diff --git a/src/icosagon/normalize.py b/src/icosagon/normalize.py
index ec09b3c..e13fb05 100644
--- a/src/icosagon/normalize.py
+++ b/src/icosagon/normalize.py
@@ -41,7 +41,8 @@ def _sparse_coo_tensor(indices, values, size):
         torch.uint8: torch.sparse.ByteTensor,
         torch.long: torch.sparse.LongTensor,
         torch.int: torch.sparse.IntTensor,
-        torch.short: torch.sparse.ShortTensor }[values.dtype]
+        torch.short: torch.sparse.ShortTensor,
+        torch.bool: torch.sparse.ByteTensor }[values.dtype]
     return ctor(indices, values, size)
 
 
diff --git a/tests/icosagon/test_normalize.py b/tests/icosagon/test_normalize.py
index e4b0fef..f3de835 100644
--- a/tests/icosagon/test_normalize.py
+++ b/tests/icosagon/test_normalize.py
@@ -46,14 +46,14 @@ def test_add_eye_sparse_04():
 
 def test_norm_adj_mat_one_node_type_sparse_01():
     adj_mat = torch.rand((10, 10))
-    adj_mat = (adj_mat > .5)
+    adj_mat = (adj_mat > .5).to(torch.float32)
     adj_mat = adj_mat.to_sparse()
     _ = norm_adj_mat_one_node_type_sparse(adj_mat)
 
 
 def test_norm_adj_mat_one_node_type_sparse_02():
     adj_mat_dense = torch.rand((10, 10))
-    adj_mat_dense = (adj_mat_dense > .5)
+    adj_mat_dense = (adj_mat_dense > .5).to(torch.float32)
     adj_mat_sparse = adj_mat_dense.to_sparse()
     adj_mat_sparse = norm_adj_mat_one_node_type_sparse(adj_mat_sparse)
     adj_mat_dense = norm_adj_mat_one_node_type_dense(adj_mat_dense)
diff --git a/tests/icosagon/test_trainprep.py b/tests/icosagon/test_trainprep.py
index b49646b..5ec86a7 100644
--- a/tests/icosagon/test_trainprep.py
+++ b/tests/icosagon/test_trainprep.py
@@ -108,7 +108,7 @@ def test_prepare_adj_mat_02():
 
 
 def test_prepare_relation_type_01():
-    adj_mat = (torch.rand((10, 10)) > .5)
+    adj_mat = (torch.rand((10, 10)) > .5).to(torch.float32)
     r = RelationType('Test', 0, 0, adj_mat, True)
     ratios = TrainValTest(.8, .1, .1)
     _ = prepare_relation_type(r, ratios, False)