DCGAN_LSTM weather predction
Purpose
This project implements a Generative Adversarial Network (GAN) to predict weather patterns, likely focusing on cloud formation and movement. This README provides instructions on how to set up, use, and understand the code. Overview
Dataset: Describes your dataset (source, format, size, pre-processing steps). Example: Carbon_Video_Set, containing a video of cloud dynamics converted into individual frames. Generative Adversarial Network (GAN): Explain the purpose of GANs: generating realistic data (images in this case). Describe the Generator and Discriminator networks: Generator: Creates realistic-looking cloud images. Discriminator: Distinguishes between real and generated images. Code Structure: List the main code files and their functions. Dependencies
List all required libraries and their versions: TensorFlow Keras NumPy OpenCV Scikit-learn Matplotlib (Potentially others, adjust accordingly) Installation
Clone the repository:
Bash git clone https://github.com/<your_username>/<repo_name>
Create a virtual environment (recommended):
Bash python3 -m venv weather-gan-env source weather-gan-env/bin/activate
Install dependencies:
Bash pip install -r requirements.txt
Dataset Setup
Download Dataset (if not included): Provide link and instructions, if applicable. Place the dataset in the appropriate folder: Indicate where to put the Carbon_Video_Set.mp4 file. Code Usage
Preprocessing: Describe any specific preprocessing scripts or commands. Training: Bash python train.py # Example command, adjust if necessary
Explain training parameters: epoch count, batch size, etc. Generating Predictions: Bash python generate.py # Example command, adjust if necessary
How to visualize the output: Describe generated images/visualizations. Model Evaluation
Metrics: Explain how you evaluate the GAN's performance (qualitative, quantitative measures). Interpretation: Discuss the results and how the GAN captures weather patterns. Contributions and Acknowledgements
If contributing to an existing project, mention guidelines. Provide links to datasets or other resources used. Additional Notes
Code Structure: Offer more specific explanations if possible. Visualization: Explain the simple_vision, threshold_vision, etc. functions. Improvements: Suggest areas for potentially enhancing the model. Contact
version1 contributer: github.com/ https://github.com/junhuk1113
contact: redcar1024@gmail.com/ https://github.com/Heisnotanimposter