change the act.name for LinearActivation() to "linear" so that it won't

fail in hl_activetype; also fix the hasinputsset in submodel

python/paddle/trainer/config_parser.py

@@ -218,7 +218,7 @@ def Inputs(*args):
 
 @config_func
 def HasInputsSet():
-    return len(g_config.model_config.input_layer_names) != 0
+    return len(g_current_submodel.input_layer_names) != 0
 
 
 # Define the name of the output layers of the NeuralNetwork.

python/paddle/trainer_config_helpers/activations.py

@@ -104,7 +104,7 @@ class IdentityActivation(BaseActivation):
     Just do nothing for output both forward/backward.
     """
 
-    def __init__(self): BaseActivation.__init__(self, '', False)
+    def __init__(self): BaseActivation.__init__(self, 'linear', False)
 
 
 LinearActivation = IdentityActivation

python/paddle/trainer_config_helpers/layers.py

@@ -1657,7 +1657,7 @@ def img_pool_layer(input, pool_size, name=None,
     :type pool_size_y: int|None
     :param num_channels: number of input channel.
     :type num_channels: int
-    :param pool_type: pooling type. MaxPooling or AveragePooling. Default is
+    :param pool_type: pooling type. MaxPooling or AvgPooling. Default is
                       MaxPooling.
     :type pool_type: BasePoolingType
     :param stride: stride width of pooling.