/// Train the network using SGD for `num_epochs` epochs, checking accuracy on
/// `validation_data` if it's provided after each epoch.
Train the network using SGD for `num_epochs` epochs, checking accuracy on
`validation_data` if it's provided after each epoch.
Train the network using SGD for ` num_epochs ` epochs checking accuracy on ` validation_data ` if it ' s provided after each epoch .
pub fn train(
        &mut self,
        mut training_data: Vec<(Input, Label)>,
        num_epochs: u32,
        minibatch_size: u32,
        learning_rate: f64,
        validation_data: Vec<(Input, Label)>,
    ) {
        let mut rng = thread_rng();
        for epoch in 0..num_epochs {
            println!("Training epoch {} / {}", epoch, num_epochs);
            // Shuffle the data so minibatch slices are randomized in each epoch
            training_data.shuffle(&mut rng);
            let pb = ProgressBar::new(training_data.len() as u64 / minibatch_size as u64);
            pb.set_style(
                ProgressStyle::default_bar().template("[{elapsed_precise}] {wide_bar} ({eta})"),
            );
            // Update each minibatch
            for start_ind in (0..training_data.len()).step_by(minibatch_size as usize) {
                // Avoid going out of bounds if if the minibatch size doesn't divide
                // the length of the input training data
                let upper_ind = std::cmp::min(start_ind + minibatch_size as usize, training_data.len());
                let minibatch = &mut training_data[start_ind..upper_ind as usize];
                self.update_minibatch(minibatch, learning_rate);
                pb.inc(1);
            }
            pb.finish_with_message("Finished epoch");
            // Assess the current performance on the provided holdout set
            let (holdout_correct, holdout_total) = self.evaluate(&validation_data);
            println!(
                "[Training] Epoch {} done. Holdout accuracy: {} / {}",
                epoch, holdout_correct, holdout_total
            );
        }
    }
pub fn train ( & mut self , mut training_data : Vec < ( Input , Label ) > , num_epochs : u32 , minibatch_size : u32 , learning_rate : f64 , validation_data : Vec < ( Input , Label ) > , ) { let mut rng = thread_rng ( ) ; for epoch in 0 .. num_epochs { println ! ( " " epoch num_epochs ) ; // Shuffle the data so minibatch slices are randomized in each epoch training_data . shuffle ( & mut rng ) ; let pb = ProgressBar :: new ( training_data . len ( ) as u64 / minibatch_size as u64 ) ; pb . set_style ( ProgressStyle :: default_bar ( ) . template ( " " ) , ) ; // Update each minibatch for start_ind in ( 0 .. training_data . len ( ) ) . step_by ( minibatch_size as usize ) { // Avoid going out of bounds if if the minibatch size doesn't divide // the length of the input training data let upper_ind = std :: cmp :: min ( start_ind + minibatch_size as usize , training_data . len ( ) ) ; let minibatch = & mut training_data [ start_ind .. upper_ind as usize ] ; self . update_minibatch ( minibatch , learning_rate ) ; pb . inc ( 1 ) ; } pb . finish_with_message ( " " ) ; // Assess the current performance on the provided holdout set let ( holdout_correct , holdout_total ) = self . evaluate ( & validation_data ) ; println ! ( " " epoch holdout_correct holdout_total ) ; } }
Train the network using SGD for `num_epochs` epochs, checking accuracy on
`validation_data` if it's provided after each epoch.
Train the network using SGD for ` num_epochs ` epochs checking accuracy on ` validation_data ` if it ' s provided after each epoch .
&mut self
training_data
Vec<(Input, Label)>
num_epochs
u32
minibatch_size
u32
learning_rate
f64
validation_data
Vec<(Input, Label)>