diff --git a/python/mxnet/initializer.py b/python/mxnet/initializer.py
index aca7c58707e2..277bfd0f4fa5 100755
--- a/python/mxnet/initializer.py
+++ b/python/mxnet/initializer.py
@@ -464,6 +464,12 @@ def __init__(self, value):
     def _init_weight(self, _, arr):
         arr[:] = self.value
 
+    def dumps(self):
+        val = self._kwargs['value']
+        if not np.isscalar(val):
+            self._kwargs['value'] = val.tolist() if isinstance(val, np.ndarray) else val.asnumpy().tolist()
+        return json.dumps([self.__class__.__name__.lower(), self._kwargs])
+
 @register
 class Uniform(Initializer):
     """Initializes weights with random values uniformly sampled from a given range.
diff --git a/tests/python/unittest/test_init.py b/tests/python/unittest/test_init.py
index c8bf01f48ca3..6d8830c1d089 100644
--- a/tests/python/unittest/test_init.py
+++ b/tests/python/unittest/test_init.py
@@ -17,6 +17,7 @@
 
 import mxnet as mx
 import numpy as np
+import json
 
 def test_default_init():
     data = mx.sym.Variable('data')
@@ -67,10 +68,26 @@ def test_bilinear_init():
     bili_1d = np.array([[1/float(4), 3/float(4), 3/float(4), 1/float(4)]])
     bili_2d = bili_1d * np.transpose(bili_1d)
     assert (bili_2d == bili_weight.asnumpy()).all()
-    
+
+def test_const_init_dumps():
+    shape = tuple(np.random.randint(1, 10, size=np.random.randint(1, 5)))
+    # test NDArray input
+    init = mx.init.Constant(mx.nd.ones(shape))
+    val = init.dumps()
+    assert val == json.dumps([init.__class__.__name__.lower(), init._kwargs])
+    # test scalar input
+    init = mx.init.Constant(1)
+    assert init.dumps() == '["constant", {"value": 1}]'
+    # test numpy input
+    init = mx.init.Constant(np.ones(shape))
+    val = init.dumps()
+    assert val == json.dumps([init.__class__.__name__.lower(), init._kwargs])
+
+
 if __name__ == '__main__':
     test_variable_init()
     test_default_init()
     test_aux_init()
     test_rsp_const_init()
     test_bilinear_init()
+    test_const_init_dumps()