uda_example.yaml

exp_setting:
  exp_name: 'uda_example'                     # Expriment title, log/checkpoint files will be named after this
  checkpoint_dir: 'checkpoint/'               # Folder for model checkpoints
  log_dir: 'log/'                             # Folder for training logs
  data_root: 'data/'
  seed: 123456
  img_size: 32
  img_depth: 3
  source_domain: 'svhn'
  target_domain: 'mnist'
  shuffle_source: False                       # Notice that here using # of images from SVHN equal as MNIST
  shuffle_target: True 
  

model:
  vae:
    encoder: [['conv', 128,4,2,1,'bn','LeakyReLU'],
              ['conv', 256,4,2,1,'bn','LeakyReLU'],
              ['conv', 512,4,2,1,'bn','LeakyReLU'],
              ['conv', 1024,4,2,1,'bn','LeakyReLU'],
              ['conv', 2048,4,2,1,  '','']
             ]
    code_dim: 2
    decoder: [['conv', 1024,4,2,1,'bn','LeakyReLU',True],
              ['conv', 512,4,2,1,'bn','LeakyReLU',False],
              ['conv', 256,4,2,1,'bn','LeakyReLU',False],
              ['conv', 128,4,2,1,'bn','LeakyReLU',False],
              ['conv',  3,4,2,1,  '','Tanh',False]
             ]
    lr: 0.0001
    betas: [0.5,0.999]
  D_feat:
    dnn: [['fc', 1024, '', 'LeakyReLU',0],
          ['fc', 256, '', 'LeakyReLU',0],
          ['fc', 2, '', '', 0]
         ]
    lr: 0.0001
    betas: [0.5,0.999]

  D_digit:
    dnn: [['fc', 10, '', '',0]
         ]
    lr: 0.0001
    betas: [0.5,0.999]

trainer:
  total_step: 500000
  batch_size: 16

  lambda:
    pix_recon:
      init:  1
      final: 1
      step:  1
    kl:
      init:  0.0000001
      final: 0.0000001
      step:  1
    feat_domain:
      init:  0.1
      final: 0.1
      step:  1

  data_augment: True        # Augmentation : pixelwise flipping value
  verbose_step: 100
  plot_step: 500
  checkpoint_step: 5000
  save_log: True
  show_fig: True
  save_fig: True
  save_checkpoint: True
  save_best_only: True