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| 7f7410a9aabd4c95042bb219452851f3db3d06 | 2019-07-24 | 389 Bytes | |
| e15e1f046966aaaa72028aaaeec515e39bb5f0 | 2019-07-24 | 319 Bytes | |
| 6093b65d3263939ccc6dcbde8c729fd23b9244 | 2019-07-24 | 441 Bytes | |
| 9c0c94dc7f3cf03c29cf07aa92a06b1dee32e2 | 2019-07-24 | 2.3 kB | |
| e0770424b2a19faf507a501ebfc23be8f54e7b | 2019-07-24 | 76 Bytes | |
| e990c5863c42546811875568f84143a3cfb9b5 | 2019-07-24 | 411 Bytes | |
| b51ff844440de2688a9b07711c6cb9990e0c6d | 2019-07-24 | 434 Bytes | |
| 9509c88bed7080d496fc5e1d87a9315e30549d | 2019-07-24 | 59 Bytes | |
| preprocess_VOC.cpython-36.pyc | 2019-07-24 | 12.8 kB | |
| update.sample | 2019-07-24 | 3.6 kB | |
| pre-rebase.sample | 2019-07-24 | 4.9 kB | |
| commit-msg.sample | 2019-07-24 | 896 Bytes | |
| HEAD | 2019-07-24 | 214 Bytes | |
| Example1.xlsx | 2019-07-24 | 63.1 kB | |
| exclude | 2019-07-24 | 240 Bytes | |
| hample.xlsx | 2019-07-24 | 7.8 kB | |
| sample.xlsx | 2019-07-24 | 7.8 kB | |
| example.xlsx | 2019-07-24 | 71.1 kB | |
| Emotions.xlsx | 2019-07-24 | 6.4 MB | |
| upload.html | 2019-07-24 | 521 Bytes | |
| home.html | 2019-07-24 | 766 Bytes | |
| layout.html | 2019-07-24 | 750 Bytes | |
| index.html | 2019-07-24 | 955 Bytes | |
| profile.html | 2019-07-24 | 538 Bytes | |
| style.css | 2019-07-24 | 3.5 kB | |
| config.cpython-36.pyc | 2019-07-24 | 460 Bytes | |
| config.cpython-37.pyc | 2019-07-24 | 464 Bytes | |
| pre-applypatch.sample | 2019-07-24 | 424 Bytes | |
| pre-push.sample | 2019-07-24 | 1.3 kB | |
| post-update.sample | 2019-07-24 | 189 Bytes | |
| prepare-commit-msg.sample | 2019-07-24 | 1.5 kB | |
| fsmonitor-watchman.sample | 2019-07-24 | 3.3 kB | |
| pre-receive.sample | 2019-07-24 | 544 Bytes | |
| applypatch-msg.sample | 2019-07-24 | 478 Bytes | |
| pre-commit.sample | 2019-07-24 | 1.6 kB | |
| plotly_VIZ_new-checkpoint.ipynb | 2019-07-24 | 984.6 kB | |
| Gender_classification-checkpoint.ipynb | 2019-07-24 | 13.5 kB | |
| Untitled1-checkpoint.ipynb | 2019-07-24 | 72 Bytes | |
| sequences.css | 2019-07-24 | 702 Bytes | |
| cool_form_1.html | 2019-07-24 | 176 Bytes | |
| cool_form.html | 2019-07-24 | 1.3 kB | |
| Solution.txt | 2019-07-24 | 89 Bytes | |
| DataSunburst.cpython-36.pyc | 2019-07-24 | 669 Bytes | |
| Gender_classification.ipynb | 2019-07-24 | 13.5 kB | |
| Sentiment_prediction_noor.cpython-36.pyc | 2019-07-24 | 813 Bytes | |
| app.cpython-36.pyc | 2019-07-24 | 353 Bytes | |
| plotly_wordcloud.cpython-37.pyc | 2019-07-24 | 1.4 kB | |
| sunburst.cpython-36.pyc | 2019-07-24 | 631 Bytes | |
| Standardizer.cpython-36.pyc | 2019-07-24 | 566 Bytes | |
| contractions.py | 2019-07-24 | 3.1 kB | |
| sentiment_model.sav | 2019-07-24 | 353.9 kB | |
| tfidf.sav | 2019-07-24 | 2.8 MB | |
| Standardizer.py | 2019-07-24 | 493 Bytes | |
| big.txt | 2019-07-24 | 6.5 MB | |
| frequency_dictionary_en_82_765.txt | 2019-07-24 | 1.3 MB | |
| dcgan_mnist.gif | 2019-07-24 | 5.8 MB | |
| words-by-frequency.txt | 2019-07-24 | 1.2 MB | |
| FETCH_HEAD | 2019-07-24 | 98 Bytes | |
| packed-refs | 2019-07-24 | 114 Bytes | |
| index | 2019-07-24 | 1.5 kB | |
| description | 2019-07-24 | 73 Bytes | |
| config | 2019-07-24 | 327 Bytes | |
| 1. Vanilla GAN PyTorch-checkpoint.ipynb | 2019-07-24 | 29.0 kB | |
| vanilla_mnist_pt_raw.png | 2019-07-24 | 36.8 kB | |
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| vanilla_mnist_pt.png | 2019-07-24 | 16.5 kB | |
| dcgan_cifar.gif | 2019-07-24 | 12.0 MB | |
| dcgan_cifar_pt_raw.png | 2019-07-24 | 251.2 kB | |
| test.csv | 2019-07-24 | 38.2 MB | |
| utils.cpython-36.pyc | 2019-07-24 | 4.0 kB | |
| helpers.py | 2019-07-24 | 172 Bytes | |
| main.py | 2019-07-24 | 6.5 kB | |
| Untitled.ipynb | 2019-07-24 | 907 Bytes | |
| config.py | 2019-07-24 | 437 Bytes | |
| COMMIT_EDITMSG | 2019-07-24 | 96 Bytes | |
| backup_script | 2019-07-24 | 334 Bytes | |
| DataSunburst.py | 2019-07-24 | 737 Bytes | |
| Feelings_file.xlsx | 2019-07-24 | 108.5 kB | |
| plotly_wordcloud.py | 2019-07-24 | 1.7 kB | |
| requirement.txt | 2019-07-24 | 3.3 kB | |
| sunburst.csv | 2019-07-24 | 20.3 kB | |
| plotly_VIZ_new.ipynb | 2019-07-24 | 158.5 kB | |
| dash_link.py | 2019-07-24 | 640 Bytes | |
| Noun_adj_file.xlsx | 2019-07-24 | 115.3 kB | |
| test_2.csv | 2019-07-24 | 38.2 MB | |
| Actions_file.xlsx | 2019-07-24 | 136.2 kB | |
| plotly-latest.min.js | 2019-07-24 | 3.1 MB | |
| dash_link | 2019-07-24 | 640 Bytes | |
| Gender_prediction_bechmark.zip | 2019-07-24 | 5.5 MB | |
| Features_file.xlsx | 2019-07-24 | 110.2 kB | |
| Untitled1.ipynb | 2019-07-24 | 10.8 kB | |
| utils.py | 2019-07-24 | 5.0 kB | |
| 2. DC-GAN TensorFlow.ipynb | 2019-07-24 | 352.0 kB | |
| README.md | 2019-07-24 | 2.2 kB | |
| 1. Vanilla GAN PyTorch.ipynb | 2019-07-24 | 29.3 kB | |
| 2. DC-GAN PyTorch-MNIST.ipynb | 2019-07-24 | 225.4 kB | |
| reprocess.xlsx | 2019-07-24 | 1.7 MB | |
| login_customize.html | 2019-07-24 | 2.2 kB | |
| login.html | 2019-07-24 | 781 Bytes | |
| Totals: 99 Items | 126.1 MB | 1 |
gans: Generative Adversarial Networks
Multiple Generative Adversarial Networks (GANs) implemented in PyTorch and Tensorflow.
Check out this blog post for an introduction to Generative Networks.
Vanilla GANs
Vanilla GANs found in this project were developed based on the original paper Generative Adversarial Networks by Goodfellow et al.
These are trained on the MNIST dataset, and learn to create hand-written digit images using a 1-Dimensional vector representation for 2D input images. - PyTorch Notebook - TensorFlow Notebook
MNIST-like generated images before & after training.
DCGANs
Deep Convolutional Generative Adversarial Networks (DCGANs) in this repository were developed based on the original paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks by Radford et al.
These are trained on the CIFAR10 and the MNIST datasets. They use 3 dimensional representations for images (length x height x colors) directly for training.
CIFAR-like generated images before & after training.