"add beam search"

python/paddle/fluid/layers/nn.py

@@ -834,11 +834,14 @@ def linear_chain_crf(input, label, param_attr=None):
 
     Args:
         input(${emission_type}): ${emission_comment}
+        input(${transition_type}): ${transition_comment}
         label(${label_type}): ${label_comment}
         param_attr(ParamAttr): The attribute of the learnable parameter.
 
     Returns:
         ${log_likelihood_comment}
+        ${transitionexps_comment}
+        ${emissionexps_comment}
 
     """
     helper = LayerHelper('linear_chain_crf', **locals())
@@ -1170,10 +1173,6 @@ def sequence_conv(input,
         Variable: output of sequence_conv
     """
 
-    # FIXME(dzh) : want to unify the argument of python layer
-    # function. So we ignore some unecessary attributes.
-    # such as, padding_trainable, context_start.
-
     helper = LayerHelper('sequence_conv', **locals())
     dtype = helper.input_dtype()
     filter_shape = [filter_size * input.shape[1], num_filters]
@@ -2051,18 +2050,31 @@ def layer_norm(input,
 
 def beam_search_decode(ids, scores, name=None):
     """
+    Beam Search Decode
+
     This layers is to pack the output of beam search layer into sentences and
     associated scores. It is usually called after the beam search layer.
+    Typically, the output of beam search layer is a tensor of selected ids, with
+    a tensor of the score of each id. Beam search layer's output ids, however, 
+    are generated directly during the tree search, and they are stacked by each 
+    level of the search tree. Thus we need to reorganize them into sentences, 
+    based on the score of each id. This layer takes the output of beam search
+    layer as input and repack them into sentences.
 
     ${beam_search_decode}
 
     Args:
-        ids (Variable): ${ids_comment}
-        scores (Variable): ${scores_comment}
+        ids (Variable): The selected ids, output of beam search layer. 
+        scores (Variable): The associated scores of the ids, out put of beam
+            search layer.
         name (str): The name of this layer. It is optional.
 
     Returns:
-        tuple(Variable): a tuple of two output variable: sentence_ids, sentence_scores
+        tuple(Variable): a tuple of two output tensors: sentence_ids, sentence_scores.
+        sentence_ids is a tensor with shape [size, length], where size is the
+        beam size of beam search, and length is the length of each sentence. 
+        Note that the length of sentences may vary.
+        sentence_scores is a tensor with the same shape as sentence_ids.
 
     Examples:
         .. code-block:: python