Struct HistogramCallback 

pub struct HistogramCallback {
    pub history: Vec<HashMap<String, HistogramStats>>,
    /* private fields */
}

Callback for tracking weight histograms during training.

This callback computes and logs histogram statistics of model parameters at regular intervals. Useful for:

• Detecting vanishing/exploding weights
• Monitoring weight distribution changes
• Debugging initialization issues
• Understanding parameter evolution

Example

use tensorlogic_train::{CallbackList, HistogramCallback};

let mut callbacks = CallbackList::new();
callbacks.add(Box::new(HistogramCallback::new(
    5,   // log_frequency: Every 5 epochs
    10,  // num_bins: 10 histogram bins
    true, // verbose: Print detailed histograms
)));

Fields

history: Vec<HashMap<String, HistogramStats>>

History of histogram statistics.

Implementations

impl HistogramCallback

pub fn new(log_frequency: usize, num_bins: usize, verbose: bool) -> Self

Create a new histogram callback.

Arguments

• log_frequency - Log histograms every N epochs
• num_bins - Number of bins in each histogram
• verbose - Print detailed ASCII histograms

Trait Implementations

impl Callback for HistogramCallback

fn on_epoch_end( &mut self, epoch: usize, state: &TrainingState, ) -> TrainResult<()>

Called at the end of an epoch.

fn on_train_begin(&mut self, _state: &TrainingState) -> TrainResult<()>

Called at the beginning of training.

fn on_train_end(&mut self, _state: &TrainingState) -> TrainResult<()>

Called at the end of training.

fn on_epoch_begin( &mut self, _epoch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the beginning of an epoch.

fn on_batch_begin( &mut self, _batch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the beginning of a batch.

fn on_batch_end( &mut self, _batch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the end of a batch.

fn on_validation_end(&mut self, _state: &TrainingState) -> TrainResult<()>

Called after validation.

fn should_stop(&self) -> bool

Check if training should stop early.