diff --git a/video_prediction_savp/HPC_scripts/generate_movingmnist.sh b/video_prediction_savp/HPC_scripts/generate_movingmnist.sh
index f6a36d1a366a1e1492546e8a8ac14760cc434098..1de81d2543d255a160ff811ff391a963ef712bde 100755
--- a/video_prediction_savp/HPC_scripts/generate_movingmnist.sh
+++ b/video_prediction_savp/HPC_scripts/generate_movingmnist.sh
@@ -10,7 +10,7 @@
 #SBATCH --gres=gpu:1
 #SBATCH --partition=develgpus
 #SBATCH --mail-type=ALL
-#SBATCH --mail-user=b.gong@fz-juelich.de
+#SBATCH --mail-user=s.stadtler@fz-juelich.de
 ##jutil env activate -p cjjsc42
 
 # Name of virtual environment 
diff --git a/video_prediction_savp/HPC_scripts/train_movingmnist.sh b/video_prediction_savp/HPC_scripts/train_movingmnist.sh
index f62d333dbf01db0affbf72a3e1ef1ecd96b94ec7..85959e52d148f2120c77e5543a79147d50427838 100755
--- a/video_prediction_savp/HPC_scripts/train_movingmnist.sh
+++ b/video_prediction_savp/HPC_scripts/train_movingmnist.sh
@@ -4,8 +4,8 @@
 #SBATCH --ntasks=1
 ##SBATCH --ntasks-per-node=1
 #SBATCH --cpus-per-task=1
-#SBATCH --output=train_era5-out.%j
-#SBATCH --error=train_era5-err.%j
+#SBATCH --output=train_moving_mnist-out.%j
+#SBATCH --error=train_moving_mnist-err.%j
 #SBATCH --time=00:20:00
 #SBATCH --gres=gpu:1
 #SBATCH --partition=develgpus
@@ -36,9 +36,9 @@ fi
 source_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/preprocessedData/moving_mnist
 destination_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/models/moving_mnist
 
-# for choosing the model, convLSTM,savp, mcnet,vae,convLSTM_Loliver
+# for choosing the model, convLSTM,savp, mcnet,vae
 model=convLSTM
 model_hparams=../hparams/era5/${model}/model_hparams.json
 
 # rund training
-srun python ../scripts/train_moving_mnist.py --input_dir  ${source_dir}/tfrecords/ --dataset moving_mnist  --model ${model} --model_hparams_dict ${model_hparams} --output_dir ${destination_dir}/${model}/  --checkpoint ${destination_dir}/${model}/ 
+srun python ../scripts/train_dummy_moving_mnist.py --input_dir  ${source_dir}/tfrecords/ --dataset moving_mnist  --model ${model} --model_hparams_dict ${model_hparams} --output_dir ${destination_dir}/${model}/ 
diff --git a/video_prediction_savp/scripts/train_moving_mnist.py b/video_prediction_savp/scripts/train_moving_mnist.py
index 0cb9a6be0d23449dbdf30cc316815e2a33b29de1..fe7d2f065e895b40844337c22c74e6007f183bd4 100644
--- a/video_prediction_savp/scripts/train_moving_mnist.py
+++ b/video_prediction_savp/scripts/train_moving_mnist.py
@@ -276,8 +276,10 @@ def main():
         val_losses=[]
         run_start_time = time.time()        
         for step in range(start_step,total_steps):
-            #global_step = sess.run(global_step):q
- 
+            #global_step = sess.run(global_step)
+            # +++ Scarlet 20200813
+            timeit_start = time.time()  
+            # --- Scarlet 20200813
             print ("step:", step)
             val_handle_eval = sess.run(val_handle)
 
@@ -342,7 +344,11 @@ def main():
                 print ("The model name does not exist")
 
             #print("saving model to", args.output_dir)
-            saver.save(sess, os.path.join(args.output_dir, "model"), global_step=step)#
+            saver.save(sess, os.path.join(args.output_dir, "model"), global_step=step)
+            # +++ Scarlet 20200813
+            timeit_end = time.time()  
+            # --- Scarlet 20200813
+            print("time needed for this step", timeit_end - timeit_start, ' s')
         train_time = time.time() - run_start_time
         results_dict = {"train_time":train_time,
                         "total_steps":total_steps}
@@ -352,6 +358,9 @@ def main():
         print("val_losses:",val_losses) 
         plot_train(train_losses,val_losses,args.output_dir)
         print("Done")
+        # +++ Scarlet 20200814
+        print("Total training time:", train_time/60., "min")
+        # +++ Scarlet 20200814
         
 if __name__ == '__main__':
     main()
diff --git a/video_prediction_savp/video_prediction/layers/layer_def.py b/video_prediction_savp/video_prediction/layers/layer_def.py
index 738e139df0e155ca294fe43edd03a2d79fc1f532..1ceac662136548fde65511815795d184fe91fac1 100644
--- a/video_prediction_savp/video_prediction/layers/layer_def.py
+++ b/video_prediction_savp/video_prediction/layers/layer_def.py
@@ -74,6 +74,8 @@ def conv_layer(inputs, kernel_size, stride, num_features, idx, initializer=tf.co
             conv_rect = tf.nn.elu(conv_biased, name = '{0}_conv'.format(idx))   
         elif activate == "leaky_relu":
             conv_rect = tf.nn.leaky_relu(conv_biased, name = '{0}_conv'.format(idx))
+        elif activate == "sigmoid":
+            conv_rect = tf.nn.sigmoid(conv_biased, name = '{0}_conv'.format(idx)) 
         else:
             raise ("activation function is not correct")
         return conv_rect
diff --git a/video_prediction_savp/video_prediction/models/__init__.py b/video_prediction_savp/video_prediction/models/__init__.py
index b71769a9d8cc523e4f108fc222fc2ed0284019f7..8010c4eeb2123fd94995c6474e7e1c8af6b02113 100644
--- a/video_prediction_savp/video_prediction/models/__init__.py
+++ b/video_prediction_savp/video_prediction/models/__init__.py
@@ -21,7 +21,6 @@ def get_model_class(model):
         'vae': 'VanillaVAEVideoPredictionModel',
         'convLSTM': 'VanillaConvLstmVideoPredictionModel',
         'mcnet': 'McNetVideoPredictionModel',
-        'convLSTM_Loliver': "ConvLstmLoliverVideoPredictionModel"
         }
     model_class = model_mappings.get(model, model)
     model_class = globals().get(model_class)