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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() | |