config.yml

# Dataset Configuration
Dataset:
  num_days: 365 # Test with sequence of 1 day
  batch_size: 128
  num_workers: 7
  valid_ratio: 0.2
  max_num_samples: None #1000
  _DEFAULT_TRAIN_FILEPATH: "/mounts/Datasets3/2022-ChallengePlankton/sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin"
  _DEFAULT_TEST_FILEPATH: "/mounts/Datasets3/2022-ChallengePlankton/sub_2CMEMS-MEDSEA-2017-testing.nc.bin"
  _ENCODING_LINEAR: "I"
  _ENCODING_INDEX: "I"  # h(short) with 2 bytes should be sufficient
  _ENCODING_OFFSET_FORMAT: ""
  _ENCODING_ENDIAN: "<"

# Data Transformation
ApproximativeStats: True
ApproximativeMean: "torch.tensor([ 4.2457e+01,  7.4651e+00,  1.6738e+02,  1.3576e+09,  2.3628e+00,
         4.6839e+01,  2.3855e-01,  3.6535e+00,  1.9776e+00,  2.2628e+02,
         8.1003e+00,  1.8691e-01,  3.8384e+01,  2.6626e+00,  1.4315e+01,
        -4.1419e-03,  6.0274e-03, -5.1017e-01])"
ApproximativeSTD: "torch.tensor([5.8939e-01, 8.1625e-01, 1.4535e+02, 5.4952e+07, 1.7543e-02, 1.3846e+02,
        2.1302e-01, 1.9558e+00, 4.1455e+00, 1.2408e+01, 2.2938e-02, 9.9070e-02,
        1.9490e-01, 9.2847e-03, 2.2575e+00, 8.5310e-02, 7.8280e-02, 8.6237e-02])"

#Optimizer selection
Optimizer: Adam # in {Adam}

#Training parameters
Training:
  Epochs: 2
  
#Model selection
Model:
  Name: LinearRegression

#Model parameters selection
LinearRegression:
  # Bias in {True, False}
  Bias: True
  HiddenSize: 35
  Initialization: init_he

#Name of directory containing logs
LogDir: ./logs/

#Visualization
Wandb:
  log_freq: 100 #log gradients and parameters every log_freq batches
  log_interval: 10 # log the train_loss every log_interval batches