Polish documentation

paddle/fluid/operators/activation_op.cc

@@ -275,7 +275,7 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
                    "The value of threshold for HardShrink. [default: 0.5]")
         .SetDefault(0.5f);
     AddComment(R"DOC(
-HardShrink Activation Operator.
+** HardShrink activation operator **
 
 ..  math::
     out = \begin{cases}
@@ -399,13 +399,12 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 ThresholdedRelu Activation Operator.
 
-$$
-out = \begin{cases} 
+..  math::
+
+    out = \begin{cases}
              x, \text{if } x > threshold \\
              0,  \text{otherwise}
           \end{cases}
-$$
-
 )DOC");
   }
 };

paddle/fluid/operators/compare_op.cc

@@ -34,16 +34,15 @@ class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(true);
     AddOutput("Out", string::Sprintf("n-dim bool tensor. Each element is %s",
                                      comment.equation));
-    AddComment(string::Sprintf(R"DOC(%s Operator
-
+    AddComment(string::Sprintf(R"DOC(
 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 $%s$
 )DOC",
-                               comment.type, comment.equation));
-    AddAttr<int>("axis",
-                 "(int, default -1). The start dimension index "
-                 "for broadcasting Y onto X.")
+                               comment.equation));
+    AddAttr<int>(
+        "axis",
+        "The start dimension index for broadcasting Y onto X. [default -1]")
         .SetDefault(-1)
         .EqualGreaterThan(-1);
   }

paddle/fluid/operators/multiplex_op.cc

@@ -96,7 +96,9 @@ 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 $k = Ids[i]$.

paddle/fluid/operators/row_conv_op.cc

@@ -94,7 +94,7 @@ class RowConvOpMaker : public framework::OpProtoAndCheckerMaker {
               "in this LodTensor is a matrix with shape T x N, i.e., the "
               "same shape as X.");
     AddComment(R"DOC(
-Row-convolution Operator.
+** Row-convolution operator **
 
 The row convolution is called lookahead convolution.  This operator was 
 introduced in the following paper for DeepSpeech2:

python/paddle/fluid/layers/control_flow.py

@@ -1008,8 +1008,28 @@ def array_read(array, i):
 
 def shrink_memory(x, i, table):
     """
-    This function creates an operator to shrink_rnn_memory using the RankTable
+    This function creates an operator to shrink rnn memory using the RankTable
     as mentioned in the input parameter.
+
+    NOTE: This API is very low-level API. It is used by DynamicRNN only.
+
+    Since the Dynamic RNN uses no-padding way to implement RNN. The sequence
+    will be sorted by order, and the length of valid memory will be shrink after
+    each time step.
+
+    Args:
+        x(Variable): The memory object in the previous time step.
+        i(Variable): The step count variable. A int scalar as LoDTensor.
+        table(Variable): The RNNRankTable object.
+
+    Returns:
+        the memory variable after shrink.
+
+    Examples:
+
+        Since this API is very low level API. The example is not provided.
+        Please reference the implementation of class DynamicRNN for detail
+        usage.
     """
     helper = LayerHelper('shrink_memory', **locals())
     out = helper.create_tmp_variable(dtype=x.dtype)
@@ -1316,10 +1336,9 @@ class IfElse(object):
 
 class DynamicRNN(object):
     """
-    Dynamic RNN.
-
-    This RNN can process a batch of sequence data. The length of each sample
-    sequence can be different. This API automatically process them in batch.
+    The dynamic RNN can process a batch of sequence data. The length of each
+    sample sequence can be different. This API automatically process them in
+    batch.
 
     The input lod must be set. Please reference `lod_tensor`
 
@@ -1500,7 +1519,7 @@ class DynamicRNN(object):
                need_reorder=False,
                dtype='float32'):
         """
-        Create a memory variable.
+        Create a memory variable for dynamic rnn.
 
         If the :code:`init` is not None, :code:`memory` will be initialized by
         this variable. The :code:`need_reorder` is used to reorder the memory as

python/paddle/fluid/layers/detection.py

@@ -210,53 +210,68 @@ def bipartite_match(dist_matrix,
                     dist_threshold=None,
                     name=None):
     """
-    **Bipartite matchint operator**
-
-    This operator is a greedy bipartite matching algorithm, which is used to
-    obtain the matching with the maximum distance based on the input
+    This operator implements a greedy bipartite matching algorithm, which is
+    used to obtain the matching with the maximum distance based on the input
     distance matrix. For input 2D matrix, the bipartite matching algorithm can
-    find the matched column for each row, also can find the matched row for
-    each column. And this operator only calculate matched indices from column
-    to row. For each instance, the number of matched indices is the number of
-    of columns of the input ditance matrix.
-
-    There are two outputs to save matched indices and distance.
-    A simple description, this algothrim matched the best (maximum distance)
+    find the matched column for each row (matched means the largest distance),
+    also can find the matched row for each column. And this operator only
+    calculate matched indices from column to row. For each instance,
+    the number of matched indices is the column number of the input distance
+    matrix.
+
+    There are two outputs, matched indices and distance.
+    A simple description, this algorithm matched the best (maximum distance)
     row entity to the column entity and the matched indices are not duplicated
     in each row of ColToRowMatchIndices. If the column entity is not matched
     any row entity, set -1 in ColToRowMatchIndices.
 
-    Please note that the input DistMat can be LoDTensor (with LoD) or Tensor.
+    NOTE: the input DistMat can be LoDTensor (with LoD) or Tensor.
     If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size.
     If Tensor, the height of ColToRowMatchIndices is 1.
 
+    NOTE: This API is a very low level API. It is used by :code:`ssd_loss`
+    layer. Please consider to use :code:`ssd_loss` instead.
+
     Args:
         dist_matrix(Variable): This input is a 2-D LoDTensor with shape
             [K, M]. It is pair-wise distance matrix between the entities
             represented by each row and each column. For example, assumed one
             entity is A with shape [K], another entity is B with shape [M]. The
-            dist_matirx[i][j] is the distance between A[i] and B[j]. The bigger
-            the distance is, the better macthing the pairs are. Please note,
-            This tensor can contain LoD information to represent a batch of
-            inputs. One instance of this batch can contain different numbers of
-            entities.
+            dist_matrix[i][j] is the distance between A[i] and B[j]. The bigger
+            the distance is, the better matching the pairs are.
+
+            NOTE: This tensor can contain LoD information to represent a batch
+            of inputs. One instance of this batch can contain different numbers
+            of entities.
         match_type(string|None): The type of matching method, should be
-           'bipartite' or 'per_prediction', 'bipartite' by defalut.
+           'bipartite' or 'per_prediction'. [default 'bipartite'].
         dist_threshold(float|None): If `match_type` is 'per_prediction',
             this threshold is to determine the extra matching bboxes based
-            on the maximum distance, 0.5 by defalut.
+            on the maximum distance, 0.5 by default.
     Returns:
-        match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
+        tuple: a tuple with two elements is returned. The first is
+        matched_indices, the second is matched_distance.
+
+        The matched_indices is a 2-D Tensor with shape [N, M] in int type.
         N is the batch size. If match_indices[i][j] is -1, it
         means B[j] does not match any entity in i-th instance.
         Otherwise, it means B[j] is matched to row
         match_indices[i][j] in i-th instance. The row number of
         i-th instance is saved in match_indices[i][j].
-        match_distance(Variable): A 2-D Tensor with shape [N, M] in float type.
-            N is batch size. If match_indices[i][j] is -1,
+
+        The matched_distance is a 2-D Tensor with shape [N, M] in float type
+        . N is batch size. If match_indices[i][j] is -1,
         match_distance[i][j] is also -1.0. Otherwise, assumed
         match_distance[i][j] = d, and the row offsets of each instance
-            are called LoD. Then match_distance[i][j] = dist_matrix[d+LoD[i]][j].
+        are called LoD. Then match_distance[i][j] =
+        dist_matrix[d+LoD[i]][j].
+
+    Examples:
+
+        >>> x = fluid.layers.data(name='x', shape=[4], dtype='float32')
+        >>> y = fluid.layers.data(name='y', shape=[4], dtype='float32')
+        >>> iou = fluid.layers.iou_similarity(x=x, y=y)
+        >>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
     """
     helper = LayerHelper('bipartite_match', **locals())
     match_indices = helper.create_tmp_variable(dtype='int32')
@@ -364,7 +379,7 @@ def ssd_loss(location,
              normalize=True,
              sample_size=None):
     """
-    **Multi-box loss layer for object dection algorithm of SSD**
+    **Multi-box loss layer for object detection algorithm of SSD**
 
     This layer is to compute dection loss for SSD given the location offset
     predictions, confidence predictions, prior boxes and ground-truth boudding
@@ -372,7 +387,7 @@ def ssd_loss(location,
     is a weighted sum of the localization loss (or regression loss) and
     confidence loss (or classification loss) by performing the following steps:
 
-    1. Find matched boundding box by bipartite matching algorithm.
+    1. Find matched bounding box by bipartite matching algorithm.
 
       1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
 
@@ -435,7 +450,7 @@ def ssd_loss(location,
         mining_type (str): The hard example mining type, should be 'hard_example'
             or 'max_negative', now only support `max_negative`.
         normalize (bool): Whether to normalize the SSD loss by the total number
-            of output locations, True by defalut.
+            of output locations, True by default.
         sample_size (int): The max sample size of negative box, used only when
             mining_type is 'hard_example'.
 

python/paddle/fluid/layers/io.py

@@ -302,15 +302,6 @@ def open_recordio_file(filename,
     """
     ${comment}
 
-    >>> 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(${filename_type}): ${filename_comment}.
        shapes(list): List of tuples which declaring data shapes.
@@ -322,6 +313,17 @@ def open_recordio_file(filename,
 
     Returns:
        ${out_comment}.
+
+    Examples:
+
+        >>> 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)
     """
     dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
     shape_concat = []
@@ -549,6 +551,29 @@ def batch(reader, batch_size):
 
 
 def double_buffer(reader, place=None, name=None):
+    """
+    Wrap a double buffer reader. The data will copy to target place with a
+    double buffer queue. If the target place is None, the place that executor
+    perform on will be used.
+
+    Args:
+        reader(Variable): the reader variable need to be wrapped.
+        place(Place): the place of target data. Default is the sample place of
+            executor perform.
+
+        name(str): Variable name. None if the user does not care.
+
+    Returns:
+        wrapped reader with double buffer.
+
+    Examples:
+
+        >>> reader = fluid.layers.open_files(filenames=['somefile'],
+        >>>                                  shapes=[[-1, 784], [-1, 1]],
+        >>>                                  dtypes=['float32', 'int64'])
+        >>> reader = fluid.layers.double_buffer(reader)
+        >>> img, label = fluid.layers.read_file(reader)
+    """
     attrs = dict()
     if place is not None:
         attrs['place'] = str(place).upper()

python/paddle/fluid/layers/ops.py

@@ -66,7 +66,6 @@ __all__ = [
     'uniform_random_batch_size_like',
     'gaussian_random',
     'gaussian_random_batch_size_like',
-    'cumsum',
     'scatter',
     'sum',
     'slice',
@@ -120,3 +119,25 @@ Examples:
     >>> data = fluid.layers.data(name="input", shape=[784])
     >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
 """
+
+__all__ += ['cumsum']
+
+_cum_sum_ = generate_layer_fn('cumsum')
+
+
+def cumsum(x, axis=None, exclusive=None, reverse=None):
+    kwargs = dict()
+    for name in locals():
+        val = locals()[name]
+        if val is not None:
+            kwargs[name] = val
+
+    return _cum_sum_(**kwargs)
+
+
+cumsum.__doc__ = _cum_sum_.__doc__ + """
+Examples:
+
+    >>> data = fluid.layers.data(name="input", shape=[32, 784])
+    >>> result = fluid.layers.cumsum(data, axis=0)
+"""