对抗性自动编码器向导指南_Python_下载.zip

# Adversarial autoencoders <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/nw_architecture.png" alt="Cover"/> This repository contains code to implement adversarial autoencoder using Tensorflow. Medium posts: 1. [A Wizard's guide to Adversarial Autoencoders: Part 1. Autoencoders?](https://medium.com/towards-data-science/a-wizards-guide-to-adversarial-autoencoders-part-1-autoencoder-d9a5f8795af4) 2. [A Wizard's guide to Adversarial Autoencoders: Part 2. Exploring the latent space with Adversarial Autoencoders.](https://medium.com/towards-data-science/a-wizards-guide-to-adversarial-autoencoders-part-2-exploring-latent-space-with-adversarial-2d53a6f8a4f9) 3. [A Wizard's guide to Adversarial Autoencoders: Part 3. Disentanglement of style and content.](https://medium.com/towards-data-science/a-wizards-guide-to-adversarial-autoencoders-part-3-disentanglement-of-style-and-content-89262973a4d7) 3. [A Wizard's guide to Adversarial Autoencoders: Part 4. Classify MNIST using 1000 labels.](https://medium.com/towards-data-science/a-wizards-guide-to-adversarial-autoencoders-part-4-classify-mnist-using-1000-labels-2ca08071f95) ## Installing the dependencies Install virtualenv and creating a new virtual environment: pip install virtualenv virtualenv -p /usr/bin/python3 aa Install dependencies pip3 install -r requirements.txt ***Note:*** * *I'd highly recommend using your GPU during training.* * *`tf.nn.sigmoid_cross_entropy_with_logits` has a `targets` parameter which has been changed to `labels` for tensorflow version > r0.12.* ## Dataset The MNIST dataset will be downloaded automatically and will be made available in `./Data` directory. ## Training! ### Autoencoder: #### Architecture: To train a basic autoencoder run: python3 autoencoder.py --train True * This trains an autoencoder and saves the trained model once every epoch in the `./Results/Autoencoder` directory. To load the trained model and generate images passing inputs to the decoder run: python3 autoencoder.py --train False ### Adversarial Autoencoder: #### Architecture: <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/AAE%20Block%20Diagram.png" alt="Cover"/> Training: python3 adversarial_autoencoder.py --train True Load model and explore the latent space: python3 adversarial_autoencoder.py --train False Example of adversarial autoencoder output when the encoder is constrained to have a stddev of 5. <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/AAE%20dist%20match.png" alt="Cover"/> **_Matching prior and posterior distributions._**  **_Distribution of digits in the latent space._** ### Supervised Adversarial Autoencoder: #### Architecture: <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/Supervised%20AAE.png" alt="Cover"/> Training: python3 supervised_adversarial_autoencoder.py --train True Load model and explore the latent space: python3 supervised_adversarial_autoencoder.py --train False Example of disentanglement of style and content: <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/disentanglement%20of%20style%20and%20content.png" alt="Cover"/> ### Semi-Supervised Adversarial Autoencoder: #### Architecture: <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/semi_AAE%20architecture.png" alt="Cover"/> Training: python3 semi_supervised_adversarial_autoencoder.py --train True Load model and explore the latent space: python3 semi_supervised_adversarial_autoencoder.py --train False Classification accuracy for 1000 labeled images: <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/semi_aae_accuracy_with_NN.png" alt="Cover"/> <img src="https://raw.githubusercontent.com/Naresh1318/Adversarial_Autoencoder/master/README/cat_n_gauss_dist_real_obtained.png" alt="Cover"/> ***Note:*** * Each run generates a required tensorboard files under `./Results/<model>/<time_stamp_and_parameters>/Tensorboard` directory. * Use `tensorboard --logdir <tensorboard_dir>` to look at loss variations and distributions of latent code. * Windows gives an error when `:` is used during folder naming (this is produced during the folder creation for each run).I would suggest you to remove the time stamp from `folder_name` variable in the `form_results()` function. Or, just dual boot linux! ## Thank You Please share this repo if you find it helpful.