diff --git a/integration_test.sh b/integration_test.sh
index d1ef3ee3..13ae3809 100755
--- a/integration_test.sh
+++ b/integration_test.sh
@@ -110,6 +110,14 @@ echo "\n\nTest multiple layers with pre-rnn attention"
 python3 train_model.py --train $TRAIN_PATH --dev $DEV_PATH --output_dir $EXPT_DIR --print_every 50 --embedding_size $EMB_SIZE --hidden_size $H_SIZE --rnn_cell $CELL --epoch $EPOCH --save_every $CP_EVERY --n_layers 3 --attention 'post-rnn' --attention_method 'dot'
 ERR=$((ERR+$?)); EX=$((EX+1))
 
+echo "\n\nTest Xavier/Glorot Initialization"
+python3 train_model.py --train $TRAIN_PATH --dev $DEV_PATH --output_dir $EXPT_DIR --print_every 50 --embedding_size $EMB_SIZE --hidden_size $H_SIZE --rnn_cell $CELL --epoch 1 --save_every $CP_EVERY --n_layers 2 --glorot_init
+ERR=$((ERR+$?)); EX=$((EX+1))
+
+echo "\n\nTest uniform Initialization"
+python3 train_model.py --train $TRAIN_PATH --dev $DEV_PATH --output_dir $EXPT_DIR --print_every 50 --embedding_size $EMB_SIZE --hidden_size $H_SIZE --rnn_cell $CELL --epoch 1 --save_every $CP_EVERY --n_layers 2 --uniform_init 0.1
+ERR=$((ERR+$?)); EX=$((EX+1))
+
 echo "\n\n\n$EX tests executed, $ERR tests failed\n\n"
 
 rm -r $EXPT_DIR
diff --git a/machine/models/seq2seq.py b/machine/models/seq2seq.py
index d90e12fe..539f3d8c 100644
--- a/machine/models/seq2seq.py
+++ b/machine/models/seq2seq.py
@@ -1,4 +1,5 @@
 import torch.nn.functional as F
+import torch.nn as nn
 
 from .baseModel import BaseModel
 
@@ -8,9 +9,13 @@ class Seq2seq(BaseModel):
     and decoder.
     """
 
-    def __init__(self, encoder, decoder, decode_function=F.log_softmax):
+    def __init__(self, encoder, decoder, decode_function=F.log_softmax,
+                 uniform_init=0, glorot_init=False):
         super(Seq2seq, self).__init__(encoder_module=encoder,
-                                      decoder_module=decoder, decode_function=decode_function)
+                                      decoder_module=decoder,
+                                      decode_function=decode_function)
+        # Initialize Weights
+        self._init_weights(uniform_init, glorot_init)
 
     def flatten_parameters(self):
         """
@@ -32,3 +37,15 @@ def forward(self, inputs, input_lengths=None, targets={},
                                      function=self.decode_function,
                                      teacher_forcing_ratio=teacher_forcing_ratio)
         return result
+
+    def _init_weights(self, uniform_init=0.0, glorot_init=False):
+        # initialize weights using uniform distribution
+        if uniform_init > 0.0:
+            for p in self.parameters():
+                p.data.uniform_(-uniform_init, uniform_init)
+
+        # xavier/glorot initialization if glorot_init
+        if glorot_init:
+            for p in self.parameters():
+                if p.dim() > 1:
+                    nn.init.xavier_uniform_(p)
diff --git a/test/test_seq2seq.py b/test/test_seq2seq.py
index b996d8c9..72cd8f35 100644
--- a/test/test_seq2seq.py
+++ b/test/test_seq2seq.py
@@ -1,5 +1,29 @@
 import unittest
 
 
+from machine.models.EncoderRNN import EncoderRNN
+from machine.models.DecoderRNN import DecoderRNN
+from machine.models.seq2seq import Seq2seq
+
+
 class TestSeq2seq(unittest.TestCase):
-    pass
+    def setUp(self):
+        self.decoder = DecoderRNN(100, 50, 16, 0, 1, input_dropout_p=0)
+        self.encoder = EncoderRNN(100, 10, 50, 16, n_layers=2, dropout_p=0.5)
+
+    def test_standard_init(self):
+        Seq2seq(self.encoder, self.decoder)
+        Seq2seq(self.encoder, self.decoder, uniform_init=-1)
+
+    def test_uniform_init(self):
+        Seq2seq(self.encoder, self.decoder, uniform_init=1)
+
+    def test_xavier_init(self):
+        Seq2seq(self.encoder, self.decoder, glorot_init=True)
+
+    def test_uniform_xavier_init(self):
+        Seq2seq(self.encoder, self.decoder, uniform_init=1, glorot_init=True)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/train_model.py b/train_model.py
index cfa92ffa..08d0b9d7 100644
--- a/train_model.py
+++ b/train_model.py
@@ -85,6 +85,11 @@ def init_argparser():
                         choices=['adam', 'adadelta', 'adagrad', 'adamax', 'rmsprop', 'sgd'])
     parser.add_argument('--max_len', type=int,
                         help='Maximum sequence length', default=50)
+    parser.add_argument('--uniform_init', type=float,
+                        help='Initializes weights of model from uniform distribution in range (-uniform_init, uniform_init). \
+                        If <= 0, standard pytorch init is used. (default, 0.0)', default=0.0)
+    parser.add_argument('--glorot_init', action='store_true',
+                        help='Initializes weights of model using glorot/xavier distribution')
     parser.add_argument(
         '--rnn_cell', help="Chose type of rnn cell", default='lstm')
     parser.add_argument('--bidirectional', action='store_true',
@@ -252,7 +257,9 @@ def initialize_model(opt, src, tgt, train):
                          bidirectional=opt.bidirectional,
                          rnn_cell=opt.rnn_cell,
                          eos_id=tgt.eos_id, sos_id=tgt.sos_id)
-    seq2seq = Seq2seq(encoder, decoder)
+    seq2seq = Seq2seq(encoder, decoder,
+                      glorot_init=opt.glorot_init,
+                      uniform_init=opt.uniform_init)
     seq2seq.to(device)
 
     return seq2seq, input_vocab, output_vocab