## Model description
Anime face generator model using [TensorFlow DCGAN example](https://www.tensorflow.org/tutorials/generative/dcgan).

## Training and evaluation data
Model is trained on [anime faces dataset](https://huggingface.co/datasets/merve/anime-faces). The dataset consists of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm [here](https://github.com/nagadomi/lbpcascade_animeface). All images are resized to 64 * 64 for the sake of convenience. The model takes a noise as input and then Conv2DTranspose is used to do upsampling. If you want to pass this to another discriminator, the output shape consists of 28x28 images.

## How to use this model
You can use this model to generate new anime faces. If you want to continuously train, use with [discriminator](https://huggingface.co/merve/anime-faces-discriminator) using `tf.GradientTape()` as mentioned in the DCGAN tutorial.

```
from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras("merve/anime-faces-generator")
```

You can generate examples using a noise.

```
seed = tf.random.normal([number_of_examples_to_generate, noise])
predictions = model(seed, training=False) # inference mode
```
## Intended use and biases
This model is not intended for production.

### Generated images 
![Example](./example.png)
{"license": "apache-2.0", "library_name": "keras", "tags": ["dcgan"], "datasets": ["merve/anime-faces"]}
keras dcgan dataset:merve/anime-faces license:apache-2.0 has_space region:us
TAGS
#keras #dcgan #dataset-merve/anime-faces #license-apache-2.0 #has_space #region-us 

## Model description
Anime face generator model using TensorFlow DCGAN example.

## Training and evaluation data
Model is trained on anime faces dataset. The dataset consists of 21551 anime faces scraped from URL, which are then cropped using the anime face detection algorithm here. All images are resized to 64 * 64 for the sake of convenience. The model takes a noise as input and then Conv2DTranspose is used to do upsampling. If you want to pass this to another discriminator, the output shape consists of 28x28 images.

## How to use this model
You can use this model to generate new anime faces. If you want to continuously train, use with discriminator using 'tf.GradientTape()' as mentioned in the DCGAN tutorial.



You can generate examples using a noise.


## Intended use and biases
This model is not intended for production.

### Generated images 
!Example
## Model description
Anime face generator model using TensorFlow DCGAN example. ## Training and evaluation data
Model is trained on anime faces dataset. The dataset consists of 21551 anime faces scraped from URL, which are then cropped using the anime face detection algorithm here. All images are resized to 64 * 64 for the sake of convenience. The model takes a noise as input and then Conv2DTranspose is used to do upsampling. If you want to pass this to another discriminator, the output shape consists of 28x28 images. ## How to use this model
You can use this model to generate new anime faces. If you want to continuously train, use with discriminator using 'tf.GradientTape()' as mentioned in the DCGAN tutorial.



You can generate examples using a noise. ## Intended use and biases
This model is not intended for production. ### Generated images 
!Example
TAGS
#keras #dcgan #dataset-merve/anime-faces #license-apache-2.0 #has_space #region-us 
 ## Model description
Anime face generator model using TensorFlow DCGAN example. ## Training and evaluation data
Model is trained on anime faces dataset. The dataset consists of 21551 anime faces scraped from URL, which are then cropped using the anime face detection algorithm here. All images are resized to 64 * 64 for the sake of convenience. The model takes a noise as input and then Conv2DTranspose is used to do upsampling. If you want to pass this to another discriminator, the output shape consists of 28x28 images. ## How to use this model
You can use this model to generate new anime faces. If you want to continuously train, use with discriminator using 'tf.GradientTape()' as mentioned in the DCGAN tutorial.



You can generate examples using a noise. ## Intended use and biases
This model is not intended for production. ### Generated images 
!Example
passage: TAGS
#keras #dcgan #dataset-merve/anime-faces #license-apache-2.0 #has_space #region-us 
## Model description
Anime face generator model using TensorFlow DCGAN example.## Training and evaluation data
Model is trained on anime faces dataset. The dataset consists of 21551 anime faces scraped from URL, which are then cropped using the anime face detection algorithm here. All images are resized to 64 * 64 for the sake of convenience. The model takes a noise as input and then Conv2DTranspose is used to do upsampling. If you want to pass this to another discriminator, the output shape consists of 28x28 images.## How to use this model
You can use this model to generate new anime faces. If you want to continuously train, use with discriminator using 'tf.GradientTape()' as mentioned in the DCGAN tutorial.



You can generate examples using a noise.## Intended use and biases
This model is not intended for production.### Generated images 
!Example