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.
@@ -1120,14 +1120,14 @@ def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
         block_expand_conf.output_x = 0
     else:
         block_expand_conf.output_x = cnn_output_size(
-            block_expand.img_size_x, block_expand.block_x, 
+            block_expand.img_size_x, block_expand.block_x,
             block_expand.padding_x, block_expand.stride_x, False)
 
     if block_expand_conf.img_size_y == 0:
         block_expand_conf.output_y = 0
     else:
         block_expand_conf.output_y = cnn_output_size(
-            block_expand.img_size_y, block_expand.block_y, 
+            block_expand.img_size_y, block_expand.block_y,
             block_expand.padding_y, block_expand.stride_y, False)
 
 def parse_maxout(maxout, input_layer_name, maxout_conf):
@@ -1135,7 +1135,7 @@ def parse_maxout(maxout, input_layer_name, maxout_conf):
     maxout_conf.groups = maxout.groups
     maxout_conf.img_size_x = maxout.img_size_x
     maxout_conf.img_size_y = maxout.img_size_y
-    
+
 # Define an evaluator
 @config_func
 def Evaluator(
@@ -1773,7 +1773,7 @@ class MaxOutLayer(LayerBase):
                      self.config.inputs[0].maxout_conf)
         maxout_conf = self.config.inputs[0].maxout_conf
         self.set_layer_size(g_layer_map[input_layer.name].size / maxout_conf.groups)
-            
+
 # key: cost type
 # value: cost class
 g_cost_map = {}

python/paddle/trainer_config_helpers/activations.py

@@ -23,9 +23,9 @@ __all__ = ["TanhActivation", "SigmoidActivation",
 
 class BaseActivation(object):
     """
-    A mark for activation class. 
+    A mark for activation class.
     Each activation inherit BaseActivation, which has two parameters.
-     
+
     :param name: activation name in paddle config.
     :type name: basestring
     :param support_hppl: True if supported by hppl. HPPL is a library used by paddle
@@ -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
@@ -194,7 +194,7 @@ class SquareActivation(BaseActivation):
 class ExpActivation(BaseActivation):
     """
     Exponential Activation.
-    
+
     .. math::
        f(z) = e^z.
     """

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.