diff --git a/egs/wsj/s5/steps/libs/nnet3/train/common.py b/egs/wsj/s5/steps/libs/nnet3/train/common.py
index e8f711e992e..62d4e302351 100644
--- a/egs/wsj/s5/steps/libs/nnet3/train/common.py
+++ b/egs/wsj/s5/steps/libs/nnet3/train/common.py
@@ -901,5 +901,16 @@ def __init__(self,
                                  then only failure notifications are sent""")
 
 
+def is_lda_added(config_dir):
+    """Returns true if there is an lda.mat in init.config
+    which suggests the LDA matrix training stage needs to be executed in the
+    nnet training"""
+    for line in open("{config_dir}/init.config".format(config_dir)):
+        line = line.strip().split("#")[0]
+        if re.search(r"lda\.mat", line):
+            return True
+    return False
+
+
 if __name__ == '__main__':
     self_test()
diff --git a/egs/wsj/s5/steps/nnet3/train_raw_dnn.py b/egs/wsj/s5/steps/nnet3/train_raw_dnn.py
index 57886afefd8..8cf17267bb9 100755
--- a/egs/wsj/s5/steps/nnet3/train_raw_dnn.py
+++ b/egs/wsj/s5/steps/nnet3/train_raw_dnn.py
@@ -80,6 +80,7 @@ def get_args():
                         rule as accepted by the --minibatch-size option of
                         nnet3-merge-egs; run that program without args to see
                         the format.""")
+
     parser.add_argument("--trainer.optimization.proportional-shrink", type=float,
                         dest='proportional_shrink', default=0.0,
                         help="""If nonzero, this will set a shrinkage (scaling)
@@ -92,6 +93,11 @@ def get_args():
                         Unlike for train_rnn.py, this is applied unconditionally,
                         it does not depend on saturation of nonlinearities.
                         Can be used to roughly approximate l2 regularization.""")
+  parser.add_argument("--compute-average-posteriors",
+                        type=str, action=common_lib.StrToBoolAction,
+                        choices=["true", "false"], default=False,
+                        help="""If true, then the average output of the
+                        network is computed and dumped as post.final.vec""")
 
     # General options
     parser.add_argument("--nj", type=int, default=4,
@@ -198,11 +204,7 @@ def train(args, run_opts):
     try:
         model_left_context = variables['model_left_context']
         model_right_context = variables['model_right_context']
-        if 'include_log_softmax' in variables:
-            include_log_softmax = common_lib.str_to_bool(
-                variables['include_log_softmax'])
-        else:
-            include_log_softmax = False
+
     except KeyError as e:
         raise Exception("KeyError {0}: Variables need to be defined in "
                         "{1}".format(str(e), '{0}/configs'.format(args.dir)))
@@ -286,7 +288,9 @@ def train(args, run_opts):
     # use during decoding
     common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir)
 
-    if (args.stage <= -3) and os.path.exists(args.dir+"/configs/init.config"):
+    add_lda = common_train_lib.is_lda_added(config_dir)
+
+    if (add_lda and args.stage <= -3):
         logger.info('Computing the preconditioning matrix for input features')
 
         train_lib.common.compute_preconditioning_matrix(
@@ -417,7 +421,7 @@ def learning_rate(iter, current_num_jobs, num_archives_processed):
             common_lib.force_symlink("{0}.raw".format(num_iters),
                                      "{0}/final.raw".format(args.dir))
 
-    if include_log_softmax and args.stage <= num_iters + 1:
+    if compute_average_posteriors and args.stage <= num_iters + 1:
         logger.info("Getting average posterior for output-node 'output'.")
         train_lib.common.compute_average_posterior(
             dir=args.dir, iter='final', egs_dir=egs_dir,