TensorFlow.NET/docs/Example-fsharp.md

Linear Regression in `Eager` mode:

```fsharp
#r "nuget: TensorFlow.Net"
#r "nuget: TensorFlow.Keras"
#r "nuget: SciSharp.TensorFlow.Redist"

open Tensorflow
open Tensorflow.NumPy
open type Tensorflow.Binding
open type Tensorflow.KerasApi

let tf = 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 = 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 regression (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(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()}"
```