experiment_group: Sample Experiments
experiments:
    - dataloader: EmnistDataLoaders
      data_loader_args:
        splits: [train, val]
        sample_to_balance: true
        subsample_fraction: null
        transform: null
        target_transform: null
        batch_size: 256
        shuffle: true
        num_workers: 8
        cuda: true
        seed: 4711
      model: CharacterModel
      metrics: [accuracy]
      # network: MLP
      # network_args:
      #   input_size: 784
      #   output_size: 62
      #   num_layers: 3
      network: LeNet
      network_args:
        input_size: [28, 28]
        output_size: 62
      train_args:
        batch_size: 256
        epochs: 16
      criterion: CrossEntropyLoss
      criterion_args:
        weight: null
        ignore_index: -100
        reduction: mean
      # optimizer: RMSprop
      # optimizer_args:
      #   lr: 1.e-3
      #   alpha: 0.9
      #   eps: 1.e-7
      #   momentum: 0
      #   weight_decay: 0
      #   centered: false
      optimizer: AdamW
      optimizer_args:
        lr: 1.e-2
        betas: [0.9, 0.999]
        eps: 1.e-08
        weight_decay: 0
        amsgrad: false
      # lr_scheduler: null
      lr_scheduler: OneCycleLR
      lr_scheduler_args:
        max_lr: 1.e-3
        epochs: 16
      callbacks: [Checkpoint, EarlyStopping, WandbCallback, WandbImageLogger, OneCycleLR]
      callback_args:
        Checkpoint:
          monitor: val_accuracy
        EarlyStopping:
          monitor: val_loss
          min_delta: 0.0
          patience: 3
          mode: min
        WandbCallback:
          log_batch_frequency: 10
        WandbImageLogger:
          num_examples: 4
        OneCycleLR:
          null
      verbosity: 1 # 0, 1, 2
      resume_experiment: null