Esempio n. 1
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# Number of iterations between two jumps
_C.alphaSizeJumps = [0, 32, 32, 32, 32, 32, 32, 32, 32, 32]

#############################################################
# Type of convolutional layer
_C.transposed = False

# Depth of a convolutional layer for each scale
_C.depthScales = [512, 512, 512, 512, 256, 128, 64, 32, 16]

# Mini batch size
_C.miniBatchSize = 16

# Dimension of the latent vector
_C.dimLatentVector = 512

# Should bias be initialized to zero ?
_C.initBiasToZero = True

# Per channel normalization
_C.perChannelNormalization = True

# Loss mode
_C.lossMode = 'WGANGP'
_C.ac_gan = False

# Gradient penalty coefficient (WGANGP)
_C.lambdaGP = 10.

# Leakyness of the leakyRelU activation function
Esempio n. 2
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from utils.config import BaseConfig

# Default configuration for ProgressiveGANTrainer
_C = BaseConfig()

############################################################

# Depth of a convolutional layer for each scale
_C.depth = 3

# Mini batch size
_C.miniBatchSize = 256  # 64

# Dimension of the latent vector
_C.dimLatentVector = 100

# Dimension of the output image
_C.dimOutput = 3

# Dimension of the generator
_C.dimG = 64

# Dimension of the discrimator
_C.dimD = 64

# Image dimension
_C.imageSize = 64

# Learning rate for optimizers
# _C.learningRate = 0.0002