6 @@ __activations__ = [
     'relu6',
     'pow',
     'stanh',
-    'hard_shrink',
     'thresholded_relu',
     'hard_sigmoid',
     'swish',
@@ -92,9 +91,32 @@ def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
             kwargs[name] = val
     return _uniform_random_(**kwargs)
 
-uniform_random.__doc__ = _uniform_random_.__doc__  + "\n"\
-+"""
+
+uniform_random.__doc__ = _uniform_random_.__doc__ + "\n" \
+                         + """
 Examples:
 
     >>> result = fluid.layers.uniform_random(shape=[32, 784])
 """
+
+__all__ += ['hard_shrink']
+
+_hard_shrink_ = generate_layer_fn('hard_shrink')
+
+
+def hard_shrink(x, threshold=None):
+    kwargs = dict()
+    for name in locals():
+        val = locals()[name]
+        if val is not None:
+            kwargs[name] = val
+    return _hard_shrink_(**kwargs)
+
+
+hard_shrink.__doc__ = _hard_shrink_.__doc__ + "\n" \
+                      + """
+Examples:
+
+    >>> data = fluid.layers.data(name="input", shape=[784])
+    >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
+"""