Struct Sequential 

pub struct Sequential<B>
where
    B: Backend,
{ /* private fields */ }

A container that chains modules sequentially.

Each module’s output becomes the next module’s input. Sequential itself implements Module, so it can be nested.

Implementations

impl<B> Sequential<B>

where B: Backend,

pub fn new() -> Sequential<B>

Create an empty Sequential.

pub fn add<M>(self, module: M) -> Sequential<B>

where M: Module<B> + 'static,

Add a layer to the end of the sequence. Returns self for chaining.

pub fn len(&self) -> usize

Number of layers.

pub fn is_empty(&self) -> bool

Whether the sequential is empty.

Trait Implementations

impl<B> Default for Sequential<B>

where B: Backend,

fn default() -> Sequential<B>

Returns the “default value” for a type.

impl<B> Module<B> for Sequential<B>

where B: Backend,

fn set_training(&self, training: bool)

Propagate training mode to all child layers.

fn forward(&self, x: &Tensor<B>) -> Result<Tensor<B>, Error>

Compute the output tensor from the input tensor. This defines the layer’s computation (forward pass).

fn parameters(&self) -> Vec<Tensor<B>>

Return all trainable parameters of this module. The optimizer uses these to update weights during training.

fn named_parameters(&self) -> Vec<(String, Tensor<B>)>

Return all trainable parameters with human-readable names.

fn is_training(&self) -> bool

Whether the module is in training mode (default: true).

fn train(&self)

Convenience: set training mode.

fn eval(&self)

Convenience: set evaluation mode.

fn num_parameters(&self) -> usize

Total number of scalar parameters in this module.

fn trainable_params_count(&self) -> usize

Number of trainable (variable) parameters.

fn frozen_parameters(&self) -> Vec<Tensor<B>>

Freeze all parameters: returns new parameter tensors with is_variable = false, preventing gradient accumulation.

fn state_dict(&self) -> Vec<(String, Tensor<B>)>

Returns a state_dict-style map of parameter name → tensor.