diff --git a/deep_speech_2/train.py b/deep_speech_2/train.py
index e6a7d076bbfe36e837767657e87a75f73cd2b6d2..89ab23c685888d86956a1c2b798447a9e79c8a47 100644
--- a/deep_speech_2/train.py
+++ b/deep_speech_2/train.py
@@ -11,6 +11,7 @@ import sys
 from model import deep_speech2
 from audio_data_utils import DataGenerator
 import numpy as np
+import os
 
 #TODO: add WER metric
 
@@ -78,6 +79,13 @@ parser.add_argument(
     default='data/eng_vocab.txt',
     type=str,
     help="Vocabulary filepath. (default: %(default)s)")
+parser.add_argument(
+    "--init_model_path",
+    default=None,
+    type=str,
+    help="If set None, the training will start from scratch. "
+    "Otherwise, the training will resume from "
+    "the existing model of this path. (default: %(default)s)")
 args = parser.parse_args()
 
 
@@ -114,8 +122,14 @@ def train():
         rnn_size=args.rnn_layer_size,
         is_inference=False)
 
-    # create parameters and optimizer
-    parameters = paddle.parameters.create(cost)
+    # create/load parameters and optimizer
+    if args.init_model_path is None:
+        parameters = paddle.parameters.create(cost)
+    else:
+        if not os.path.isfile(args.init_model_path):
+            raise IOError("Invalid model!")
+        parameters = paddle.parameters.Parameters.from_tar(
+            gzip.open(args.init_model_path))
     optimizer = paddle.optimizer.Adam(
         learning_rate=args.adam_learning_rate, gradient_clipping_threshold=400)
     trainer = paddle.trainer.SGD(