diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
index c6929a4b..91e5e85a 100644
--- a/src/TensorFlowNET.Keras/Layers/LayersApi.cs
+++ b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
@@ -85,7 +85,7 @@ namespace Tensorflow.Keras.Layers
         /// <param name="bias_initializer">Initializer for the bias vector (see keras.initializers).</param>
         /// <returns>A tensor of rank 3 representing activation(conv1d(inputs, kernel) + bias).</returns>
         public Conv1D Conv1D(int filters,
-            Shape? kernel_size = null,
+            Shape kernel_size,
             int? strides = null,
             string padding = "valid",
             string data_format = null,
diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs
index 81fb3ea1..fbe4330c 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Convolution.Test.cs
@@ -14,14 +14,12 @@ namespace TensorFlowNET.Keras.UnitTest
         {
             var filters = 8;
 
-            var conv = keras.layers.Conv1D(filters, activation: "linear");
+            var conv = keras.layers.Conv1D(filters, kernel_size: 3,  activation: "linear");
 
             var x = np.arange(256.0f).reshape((8, 8, 4));
             var y = conv.Apply(x);
 
-            Assert.AreEqual(3, y.shape.ndim);
-            Assert.AreEqual(x.dims[0], y.shape[0]);
-            Assert.AreEqual(x.dims[1] - 4, y.shape[1]);
+            Assert.AreEqual(y.shape, (8, 6, 8));
             Assert.AreEqual(filters, y.shape[2]);
         }