diff --git a/src/icosagon/decode.py b/src/icosagon/decode.py
index 16efd83..dccf508 100644
--- a/src/icosagon/decode.py
+++ b/src/icosagon/decode.py
@@ -11,13 +11,13 @@ from .dropout import dropout
 
 class DEDICOMDecoder(torch.nn.Module):
     """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
-    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
+    def __init__(self, input_dim, num_relation_types, keep_prob=1.,
         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.keep_prob = keep_prob
         self.activation = activation
 
         self.global_interaction = init_glorot(input_dim, input_dim)
@@ -29,8 +29,8 @@ class DEDICOMDecoder(torch.nn.Module):
     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)
+            inputs_row = dropout(inputs_row, self.keep_prob)
+            inputs_col = dropout(inputs_col, self.keep_prob)
 
             relation = torch.diag(self.local_variation[k])
 
@@ -46,13 +46,13 @@ class DEDICOMDecoder(torch.nn.Module):
 
 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.,
+    def __init__(self, input_dim, num_relation_types, keep_prob=1.,
         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.keep_prob = keep_prob
         self.activation = activation
 
         self.relation = [
@@ -63,8 +63,8 @@ class DistMultDecoder(torch.nn.Module):
     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)
+            inputs_row = dropout(inputs_row, self.keep_prob)
+            inputs_col = dropout(inputs_col, self.keep_prob)
 
             relation = torch.diag(self.relation[k])
 
@@ -78,13 +78,13 @@ class DistMultDecoder(torch.nn.Module):
 
 class BilinearDecoder(torch.nn.Module):
     """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
-    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
+    def __init__(self, input_dim, num_relation_types, keep_prob=1.,
         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.keep_prob = keep_prob
         self.activation = activation
 
         self.relation = [
@@ -95,8 +95,8 @@ class BilinearDecoder(torch.nn.Module):
     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)
+            inputs_row = dropout(inputs_row, self.keep_prob)
+            inputs_col = dropout(inputs_col, self.keep_prob)
 
             intermediate_product = torch.mm(inputs_row, self.relation[k])
             rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
@@ -108,21 +108,21 @@ class BilinearDecoder(torch.nn.Module):
 
 class InnerProductDecoder(torch.nn.Module):
     """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
-    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
+    def __init__(self, input_dim, num_relation_types, keep_prob=1.,
         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.keep_prob = keep_prob
         self.activation = activation
 
 
     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)
+            inputs_row = dropout(inputs_row, self.keep_prob)
+            inputs_col = dropout(inputs_col, self.keep_prob)
 
             rec = torch.bmm(inputs_row.view(inputs_row.shape[0], 1, inputs_row.shape[1]),
                 inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
diff --git a/tests/icosagon/test_decode.py b/tests/icosagon/test_decode.py
new file mode 100644
index 0000000..001f411
--- /dev/null
+++ b/tests/icosagon/test_decode.py
@@ -0,0 +1,86 @@
+from icosagon.decode import DEDICOMDecoder, \
+    DistMultDecoder, \
+    BilinearDecoder, \
+    InnerProductDecoder
+import decagon_pytorch.decode.pairwise
+import torch
+
+
+def test_dedicom_decoder_01():
+    repr_ = torch.rand(20, 32)
+    dec_1 = DEDICOMDecoder(32, 7, keep_prob=1.,
+        activation=torch.sigmoid)
+    dec_2 = decagon_pytorch.decode.pairwise.DEDICOMDecoder(32, 7, drop_prob=0.,
+        activation=torch.sigmoid)
+    dec_2.global_interaction = dec_1.global_interaction
+    dec_2.local_variation = dec_1.local_variation
+
+    res_1 = dec_1(repr_, repr_)
+    res_2 = dec_2(repr_, repr_)
+
+    assert isinstance(res_1, list)
+    assert isinstance(res_2, list)
+
+    assert len(res_1) == len(res_2)
+
+    for i in range(len(res_1)):
+        assert torch.all(res_1[i] == res_2[i])
+
+
+def test_dist_mult_decoder_01():
+    repr_ = torch.rand(20, 32)
+    dec_1 = DistMultDecoder(32, 7, keep_prob=1.,
+        activation=torch.sigmoid)
+    dec_2 = decagon_pytorch.decode.pairwise.DistMultDecoder(32, 7, drop_prob=0.,
+        activation=torch.sigmoid)
+    dec_2.relation = dec_1.relation
+
+    res_1 = dec_1(repr_, repr_)
+    res_2 = dec_2(repr_, repr_)
+
+    assert isinstance(res_1, list)
+    assert isinstance(res_2, list)
+
+    assert len(res_1) == len(res_2)
+
+    for i in range(len(res_1)):
+        assert torch.all(res_1[i] == res_2[i])
+
+
+def test_bilinear_decoder_01():
+    repr_ = torch.rand(20, 32)
+    dec_1 = BilinearDecoder(32, 7, keep_prob=1.,
+        activation=torch.sigmoid)
+    dec_2 = decagon_pytorch.decode.pairwise.BilinearDecoder(32, 7, drop_prob=0.,
+        activation=torch.sigmoid)
+    dec_2.relation = dec_1.relation
+
+    res_1 = dec_1(repr_, repr_)
+    res_2 = dec_2(repr_, repr_)
+
+    assert isinstance(res_1, list)
+    assert isinstance(res_2, list)
+
+    assert len(res_1) == len(res_2)
+
+    for i in range(len(res_1)):
+        assert torch.all(res_1[i] == res_2[i])
+
+
+def test_inner_product_decoder_01():
+    repr_ = torch.rand(20, 32)
+    dec_1 = InnerProductDecoder(32, 7, keep_prob=1.,
+        activation=torch.sigmoid)
+    dec_2 = decagon_pytorch.decode.pairwise.InnerProductDecoder(32, 7, drop_prob=0.,
+        activation=torch.sigmoid)
+
+    res_1 = dec_1(repr_, repr_)
+    res_2 = dec_2(repr_, repr_)
+
+    assert isinstance(res_1, list)
+    assert isinstance(res_2, list)
+
+    assert len(res_1) == len(res_2)
+
+    for i in range(len(res_1)):
+        assert torch.all(res_1[i] == res_2[i])