Protein Engineering

avgfp · Sequence CNN

reference_avgfp_cnn_s4_20261104-125432

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
test0.09760.95770.86510.9130490.00000.298818.00000.60003.77033.95800.00000.00000.82480.64003.77844.11360.22000.00000.88050.60003.76214.11360.29000.00000.89660.62963.77804.11360.22220.00000.8833
train0.06670.97250.88700.94074088.00000.27330.10000.80650.10000.84540.11000.86510.19860.8855
validation0.09490.95880.86810.9157466.00000.2548406.00000.80003.76423.88410.00000.00000.81500.68003.75964.03140.16000.00000.87020.61003.75854.03140.24000.00000.89880.67353.75884.03140.16330.00000.8688

Loss per epoch

training validation

Config

name: reference_avgfp_cnn_s4
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: 4