Protein Engineering

avgfp · Fully connected

reference_avgfp_fc_contiguousf2_20261104-163841

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
train0.30070.85490.80900.7011835.00000.1370168.00000.20003.63983.77780.00000.00000.68810.36003.66813.88380.00000.00000.75420.39003.66063.93630.06000.00000.77120.38893.66853.93630.04170.00000.7666
validation0.2462-0.0977-0.1571-0.0399366.00000.0321916.00000.10003.20893.79190.00000.0000-0.60250.22003.36513.87900.02000.0000-0.39890.21003.34993.87900.04000.0000-0.49540.07693.13243.79190.00000.0000-0.7767
test0.5745-0.2997-0.2027-0.1509142.0000-0.084045.00000.00001.93093.70020.00000.0000-2.33130.04002.51113.96120.02000.0000-1.58290.14002.70683.96120.10000.0000-1.30100.00002.05833.70020.00000.0000-2.1373

Loss per epoch

training validation

Config

name: reference_avgfp_fc_contiguousf2
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: contiguous
  split_fold: 2
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