Framing the Future

[manishh12]

Pull Request for DL-Simplified 💡

Issue Title : Framing the Future

• Info about the related issue (Aim of the project) : The main objective of this project is to predict future frames in a sequence of Moving MNIST dataset using Convolutional LSTM (ConvLSTM) model. The ConvLSTM architecture is capable of capturing both spatial and temporal dependencies in sequential data, making it suitable for this task.
• Name: Manish Kumar Gupta
• GitHub ID: manishh12
• Email ID: manishdid360@gmail.com
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSOC'24 Contributor

Closes: #604

Describe the add-ons or changes you've made 📃

This project involves building a ConvLSTM model to predict future frames in Moving MNIST sequences. It includes data preprocessing, model creation, training, evaluation, and prediction on unseen examples.

Type of change ☑️

What sort of change have you made:

• [ ] Bug fix (non-breaking change which fixes an issue)
• [x] New feature (non-breaking change which adds functionality)
• [ ] Code style update (formatting, local variables)
• [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
• [ ] This change requires a documentation update

Checklist: ☑️

• [x] My code follows the guidelines of this project.
• [x] I have performed a self-review of my own code.
• [x] I have commented my code, particularly wherever it was hard to understand.
• [x] I have made corresponding changes to the documentation.
• [x] My changes generate no new warnings.
• [x] I have added things that prove my fix is effective or that my feature works.
• [ ] Any dependent changes have been merged and published in downstream modules.

[github-actions[bot]]

Our team will soon review your PR. Thanks @manishh12 :)

[manishh12]

@abhisheks008 Please could you kindly review this pull request? I'm not very familiar with a wide range of models, so I included both ANN and CNN in the analysis. Based on their performance, I ultimately chose to focus on LSTM for the conclusion.