Protein Engineering

avgfp · Fully connected

reference_avgfp_fc_plm_contiguousf1_20261104-181527

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
train0.26660.83130.71560.61011898.00000.18804118.00000.60003.73373.95450.00000.00000.61280.50003.71754.01930.06000.00000.64280.48003.70204.03780.05000.00000.65570.47273.70284.03780.05450.00000.6617
validation0.49950.09170.0959-0.0008142.0000-0.0090121.00000.20003.04123.80980.00000.0000-0.47490.18003.37663.85690.08000.00000.09990.13003.35703.90820.11000.00000.06670.18183.09493.80980.00000.0000-0.3887
test1.36430.0233-0.0404-0.357160.0000-0.0372196.00000.00002.43263.64800.00000.0000-0.37920.08002.72923.76030.08000.0000-0.11110.06002.63983.99120.09000.0000-0.21440.00002.20303.64800.00000.0000-0.5774

Loss per epoch

training validation

Config

name: reference_avgfp_fc_plm_contiguousf1
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: contiguous
  split_fold: 1
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