Plot Twist: Introducing Training Metrics Visualization with Plotting_Utils in model.py for Deep Learning Insights

[SujitSakore]

This pull request introduces a new utility class for visualizing training metrics (loss and accuracy) in the Multi-Layer Artificial Neural Network (ANN) model. The enhancements aim to provide users with visual feedback on model training and performance, facilitating better tracking and debugging.

Key Changes: 1.Added Plotting_Utils Class:

Introduced a utility class for plotting training and validation metrics. Implemented a method plot_training_history() that: Accepts a history dictionary containing training and validation loss/accuracy. Utilizes Matplotlib for visualizations. Allows users to specify which metrics to visualize (loss and/or accuracy). Saves generated plots as images.

2.Enhanced Multi_Layer_ANN Class:

Modified the training loop to store training and validation metrics in a history dictionary for later visualization. Ensured` compatibility with the new plotting utility.

3.Updated runner.py:

Integrated` the Plotting_Utils class to plot the training history after model training. Provided examples of how to use the plotting utility with the training history data.

4.Requirements Update:

Added Matplotlib to the requirements.txt file to ensure that the necessary libraries for plotting are included in the project dependencies.

Benefits: Users can visually track model performance over epochs, making it easier to identify trends and diagnose potential issues during training. Improved overall user experience by incorporating visual feedback into the training process.

Testing: The new plotting functionality has been tested, ensuring that the plots accurately reflect the training metrics and are saved as specified.

[ravin-d-27]

Thank you @SujitSakore for your contribution !

[ravin-d-27]

Please consider starring the repository !