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--- |
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library_name: tf-keras |
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tags: |
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- gan |
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- dcgan |
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- huggan |
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- tensorflow |
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- unconditional-image-generation |
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--- |
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## Model description |
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Simple DCGAN implementation in TensorFlow to generate CryptoPunks. |
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## Generated samples |
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<img src="https://github.com/dimitreOliveira/cryptogans/raw/main/assets/gen_samples.png" width="350" height="350"> |
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Project repository: [CryptoGANs](https://github.com/dimitreOliveira/cryptogans). |
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## Usage |
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You can play with the HuggingFace [space demo](https://huggingface.co/spaces/huggan/crypto-gan). |
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Or try it yourself |
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```python |
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import tensorflow as tf |
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import matplotlib.pyplot as plt |
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from huggingface_hub import from_pretrained_keras |
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seed = 42 |
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n_images = 36 |
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codings_size = 100 |
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generator = from_pretrained_keras("huggan/crypto-gan") |
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def generate(generator, seed): |
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noise = tf.random.normal(shape=[n_images, codings_size], seed=seed) |
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generated_images = generator(noise, training=False) |
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fig = plt.figure(figsize=(10, 10)) |
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for i in range(generated_images.shape[0]): |
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plt.subplot(6, 6, i+1) |
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plt.imshow(generated_images[i, :, :, :]) |
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plt.axis('off') |
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plt.savefig("samples.png") |
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generate(generator, seed) |
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``` |
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## Training data |
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For training, I used the 10000 CryptoPunks images. |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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 |
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</details> |
