Implement validation loss using FID scores and add corresponding documentation

[IndigoDosSantos]

Description

This pull request introduces the implementation of validation loss using FID (Fréchet Inception Distance) scores and provides comprehensive documentation for this feature (for the wiki). The implementation includes:

• Calculation of FID scores at regular intervals (currently after each epoch) using a separate validation image set
• Storage of FID scores and generated images in the "epochs" folder within the workspace directory
• Logging of FID scores to TensorBoard for visualization and monitoring

The accompanying documentation covers the following aspects:

• Explanation of validation loss and its implementation using FID scores
• Description of how validation loss complements other training metrics
• Guidance on interpreting validation loss and its benefits for monitoring model performance
• Details on the relationship between FID scores, model generalization, and overfitting
• Recommendations for the size of the validation set, with 15% of the total dataset being a good middle ground
• Implementation considerations for effectively utilizing validation loss, including:
• Creating a separate validation image set
• Configuring the "validation_images" concept in concepts.json
• Storing FID scores and generated images in the "epochs" folder
• Calculating FID scores after each epoch and logging them to TensorBoard