Protein Engineering

avgfp · Fully connected

reference_avgfp_fc_modulof2_20261104-163006

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
train0.32330.81350.74050.60631577.00000.21711120.00000.10003.62793.82910.00000.00000.55570.40003.61863.94530.00000.00000.58880.42003.65574.10680.04000.00000.65480.43523.65924.10680.04630.00000.6622
validation0.7383-0.1563-0.1862-0.0493111.00000.1443397.00000.00002.04283.69390.00000.0000-1.59660.10002.73843.81510.04000.0000-0.71710.10002.82853.81770.09000.0000-0.61250.00002.03163.69390.00000.0000-1.5669
test0.4754-0.1033-0.0863-0.0227135.00000.1106129.00000.00002.65053.70340.00000.0000-1.25440.08003.02153.78640.02000.0000-0.68470.10003.15463.81820.08000.0000-0.44290.00002.45903.70340.00000.0000-1.5758

Loss per epoch

training validation

Config

name: reference_avgfp_fc_modulof2
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  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