fix unit test.

test/TensorFlowNET.UnitTest/Keras/LayersTest.cs

@@ -30,10 +30,10 @@ namespace TensorFlowNET.UnitTest.Keras
             var inputs = keras.Input(shape: 784);
             Assert.AreEqual((None, 784), inputs.TensorShape);
 
-            var dense = layers.Dense(64, activation: "relu");
+            var dense = layers.Dense(64, activation: tf.keras.activations.Relu);
             var x = dense.Apply(inputs);
 
-            x = layers.Dense(64, activation: "relu").Apply(x);
+            x = layers.Dense(64, activation: tf.keras.activations.Relu).Apply(x);
             var outputs = layers.Dense(10).Apply(x);
 
             var model = keras.Model(inputs, outputs, name: "mnist_model");
@@ -75,7 +75,7 @@ namespace TensorFlowNET.UnitTest.Keras
             // Create a `Sequential` model and add a Dense layer as the first layer.
             var model = tf.keras.Sequential();
             model.add(tf.keras.Input(shape: 16));
-            model.add(tf.keras.layers.Dense(32, activation: "relu"));
+            model.add(tf.keras.layers.Dense(32, activation: tf.keras.activations.Relu));
             // Now the model will take as input arrays of shape (None, 16)
             // and output arrays of shape (None, 32).
             // Note that after the first layer, you don't need to specify