Protein Engineering

avgfp · Fully connected

reference_avgfp_fc_plm_randomf0_20261104-174442

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10tie_share_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50tie_share_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100tie_share_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pcttie_share_at_1pctcaptured_at_1pct
train0.05170.97660.87460.95382984.00000.28821790.00000.70003.79363.95800.00000.00000.81240.84003.82224.10790.10000.00000.86780.77003.80634.10790.13000.00000.87350.68833.77594.10790.23770.00000.8896
validation0.07210.96760.86280.93621025.00000.2299146.00001.00003.85274.07250.10000.00000.84390.80003.80204.11360.12000.00000.86660.69003.77524.11360.18000.00000.87610.67593.77234.11360.17590.00000.8772
test0.07530.96620.86240.93341035.00000.260835.00000.90003.85054.03110.20000.00000.85990.66003.76794.10680.12000.00000.84460.65003.72814.10680.16000.00000.84310.64813.73194.10680.16670.00000.8489

Loss per epoch

training validation

Config

name: reference_avgfp_fc_plm_randomf0
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: random
  split_fold: 0
model:
  name: fcn
  hidden:
  - 100
  - 100
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 32
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7