diff --git a/src/decagon_pytorch/convolve.py b/src/decagon_pytorch/convolve.py
index d125e98..aec9c04 100644
--- a/src/decagon_pytorch/convolve.py
+++ b/src/decagon_pytorch/convolve.py
@@ -26,7 +26,7 @@ class SparseDropoutGraphConvActivation(torch.nn.Module):
         activation=torch.nn.functional.relu,
         **kwargs):
         super().__init__(**kwargs)
-        self.sparse_graph_conv = SparseGraphConv(input_dim, output_dim)
+        self.sparse_graph_conv = SparseGraphConv(input_dim, output_dim, adjacency_matrix)
         self.keep_prob = keep_prob
         self.activation = activation
 
diff --git a/tests/decagon_pytorch/test_convolve.py b/tests/decagon_pytorch/test_convolve.py
index 611f0a1..2bf603a 100644
--- a/tests/decagon_pytorch/test_convolve.py
+++ b/tests/decagon_pytorch/test_convolve.py
@@ -19,6 +19,26 @@ def prepare_data():
     return latent, adjacency_matrices
 
 
+def dense_to_sparse_tf(x):
+    a, b = np.where(x)
+    indices = np.array([a, b]).T
+    values = x[a, b]
+    return tf.sparse.SparseTensor(indices, values, x.shape)
+
+
+def dropout_sparse_tf(x, keep_prob, num_nonzero_elems):
+    """Dropout for sparse tensors. Currently fails for very large sparse tensors (>1M elements)
+    """
+    noise_shape = [num_nonzero_elems]
+    random_tensor = keep_prob
+    random_tensor += tf.convert_to_tensor(torch.rand(noise_shape).detach().numpy())
+    # tf.convert_to_tensor(np.random.random(noise_shape))
+    # tf.random_uniform(noise_shape)
+    dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
+    pre_out = tf.sparse_retain(x, dropout_mask)
+    return pre_out * (1./keep_prob)
+
+
 def sparse_graph_conv_torch():
     torch.random.manual_seed(0)
     latent, adjacency_matrices = prepare_data()
@@ -34,24 +54,51 @@ def sparse_graph_conv_torch():
     return latent
 
 
-def dense_to_sparse_tf(x):
-    a, b = np.where(x)
-    indices = np.array([a, b]).T
-    values = x[a, b]
-    return tf.sparse.SparseTensor(indices, values, x.shape)
+def sparse_graph_conv_tf():
+    torch.random.manual_seed(0)
+    latent, adjacency_matrices = prepare_data()
+    conv_torch = decagon_pytorch.convolve.SparseGraphConv(10, 10,
+        torch.tensor(adjacency_matrices[0]).to_sparse())
+    weight = tf.constant(conv_torch.weight.detach().numpy())
+    latent = dense_to_sparse_tf(latent)
+    adj_mat = dense_to_sparse_tf(adjacency_matrices[0])
+    latent = tf.sparse_tensor_dense_matmul(latent, weight)
+    latent = tf.sparse_tensor_dense_matmul(adj_mat, latent)
+    return latent
 
 
+def sparse_dropout_graph_conv_activation_torch(keep_prob=1.):
+    torch.random.manual_seed(0)
+    latent, adjacency_matrices = prepare_data()
+    latent = torch.tensor(latent).to_sparse()
+    adj_mat = adjacency_matrices[0]
+    adj_mat = torch.tensor(adj_mat).to_sparse()
+    conv = decagon_pytorch.convolve.SparseDropoutGraphConvActivation(10, 10,
+        adj_mat, keep_prob=keep_prob)
+    latent = conv(latent)
+    return latent
 
-def sparse_graph_conv_tf():
+
+def sparse_dropout_graph_conv_activation_tf(keep_prob=1.):
     torch.random.manual_seed(0)
     latent, adjacency_matrices = prepare_data()
     conv_torch = decagon_pytorch.convolve.SparseGraphConv(10, 10,
         torch.tensor(adjacency_matrices[0]).to_sparse())
+
     weight = tf.constant(conv_torch.weight.detach().numpy())
+    nonzero_feat = np.sum(latent > 0)
+
     latent = dense_to_sparse_tf(latent)
+    latent = dropout_sparse_tf(latent, keep_prob,
+        nonzero_feat)
+
     adj_mat = dense_to_sparse_tf(adjacency_matrices[0])
+
     latent = tf.sparse_tensor_dense_matmul(latent, weight)
     latent = tf.sparse_tensor_dense_matmul(adj_mat, latent)
+
+    latent = tf.nn.relu(latent)
+
     return latent
 
 
@@ -62,7 +109,18 @@ def test_sparse_graph_conv():
 
 
 def test_sparse_dropout_grap_conv_activation():
-    pass
+    for i in range(11):
+        keep_prob = i/10. + np.finfo(np.float32).eps
+
+        latent_torch = sparse_dropout_graph_conv_activation_torch(keep_prob)
+        latent_tf = sparse_dropout_graph_conv_activation_tf(keep_prob)
+
+        latent_torch = latent_torch.detach().numpy()
+        latent_tf = latent_tf.eval(session = tf.Session())
+        print('latent_torch:', latent_torch)
+        print('latent_tf:', latent_tf)
+
+        assert np.all(latent_torch - latent_tf < .000001)
 
 
 def test_sparse_multi_dgca():