test for the new infer storage

tests/python/mkl/test_mkldnn.py

@@ -22,7 +22,7 @@
 import os
 import numpy as np
 import mxnet as mx
-from mxnet.test_utils import assert_almost_equal
+from mxnet.test_utils import rand_ndarray, assert_almost_equal
 from mxnet import gluon
 from mxnet.gluon import nn
 from mxnet.test_utils import *
@@ -240,5 +240,25 @@ def check_batchnorm_training(stype):
     for stype in stypes:
         check_batchnorm_training(stype)
 
+
+@with_seed()
+def test_fullyconnected():
+    def check_fullyconnected_training(stype):
+        data_shape = rand_shape_nd(2)
+        weight_shape = rand_shape_nd(2)
+        weight_shape = (weight_shape[0], data_shape[1])
+        for density in [1.0, 0.5, 0.0]:
+            x = rand_ndarray(shape=data_shape, stype=stype, density=density)
+            w = rand_ndarray(shape=weight_shape, stype=stype, density=density)
+            x_sym = mx.sym.Variable("data")
+            w_sym = mx.sym.Variable("weight")
+            sym = mx.sym.FullyConnected(data=x_sym, weight=w_sym, num_hidden=weight_shape[0], no_bias=True)
+            in_location = [x, w]
+            check_numeric_gradient(sym, in_location, numeric_eps=1e-3, rtol=1e-3, atol=5e-3)
+    stypes = ['row_sparse', 'default']
+    for stype in stypes:
+        check_fullyconnected_training(stype)
+
+
 if __name__ == '__main__':
     test_mkldnn_install()