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() 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()}" ```