Polish example

Author: reyoung

paddle/fluid/operators/row_conv_op.cc

@@ -114,7 +114,7 @@ and a filter ($W$) of size $context \times d$,
 the output sequence is convolved as:
 
 $$
-out_{i, :} = \sum_{j=i}^{i + context} in_{j,:} \dot W_{i-j, :}
+out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
 $$
 
 In the above equation:

paddle/fluid/operators/uniform_random_op.cc

@@ -88,8 +88,6 @@ class UniformRandomOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddOutput("Out", "The output tensor of uniform random op");
     AddComment(R"DOC(
-Uniform random operator.
-
 This operator initializes a tensor with random values sampled from a
 uniform distribution. The random result is in set [min, max].
 

python/paddle/fluid/layers/nn.py

@@ -1718,10 +1718,14 @@ def layer_norm(input,
 
         h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
 
-    >>> import paddle.fluid as fluid
-    >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
-    >>>                          dtype='float32')
-    >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
+    * :math:`a`: the vector representation of the summed inputs to the neurons
+    in that layer.
+
+    * :math:`H`: the number of hidden units in a layers
+
+    * :math:`g`: the trainable scale parameter.
+
+    * :math:`b`: the trainable bias parameter.
 
     Args:
         input(Variable): The input tensor variable.
@@ -1742,6 +1746,12 @@ def layer_norm(input,
 
     Returns:
         ${y_comment}
+
+    Examples:
+
+        >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
+        >>>                          dtype='float32')
+        >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
     """
     helper = LayerHelper('layer_norm', **locals())
     dtype = helper.input_dtype()
@@ -3262,12 +3272,6 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
     """
     ${comment}
 
-    >>> import paddle.fluid as fluid
-    >>> x = fluid.layers.data(name='x', shape=[16],
-    >>>                        dtype='float32', lod_level=1)
-    >>> out = fluid.layers.row_conv(input=x, future_context_size=2)
-
-
     Args:
         input (${x_type}): ${x_comment}.
         future_context_size (int): Future context size. Please note, the shape
@@ -3278,6 +3282,12 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
 
     Returns:
         ${out_comment}.
+
+    Examples:
+        >>> import paddle.fluid as fluid
+        >>> x = fluid.layers.data(name='x', shape=[16],
+        >>>                        dtype='float32', lod_level=1)
+        >>> out = fluid.layers.row_conv(input=x, future_context_size=2)
     """
     helper = LayerHelper('row_conv', **locals())
     dtype = helper.input_dtype()

python/paddle/fluid/layers/ops.py

@@ -64,7 +64,6 @@
     'logical_or',
     'logical_xor',
     'logical_not',
-    'uniform_random',
     'uniform_random_batch_size_like',
     'gaussian_random',
     'gaussian_random_batch_size_like',
@@ -79,3 +78,23 @@
 
 for _OP in set(__all__):
     globals()[_OP] = generate_layer_fn(_OP)
+
+__all__ += ["uniform_random"]
+
+_uniform_random_ = generate_layer_fn('uniform_random')
+
+
+def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
+    kwargs = dict()
+    for name in locals():
+        val = locals()[name]
+        if val is not None:
+            kwargs[name] = val
+    return _uniform_random_(**kwargs)
+
+uniform_random.__doc__ = _uniform_random_.__doc__  + "\n"\
++"""
+Examples:
+
+    >>> result = fluid.layers.uniform_random(shape=[32, 784])
+"""

python/paddle/fluid/layers/tensor.py

@@ -6,7 +6,7 @@
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
+# Unlessf 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
@@ -57,12 +57,6 @@ def create_parameter(shape,
     NOTE: this is a very low-level API. This API is useful when you create
     operator by your self. instead of using layers.
 
-    >>> import paddle.fluid as fluid
-    >>> W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
-    >>> data = fluid.layers.data(name="img", shape=[64, 784],
-    >>>           append_batch_size=False)
-    >>> hidden = fluid.layers.matmul(x=data, y=W)
-
     Args:
         shape(list[int]): shape of the parameter
         dtype(string): element type of the parameter
@@ -74,7 +68,12 @@ def create_parameter(shape,
         default_initializer(Initializer): initializer for the parameter
 
     Returns:
-        the created parameter
+        the created parameter.
+
+    Examples:
+        >>> W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
+        >>> data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False)
+        >>> hidden = fluid.layers.matmul(x=data, y=W)
     """
     helper = LayerHelper("create_parameter", **locals())
     if attr is None: