🎨 Introduce builder pattern for NeuraBatchedTrainer

README.md

@@ -0,0 +1,34 @@
+# NeurAmethyst
+
+A neural network library written in [Rust](https://www.rust-lang.org/) and for Rust, that focuses on flexibility and ease of use.
+
+```rust
+use neuramethyst::prelude::*;
+use neuramethyst::derivable::loss::CrossEntropy;
+
+// Create the network
+let network = neura_sequential![
+    neura_layer!("dense", 100),
+    neura_layer!("dropout", 0.5),
+    neura_layer!("dense", 40),
+    neura_layer!("dropout", 0.5),
+    neura_layer!("dense", 10),
+    neura_layer!("softmax"),
+];
+
+// Assemble the network together, allowing layers to infer the shape of the input data
+let mut network = network.construct(NeuraShape::Vector(100)).unwrap();
+
+// Train the network
+let trainer = NeuraBatchedTrainer::new()
+    .learning_rate(0.03)
+    .batch_size(128)
+    .epochs(20, 50000); // number of epochs and size of the training set
+
+trainer.train(
+    &NeuraBackprop::new(CrossEntropy),
+    &mut network,
+    input_data(),
+    test_data(),
+);
+```

examples/bivariate-forward.rs

@@ -45,7 +45,7 @@ fn main() {
 
     if std::env::args().any(|arg| arg == "draw") {
         for epoch in 0..200 {
-            let mut trainer = NeuraBatchedTrainer::new(0.03, 10);
+            let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.03).iterations(0);
             trainer.batch_size = 10;
 
             trainer.train(
@@ -84,7 +84,9 @@ fn main() {
             std::thread::sleep(std::time::Duration::new(0, 50_000_000));
         }
     } else {
-        let mut trainer = NeuraBatchedTrainer::new(0.03, 20 * 50);
+        let mut trainer = NeuraBatchedTrainer::new()
+            .learning_rate(0.03)
+            .iterations(20 * 50);
         trainer.batch_size = 10;
         trainer.log_iterations = 20;
 

examples/bivariate.rs

@@ -42,7 +42,7 @@ fn main() {
 
     if std::env::args().any(|arg| arg == "draw") {
         for epoch in 0..200 {
-            let mut trainer = NeuraBatchedTrainer::new(0.03, 10);
+            let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.03).iterations(0);
             trainer.batch_size = 10;
 
             trainer.train(
@@ -72,7 +72,9 @@ fn main() {
             std::thread::sleep(std::time::Duration::new(0, 50_000_000));
         }
     } else {
-        let mut trainer = NeuraBatchedTrainer::new(0.03, 20 * 50);
+        let mut trainer = NeuraBatchedTrainer::new()
+            .learning_rate(0.03)
+            .iterations(20 * 50);
         trainer.batch_size = 10;
         trainer.log_iterations = 20;
 

examples/densenet-fwdfwd.rs

@@ -42,7 +42,7 @@ fn main() {
 
     if std::env::args().any(|arg| arg == "draw") {
         for epoch in 0..200 {
-            let mut trainer = NeuraBatchedTrainer::new(0.03, 10);
+            let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.03).iterations(0);
             trainer.batch_size = 50;
 
             trainer.train(
@@ -81,7 +81,9 @@ fn main() {
             std::thread::sleep(std::time::Duration::new(0, 50_000_000));
         }
     } else {
-        let mut trainer = NeuraBatchedTrainer::new(0.03, 20 * 50);
+        let mut trainer = NeuraBatchedTrainer::new()
+            .learning_rate(0.03)
+            .iterations(20 * 50);
         trainer.batch_size = 50;
         trainer.log_iterations = 20;
 

examples/densenet.rs

@@ -41,7 +41,7 @@ fn main() {
 
     if std::env::args().any(|arg| arg == "draw") {
         for epoch in 0..200 {
-            let mut trainer = NeuraBatchedTrainer::new(0.03, 10);
+            let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.03).iterations(0);
             trainer.batch_size = 10;
 
             trainer.train(

examples/forward-progressive.rs

@@ -39,7 +39,7 @@ pub fn main() {
     let test_inputs = generator().filter(|x| x.1).take(50).collect::<Vec<_>>();
 
     let gradient_solver = NeuraForwardForward::new(Tanh, 0.5);
-    let mut trainer = NeuraBatchedTrainer::new(0.01, 200);
+    let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.01).iterations(0);
     trainer.batch_size = 256;
 
     for _epoch in 0..EPOCHS {

examples/generate-tests.rs

@@ -23,7 +23,7 @@ fn main() {
         (dvector![1.0, 1.0], dvector![0.0]),
     ];
 
-    let mut trainer = NeuraBatchedTrainer::new(0.05, 1);
+    let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.05).iterations(1);
     trainer.batch_size = 1;
 
     let mut parameters = vec![(

examples/xor.rs

@@ -32,7 +32,7 @@ fn main() {
         );
     }
 
-    let mut trainer = NeuraBatchedTrainer::new(0.05, 1000);
+    let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.05).iterations(0);
     trainer.batch_size = 6;
     trainer.log_iterations = 250;
     trainer.learning_momentum = 0.01;

src/axis.rs

@@ -33,7 +33,7 @@ impl NeuraAxisBase for NeuraAxisDefault {
         if inputs.len() != 1 {
             Err(NeuraAxisErr::InvalidAmount(inputs.len(), 1, Some(1)))
         } else {
-            Ok(*inputs[0].borrow())
+            Ok(inputs[0])
         }
     }
 }

src/gradient_solver/forward_forward.rs

@@ -202,7 +202,9 @@ mod test {
 
         let solver = NeuraForwardForward::new(Tanh, 0.25);
 
-        let trainer = NeuraBatchedTrainer::new(0.01, 20);
+        let trainer = NeuraBatchedTrainer::new()
+            .learning_rate(0.01)
+            .iterations(20);
 
         let inputs = (0..1).cycle().map(|_| {
             let mut rng = rand::thread_rng();

src/train.rs

@@ -44,14 +44,39 @@ impl Default for NeuraBatchedTrainer {
 }
 
 impl NeuraBatchedTrainer {
-    pub fn new(learning_rate: f64, iterations: usize) -> Self {
+    pub fn new() -> Self {
-        Self {
+        Self::default()
-            learning_rate,
+    }
-            iterations,
+
-            ..Default::default()
+    pub fn batch_size(mut self, batch_size: usize) -> Self {
+        self.batch_size = batch_size;
+        self
+    }
+
+    pub fn learning_rate(mut self, learning_rate: f64) -> Self {
+        self.learning_rate = learning_rate;
+        self
+    }
+
+    pub fn iterations(mut self, iterations: usize) -> Self {
+        self.iterations = iterations;
+        self
+    }
+
+    pub fn log_iterations(mut self, log_iterations: usize) -> Self {
+        self.log_iterations = log_iterations;
+        self
+    }
+
+    pub fn epochs(mut self, epochs: usize, training_size: usize) -> Self {
+        if self.log_iterations == 0 {
+            self.log_iterations = (training_size / self.batch_size).max(1);
+            self.iterations = (training_size * epochs / self.batch_size).max(1);
         }
+        self
     }
 
+    #[deprecated]
     pub fn with_epochs(
         learning_rate: f64,
         epochs: usize,

tests/xor.rs

@@ -36,7 +36,7 @@ fn test_xor_training() {
         (dvector![1.0, 1.0], dvector![0.0]),
     ];
 
-    let mut trainer = NeuraBatchedTrainer::new(0.05, 1);
+    let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.05).iterations(1);
     trainer.batch_size = 1;
 
     for iteration in 0..4 {