Protein Engineering

avgfp · Sequence CNN

reference_avgfp_cnn_s2_20261104-022537

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
test0.09130.95960.86680.9187490.00000.316385.00000.60003.74663.89960.00000.00000.80730.60003.76893.95850.20000.00000.87320.59003.76244.11360.33000.00000.89690.59263.76813.95850.24070.00000.8755
train0.05840.97520.89210.94804088.00000.26530.00000.79720.08000.83600.07000.84880.19860.8855
validation0.09320.95870.86920.9171466.00000.2459129.00000.80003.75413.88500.00000.00000.80790.68003.76174.03140.14000.00000.87180.64003.75084.03140.20000.00000.89270.69393.76354.03140.14290.00000.8724

Loss per epoch

training validation

Config

name: reference_avgfp_cnn_s2
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: avgfp/splits/main
model:
  name: cnn
  conv:
  - - 128
    - 3
  - - 128
    - 3
  - - 128
    - 3
  - - 128
    - 3
  - - 128
    - 3
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 64
  learning_rate: 0.001
  patience: 15
  min_delta: 1.0e-05
  seed: 2