# Chapter. Linear Regression

```csharp
// Prepare training 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, 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, 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
var n_samples = train_X.shape[0];
```

```csharp
// tf Graph Input
var X = tf.placeholder(tf.float32);
var Y = tf.placeholder(tf.float32);

// Set model weights 
// We can set a fixed init value in order to debug
// var rnd1 = rng.randn<float>();
// var rnd2 = rng.randn<float>();
var W = tf.Variable(-0.06f, name: "weight");
var b = tf.Variable(-0.73f, name: "bias");

// Construct a linear model
var pred = tf.add(tf.multiply(X, W), b);

// Mean squared error
var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);

// Gradient descent
// Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
```