Struct MaxPool2d 

pub struct MaxPool2d { /* private fields */ }

2D max-pooling layer.

Slides a window of kernel_size over the input’s spatial dimensions and takes the max in each window.

Input: [N, C, H, W] Output: [N, C, H_out, W_out]

Implementations

impl MaxPool2d

pub fn new( kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], ) -> MaxPool2d

Create a new MaxPool2d layer.

Arguments

• kernel_size: [kH, kW]
• stride: [sH, sW] — typically equal to kernel_size
• padding: [pH, pW]

Trait Implementations

impl<B> Module<B> for MaxPool2d

where B: Backend,

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 set_training(&self, _training: bool)

Set training or evaluation mode.

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

Return all trainable parameters with human-readable names.

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

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