Merge pull request #416 from yu239/hl_activetype

change the act.name for LinearActivation() to "linear" so that it won't fail in hl_activetype; also fix the hasinputsset in submodel

paddle/utils/Util.cpp

@@ -378,7 +378,7 @@ hl_activation_mode_t hlActiveType(const std::string& type) {
     return HL_ACTIVATION_RELU;
   } else if (type == "tanh") {
     return HL_ACTIVATION_TANH;
-  } else if (type == "linear") {
+  } else if (type == "linear" || type == "") {
     return HL_ACTIVATION_LINEAR;
   } else {
     LOG(FATAL) << "Do not support activation type " << type;

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/layers.py

@@ -1766,7 +1766,7 @@ 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.