libs/solaris/nets/configs/selimsef_densenet121unet_spacenet4.yml [104:127]:
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  steps_per_epoch:
  optimizer: AdamW
  lr: 2e-4
  opt_args:
    weight_decay: 0.0001
  loss:
    focal:
    dice:
  loss_weights:
    focal: 1
    dice: 1
  metrics:
    training:
    validation:
  checkpoint_frequency: 10
  callbacks:
    lr_schedule:
      schedule_type: 'arbitrary'
      schedule_dict:
        milestones:
          - 1
          - 5
          - 15
          - 30
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libs/solaris/nets/configs/selimsef_scse50unet_spacenet4.yml [143:176]:
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  steps_per_epoch:  # optional argument defining # steps/epoch. If not provided,
                    # each epoch will include the number of steps needed to go
                    # through the entire training dataset.
  optimizer: AdamW # optimizer function name. see docs for options.
  lr: 2e-4 # learning rate.
  opt_args:  # dictionary of values (e.g. alpha, gamma, momentum) specific to
    weight_decay: 0.0001
  loss:
    focal:  # loss function(s). See docs for options. This should be a list of loss
    dice:     # names with sublists of loss function hyperparameters (if applicable).
         # See the docs for more details and usage examples.
  loss_weights:
    focal: 1 # (optional) weights to use for each loss function if using
    dice: 1 # loss: composite. This must be a set of key:value pairs where
                 # defining the weight for each sub-loss within the composite.
                 # If using a composite and a value isn't provided here, all
                 # losses will be weighted equally.
  metrics:  # metrics to monitor on the training and validation sets.
    training:      # training set metrics.
    validation:  # validation set metrics.
  checkpoint_frequency: 10 # how frequently should checkpoints be saved?
                         # this can be an int, in which case every n epochs
                         # a checkpoint will be made, or a list, in which case
                         # checkpoints will be saved on those specific epochs.
                         # if not provided, only the final model is saved.
  callbacks:
    lr_schedule:
      schedule_type: 'arbitrary'
      schedule_dict:
        milestones:
          - 1
          - 5
          - 15
          - 30
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