diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
index dff9ff1b..6c3e3556 100644
--- a/src/TensorFlowNET.Keras/Layers/LayersApi.cs
+++ b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
@@ -66,7 +66,7 @@ namespace Tensorflow.Keras.Layers
                     Trainable = trainable,
                     Name = name
                 });
-#if false
+
         /// <summary>
         /// 2D convolution layer (e.g. spatial convolution over images).
         /// This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
@@ -119,7 +119,6 @@ namespace Tensorflow.Keras.Layers
                     ActivityRegularizer = activity_regularizer,
                     Activation = activation ?? keras.activations.Linear
                 });
-#endif
 
         /// <summary>
         /// 2D convolution layer (e.g. spatial convolution over images).
diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs
index b24b214a..5e71ece6 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs
@@ -12,7 +12,7 @@ namespace TensorFlowNET.Keras.UnitTest
         public void BasicConv2D()
         {
             var filters = 8;
-            var conv = keras.layers.Conv2D(filters);
+            var conv = keras.layers.Conv2D(filters, activation: "linear");
 
             var x = np.arange(256.0f).reshape(1,8,8,4);
             var y = conv.Apply(x);
@@ -28,7 +28,7 @@ namespace TensorFlowNET.Keras.UnitTest
         public void BasicConv2D_ksize()
         {
             var filters = 8;
-            var conv = keras.layers.Conv2D(filters, kernel_size: 3);
+            var conv = keras.layers.Conv2D(filters, kernel_size: 3, activation: "linear");
 
             var x = np.arange(256.0f).reshape(1, 8, 8, 4);
             var y = conv.Apply(x);
@@ -44,7 +44,7 @@ namespace TensorFlowNET.Keras.UnitTest
         public void BasicConv2D_ksize_same()
         {
             var filters = 8;
-            var conv = keras.layers.Conv2D(filters, kernel_size: 3, padding: "same");
+            var conv = keras.layers.Conv2D(filters, kernel_size: 3, padding: "same", activation: "linear");
 
             var x = np.arange(256.0f).reshape(1, 8, 8, 4);
             var y = conv.Apply(x);
@@ -60,7 +60,7 @@ namespace TensorFlowNET.Keras.UnitTest
         public void BasicConv2D_ksize_strides()
         {
             var filters = 8;
-            var conv = keras.layers.Conv2D(filters, kernel_size: 3, strides: 2);
+            var conv = keras.layers.Conv2D(filters, kernel_size: 3, strides: 2, activation: "linear");
 
             var x = np.arange(256.0f).reshape(1, 8, 8, 4);
             var y = conv.Apply(x);
@@ -76,7 +76,7 @@ namespace TensorFlowNET.Keras.UnitTest
         public void BasicConv2D_ksize_dilation()
         {
             var filters = 8;
-            var conv = keras.layers.Conv2D(filters, kernel_size: 3, dilation_rate: 2);
+            var conv = keras.layers.Conv2D(filters, kernel_size: 3, dilation_rate: 2, activation: "linear");
 
             var x = np.arange(256.0f).reshape(1, 8, 8, 4);
             var y = conv.Apply(x);
@@ -87,5 +87,21 @@ namespace TensorFlowNET.Keras.UnitTest
             Assert.AreEqual(x.shape[2] - 4, y.shape[2]);
             Assert.AreEqual(filters, y.shape[3]);
         }
+
+        [TestMethod]
+        public void BasicConv2D_ksize_dilation_same()
+        {
+            var filters = 8;
+            var conv = keras.layers.Conv2D(filters, kernel_size: 3, dilation_rate: 2, padding: "same", activation: "linear");
+
+            var x = np.arange(256.0f).reshape(1, 8, 8, 4);
+            var y = conv.Apply(x);
+
+            Assert.AreEqual(4, y.shape.ndim);
+            Assert.AreEqual(x.shape[0], y.shape[0]);
+            Assert.AreEqual(x.shape[1], y.shape[1]);
+            Assert.AreEqual(x.shape[2], y.shape[2]);
+            Assert.AreEqual(filters, y.shape[3]);
+        }
     }
 }