Add F# example to readme

- Added example of eager mode linear regression to the readme
- Added the example data to the C# example

README.md

@@ -33,6 +33,14 @@ In comparison to other projects, like for instance [TensorFlowSharp](https://www
 | tf.net 0.15               | x             | x              |               |
 | tf.net 0.14               | x             |                |               |
 
+Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
+
+There are many examples reside at [TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples).
+
+Troubleshooting of running example or installation, please  refer [here](tensorflowlib/README.md).
+
+#### C# Example
+
 Install TF.NET and TensorFlow binary through NuGet.
 ```sh
 ### install tensorflow C#/F# binding
@@ -63,6 +71,13 @@ int training_steps = 1000;
 float learning_rate = 0.01f;
 int display_step = 100;
 
+// Sample data
+train_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);
+train_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);
+n_samples = train_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");
@@ -142,11 +157,65 @@ model.fit(x_train[new Slice(0, 1000)], y_train[new Slice(0, 1000)],
           validation_split: 0.2f);
 ```
 
-Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
+#### F# Example
 
-There are many examples reside at [TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples).
+Linear Regression in `Eager` mode:
+
+```fsharp
+#r "nuget: TensorFlow.Net"
+#r "nuget: TensorFlow.Keras"
+#r "nuget: SciSharp.TensorFlow.Redist"
+#r "nuget: NumSharp"
+
+open System
+open NumSharp
+open Tensorflow
+open Tensorflow.Keras
+
+let tf = Binding.New<tensorflow>()
+tf.enable_eager_execution()
+
+// Parameters
+let training_steps = 1000
+let learning_rate = 0.01f
+let display_step = 100
+
+// Sample data
+let train_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)
+let train_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)
+let n_samples = train_X.shape.[0]
+
+// We can set a fixed init value in order to demo
+let W = tf.Variable(-0.06f,name = "weight")
+let b = tf.Variable(-0.73f, name = "bias")
+let optimizer = KerasApi.keras.optimizers.SGD(learning_rate)
+
+// Run training for the given number of steps.
+for step = 1 to  (training_steps + 1) do 
+    // Run the optimization to update W and b values.
+    // Wrap computation inside a GradientTape for automatic differentiation.
+    use g = tf.GradientTape()
+    // Linear regressoin (Wx + b).
+    let pred = W * train_X + b
+    // Mean square error.
+    let loss = tf.reduce_sum(tf.pow(pred - train_Y,2)) / (2 * n_samples)
+    // should stop recording
+    // compute gradients
+    let gradients = g.gradient(loss,struct (W,b))
+
+    // Update W and b following gradients.
+    optimizer.apply_gradients(Binding.zip(gradients, struct (W,b)))
+
+    if (step % display_step) = 0 then
+        let pred = W * train_X + b
+        let loss = tf.reduce_sum(tf.pow(pred-train_Y,2)) / (2 * n_samples)
+        printfn $"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}"
+```
 
-Troubleshooting of running example or installation, please  refer [here](tensorflowlib/README.md).
 
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