https://github.com/yeonghyeon/cgan-tf
TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.
https://github.com/yeonghyeon/cgan-tf
cgan cgans conditional-gan conditions gan gans generative-adversarial-network generative-model mnist mnist-dataset tensorflow tensorflow-examples
Last synced: 4 months ago
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TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.
- Host: GitHub
- URL: https://github.com/yeonghyeon/cgan-tf
- Owner: YeongHyeon
- License: mit
- Created: 2020-11-10T02:33:09.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-11-11T04:58:47.000Z (over 4 years ago)
- Last Synced: 2025-01-31T03:12:31.602Z (4 months ago)
- Topics: cgan, cgans, conditional-gan, conditions, gan, gans, generative-adversarial-network, generative-model, mnist, mnist-dataset, tensorflow, tensorflow-examples
- Language: Python
- Homepage:
- Size: 3.81 MB
- Stars: 7
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[TensorFlow] Conditional Generative Adversarial Nets (CGAN)
=====
TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.
## Architecture
### Training algorithm
The algorithm for training CGAN [1].
### CGAN architecture
The architecture of CGAN [1].
### Graph in TensorBoard
Graph of CGAN.
## Results
### Training Procedure
Loss graph in the training procedure. Each graph shows loss of the discriminator and loss of the generator respectively.
### Test Procedure
#### From random noise without conditions
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#### From random noise with conditions
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#### Latent space walking with conditions
|Class-0 (z:2)|Class-1 (z:2)|Class-2 (z:2)|Class-3 (z:2)|Class-4 (z:2)|
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|Class-5 (z:2)|Class-6 (z:2)|Class-7 (z:2)|Class-8 (z:2)|Class-9 (z:2)|
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## Environment
* Python 3.7.4
* Tensorflow 1.14.0
* Numpy 1.17.1
* Matplotlib 3.1.1
* Scikit Learn (sklearn) 0.21.3
## Reference
[1] Mehdi Mirza and Simon Osindero. (2014). Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784.