diff --git a/README.md b/README.md
index 1e53d2ab..4f8f62ee 100644
--- a/README.md
+++ b/README.md
@@ -112,46 +112,40 @@ Run this example in [Jupyter Notebook](https://github.com/SciSharp/SciSharpCube)
 Toy version of `ResNet` in `Keras` functional API:
 
 ```csharp
+var layers = new LayersApi();
 // input layer
 var inputs = keras.Input(shape: (32, 32, 3), name: "img");
-
 // convolutional layer
 var x = layers.Conv2D(32, 3, activation: "relu").Apply(inputs);
 x = layers.Conv2D(64, 3, activation: "relu").Apply(x);
 var block_1_output = layers.MaxPooling2D(3).Apply(x);
-
 x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_1_output);
 x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
-var block_2_output = layers.add(x, block_1_output);
-
+var block_2_output = layers.Add().Apply(new Tensors(x, block_1_output));
 x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_2_output);
 x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
-var block_3_output = layers.add(x, block_2_output);
-
+var block_3_output = layers.Add().Apply(new Tensors(x, block_2_output));
 x = layers.Conv2D(64, 3, activation: "relu").Apply(block_3_output);
 x = layers.GlobalAveragePooling2D().Apply(x);
 x = layers.Dense(256, activation: "relu").Apply(x);
 x = layers.Dropout(0.5f).Apply(x);
-
 // output layer
 var outputs = layers.Dense(10).Apply(x);
-
 // build keras model
-model = keras.Model(inputs, outputs, name: "toy_resnet");
+var model = keras.Model(inputs, outputs, name: "toy_resnet");
 model.summary();
-
 // compile keras model in tensorflow static graph
 model.compile(optimizer: keras.optimizers.RMSprop(1e-3f),
-	loss: keras.losses.CategoricalCrossentropy(from_logits: true),
-	metrics: new[] { "acc" });
-
+    loss: keras.losses.CategoricalCrossentropy(from_logits: true),
+    metrics: new[] { "acc" });
 // prepare dataset
 var ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data();
-
+x_train = x_train / 255.0f;
+y_train = np_utils.to_categorical(y_train, 10);
 // training
-model.fit(x_train[new Slice(0, 1000)], y_train[new Slice(0, 1000)], 
-          batch_size: 64, 
-          epochs: 10, 
+model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)],
+          batch_size: 64,
+          epochs: 10,
           validation_split: 0.2f);
 ```
 
@@ -260,4 +254,4 @@ WeChat Sponsor 微信打赏:
 
 TensorFlow.NET is a part of [SciSharp STACK](https://scisharp.github.io/SciSharp/)
 <br>
-<a href="http://scisharpstack.org"><img src="https://github.com/SciSharp/SciSharp/blob/master/art/scisharp-stack.png" width="391" height="100" /></a>
+<a href="http://scisharpstack.org"><img src="https://github.com/SciSharp/SciSharp/blob/master/art/scisharp-stack.png" width="391" height="100" /></a>
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