Fix csharp example

README.md

@@ -56,30 +56,32 @@ PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
 
 Import TF.NET and Keras API in your project.
 
-```cs
+```csharp
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
+using Tensorflow;
+using NumSharp;
 ```
 
 Linear Regression in `Eager` mode:
 
-```c#
+```csharp
 // Parameters        
 var training_steps = 1000;
 var learning_rate = 0.01f;
 var display_step = 100;
 
 // Sample data
-var train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
+var X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
              7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
-var train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
+var Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
              2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
-var n_samples = train_X.shape[0];
+var n_samples = X.shape[0];
 
 // We can set a fixed init value in order to demo
 var W = tf.Variable(-0.06f, name: "weight");
 var b = tf.Variable(-0.73f, name: "bias");
-var optimizer = tf.optimizers.SGD(learning_rate);
+var optimizer = keras.optimizers.SGD(learning_rate);
 
 // Run training for the given number of steps.
 foreach (var step in range(1, training_steps + 1))