diff --git a/docs/ICLR-2019-DARTS.md b/docs/ICLR-2019-DARTS.md
index 9b321ce..a9a6bfe 100644
--- a/docs/ICLR-2019-DARTS.md
+++ b/docs/ICLR-2019-DARTS.md
@@ -4,17 +4,22 @@ DARTS: Differentiable Architecture Search is accepted by ICLR 2019.
 In this paper, Hanxiao proposed a differentiable neural architecture search method, named as DARTS.
 Recently, DARTS becomes very popular due to its simplicity and performance.
 
-**Run DARTS on the NAS-Bench-201 search space**:
+## Run DARTS on the NAS-Bench-201 search space
 ```
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh     cifar10 1 -1
 ```
 
-**Run the first-order DARTS on the NASNet search space**:
+## Run the first-order DARTS on the NASNet/DARTS search space
+This command will start to use the first-order DARTS to search architectures on the DARTS search space.
 ```
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1
 ```
 
+After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
+In future, I will add a more eligent way to train the searched architecture from the DARTS search space.
+
+
 # Citation
 
 ```
diff --git a/exps/algos/DARTS-V1.py b/exps/algos/DARTS-V1.py
index f0369ce..b06f779 100644
--- a/exps/algos/DARTS-V1.py
+++ b/exps/algos/DARTS-V1.py
@@ -199,7 +199,8 @@ def main(xargs):
       logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
       copy_checkpoint(model_base_path, model_best_path, logger)
     with torch.no_grad():
-      logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+      #logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+      logger.log('{:}'.format(search_model.show_alphas()))
     if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
     # measure elapsed time
     epoch_time.update(time.time() - start_time)
diff --git a/lib/models/cell_searchs/search_model_darts.py b/lib/models/cell_searchs/search_model_darts.py
index fd6f4cf..e7e61a7 100644
--- a/lib/models/cell_searchs/search_model_darts.py
+++ b/lib/models/cell_searchs/search_model_darts.py
@@ -53,6 +53,10 @@ class TinyNetworkDarts(nn.Module):
   def get_alphas(self):
     return [self.arch_parameters]
 
+  def show_alphas(self):
+    with torch.no_grad():
+      return 'arch-parameters :\n{:}'.format( nn.functional.softmax(self.arch_parameters, dim=-1).cpu() )
+
   def get_message(self):
     string = self.extra_repr()
     for i, cell in enumerate(self.cells):