LinearModel | UTKFace-Age-Regression Workspace – Weights & Biases

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Moritzm00's group workspace

Group: LinearModel

1-2

of 2

Tags

Notes

Tags

LinearModelv1

Author

State

Finished

Start time

May 10th, 2023 2:12:25 PM

Runtime

1m 13s

Tracked hours

1m 8s

Run path

moritzm00/UTKFace-Age-Regression/90n6487c

OS

macOS-13.3.1-arm64-arm-64bit

Python version

3.9.15

Git repository

git clone git@github.com:MoritzM00/AgePrediction-UTKFace.git

Git state

git checkout -b "lemon-forest-101" 3eeba1ffbe4414a821a75a221cf5346ca0dddc3a

Command

/opt/homebrew/Caskroom/miniforge/base/envs/age-prediction/bin/train model=linearmodel train.target_size=[64,64] callbacks.early_stopping_patience=15

System Hardware

CPU count	8
Logical CPU count	8

W&B CLI Version

0.15.2

Group

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 12 keys
- ▶
  augment:{} 2 keys
  - active:
    true
  - ▶
    factors:{} 4 keys
    - random_brightness:
      null
    - random_flip:
      "horizontal"
    - random_rotation:
      0.1
    - random_translation:
      null
- ▶
  callbacks:{} 7 keys
- ckpt_dir:
  "ckpts"
- data_dir:
  "data"
- ▶
  dataset:{} 6 keys
- log_dir:
  "logs"
- ▶
  lr_schedule:{} 2 keys
  - ▶
    stage_1:{} 4 keys
    - _target_:
      "tensorflow.keras.optimizers.schedules.ExponentialDecay"
    - decay_rate:
      0.96
    - decay_steps:
      100,000
    - initial_learning_rate:
      0.004
  - ▶
    stage_2:{} 4 keys
    - _target_:
      "tensorflow.keras.optimizers.schedules.ExponentialDecay"
    - decay_rate:
      0.99
    - decay_steps:
      100,000
    - initial_learning_rate:
      0.00005
- ▶
  model:{} 9 keys
- model_dir:
  "models"
- ▶
  optimizer:{} 2 keys
  - _partial_:
    true
  - _target_:
    "tensorflow.keras.optimizers.Adam"
- ▶
  train:{} 10 keys
- ▶
  wandb:{} 9 keys

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 11 keys
- ▶
  age_distribution:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/age_distribution_64_9539f05d582bbfd8ef5e.plotly.json"
  - sha256:
    "9539f05d582bbfd8ef5e1839186549f90df6ce23fdeeae37cd47530a8036c28f"
  - size:
    153,610
- ▶
  empirical_cdfs:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/empirical_cdfs_64_f3fca2d5c5fd3ee6a5b7.plotly.json"
  - sha256:
    "f3fca2d5c5fd3ee6a5b741be57c5cb12301cf2a8382b9cbde784837ec0257f04"
  - size:
    309,161
- epoch/epoch:
  63
- epoch/learning_rate:
  0.003938506823033094
- epoch/loss:
  277.3766174316406
- epoch/mae:
  12.797863960266112
- epoch/val_loss:
  295.5289306640625
- epoch/val_mae:
  13.814743995666504
- ▶
  predictions:{} 7 keys
- ▶
  residuals:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/residuals_64_e70aafe2f846ea67bca5.plotly.json"
  - sha256:
    "e70aafe2f846ea67bca5e7b477b4e82a580b425bcb3cac6b7be42972ce825921"
  - size:
    119,583
- test_mae:
  12.462441120606242

This run produced these artifacts as outputs. Total: 2. Learn more

run_90n6487c_model:v0

run-90n6487c-history:v0

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