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visualization.model

use_gpu​

plot_reconstruction​

def plot_reconstruction(filepath: str,
                        test_loader: Data.DataLoader,
                        seq_len_half: int,
                        model: RNN_VAE,
                        model_name: str,
                        FUTURE_DECODER: bool,
                        FUTURE_STEPS: int,
                        suffix: Optional[str] = None,
                        show_figure: bool = False) -> None

Plot the reconstruction and future prediction of the input sequence. Saves the plot to:

  • project_name/
    • model/
      • evaluate/
        • Reconstruction_model_name.png

Parameters

  • filepath (str): Path to save the plot.
  • test_loader (Data.DataLoader): DataLoader for the test dataset.
  • seq_len_half (int): Half of the temporal window size.
  • model (RNN_VAE): Trained VAE model.
  • model_name (str): Name of the model.
  • FUTURE_DECODER (bool): Flag indicating whether the model has a future prediction decoder.
  • FUTURE_STEPS (int): Number of future steps to predict.
  • suffix (str, optional): Suffix for the saved plot filename. Defaults to None.
  • show_figure (bool, optional): Flag indicating whether to show the plot. Defaults to False.

Returns

  • None

plot_loss​

def plot_loss(config: dict,
              model_name: str,
              save_to_file: bool = False,
              show_figure: bool = True) -> None

Plot the losses of the trained model. Saves the plot to:

  • project_name/
    • model/
      • evaluate/
        • mse_and_kl_loss_model_name.png

Parameters

  • config (dict): Configuration dictionary.
  • model_name (str): Name of the model.
  • save_to_file (bool, optional): Flag indicating whether to save the plot. Defaults to False.
  • show_figure (bool, optional): Flag indicating whether to show the plot. Defaults to True.

Returns

  • None