hyper_tcg.yaml

# @package _global_
#
# to execute this experiment run:
# python train.py experiment=tcg

defaults:
  - override /model: hyper_tcg #hyper_tcg
  - override /datamodule: linear_unidentifiable_velocity #linear_velocity
  - override /logger:
      - csv
      - wandb
  - override /trainer: gpu
name: "hyper_tcg_gfn"

seed: 0

datamodule:
  batch_size: 500 #500
  T: 2
  p: 5 #20
  vars_to_deidentify: [0]
  sparsity: 0.85 #0.95
  system: "linear"
  sigma: 0
  seed: 13

model:
  env_batch_size: 1024
  eval_batch_size: 1000
  full_posterior_eval: False
  uniform_backwards: True
  debug_use_shd_energy: False
  analytic_use_simple_mse_energy: True
  loss_fn: "detailed_balance"
  alpha: 0
  temperature: 0.00001
  temper_period: 5
  prior_lambda: 15
  beta: 0.01
  confidence: 0.0
  hidden_dim: 128
  gfn_freq: 1
  energy_freq: 1
  pretraining_epochs: 0
  lr: 1e-4
  hyper: "mlp"
  bias: True

trainer:
  max_epochs: 500
  min_epochs: 500
  check_val_every_n_epoch: 5

logger:
  wandb:
    tags: ["analytic", "per-vs-full", "gfn", "${name}", "v10"]