Polish documents

* less_than
* cumsum
* multiplex
* open_recordio_file

paddle/fluid/operators/compare_op.cc

@@ -23,25 +23,22 @@ class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     OpComment comment;
-    AddInput("X",
-             string::Sprintf("(LoDTensor) the left hand operand of %s operator",
+    AddInput("X", string::Sprintf("the left hand operand of %s operator",
                                   comment.type));
-    AddInput("Y", string::Sprintf(
-                      "(LoDTensor) the right hand operand of %s operator",
+    AddInput("Y", string::Sprintf("the right hand operand of %s operator",
                                   comment.type));
     AddAttr<bool>("force_cpu",
-                  "(bool, default false) Force fill output variable to cpu "
+                  "Force fill output variable to cpu "
                   "memory. Otherwise, fill output variable to the running "
-                  "device")
-        .SetDefault(false);
-    AddOutput("Out", string::Sprintf(
-                         "(LoDTensor) n-dim bool tensor. Each element is %s",
+                  "device [default true].")
+        .SetDefault(true);
+    AddOutput("Out", string::Sprintf("n-dim bool tensor. Each element is %s",
                                      comment.equation));
     AddComment(string::Sprintf(R"DOC(%s Operator
 
 It operates element-wise on X and Y, and returns the Out. Each of them is a
 N-dim tensor. X and Y could be any type.  The each element of the Out tensor is
-calculated by %s
+calculated by $%s$
 )DOC",
                                comment.type, comment.equation));
     AddAttr<int>("axis",

paddle/fluid/operators/cumsum_op.cc

@@ -33,16 +33,16 @@ class CumsumOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X", "Input of Cumsum operator");
     AddOutput("Out", "Output of Cumsum operator");
     AddAttr<int>("axis",
-                 "(int, default -1). The dimenstion to accumulate along. "
-                 "-1 means the last dimenstion")
+                 "The dimenstion to accumulate along. -1 means the last "
+                 "dimenstion [default -1].")
         .SetDefault(-1)
         .EqualGreaterThan(-1);
     AddAttr<bool>("exclusive",
-                  "bool, default false). Whether to perform exclusive cumsum")
+                  "Whether to perform exclusive cumsum. [default false].")
         .SetDefault(false);
     AddAttr<bool>("reverse",
-                  "bool, default false). If true, the cumsum is performed in "
-                  "the reversed direction")
+                  "If true, the cumsum is performed in the reversed direction. "
+                  "[default false].")
         .SetDefault(false);
     AddComment(R"DOC(
 The cumulative sum of the elements along a given axis.

paddle/fluid/operators/multiplex_op.cc

@@ -62,26 +62,44 @@ class MultiplexOp : public framework::OperatorWithKernel {
 class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("Ids", "The index tensor of multiplex operator.");
-    AddInput("X", "The candidate tensors of multiplex operator.")
+    AddInput("Ids",
+             "Tensor<int32>, index variable which is a 2-D tensor with shape "
+             "[M, 1] where M is the batch size.");
+    AddInput("X",
+             "A list of variables to gather from. All variables have the same "
+             "shape and the rank is at least 2.")
         .AsDuplicable();
     AddOutput("Out", "The output tensor of multiplex operator.");
     AddComment(R"DOC(
-Multiplex Operator.
-
-Multiplex multiple tensors according to the index provided by the index tensor.
-
-Ids: the index tensor.
-X[0 : N - 1]: the candidate tensors for output (N >= 2).
-For each index i from 0 to batchSize - 1, the output is the i-th row of the
+Referring to the given index variable, this layer selects rows from the
+input variables to construct a multiplex variable. Assuming that there are
+:math:`m` input variables and :math:`I_i` represents the i-th input
+variable and :math:`i` is in [0, :math:`m`). All input variables are
+tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
+Please note that rank of the input tensor should be at least 2. Each input
+variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
+where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
+* ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
+variable. The given index variable should be a 2-D tensor with shape
+[:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
+Then the output variable will be a tensor with shape [:math:`d_0`,
+:math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
+matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
+row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
+
+* Ids: the index tensor.
+
+* X[0 : N - 1]: the candidate tensors for output (N >= 2).
+
+* For each index i from 0 to batchSize - 1, the output is the i-th row of the
 the (Ids[i])-th tensor.
 
 For i-th row of the output tensor:
 
-$$y[i] = x_{k}[i]$$
+$ y[i] = x_{k}[i] $
 
-where `y` is the output tensor, `x_{k}` is the k-th input tensor,
-and `k = Ids[i]`.
+where $y$ is the output tensor, $x_{k}$ is the k-th input tensor,
+and $k = Ids[i]$.
 
 )DOC");
   }

paddle/fluid/operators/reader/create_recordio_file_reader_op.cc

@@ -78,11 +78,15 @@ class CreateRecordIOReaderOp : public framework::OperatorBase {
 class CreateRecordIOReaderOpMaker : public FileReaderMakerBase {
  protected:
   void Apply() override {
-    AddAttr<std::string>("filename", "The filename of record io reader");
+    AddAttr<std::string>(
+        "filename",
+        "The filename of record file. This file will given to reader.");
     AddComment(R"DOC(
-      CreateRecordIOReader Operator
+Open a recordio file and return the reader object. The returned reader object
+is thread-safe.
 
-      Create a reader from a record io file
+NOTE: This is a very low-level API. It is used for debugging data file or
+training. Please use `open_files` instead of this API for production usage.
     )DOC");
   }
 };

paddle/fluid/operators/reader/reader_op_registry.cc

@@ -54,7 +54,7 @@ std::unique_ptr<framework::ReaderBase> CreateReaderByFileName(
 }
 
 void FileReaderMakerBase::Make() {
-  AddOutput("Out", "(ReaderHolder) The created random reader.").AsDuplicable();
+  AddOutput("Out", "(ReaderHolder): The created random reader.").AsDuplicable();
   AddAttr<std::vector<int>>("shape_concat", "The concat of all data's shapes.");
   AddAttr<std::vector<int>>(
       "ranks",

python/paddle/fluid/layers/control_flow.py

@@ -909,37 +909,40 @@ def create_array(dtype):
         dtype=dtype)
 
 
-def less_than(x, y, force_cpu=True, cond=None, **ignored):
+@templatedoc()
+def less_than(x, y, force_cpu=None, cond=None, **ignored):
     """
-    **Less than**
+    ${comment}
 
-    This layer returns the truth value of :math:`x < y` elementwise.
+    >>> import paddle.fluid as fluid
+    >>> less = fluid.layers.less_than(x=label, y=limit)
 
     Args:
-        x(Variable): First operand of *less_than*
-        y(Variable): Second operand of *less_than*
-        force_cpu(Bool|True): The output data will be on CPU if set true.
+        x(${x_type}): ${x_comment}.
+        y(${y_type}): ${y_comment}.
+        force_cpu(${force_cpu_type}): ${force_cpu_comment}.
         cond(Variable|None): Optional output variable to store the result of *less_than*
 
     Returns:
-        Variable: The tensor variable storing the output of *less_than*.
-
-    Examples:
-        .. code-block:: python
-
-          less = fluid.layers.less_than(x=label, y=limit)
+        ${out_comment}.
     """
     helper = LayerHelper("less_than", **locals())
     if cond is None:
         cond = helper.create_tmp_variable(dtype='bool')
         cond.stop_gradient = True
 
+    attrs = dict()
+    if force_cpu is not None:
+        attrs['force_cpu'] = force_cpu
+    elif force_init_on_cpu():
+        attrs['force_cpu'] = force_init_on_cpu()
+
     helper.append_op(
         type='less_than',
         inputs={'X': [x],
                 'Y': [y]},
         outputs={'Out': [cond]},
-        attrs={'force_cpu': force_cpu or force_init_on_cpu()})
+        attrs=attrs)
     return cond
 
 

python/paddle/fluid/layers/io.py

@@ -292,6 +292,7 @@ def _copy_reader_create_op_(block, op):
     return new_op
 
 
+@templatedoc(op_type='create_recordio_file_reader')
 def open_recordio_file(filename,
                        shapes,
                        lod_levels,
@@ -299,34 +300,28 @@ def open_recordio_file(filename,
                        pass_num=1,
                        for_parallel=True):
     """
-    Open a RecordIO file
+    ${comment}
 
-    This layer takes a RecordIO file to read from and returns a Reader Variable.
-    Via the Reader Variable, we can get data from the given RecordIO file.
+    >>> import paddle.fluid as fluid
+    >>> reader = fluid.layers.io.open_recordio_file(
+    >>>                               filename='./data.recordio',
+    >>>                               shapes=[(3,224,224), (1)],
+    >>>                               lod_levels=[0, 0],
+    >>>                               dtypes=['float32', 'int64'])
+    >>> # Via the reader, we can use 'read_file' layer to get data:
+    >>> image, label = fluid.layers.io.read_file(reader)
 
     Args:
-       filename(str): The RecordIO file's name.
+       filename(${filename_type}): ${filename_comment}.
        shapes(list): List of tuples which declaring data shapes.
-       lod_levels(list): List of ints which declaring data lod_level.
+       lod_levels(${lod_levels_type}): ${lod_levels_comment}.
        dtypes(list): List of strs which declaring data type.
        pass_num(int): Number of passes to run.
        for_parallel(Bool): Set it as True if you are going to run
             subsequent operators in parallel.
 
     Returns:
-       Variable: A Reader Variable via which we can get RecordIO file data.
-
-    Examples:
-       .. code-block:: python
-
-         reader = fluid.layers.io.open_recordio_file(
-                                          filename='./data.recordio',
-                                          shapes=[(3,224,224), (1)],
-                                          lod_levels=[0, 0],
-                                          dtypes=['float32', 'int64'])
-
-         # Via the reader, we can use 'read_file' layer to get data:
-         image, label = fluid.layers.io.read_file(reader)
+       ${out_comment}.
     """
     dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
     shape_concat = []

python/paddle/fluid/layers/nn.py

@@ -3210,42 +3210,23 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
     return helper.append_activation(out)
 
 
+@templatedoc()
 def multiplex(inputs, index):
     """
-    **Multiplex Layer**
-
-    Referring to the given index variable, this layer selects rows from the
-    input variables to construct a multiplex variable. Assuming that there are
-    :math:`m` input variables and :math:`I_i` represents the i-th input
-    variable and :math:`i` is in [0, :math:`m`). All input variables are
-    tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
-    Please note that rank of the input tensor should be at least 2. Each input
-    variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
-    where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
-    * ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
-    variable. The given index variable should be a 2-D tensor with shape
-    [:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
-    Then the output variable will be a tensor with shape [:math:`d_0`,
-    :math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
-    matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
-    row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
+    ${comment}
+
+    >>> import paddle.fluid as fluid
+    >>> x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
+    >>> x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
+    >>> index = fluid.layers.data(name='index', shape=[1], dtype='int32')
+    >>> out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
 
     Args:
-       inputs (list): A list of variables to gather from. All variables have the
-                same shape and the rank is at least 2.
-       index (Variable): Tensor<int32>, index variable which is a 2-D tensor
-                with shape [M, 1] where M is the batch size.
+       inputs (list): ${x_comment}.
+       index (${ids_type}): ${ids_comment}.
 
     Returns:
-        Variable: Multiplex variable gathered from input variables.
-
-    Examples:
-        .. code-block:: python
-
-            x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
-            x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
-            index = fluid.layers.data(name='index', shape=[1], dtype='int32')
-            out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
+        ${out_comment}.
     """
     helper = LayerHelper('multiplex', **locals())