PyTorch implementation of Deep Convolutional Generative Adversarial Networks (DCGAN), which is a stabilize Generative Adversarial Networks. The origin code can be found here.
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Generator
- input: a vector with z_size.
- hidden layers: Four 4x4 transposed convolutional layers (1024, 512, 256, and 128 kernels, respectively) with ReLU
- output layer: 4x4 transposed convolutional layer (channel_size kernels, 4096 nodes = 64x64 size image) with Tanh.
- BatchNormalization is used except for output layer.
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Discriminator
- input: a vector with channel_size * image_size * image_size.
- hidden layers: Four 4x4 convolutional layers (128, 256, 512, and 1024 kernels, respectively) with LeakyReLU (negative slope is 0.2).
- output layer: 4x4 convolutional layer (1 node) with Sigmoid.
- BatchNormalization is used except for 1st hidden layer and output layer.
All of the transposed convolutional layer and convolutional layer are initilized by a normal distribution with 0.0 mean and 0.02 std.
- For MNIST image, the channel_size is 1 and image_size is 64.
- The learning rate is 0.0002, batch size is 128 and the optimizer is Adam.
| Gnerated Images | DCGAN Loss |
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- For CelebA image, the channel_size is 3 and image_size is 180 x 180, which has been aligned and cropped. And then, it will be resized to 64 x 64.
- The learning rate is 0.0002, batch size is 128 and the optimizer is Adam.
| Gnerated Images | DCGAN Loss |
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- For LSUN image, the channel_size is 3. And then, it will be resized to 64 x 64.
- The learning rate is 0.0002, batch size is 1024 and the optimizer is Adam.
| Gnerated Images | DCGAN Loss |
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