ResNet | moritzm00 Workspace – Weights & Biases

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Moritzm00's group workspace

Group: ResNet

1-12

of 12

Tags

Notes

Tags

ResNet50

Author

State

Finished

Start time

May 8th, 2023 4:06:16 PM

Runtime

12m 17s

Tracked hours

12m 10s

Run path

moritzm00/UTKFace-Age-Regression/o6ha4exz

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 "pleasant-breeze-85" d8873dfbf9756ed29e5a49372bf89a4aee51efe2

Command

/usr/local/bin/train model=resnet optimizer=adamw

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

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:{} 3 keys
  - _partial_:
    true
  - _target_:
    "tensorflow.keras.optimizers.Adam"
  - decay:
    0.0005
- ▶
  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_24_8bd3532e1eff7ac2bad9.plotly.json"
  - sha256:
    "8bd3532e1eff7ac2bad95308d606e7456af727b4466154b3f5ce0e190a6d9ac8"
  - size:
    148,406
- ▶
  empirical_cdfs:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/empirical_cdfs_24_94c17feee47be2085244.plotly.json"
  - sha256:
    "94c17feee47be20852449d7edd470de565fc64c8c49475384ec2151f9e8b94f8"
  - size:
    303,860
- epoch/epoch:
  23
- epoch/learning_rate:
  0.00004995824929210357
- epoch/loss:
  120.2540054321289
- epoch/mae:
  8.026667594909668
- epoch/val_loss:
  99.61114501953124
- epoch/val_mae:
  7.200781345367432
- ▶
  predictions:{} 7 keys
- ▶
  residuals:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/residuals_24_f79e0fe16e48427e99f2.plotly.json"
  - sha256:
    "f79e0fe16e48427e99f2a4d376e5dda174917cb39b0bb63d2dcab7b291f1d76d"
  - size:
    114,470
- test_mae:
  7.193877692943583

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

run_o6ha4exz_model:v0

run-o6ha4exz-history:v0

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