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//! TensorBoard logger callback implementation
use crate::callbacks::{Callback, CallbackContext, CallbackTiming};
use crate::error::Result;
use scirs2_core::ndarray::ScalarOperand;
use scirs2_core::numeric::{Float, NumAssign};
use std::fmt::Debug;
use std::path::{Path, PathBuf};
/// TensorBoard logger callback that writes training metrics to TensorBoard.
///
/// Note: This is a placeholder implementation. A full implementation would
/// require integration with TensorBoard, which is beyond the scope of this example.
pub struct TensorBoardLogger<F: Float + Debug + ScalarOperand> {
/// Directory to store TensorBoard logs
log_dir: PathBuf,
/// Whether to log histograms of model parameters
log_histograms: bool,
/// Frequency of logging (in batches)
update_freq: usize,
/// Phantom data for generic type
_phantom: std::marker::PhantomData<F>,
}
impl<F: Float + Debug + ScalarOperand + NumAssign> TensorBoardLogger<F> {
/// Create a new TensorBoard logger callback
///
/// # Arguments
/// * `log_dir` - Directory to store TensorBoard logs
/// * `log_histograms` - Whether to log histograms of model parameters
/// * `update_freq` - Frequency of logging (in batches)
pub fn new<P: AsRef<Path>>(log_dir: P, log_histograms: bool, update_freq: usize) -> Self {
Self {
log_dir: log_dir.as_ref().to_path_buf(),
log_histograms,
update_freq,
_phantom: std::marker::PhantomData,
}
}
}
impl<F: Float + Debug + ScalarOperand + NumAssign> Callback<F> for TensorBoardLogger<F> {
fn on_event(&mut self, timing: CallbackTiming, context: &mut CallbackContext<F>) -> Result<()> {
match timing {
CallbackTiming::BeforeTraining => {
println!(
"TensorBoard: Initializing logger at {}",
self.log_dir.display()
);
// In a real implementation, we'd initialize the TensorBoard writer here
}
CallbackTiming::AfterBatch
// Log batch metrics at specified frequency
if context.batch.is_multiple_of(self.update_freq) => {
if let Some(batch_loss) = context.batch_loss {
let global_step = context.epoch * context.total_batches + context.batch;
println!(
"TensorBoard: Logging batch {} loss: {:.6?}",
global_step, batch_loss
);
// In a real implementation, we'd log to TensorBoard here
// writer.add_scalar("train/batch_loss", batch_loss, global_step);
}
}
CallbackTiming::AfterEpoch => {
let epoch = context.epoch;
// Log epoch metrics
if let Some(epoch_loss) = context.epoch_loss {
println!(
"TensorBoard: Logging epoch {} train loss: {:.6?}",
epoch + 1,
epoch_loss
);
// writer.add_scalar("train/epoch_loss", epoch_loss, epoch);
}
if let Some(val_loss) = context.val_loss {
println!(
"TensorBoard: Logging epoch {} validation loss: {:.6?}",
epoch + 1,
val_loss
);
// writer.add_scalar("validation/loss", val_loss, epoch);
}
// Log metrics if available
if !context.metrics.is_empty() {
println!(
"TensorBoard: Logging epoch {} metrics: {:.6?}",
epoch + 1,
context.metrics
);
// In a real implementation, we'd log each metric with a name
// For now just log the raw values
// for (i, metric) in context.metrics.iter().enumerate() {
// writer.add_scalar(&format!("metrics/metric_{}", i), *metric, epoch);
// }
}
// Log model parameter histograms
if self.log_histograms {
println!("TensorBoard: Logging model parameter histograms");
// In a real implementation, we'd log parameter histograms here
// for (name, param) in model.named_parameters() {
// writer.add_histogram(&format!("parameters/{}", name), param, epoch);
// }
}
}
CallbackTiming::AfterTraining => {
println!("TensorBoard: Closing logger");
// In a real implementation, we'd close the TensorBoard writer here
// writer.close();
}
_ => {}
}
Ok(())
}
}