diff --git a/egs/wsj/s5/steps/libs/nnet3/xconfig/basic_layers.py b/egs/wsj/s5/steps/libs/nnet3/xconfig/basic_layers.py
index aa301eb4398..7c01689e86c 100644
--- a/egs/wsj/s5/steps/libs/nnet3/xconfig/basic_layers.py
+++ b/egs/wsj/s5/steps/libs/nnet3/xconfig/basic_layers.py
@@ -998,8 +998,7 @@ def set_default_configs(self):
         self.config = {'input': '[-1]',
                        'dim': -1,
                        'cepstral-lifter': 22.0,
-                       'affine-transform-file': '',
-                       'write-init-config': True}
+                       'affine-transform-file': ''}
 
     def check_configs(self):
         if self.config['affine-transform-file'] is None:
@@ -1031,13 +1030,6 @@ def get_full_config(self):
         output_dim = self.output_dim()
         transform_file = self.config['affine-transform-file']
 
-        if self.config['write-init-config']:
-            # to init.config we write an output-node with the name 'output' and
-            # with a Descriptor equal to the descriptor that's the input to this
-            # layer.  This will be used to accumulate stats to learn the LDA transform.
-            line = 'output-node name=output input={0}'.format(descriptor_final_string)
-            ans.append(('init', line))
-
         idct_mat = common_lib.compute_idct_matrix(
             input_dim, output_dim, self.config['cepstral-lifter'])
         # append a zero column to the matrix, this is the bias of the fixed
diff --git a/egs/wsj/s5/steps/nnet3/chain/train.py b/egs/wsj/s5/steps/nnet3/chain/train.py
index 65b09f9f1eb..6bc51dcbd3f 100755
--- a/egs/wsj/s5/steps/nnet3/chain/train.py
+++ b/egs/wsj/s5/steps/nnet3/chain/train.py
@@ -307,7 +307,7 @@ def train(args, run_opts):
         logger.info("Creating denominator FST")
         chain_lib.create_denominator_fst(args.dir, args.tree_dir, run_opts)
 
-    if (args.stage <= -4):
+    if (args.stage <= -4) and os.path.exists(args.dir+"/configs/init.config"):
         logger.info("Initializing a basic network for estimating "
                     "preconditioning matrix")
         common_lib.execute_command(
@@ -375,7 +375,7 @@ def train(args, run_opts):
     logger.info("Copying the properties from {0} to {1}".format(egs_dir, args.dir))
     common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir)
 
-    if (args.stage <= -2):
+    if (args.stage <= -2) and os.path.exists(args.dir+"/configs/init.config"):
         logger.info('Computing the preconditioning matrix for input features')
 
         chain_lib.compute_preconditioning_matrix(