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| 🙂 | |
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| license: gpl-2.0 | |
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| # DCGAN to generate face images | |
| This is an example notebook for Keras sprint prepared by Hugging Face. Keras Sprint aims to reproduce Keras examples and build interactive demos to them. The markdown parts beginning with 🤗 and the following code snippets are the parts added by the Hugging Face team to give you an example of how to host your model and build a demo. | |
| **Original Author of the DCGAN to generate face images Example:** [fchollet](https://twitter.com/fchollet) | |
| ## Steps to Train the DCGAN | |
| 1. Create the discriminator | |
| - It maps a 64x64 image to a binary classification score. | |
| ```py | |
| discriminator = keras.Sequential( | |
| [ | |
| keras.Input(shape=(64, 64, 3)), | |
| layers.Conv2D(64, kernel_size=4, strides=2, padding="same"), | |
| layers.LeakyReLU(alpha=0.2), | |
| layers.Conv2D(128, kernel_size=4, strides=2, padding="same"), | |
| layers.LeakyReLU(alpha=0.2), | |
| layers.Conv2D(128, kernel_size=4, strides=2, padding="same"), | |
| layers.LeakyReLU(alpha=0.2), | |
| layers.Flatten(), | |
| layers.Dropout(0.2), | |
| layers.Dense(1, activation="sigmoid"), | |
| ], | |
| name="discriminator", | |
| ) | |
| ``` | |
| 2. Create the generator | |
| - It mirrors the discriminator, replacing Conv2D layers with Conv2DTranspose layers | |
| ```py | |
| latent_dim = 128 | |
| generator = keras.Sequential( | |
| [ | |
| keras.Input(shape=(latent_dim,)), | |
| layers.Dense(8 * 8 * 128), | |
| layers.Reshape((8, 8, 128)), | |
| layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding="same"), | |
| layers.LeakyReLU(alpha=0.2), | |
| layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"), | |
| layers.LeakyReLU(alpha=0.2), | |
| layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"), | |
| layers.LeakyReLU(alpha=0.2), | |
| layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"), | |
| ], | |
| name="generator", | |
| ) | |
| ``` | |
| HF Contributor: [Tarun Jain](https://twitter.com/TRJ_0751) |