basic.yaml

entrypoint: classification

model_params:
  arch: 'ResNetClassifier1d'
  name: 'ResNetClassifier1d'
  hidden_dims: [128, 256, 128]
  num_classes: 6

exp_params:
  manual_seed: 6602
  warmup_steps: 512
  reduce_lr_on_plateau:
    factor: 0.1
    patience: 3
    threshold: 1.0e-04
  save_weights:
    every_n_steps: 10_000
    local: {}
  data:
    name: grasp-and-lift-eeg
    training:
      root: "C:/data/grasp-and-lift-eeg" #/data/grasp-and-lift-eeg-detection
      num_samples: 1024
      last_label_only: true
      subjects: [1, 2, 3, 4, 5, 6, 7, 8, 9]
    validation:
      subjects: [10, 11, 12]
    loader:
      pin_memory: false
      num_workers: 0
  batch_size: 32
  optimizer:
    lr: 0.00005
    weight_decay: 0.0000001
  plot:
    fn: classifier1d_multicolumn
    sample_every_n_steps: 20_000
    examples_per_class: 6
    classes:
    - name: Control
      labels: [0, 0, 0, 0, 0, 0]
      all: true
    - name: HandStart
      labels: [1, 0, 0, 0, 0, 0]
      all: true
    - name: FirstDigitTouch
      labels: [0, 1, 0, 0, 0, 0]
      all: true
    - name: BothStartLoadPhase
      labels: [0, 0, 1, 0, 0, 0]
      all: true
    - name: LiftOff
      labels: [0, 0, 0, 1, 0, 0]
      all: true
    - name: Replace
      labels: [0, 0, 0, 0, 1, 0]
      all: true
    - name: BothReleased
      labels: [0, 0, 0, 0, 0, 1]
      all: true
    params:
      width: 1024
      height: 256
      indicator_thickness: 12
      line_opacity: 0.3
      layout_params:
        showlegend: false
        margin:
          l: 0
          r: 0
          b: 0
          t: 0
    layout_params:
      showlegend: false

trainer_params:
  max_epochs: 1_000_000
  log_every_n_steps: 100
  val_check_interval: 20_000
  limit_val_batches: 2_000

logging_params:
  save_dir: "logs/"
  name: "GraspLift_Basic"

manual_seed: 6602