test(mlp/new,default): add doc tests

src/neural_network/mlp.rs

@@ -13,6 +13,35 @@ pub struct Network {
 
 impl Network {
     /// Creates a new `Network` with the specified layers, activation function, learning rate, and optimizer.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// # use engram::*;
+    /// let network = Network::new(
+    ///     &[6, 4, 1],
+    ///     Initializer::Xavier,
+    ///     Activation::ReLU,
+    ///     LossFunction::MeanSquaredError,
+    ///     Optimizer::Adagrad {
+    ///         learning_rate: 0.1,
+    ///         epsilon: 1e-8,
+    ///         weight_decay: Some(0.01),
+    ///         shape: (3, 1),
+    ///     },
+    /// );
+    /// assert_eq!(network.layers.len(), 2);
+    /// assert_eq!(network.layers[0].weights.shape(), (6, 4));
+    /// assert_eq!(network.layers[1].weights.shape(), (4, 1));
+    /// assert_eq!(network.layers[0].activation, Activation::ReLU);
+    /// assert_eq!(network.loss_function, LossFunction::MeanSquaredError);
+    /// assert_eq!(network.optimizer, Optimizer::Adagrad {
+    ///    learning_rate: 0.1,
+    ///    epsilon: 1e-8,
+    ///    weight_decay: Some(0.01),
+    ///    shape: (3, 1),
+    /// })
+    /// ```
     pub fn new(
         layer_sizes: &[usize],
         initializer: Initializer,
@@ -42,6 +71,20 @@ impl Network {
 
     /// Creates a new `Network` with defaults: xavier initialization, sigmoid activation,
     /// and stochastic gradient descent optimizer with a learning rate of 0.1.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// # use engram::*;
+    /// let network = Network::default(&[6, 4, 2, 3]);
+    /// assert_eq!(network.layers.len(), 3);
+    /// assert_eq!(network.layers[0].weights.shape(), (6, 4));
+    /// assert_eq!(network.layers[1].weights.shape(), (4, 2));
+    /// assert_eq!(network.layers[2].weights.shape(), (2, 3));
+    /// assert_eq!(network.layers[0].activation, Activation::Sigmoid);
+    /// assert_eq!(network.loss_function, LossFunction::BinaryCrossEntropy);
+    /// assert_eq!(network.optimizer, Optimizer::SGD { learning_rate: 0.1 })
+    /// ```
     pub fn default(layer_sizes: &[usize]) -> Network {
         Network::new(
             layer_sizes,