Convenient GAN implementation in Keras
This is a WIP
from GAN import GAN
# 128x128, 3 channels
shape = (128, 128, 3)
model = GAN(shape)
model.train(x, y, epochs=100)
pred = model.generate(x[0])You may construct the model with some custom hyperparameters by passing in a settings object at construction.
from keras.optimizers import Adagrad
shape = (64, 64, 1) # 64x64 grayscal images
my_settings = {
'input_mask':True,
'g_ksize': 3,
'd_optimizer': Adagrad()
}
model = GAN(shape, settings=my_settings)| Key | Type | Default Value | Description |
|---|---|---|---|
| input_mask | boolean | False |
If set to True, the model with replace the output of the generator with the input where the input != 0. The masked output is used when computing loss |
| d_loss_target | 0.3 | float between 0 .. 1 | Each epoch, the discriminator will be trained if its loss is less than the target, the generator will be trained otherwise |
| g_optimizer | str, keras Optimizer, or lambda |
Adam(1e-3) |
The optimizer that will be used for the Generator. |
| g_ksize | int | 5 | Convolution kernel size for the generator (g_ksize * g_ksize) |
| g_depth | int | 64 | Output depth of each hidden layer of the generator |
| g_activation | str or lambda |
lambda: LeakyReLU() |
Activation function to use for the generator |
| g_regularizer | str of lambda |
None |
Regularization to use for the generator |
| d_optimizer | str, keras Optimizer, or lambda |
SGD() |
The optimizer that will be used for the Discriminator. |
| d_ksize | int | 5 | Convolution kernel size for the discriminator (d_ksize * d_ksize) |
| d_depth | int | 32 | Output depth of each hidden layer of the discriminator |
| d_activation | str or lambda |
lambda: LeakyReLU() |
Activation function to use for the discriminator |
| d_output_activation | str or lambda |
'sigmoid' |
Activation function to use for output layer the discriminator |
| d_regularizer | str of lambda |
None |
Regularization to use for the discriminator |
lambdasettings values should always return a new instance
- Training set was ~400 128x128 RGB images of landscapes
- Default settings
- epochs = 100
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