gsnn.gsnn.optim.TrainingDiagnostics

class gsnn.gsnn.optim.TrainingDiagnostics(model: torch.nn.Module, track_every: int = 10, window_size: int = 100, track_activations: bool = True, track_weights: bool = True, track_gradients: bool = True, track_curvature: bool = False, verbose: bool = True)

Bases: object

Comprehensive training diagnostics for monitoring model optimization.

Tracks gradient flow, activation patterns, weight spectral properties, and basic curvature metrics to help diagnose training issues and guide optimization decisions.

Usage:

diagnostics = TrainingDiagnostics(model, track_every=10)

# During training loop: loss.backward() diagnostics.update(model, loss.item(), batch_idx) optimizer.step()

# Generate reports: summary = diagnostics.get_summary() diagnostics.plot_diagnostics()

__init__(model: torch.nn.Module, track_every: int = 10, window_size: int = 100, track_activations: bool = True, track_weights: bool = True, track_gradients: bool = True, track_curvature: bool = False, verbose: bool = True)

Initialize training diagnostics tracker.

Parameters:

• model – PyTorch model to monitor

• track_every – Update diagnostics every N steps

• window_size – Size of rolling window for statistics

• track_activations – Whether to track activation statistics

• track_weights – Whether to track weight spectral properties

• track_gradients – Whether to track gradient flow metrics

• track_curvature – Whether to track curvature (expensive!)

• verbose – Whether to print diagnostic warnings

Methods

__init__(model[, track_every, window_size, ...])	Initialize training diagnostics tracker.
get_gradient_histogram_data()	Get gradient histograms for current step.
get_summary()	Generate a comprehensive diagnostic summary.
plot_diagnostics([save_path, figsize])	Generate comprehensive diagnostic plots.
reset()	Reset all diagnostic tracking.
update(model, loss[, step])	Update diagnostics with current training state.

get_gradient_histogram_data() → Dict[str, numpy.ndarray]

Get gradient histograms for current step.

Returns:

Dictionary mapping layer names to gradient histograms

get_summary() → Dict[str, Any]

Generate a comprehensive diagnostic summary.

Returns:

Dictionary containing current diagnostic state and recommendations

plot_diagnostics(save_path: Optional[str] = None, figsize: Tuple[int, int] = (15, 12))

Generate comprehensive diagnostic plots.

Parameters:

• save_path – Optional path to save the plot

• figsize – Figure size tuple

reset()

Reset all diagnostic tracking.

update(model: torch.nn.Module, loss: float, step: Optional[int] = None)

Update diagnostics with current training state.

Parameters:

• model – Current model state

• loss – Current loss value

• step – Optional step counter (uses internal if None)