add missing configuration functions in v2 API.

doc/api/v2/config/layer.rst

@@ -434,10 +434,18 @@ smooth_l1_cost
 ..  autoclass:: paddle.v2.layer.smooth_l1_cost
     :noindex:
 
-Check Layer 
+Check Layer
 ============
 
 eos
 ---
 ..  autoclass:: paddle.v2.layer.eos
     :noindex:
+
+Miscs
+=====
+
+dropout
+--------------
+..  autoclass:: paddle.v2.layer.dropout
+    :noindex:

doc/api/v2/config/networks.rst

@@ -125,11 +125,3 @@ simple_attention
     :members: simple_attention
     :noindex:
 
-Miscs
-=====
-
-dropout_layer
---------------
-..  automodule:: paddle.v2.networks
-    :members: dropout_layer
-    :noindex:

python/paddle/trainer/config_parser.py

@@ -3546,11 +3546,7 @@ def update_g_config():
     return g_config
 
 
-def begin_parse(config_arg_str=''):
-    '''
-    @param config_arg_str: a string of the form var1=val1,var2=val2. It will be
-    passed to config script as a dictionary CONFIG_ARGS
-    '''
+def begin_parse():
     init_config_environment()
     for hook in _parse_config_hooks:
         hook()
@@ -3568,8 +3564,12 @@ def begin_parse(config_arg_str=''):
 
 
 def parse_config(trainer_config, config_arg_str):
-    begin_parse(config_arg_str)
+    '''
+    @param config_arg_str: a string of the form var1=val1,var2=val2. It will be
+    passed to config script as a dictionary CONFIG_ARGS
+    '''
 
+    begin_parse()
     config_args = {}
 
     if config_arg_str:

python/paddle/trainer_config_helpers/layers.py

@@ -120,6 +120,7 @@ __all__ = [
     'smooth_l1_cost',
     'layer_support',
     'multiplex_layer',
+    'dropout_layer',
 ]
 
 
@@ -3760,7 +3761,6 @@ def beam_search(step,
     assert generated_input_index != -1
 
     gipt = input[generated_input_index]
-    assert isinstance(gipt, BaseGeneratedInput)
 
     gipt.bos_id = bos_id
     gipt.eos_id = eos_id
@@ -3780,7 +3780,6 @@ def beam_search(step,
         predict = gipt.after_real_step(step(*args))
 
         eos_layer(input=predict, eos_id=eos_id, name=eos_name)
-
         return predict
 
     tmp = recurrent_group(
@@ -5543,3 +5542,26 @@ def multiplex_layer(input, name=None, layer_attr=None):
         layer_type=LayerType.MULTIPLEX_LAYER,
         parents=input,
         size=l.config.size)
+
+
+############################################################################
+#                         Miscs                                            #
+############################################################################
+
+
+@wrap_name_default("dropout")
+def dropout_layer(input, dropout_rate, name=None):
+    """
+    @TODO(yuyang18): Add comments.
+
+    :param name:
+    :param input:
+    :param dropout_rate:
+    :return:
+    """
+    return addto_layer(
+        name=name,
+        input=input,
+        act=LinearActivation(),
+        bias_attr=False,
+        layer_attr=ExtraAttr(drop_rate=dropout_rate))

python/paddle/trainer_config_helpers/networks.py

@@ -26,10 +26,10 @@ from paddle.trainer.config_parser import *
 
 __all__ = [
     'sequence_conv_pool', 'simple_lstm', "simple_img_conv_pool",
-    "img_conv_bn_pool", 'dropout_layer', 'lstmemory_group', 'lstmemory_unit',
-    'small_vgg', 'img_conv_group', 'vgg_16_network', 'gru_unit', 'gru_group',
-    'simple_gru', 'simple_attention', 'simple_gru2', 'bidirectional_gru',
-    'text_conv_pool', 'bidirectional_lstm', 'inputs', 'outputs'
+    "img_conv_bn_pool", 'lstmemory_group', 'lstmemory_unit', 'small_vgg',
+    'img_conv_group', 'vgg_16_network', 'gru_unit', 'gru_group', 'simple_gru',
+    'simple_attention', 'simple_gru2', 'bidirectional_gru', 'text_conv_pool',
+    'bidirectional_lstm', 'inputs', 'outputs'
 ]
 
 ######################################################
@@ -1366,29 +1366,6 @@ def simple_attention(encoded_sequence,
         input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name)
 
 
-############################################################################
-#                         Miscs                                            #
-############################################################################
-
-
-@wrap_name_default("dropout")
-def dropout_layer(input, dropout_rate, name=None):
-    """
-    @TODO(yuyang18): Add comments.
-
-    :param name:
-    :param input:
-    :param dropout_rate:
-    :return:
-    """
-    return addto_layer(
-        name=name,
-        input=input,
-        act=LinearActivation(),
-        bias_attr=False,
-        layer_attr=ExtraAttr(drop_rate=dropout_rate))
-
-
 def inputs(layers, *args):
     """
     Declare the inputs of network. The order of input should be as same as

python/paddle/v2/layer.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 """
 `paddle.v2.layer` is a part of model config packages in paddle.v2. In API v2,
-we want to make Paddle a plain Python package. The model config package defined
+we want to make Paddle a plain Python package. The model config package defines
 the way how to configure a neural network topology in Paddle Python code.
 
 The primary usage shows below.
@@ -30,7 +30,6 @@ The primary usage shows below.
     # use prediction instance where needed.
     parameters = paddle.parameters.create(cost)
 """
-
 import collections
 import copy
 import re
@@ -44,9 +43,10 @@ __all__ = ['data', 'parse_network']
 
 
 def __need_to_keep__(name):
-    if name in ['StaticInput', 'LayerType', 'layer_support']:
-        return False
-    return True
+    return name in [
+        'StaticInput', 'SubsequenceInput', 'GeneratedInput', 'LayerType',
+        'layer_support'
+    ]
 
 
 def __need_to_wrap__(name):
@@ -54,6 +54,8 @@ def __need_to_wrap__(name):
 
 
 def __convert_name__(inname):
+    if __need_to_keep__(inname):
+        return inname
     if inname == 'maxid_layer':
         return 'max_id'
     elif inname.endswith('memory') or inname.endswith(
@@ -74,8 +76,6 @@ def __convert_name__(inname):
 
 for name in v1_layers.__all__:
     obj = getattr(v1_layers, name)
-    if not __need_to_keep__(name):
-        continue
     new_name = __convert_name__(name)
     if callable(obj) and __need_to_wrap__(name):
         globals()[new_name] = __convert_to_v2__(obj, new_name, __name__)
@@ -107,7 +107,7 @@ __data_layer__.__doc__ = __map_data_docstr__(v1_layers.data_layer.__doc__)
 data = __convert_to_v2__(__data_layer__, 'name', __name__)
 
 
-def __get_used_layers__(output_layers, extra_layers=None):
+def __get_used_layers__(output_layers):
     layer_names = set()
     parents = {}
 
@@ -175,6 +175,8 @@ def __get_used_submodels__(layer_names):
     for submodel in cp.g_config.model_config.sub_models:
         if submodel.name in layer_names:
             submodel_names.add(submodel.name)
+            if submodel.is_recurrent_layer_group:
+                layer_names |= set(submodel.layer_names)
     return submodel_names
 
 
@@ -248,18 +250,21 @@ def parse_network(output_layers, extra_layers=None):
 
     model_config = ModelConfig()
     model_config.type = cp.g_config.model_config.type
+
+    for layer in output_layers:
+        model_config.output_layer_names.append(layer.full_name)
+        output_layer_names.add(layer.full_name)
+
     for l in cp.g_config.model_config.layers:
         if l.name not in layer_names:
             continue
         model_config.layers.extend([l])
         if l.type == 'data':
+            if l.name in model_config.output_layer_names:
+                continue
             model_config.input_layer_names.append(l.name)
             input_layer_names.add(l.name)
 
-    for layer in output_layers:
-        model_config.output_layer_names.append(layer.full_name)
-        output_layer_names.add(layer.full_name)
-
     for e in cp.g_config.model_config.evaluators:
         if e.name in evaluator_names:
             model_config.evaluators.extend([e])

python/paddle/v2/topology.py

@@ -91,8 +91,9 @@ class Topology(object):
         [('image', dense_vector(768)), ('label', integer_value(10))]
         """
         data_layers = self.data_layers()
+
         return [(nm, data_layers[nm].data_type)
-                for nm in self.proto().input_layer_names]
+                for nm in self.proto().input_layer_names if nm in data_layers]
 
     def get_layer_proto(self, name):
         for layer in self.__model_config__.layers: