Protein Engineering

avgfp · Fully connected

reference_avgfp_fc_plm_modulof2_20261104-183459

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
train0.43860.77910.73660.46601577.00000.2051974.00000.20003.60383.75130.00000.00000.53120.44003.67203.90080.00000.00000.64720.49003.65753.92700.01000.00000.65690.50003.66443.92700.01850.00000.6680
validation0.70880.1030-0.0376-0.0073111.0000-0.1131328.00000.00002.25913.51910.00000.0000-1.29570.10002.70333.88410.04000.0000-0.77260.09002.82313.88410.09000.0000-0.62160.00002.04803.51910.00000.0000-1.5447
test0.46760.05790.0065-0.0059135.00000.0799662.00000.00003.10123.70350.00000.0000-0.44700.16003.39973.92880.08000.00000.10200.18003.36273.92880.12000.00000.02700.00002.96873.64730.00000.0000-0.6750

Loss per epoch

training validation

Config

name: reference_avgfp_fc_plm_modulof2
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: modulo
  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