add beam search doc (#11469)

paddle/fluid/operators/activation_op.cc

@@ -143,7 +143,7 @@ $$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
 __attribute__((unused)) constexpr char TanhShrinkDoc[] = R"DOC(
 TanhShrink Activation Operator.
 
-$$out = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
+$$out = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
 
 )DOC";
 
@@ -385,7 +385,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 STanh Activation Operator.
 
-$$out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
+$$out = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
 
 )DOC");
   }

python/paddle/fluid/layers/control_flow.py

@@ -185,12 +185,14 @@ def Print(input,
     Returns:
         Variable: Output tensor, same data with input tensor.
 
+
     Examples:
+
         .. code-block:: python
 
-        value = some_layer(...)
-        Print(value, summarize=10,
-              message="The content of some_layer: ")
+           value = some_layer(...)
+           Print(value, summarize=10,
+               message="The content of some_layer: ")
     '''
     helper = LayerHelper('print', **locals())
     out = helper.create_tmp_variable(dtype=helper.input_dtype())
@@ -1201,6 +1203,31 @@ class ConditionalBlockGuard(BlockGuard):
 
 
 class ConditionalBlock(object):
+    '''
+    **ConditionalBlock**
+
+    ConditionalBlock is an operator that bind a block to a specific condition,
+    if the condition matches, the corresponding block will be executed.
+
+    Args:
+        inputs (Variable): bool conditions.
+        is_scalar_condition (bool): whether the branch is controled by a scalar.
+        name(str): name of this ConditionalBlock.
+
+    Examples:
+        .. code-block:: python
+
+             cond = layers.less_than(x=label, y=limit)
+             true_image, false_image = layers.split_lod_tensor(
+                 input=image, mask=cond)
+             true_cond = layers.ConditionalBlock([true_image])
+
+             with true_cond.block():
+                 ...
+             with false_cond.block():
+                 ...
+    '''
+
     def __init__(self, inputs, is_scalar_condition=False, name=None):
         for each_input in inputs:
             if not isinstance(each_input, Variable):

python/paddle/fluid/layers/nn.py

@@ -2678,18 +2678,35 @@ def sequence_expand(x, y, ref_level=-1, name=None):
 
 def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
     '''
+    **beam search**
+
     This function implements the beam search algorithm.
 
+    Beam search is a classical algorithm for selecting candidate words
+    in a machine translation task.
+
+    Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
+    for more details.
+
     Args:
-        pre_ids (Variable): ${pre_ids_comment}
-        ids (Variable): ${ids_comment}
-        scores (Variable): ${scores_comment}
-        beam_size (int): ${beam_size_comment}
-        end_id (int): ${end_id_comment}
-        level (int): ${level_comment}
+        pre_ids (Variable): ids in previous step.
+        ids (Variable): a LoDTensor of shape of [None,k]
+        scores (Variable): a LoDTensor that has the same shape and LoD with `ids`
+        beam_size (int): beam size for beam search
+        end_id (int): the token id which indicates the end of a sequence
+        level (int): the level of LoDTensor
 
     Returns:
-        tuple: a tuple of beam_search output variables: selected_ids, selected_scores
+        tuple: a tuple of beam_search output variables: `selected_ids`, `selected_scores`
+
+    Examples:
+        .. code-block:: python
+
+             # current_score is a Tensor of shape (num_batch_size, embed_size), which
+             # consists score of each candidate word.
+             topk_scores, topk_indices = pd.topk(current_score, k=50)
+             selected_ids, selected_scores = pd.beam_search(
+                 pre_ids, topk_indices, topk_scores, beam_size, end_id=10, level=0)
     '''
     helper = LayerHelper('beam_search', **locals())
     score_type = scores.dtype