sweep.yml

program: sweep.py
method: bayes
metric:
  goal: maximize
  name: best_acc
parameters:
  scaling_mode:
    values: [1, 2, 3]
    distribution: categorical
  disable_bias:
    values: [True, False]
    distribution: categorical
  # critic_score_lambda:
  #   max: 1
  #   min: 0
  #   distribution: uniform
  lr:
    max: 0.01
    min: 0.0001
    distribution: uniform
  weight_decay:
    max: 1
    min: 0.0
    distribution: uniform
  drop_path:
    max: 0.5
    min: 0.0
    distribution: uniform
  m_dropout:
    max: 0.5
    min: 0.0
    distribution: uniform
  a_dropout:
    max: 0.5
    min: 0.0
    distribution: uniform
  mlp_dim:
    values: [32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152,  160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256]
    distribution: categorical
  emb_type:
    values:
      - downsample
      - mlp
      - time2vec
      - convnet
    distribution: categorical
  emb_dim:
    values: [32, 64, 128, 256, 512]
    distribution: categorical
  num_units:
    values: [32, 64, 128, 256, 512]
  num_blocks:
    max: 3
    min: 1
    distribution: int_uniform
  num_heads:
    max: 3
    min: 1
    distribution: int_uniform
  batch_size:
    values: [8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128]
    distribution: categorical