SeparableConvCNN | moritzm00 Workspace – Weights & Biases

Skip to main content

Moritzm00's group workspace

Group: SeparableConvCNN

1-1

of 1

Tags

Notes

Tags

SeparableConvCNNv1

Author

State

Finished

Start time

May 8th, 2023 3:29:20 PM

Runtime

27m 37s

Tracked hours

27m 32s

Run path

moritzm00/UTKFace-Age-Regression/cv9bxjhx

OS

Linux-5.4.0-122-generic-x86_64-with-glibc2.31

Python version

3.9.16

Git repository

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

Git state

git checkout -b "robust-night-82" f2049f33cb30a359467f57a0de175aac06dc91ec

Command

/usr/local/bin/train model=customcnn

System Hardware

CPU count	8
Logical CPU count	8
GPU count	1
GPU type	Quadro RTX 5000

W&B CLI Version

0.15.2

Group

SeparableConvCNN

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_31_d15b0485c31f8853672e.plotly.json"
  - sha256:
    "d15b0485c31f8853672e3a6487a6158f940cafd6c639d7e1d0c418c933524816"
  - size:
    154,949
- ▶
  empirical_cdfs:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/empirical_cdfs_31_bf9742be521d811cba8b.plotly.json"
  - sha256:
    "bf9742be521d811cba8be059d2bc7ed03600f6445a51446b03d1f83a926dc552"
  - size:
    310,269
- epoch/epoch:
  30
- epoch/learning_rate:
  0.003970095422118902
- epoch/loss:
  118.76026916503906
- epoch/mae:
  8.272697448730469
- epoch/val_loss:
  123.45489501953124
- epoch/val_mae:
  8.501372337341309
- ▶
  predictions:{} 7 keys
- ▶
  residuals:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/residuals_31_db8d5da6b226785ef72e.plotly.json"
  - sha256:
    "db8d5da6b226785ef72e27d99247e0c59c15af2d488d70ec8da8e4dad7b9d7f8"
  - size:
    120,808
- test_mae:
  8.441869857189577

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

run_cv9bxjhx_model:v0

run-cv9bxjhx-history:v0

Loading...