diff --git a/src/decagon_pytorch/decode.py b/src/decagon_pytorch/decode.py
index 8ff6096..3643652 100644
--- a/src/decagon_pytorch/decode.py
+++ b/src/decagon_pytorch/decode.py
@@ -14,7 +14,6 @@ class DEDICOMDecoder(torch.nn.Module):
         self.drop_prob = drop_prob
         self.activation = activation
 
-
         self.global_interaction = init_glorot(input_dim, input_dim)
         self.local_variation = [
             torch.flatten(init_glorot(input_dim, 1)) \
@@ -35,3 +34,33 @@ class DEDICOMDecoder(torch.nn.Module):
             rec = torch.mm(product3, torch.transpose(inputs_col, 0, 1))
             outputs.append(self.activation(rec))
         return outputs
+
+
+class DistMultDecoder(torch.nn.Module):
+    """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
+    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
+        activation=torch.sigmoid, **kwargs):
+
+        super().__init__(**kwargs)
+        self.input_dim = input_dim
+        self.num_relation_types = num_relation_types
+        self.drop_prob = drop_prob
+        self.activation = activation
+
+        self.relation = [
+            torch.flatten(init_glorot(input_dim, 1)) \
+                for _ in range(num_relation_types)
+        ]
+
+    def forward(self, inputs_row, inputs_col):
+        outputs = []
+        for k in range(self.num_relation_types):
+            inputs_row = dropout(inputs_row, 1.-self.drop_prob)
+            inputs_col = dropout(inputs_col, 1.-self.drop_prob)
+
+            relation = torch.diag(self.relation[k])
+
+            intermediate_product = torch.mm(inputs_row, relation)
+            rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
+            outputs.append(self.activation(rec))
+        return outputs
diff --git a/tests/decagon_pytorch/test_decode.py b/tests/decagon_pytorch/test_decode.py
index 7e8c4ed..a374480 100644
--- a/tests/decagon_pytorch/test_decode.py
+++ b/tests/decagon_pytorch/test_decode.py
@@ -5,25 +5,14 @@ import tensorflow as tf
 import torch
 
 
-def test_dedicom():
-    dedicom_torch = decagon_pytorch.decode.DEDICOMDecoder(input_dim=10,
-        num_relation_types=7)
-    dedicom_tf = decagon.deep.layers.DEDICOMDecoder(input_dim=10, num_types=7,
-        edge_type=(0, 0))
-
-    dedicom_tf.vars['global_interaction'] = \
-        tf.convert_to_tensor(dedicom_torch.global_interaction.detach().numpy())
-    for i in range(dedicom_tf.num_types):
-        dedicom_tf.vars['local_variation_%d' % i] = \
-            tf.convert_to_tensor(dedicom_torch.local_variation[i].detach().numpy())
-
+def _common(decoder_torch, decoder_tf):
     inputs = np.random.rand(20, 10).astype(np.float32)
     inputs_torch = torch.tensor(inputs)
     inputs_tf = {
         0: tf.convert_to_tensor(inputs)
     }
-    out_torch = dedicom_torch(inputs_torch, inputs_torch)
-    out_tf = dedicom_tf(inputs_tf)
+    out_torch = decoder_torch(inputs_torch, inputs_torch)
+    out_tf = decoder_tf(inputs_tf)
 
     assert len(out_torch) == len(out_tf)
     assert len(out_tf) == 7
@@ -39,3 +28,31 @@ def test_dedicom():
         print('item_tf:', item_tf)
         assert np.all(item_torch - item_tf < .000001)
     sess.close()
+
+
+def test_dedicom_decoder():
+    dedicom_torch = decagon_pytorch.decode.DEDICOMDecoder(input_dim=10,
+        num_relation_types=7)
+    dedicom_tf = decagon.deep.layers.DEDICOMDecoder(input_dim=10, num_types=7,
+        edge_type=(0, 0))
+
+    dedicom_tf.vars['global_interaction'] = \
+        tf.convert_to_tensor(dedicom_torch.global_interaction.detach().numpy())
+    for i in range(dedicom_tf.num_types):
+        dedicom_tf.vars['local_variation_%d' % i] = \
+            tf.convert_to_tensor(dedicom_torch.local_variation[i].detach().numpy())
+
+    _common(dedicom_torch, dedicom_tf)
+
+
+def test_dist_mult_decoder():
+    distmult_torch = decagon_pytorch.decode.DistMultDecoder(input_dim=10,
+        num_relation_types=7)
+    distmult_tf = decagon.deep.layers.DistMultDecoder(input_dim=10, num_types=7,
+        edge_type=(0, 0))
+
+    for i in range(distmult_tf.num_types):
+        distmult_tf.vars['relation_%d' % i] = \
+            tf.convert_to_tensor(distmult_torch.relation[i].detach().numpy())
+
+    _common(distmult_torch, distmult_tf)