"/home/yossef/notes/personal/ml/torch_study/helper_utils.md"

path: personal/ml/torch_study/helper_utils.md

- **fileName**: helper_utils
- **Created on**: 2026-04-02 19:54:35

helper_utils.py Documentation

This script provides utility functions for plotting data, model fit,
and training progress in PyTorch.

Functions

plot_data(distances, times, normalize=False)

Creates a scatter plot of the data points.

• Args:
• distances: The input data points for the x-axis. - times: The
  target data points for the y-axis. - normalize: A boolean flag
  indicating whether the data is normalized.

plot_final_fit(model, distances, times, distances_norm, times_std, times_mean)

Plots the predictions of a trained model against the original data,
after de-normalizing the predictions.

• Args:
• model: The trained model used for prediction. - distances: The
  original, un-normalized input data. - times: The original, un-
  normalized target data. - distances_norm: The normalized input data
  for the model. - times_std: The standard deviation used for de-
  normalization. - times_mean: The mean value used for de-
  normalization.

plot_training_progress(epoch, loss, model, distances_norm, times_norm)

Plots the training progress of a model on normalized data, showing the
current fit at each epoch.

• Args:
• epoch: The current training epoch number. - loss: The loss value
  at the current epoch. - model: The model being trained. -
  distances_norm: The normalized input data. - times_norm: The
  normalized target data.

before:[[]]
continue:./torch_study_1.md