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""" hardcoded parameter
these can be changed in a jupyter notebook during runtime via
>>> import parameter
>>> parameter.parameter = new_value
"""
from torch.optim import Adam
###############
## hardcoded ##
###############
# Input
image_dim = 64
colors_dim = 3
labels_dim = 37 #3
input_size = (colors_dim,image_dim,image_dim)
#############
## mutable ##
#############
class Parameter:
""" container for hyperparameters"""
def __init__(self):
# Encoder/Decoder
self.latent_dim = 8
self.decoder_dim = self.latent_dim # differs from latent_dim if PCA applied before decoder
# General
self.learning_rate = 0.0002
self.betas = (0.5,0.999) ## 0.999 is default beta2 in tensorflow
self.optimizer = Adam
self.negative_slope = 0.2 # for LeakyReLU
self.momentum = 0.99 # for BatchNorm
# Loss weights
self.alpha = 1 # switch VAE (1) / AE (0)
self.beta = 1 # weight for KL-loss
self.gamma = 1024 # weight for learned-metric-loss (https://arxiv.org/pdf/1512.09300.pdf)
self.delta = 1 # weight for class-loss
self.zeta = 0.5 # weight for MSE-loss
def return_parameter_dict(self):
return(self.__dict__)
parameter = Parameter()
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