Implementation of Autoencoders with Tensorflow and Pytorch This repository contains an implementation of a simple autoencoder using PyTorch and Tensorflow. The code is implemented in a Jupyter Notebook and demonstrates the steps involved in building and training an autoencoder model on the MNIST dataset.
AutoEncoders_Pytorch.ipynb: Jupyter Notebook file containing the Pytorch code for the autoencoder implementation.AutoEncoders_Tensorflow.ipynb: Jupyter Notebook file containing the Tensorflow code for the autoencoder implementation.data: Folder containing the MNIST dataset (downloaded automatically if not present).README.md: This README file providing an overview of the repository.
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Clone the repository:
git clone https://github.com/your-username/autoencoder.git -
Open the
AutoEncoders_Pytorch.ipynborAutoEncoders_Tensorflow.ipynbfile in Jupyter Notebook or any compatible environment. -
Run the notebook cells step-by-step to fetch the data, preprocess it, create the autoencoder model, train the model, and perform inference.
The following dependencies are required to run the code:
- Python 3.x
- PyTorch
- torchvision
- TensorFlow
- Keras
- numpy
- matplotlib
- Jupyter Notebook
You can install the necessary packages using the following command:
pip install torch torchvision jupyter notebook
pip install tensorflow keras numpy matplotlib
Note: Make sure you have a compatible GPU and the required drivers installed if you want to leverage GPU acceleration with TensorFlow or Pytorch.
Feel free to use the code provided in this repository as a starting point for your own autoencoder projects. If you find this repository helpful, consider giving it a star!
- The code in this repository is inspired by various autoencoder implementations and tutorials available in the TensorFlow and Keras and Pytorch communities.
If you have any questions or feedback, feel free to open an issue or reach out to me. Enjoy experimenting with autoencoders!