diff --git a/decagon_pytorch/__init__.py b/src/decagon_pytorch/__init__.py
similarity index 100%
rename from decagon_pytorch/__init__.py
rename to src/decagon_pytorch/__init__.py
diff --git a/decagon_pytorch/convolve.py b/src/decagon_pytorch/convolve.py
old mode 100755
new mode 100644
similarity index 100%
rename from decagon_pytorch/convolve.py
rename to src/decagon_pytorch/convolve.py
diff --git a/decagon_pytorch/dropout.py b/src/decagon_pytorch/dropout.py
similarity index 100%
rename from decagon_pytorch/dropout.py
rename to src/decagon_pytorch/dropout.py
diff --git a/decagon_pytorch/model.py b/src/decagon_pytorch/model.py
similarity index 100%
rename from decagon_pytorch/model.py
rename to src/decagon_pytorch/model.py
diff --git a/decagon_pytorch/weights.py b/src/decagon_pytorch/weights.py
similarity index 100%
rename from decagon_pytorch/weights.py
rename to src/decagon_pytorch/weights.py
diff --git a/tests/decagon_pytorch/test_dropout.py b/tests/decagon_pytorch/test_dropout.py
new file mode 100644
index 0000000..60366ec
--- /dev/null
+++ b/tests/decagon_pytorch/test_dropout.py
@@ -0,0 +1,34 @@
+from decagon_pytorch.dropout import dropout_sparse
+import torch
+import numpy as np
+
+
+def dropout_dense(a, keep_prob):
+    i = np.array(np.where(a))
+    v = a[i[0, :], i[1, :]]
+
+    # torch.random.manual_seed(0)
+    n = keep_prob + torch.rand(len(v))
+    n = torch.floor(n).to(torch.bool)
+    i = i[:, n]
+    v = v[n]
+    x = torch.sparse_coo_tensor(i, v, size=a.shape)
+
+    return x * (1./keep_prob)
+
+
+def test_dropout_sparse():
+    for i in range(11):
+        torch.random.manual_seed(i)
+        a = torch.rand((5, 10))
+        a[a < .5] = 0
+
+        keep_prob=i/10. + np.finfo(np.float32).eps
+
+        torch.random.manual_seed(i)
+        b = dropout_dense(a, keep_prob=keep_prob)
+
+        torch.random.manual_seed(i)
+        c = dropout_sparse(a.to_sparse(), keep_prob=keep_prob)
+
+        assert np.all(np.array(b.to_dense()) == np.array(c.to_dense()))