Data:
  # Basics
  log_dir: 'tasks/models'
  # Data
  dataset: "FFTDataset"
  data_dir: None
  model_name: "CNNEncoder"
  batch_size: 32
  num_epochs: 10
  exp_num: 2
  max_len_spectra: 4096
  max_days_lc: 270
  lc_freq: 0.0208
  create_umap: True
  checkpoint_path: 'tasks/models/frugal_2025-01-21/frugal_kan_2.pth'

CNNEncoder:
  # Model
  in_channels: 2
  num_layers: 4
  stride: 1
  encoder_dims: [32,64,128]
  kernel_size: 3
  dropout_p: 0.3
  output_dim: 2
  beta: 1
  load_checkpoint: False
  checkpoint_num: 1
  activation: "silu"
  sine_w0: 1.0
  avg_output: False

KAN:
  layers_hidden: [1125,32,8,8,1]
  grid_min:  -1.2
  grid_max:  1.2
  num_grids:  8
  exponent:  2

CNNEncoder_f:
  # Model
  in_channels: 1
  num_layers: 4
  stride: 1
  encoder_dims: [32,64,128]
  kernel_size: 3
  dropout_p: 0.3
  output_dim: 2
  beta: 1
  load_checkpoint: True
  checkpoint_num: 1
  activation: "silu"
  sine_w0: 1.0
  avg_output: True


Conformer:
  encoder: ["mhsa_pro", "conv"]
  timeshift: false
  num_layers: 4
  encoder_dim: 128
  num_heads: 8
  kernel_size: 3
  dropout_p: 0.2
  norm: "postnorm"


Optimization:
  # Optimization
  max_lr: 1e-5
  weight_decay: 5e-6
  warmup_pct: 0.3
  steps_per_epoch: 3500