Protein Engineering

avgfp · Fully connected

reference_avgfp_fc_contiguousf0_20261104-144834

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pctcaptured_at_1pct
train0.40440.80890.66920.36722295.00000.1580601.00000.40003.67983.92710.00000.52750.40003.68214.00420.02000.57910.39003.68494.01930.05000.61070.41183.69274.01930.05150.6361
validation1.13250.07230.0785-0.126560.0000-0.0681549.00000.00002.00393.68760.00000.0000-0.78300.04002.52083.73120.00000.0000-0.32960.11002.76163.86780.14000.0000-0.08070.00002.02953.19270.00000.0000-0.7359
test0.7207-0.2720-0.2466-0.0149109.0000-0.0837206.00000.00001.83193.49380.00000.0000-1.77500.04002.51013.75180.00000.0000-1.02330.05002.66283.75180.04000.0000-0.84360.00002.00653.49380.00000.0000-1.5070

Loss per epoch

training validation

Config

name: reference_avgfp_fc_contiguousf0
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: contiguous
  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