{"id":13614335,"url":"https://github.com/carpedm20/DCGAN-tensorflow","last_synced_at":"2025-04-13T18:32:56.554Z","repository":{"id":37470813,"uuid":"47799229","full_name":"carpedm20/DCGAN-tensorflow","owner":"carpedm20","description":"A tensorflow implementation of \"Deep Convolutional Generative Adversarial Networks\"","archived":false,"fork":false,"pushed_at":"2021-01-06T05:38:06.000Z","size":79859,"stargazers_count":7161,"open_issues_count":188,"forks_count":2625,"subscribers_count":244,"default_branch":"master","last_synced_at":"2025-04-10T22:35:13.468Z","etag":null,"topics":["dcgan","gan","generative-model","tensorflow"],"latest_commit_sha":null,"homepage":"http://carpedm20.github.io/faces/","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/carpedm20.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-12-11T02:06:40.000Z","updated_at":"2025-04-10T20:39:35.000Z","dependencies_parsed_at":"2022-07-12T23:10:33.180Z","dependency_job_id":null,"html_url":"https://github.com/carpedm20/DCGAN-tensorflow","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carpedm20%2FDCGAN-tensorflow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carpedm20%2FDCGAN-tensorflow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carpedm20%2FDCGAN-tensorflow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carpedm20%2FDCGAN-tensorflow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/carpedm20","download_url":"https://codeload.github.com/carpedm20/DCGAN-tensorflow/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248760644,"owners_count":21157401,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dcgan","gan","generative-model","tensorflow"],"created_at":"2024-08-01T20:01:00.235Z","updated_at":"2025-04-13T18:32:56.532Z","avatar_url":"https://github.com/carpedm20.png","language":"JavaScript","readme":"# DCGAN in Tensorflow\n\nTensorflow implementation of [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) which is a stabilize Generative Adversarial Networks. The referenced torch code can be found [here](https://github.com/soumith/dcgan.torch).\n\n![alt tag](DCGAN.png)\n\n* [Brandon Amos](http://bamos.github.io/) wrote an excellent [blog post](http://bamos.github.io/2016/08/09/deep-completion/) and [image completion code](https://github.com/bamos/dcgan-completion.tensorflow) based on this repo.\n* *To avoid the fast convergence of D (discriminator) network, G (generator) network is updated twice for each D network update, which differs from original paper.*\n\n\n## Online Demo\n\n[\u003cimg src=\"https://raw.githubusercontent.com/carpedm20/blog/master/content/images/face.png\"\u003e](http://carpedm20.github.io/faces/)\n\n[link](http://carpedm20.github.io/faces/)\n\n\n## Prerequisites\n\n- Python 2.7 or Python 3.3+\n- [Tensorflow 0.12.1](https://github.com/tensorflow/tensorflow/tree/r0.12)\n- [SciPy](http://www.scipy.org/install.html)\n- [pillow](https://github.com/python-pillow/Pillow)\n- [tqdm](https://pypi.org/project/tqdm/)\n- (Optional) [moviepy](https://github.com/Zulko/moviepy) (for visualization)\n- (Optional) [Align\u0026Cropped Images.zip](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) : Large-scale CelebFaces Dataset\n\n\n## Usage\n\nFirst, download dataset with:\n\n    $ python download.py mnist celebA\n\nTo train a model with downloaded dataset:\n\n    $ python main.py --dataset mnist --input_height=28 --output_height=28 --train\n    $ python main.py --dataset celebA --input_height=108 --train --crop\n\nTo test with an existing model:\n\n    $ python main.py --dataset mnist --input_height=28 --output_height=28\n    $ python main.py --dataset celebA --input_height=108 --crop\n\nOr, you can use your own dataset (without central crop) by:\n\n    $ mkdir data/DATASET_NAME\n    ... add images to data/DATASET_NAME ...\n    $ python main.py --dataset DATASET_NAME --train\n    $ python main.py --dataset DATASET_NAME\n    $ # example\n    $ python main.py --dataset=eyes --input_fname_pattern=\"*_cropped.png\" --train\n\nIf your dataset is located in a different root directory:\n\n    $ python main.py --dataset DATASET_NAME --data_dir DATASET_ROOT_DIR --train\n    $ python main.py --dataset DATASET_NAME --data_dir DATASET_ROOT_DIR\n    $ # example\n    $ python main.py --dataset=eyes --data_dir ../datasets/ --input_fname_pattern=\"*_cropped.png\" --train\n    \n\n## Results\n\n![result](assets/training.gif)\n\n### celebA\n\nAfter 6th epoch:\n\n![result3](assets/result_16_01_04_.png)\n\nAfter 10th epoch:\n\n![result4](assets/test_2016-01-27%2015:08:54.png)\n\n### Asian face dataset\n\n![custom_result1](web/img/change5.png)\n\n![custom_result1](web/img/change2.png)\n\n![custom_result2](web/img/change4.png)\n\n### MNIST\n\nMNIST codes are written by [@PhoenixDai](https://github.com/PhoenixDai).\n\n![mnist_result1](assets/mnist1.png)\n\n![mnist_result2](assets/mnist2.png)\n\n![mnist_result3](assets/mnist3.png)\n\nMore results can be found [here](./assets/) and [here](./web/img/).\n\n\n## Training details\n\nDetails of the loss of Discriminator and Generator (with custom dataset not celebA).\n\n![d_loss](assets/d_loss.png)\n\n![g_loss](assets/g_loss.png)\n\nDetails of the histogram of true and fake result of discriminator (with custom dataset not celebA).\n\n![d_hist](assets/d_hist.png)\n\n![d__hist](assets/d__hist.png)\n\n\n## Related works\n\n- [BEGAN-tensorflow](https://github.com/carpedm20/BEGAN-tensorflow)\n- [DiscoGAN-pytorch](https://github.com/carpedm20/DiscoGAN-pytorch)\n- [simulated-unsupervised-tensorflow](https://github.com/carpedm20/simulated-unsupervised-tensorflow)\n\n\n## Author\n\nTaehoon Kim / [@carpedm20](http://carpedm20.github.io/)\n","funding_links":[],"categories":["Code 💻","JavaScript"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarpedm20%2FDCGAN-tensorflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcarpedm20%2FDCGAN-tensorflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarpedm20%2FDCGAN-tensorflow/lists"}