Struct Sequential 

pub struct Sequential { /* private fields */ }

Implementations

impl Sequential

pub fn new() -> Self

pub fn new_simple(net_cfg: &Vec<usize>) -> Self

Examples found in repository

examples/xor/main.rs (line 32)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    init_logger();
    log::info!("nevermind-neu xor example starting...");

    // prepare data set
    let mut dataset_train: Vec<LabeledEntry> = Vec::new();
    dataset_train.push(LabeledEntry::new(vec![0.0, 0.0], vec![0.0]));
    dataset_train.push(LabeledEntry::new(vec![0.0, 1.0], vec![1.0]));
    dataset_train.push(LabeledEntry::new(vec![1.0, 0.0], vec![1.0]));
    dataset_train.push(LabeledEntry::new(vec![1.0, 1.0], vec![0.0]));

    let dataloader = Box::new(SimpleDataLoader::new(dataset_train));

    let net_cfg = vec![2, 10, 1];
    let mut seq_mdl = Sequential::new_simple(&net_cfg);
    seq_mdl.set_batch_size(4);

    let opt = Box::new(OptimizerRMS::new(1e-2, 0.8));
    seq_mdl.set_optim(opt);

    let mut net = Orchestra::new(seq_mdl).test_batch_size(4);

    net.set_save_on_finish_flag(false);
    net.set_train_dataset(dataloader);

    let now_time = Instant::now();

    net.train_for_error_or_iter(0.05, 2000)?;

    let elapsed_bench = now_time.elapsed();

    info!("Elapsed for training : {} ms", elapsed_bench.as_millis());

    info!("Now testing net !!!");

    let out = net.eval(array![[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]).unwrap();
    let out_b = out.borrow();

    info!("Trained-net XOR out : {}", out_b);

    Ok(())
}

pub fn from_yaml(yaml_str: &str) -> Result<Self, Box<dyn Error>>

pub fn new_with_layers(ls: SequentialLayersStorage) -> Self

pub fn from_file(filepath: &str) -> Result<Self, Box<dyn Error>>

pub fn to_file(&self, filepath: &str) -> Result<(), Box<dyn Error>>

pub fn compile_shapes(&mut self)

pub fn set_optim(&mut self, optim: Box<dyn Optimizer>)

Examples found in repository

examples/xor/main.rs (line 36)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    init_logger();
    log::info!("nevermind-neu xor example starting...");

    // prepare data set
    let mut dataset_train: Vec<LabeledEntry> = Vec::new();
    dataset_train.push(LabeledEntry::new(vec![0.0, 0.0], vec![0.0]));
    dataset_train.push(LabeledEntry::new(vec![0.0, 1.0], vec![1.0]));
    dataset_train.push(LabeledEntry::new(vec![1.0, 0.0], vec![1.0]));
    dataset_train.push(LabeledEntry::new(vec![1.0, 1.0], vec![0.0]));

    let dataloader = Box::new(SimpleDataLoader::new(dataset_train));

    let net_cfg = vec![2, 10, 1];
    let mut seq_mdl = Sequential::new_simple(&net_cfg);
    seq_mdl.set_batch_size(4);

    let opt = Box::new(OptimizerRMS::new(1e-2, 0.8));
    seq_mdl.set_optim(opt);

    let mut net = Orchestra::new(seq_mdl).test_batch_size(4);

    net.set_save_on_finish_flag(false);
    net.set_train_dataset(dataloader);

    let now_time = Instant::now();

    net.train_for_error_or_iter(0.05, 2000)?;

    let elapsed_bench = now_time.elapsed();

    info!("Elapsed for training : {} ms", elapsed_bench.as_millis());

    info!("Now testing net !!!");

    let out = net.eval(array![[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]).unwrap();
    let out_b = out.borrow();

    info!("Trained-net XOR out : {}", out_b);

    Ok(())
}

pub fn add_layer(&mut self, l: Box<dyn AbstractLayer>)

Trait Implementations

impl Clone for Sequential

fn clone(&self) -> Sequential

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'de> Deserialize<'de> for Sequential

fn deserialize<D>(deserializer: D) -> Result<Sequential, D::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Model for Sequential

fn feedforward(&mut self, train_data: Array2D)

fn backpropagate(&mut self, expected: Array2D)

fn optimize(&mut self)

fn optimizer(&self) -> &Box<dyn WithParams>

fn optimizer_mut(&mut self) -> &mut Box<dyn WithParams>

fn model_type(&self) -> &str

fn output_params(&self) -> CpuParams

fn last_layer_metrics(&self) -> Option<&Metrics>

fn batch_size(&self) -> usize

fn set_batch_size(&mut self, batch_size: usize)

fn set_batch_size_for_tests(&mut self, batch_size: usize)

fn layer(&self, id: usize) -> &Box<dyn AbstractLayer>

fn layers_count(&self) -> usize

fn last_layer(&self) -> &Box<dyn AbstractLayer>

fn save_state(&self, filepath: &str) -> Result<(), Box<dyn Error>>

fn load_state(&mut self, filepath: &str) -> Result<(), Box<dyn Error>>

impl Serialize for Sequential

fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>

where S: Serializer,

Serialize this value into the given Serde serializer.