Struct Softmax 

pub struct Softmax;

Softmax is a builder for Softmax Activation Function

It converts a vector of values into a normalized probability distribution, where each element is in the range (0, 1) and all elements sum to 1. It is typically used in the output layer of a classification model to represent confidence scores across multiple classes.

Range: (0, 1) for each output element Best for: Output layers of multi-class classification models.

Implementations

impl Softmax

pub fn build() -> Result<Box<dyn ActivationFunction>, NetworkError>

Examples found in repository

examples/triplets/triplets.rs (line 182)

fn triplets_network(inp_size: usize, targ_size: usize) -> Network {
    let network = NetworkBuilder::new(inp_size, targ_size)
        .layer(Dense::default().size(24).activation(ReLU::build()).build())
        .layer(Dense::default().size(targ_size).activation(Softmax::build()).build())
        .loss_function(CrossEntropy::default().epsilon(1e-8).build())
        .optimizer(Adam::default().beta1(0.99).beta2(0.999).learning_rate(0.0035).build())
        .batch_size(8)
        .batch_group_size(2)
        .parallelize(2)
        .epochs(1000)
        .seed(55)
        .build();

    match network {
        Ok(net) => net,
        Err(e) => {
            eprintln!("Failed to build network: {}", e);
            std::process::exit(1);
        }
    }
}

examples/wine/wine.rs (line 119)

fn one_hot_encode_network(inp_size: usize, targ_size: usize) -> Network {
    let network = NetworkBuilder::new(inp_size, targ_size)
        .layer(Dense::default().size(7).activation(ReLU::build()).build())
        .layer(Dense::default().size(5).activation(ReLU::build()).build())
        .layer(Dense::default().size(targ_size).activation(Softmax::build()).build())
        .optimizer(Adam::default().beta1(0.99).beta2(0.999).learning_rate(0.0035).build())
        .loss_function(CrossEntropy::default().epsilon(1e-8).build())
        .batch_size(4)
        .normalize_input(MinMax::default())
        .epochs(500)
        .seed(55)
        .build();

    match network {
        Ok(net) => net,
        Err(e) => {
            error!("Failed to build network: {}", e);
            std::process::exit(1);
        }
    }
}

examples/iris/iris.rs (line 112)

fn iris_network(inp_size: usize, targ_size: usize) -> Network {
    let network = NetworkBuilder::new(inp_size, targ_size)
        .layer(Dense::default().size(12).activation(ReLU::build()).build())
        .layer(Dense::default().size(12).activation(ReLU::build()).build())
        .layer(Dense::default().size(targ_size).activation(Softmax::build()).build())
        .loss_function(CrossEntropy::default().epsilon(1e-8).build())
        .optimizer(Adam::default().beta1(0.99).beta2(0.999).learning_rate(0.0035).build())
        .batch_size(9)
        .batch_group_size(2)
        .parallelize(2)
        .epochs(3000)
        .seed(55)
        .build();

    match network {
        Ok(net) => net,
        Err(e) => {
            eprintln!("Failed to build network: {}", e);
            std::process::exit(1);
        }
    }
}

Trait Implementations

impl Default for Softmax

fn default() -> Self

Creates a new Softmax activation function Softmax weight initialization factor is set to Xavier initialization.