stouputils.data_science.models.keras_utils.callbacks.colored_progress_bar module#
- class ColoredProgressBar(desc: str = 'Training', track_epochs: bool = False, show_lr: bool = False, update_frequency: int = 1, color: str = '\x1b[95m')[source]#
Bases:
CallbackProgress bar using tqdm for Keras training.
A callback that displays a progress bar using tqdm during model training. Shows the training progress across steps with a customized format instead of the default Keras one showing multiple lines.
- desc: str#
Description of the progress bar.
- track_epochs: bool#
Whether to track epochs instead of batches.
- show_lr: bool#
Whether to show the learning rate.
- latest_val_loss: float#
Latest validation loss, updated at the end of each epoch.
- latest_lr: float#
Latest learning rate, updated during batch and epoch processing.
- batch_count: int#
Counter to update the progress bar less frequently.
- update_frequency: int#
How often to update the progress bar (every N batches).
- color: str#
Color of the progress bar.
- pbar: tqdm[Any] | None#
The tqdm progress bar instance.
- epochs: int#
Total number of epochs.
- steps: int#
Number of steps per epoch.
- total: int#
Total number of steps/epochs to track.
- params: dict[str, Any]#
Training parameters.
- on_train_begin(logs: dict[str, Any] | None = None) None[source]#
Initialize the progress bar at the start of training.
- Parameters:
logs (dict | None) – Training logs.
- on_batch_end(batch: int, logs: dict[str, Any] | None = None) None[source]#
Update the progress bar after each batch, based on update frequency.
- Parameters:
batch (int) – Current batch number (0-indexed).
logs (dict | None) – Dictionary of logs containing metrics for the batch.