https://github.com/rapsssito/cats-gan
Example of how to generate images of cat faces using a Deep Convolutional Generative Adversarial Network (DCGAN).
https://github.com/rapsssito/cats-gan
deep-learning gan generative-adversarial-network tensorflow
Last synced: about 1 month ago
JSON representation
Example of how to generate images of cat faces using a Deep Convolutional Generative Adversarial Network (DCGAN).
- Host: GitHub
- URL: https://github.com/rapsssito/cats-gan
- Owner: Rapsssito
- Created: 2021-09-21T15:31:30.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-21T16:37:47.000Z (almost 5 years ago)
- Last Synced: 2026-03-01T09:53:49.531Z (4 months ago)
- Topics: deep-learning, gan, generative-adversarial-network, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 5.04 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# CATS - Deep Convolutional Generative Adversarial Network
This repository serves as an example of how to generate images of cat faces using a [Deep Convolutional Generative Adversarial Network](https://arxiv.org/pdf/1511.06434.pdf) (DCGAN) developed by [@Rapsssito](https://github.com/rapsssito) and [@pablotix20](https://github.com/pablotix20). It is heavily inspired by [TensorFlow's tutorial](https://www.tensorflow.org/tutorials/generative/dcgan) about this topic. This repository does not provide a training dataset, but several are available online:
* [`fferlito/Cat-faces-dataset`](https://github.com/fferlito/Cat-faces-dataset)
* [`Cats faces 64x64`](https://www.kaggle.com/spandan2/cats-faces-64x64-for-generative-models)
## Our model
Our particular model, provided in [cats_gan.ipynb](./cats_gan.ipynb), uses more than 30k 64x64 resized RGB images of cat faces as the training dataset. Below the structures for the generator and discriminator are provided:
### Generator structure
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 50) 2500
_________________________________________________________________
batch_normalization (BatchNo (None, 50) 200
_________________________________________________________________
leaky_re_lu (LeakyReLU) (None, 50) 0
_________________________________________________________________
reshape (Reshape) (None, 1, 1, 50) 0
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 2, 2, 512) 921600
_________________________________________________________________
batch_normalization_1 (Batch (None, 2, 2, 512) 2048
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 2, 2, 512) 0
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 4, 4, 256) 4718592
_________________________________________________________________
batch_normalization_2 (Batch (None, 4, 4, 256) 1024
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 4, 4, 256) 0
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 8, 8, 128) 1179648
=================================================================
Total params: 7,215,372
Trainable params: 7,213,256
Non-trainable params: 2,116
```
### Discriminator structure
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_18 (Conv2D) (None, 32, 32, 16) 1744
_________________________________________________________________
leaky_re_lu_25 (LeakyReLU) (None, 32, 32, 16) 0
_________________________________________________________________
dropout_18 (Dropout) (None, 32, 32, 16) 0
_________________________________________________________________
conv2d_19 (Conv2D) (None, 16, 16, 32) 18464
_________________________________________________________________
leaky_re_lu_26 (LeakyReLU) (None, 16, 16, 32) 0
_________________________________________________________________
dropout_19 (Dropout) (None, 16, 16, 32) 0
_________________________________________________________________
conv2d_20 (Conv2D) (None, 8, 8, 64) 73792
_________________________________________________________________
leaky_re_lu_27 (LeakyReLU) (None, 8, 8, 64) 0
_________________________________________________________________
dropout_20 (Dropout) (None, 8, 8, 64) 0
_________________________________________________________________
conv2d_21 (Conv2D) (None, 4, 4, 128) 295040
_________________________________________________________________
leaky_re_lu_28 (LeakyReLU) (None, 4, 4, 128) 0
Total params: 6,288,561
Trainable params: 6,288,561
Non-trainable params: 0
```
### Experiment
An experiment with 3484 epochs provided the following results:
#### Generator and discriminator losses
#### 16 sample images (epoch 0301)
#### 16 sample images (epoch 3476)
