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52 lines
1.3 KiB
Markdown
52 lines
1.3 KiB
Markdown
## Face Generation GAN based on DCGAN architecture
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### Architecture
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### Examples
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#### Mnist
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#### Celeba
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### Details
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**codes for layers**
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- **1 layer** = (size)F|C(ks,str,p)|DC(ks,str)|BN|D|LR
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- **(size):** size of layer or filters
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- **F:** Fully Connected
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- **C:** Convulution
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- **DC(ks,str):** Deconvulution with (Kernel Size, Stride, padding)
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- **BN:** Batch normalization
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- **D:** Dropout
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- **LR:** Leaky Relu
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### Generator:
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#### Full: (7x7x1024)F -> D -> LR
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#### DCONV1: (512)DC(3,2,same) -> BN -> D -> LR
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#### DCONV2: (256)DC(3,2,same) -> BN -> D -> LR
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#### DCONV3: (128)DC(5,1,same) -> BN -> D -> LR
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#### DCONV_OUT: (channles)DC(5,1)
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### Discriminator:
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#### CONV1: (64)C(5,1,valid) -> LR
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#### CONV2: (128)C(5,1,valid) -> BN -> D -> LR
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#### CONV3: (256)C(5,1,valid) -> BN -> D -> LR
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#### CONV4: (512)C(5,2,valid) -> BN -> D -> LR
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#### OUT: (flat)F
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### Hyperparameters for celeba set
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- Leaky Relu Slope: 0.2
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- Adam Optimizaer beta: 0.5
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- Learning rate: 0.0003
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- Batch Size: 16
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- latent vector dimension: 100
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- dropout: 0.5
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- 1 epoch
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