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

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Moritzm00's group workspace

Group: SENet

1-4

of 4

Tags

Notes

Tags

SENet50

Author

State

Finished

Start time

May 13th, 2023 11:35:56 AM

Runtime

59m 43s

Tracked hours

59m 38s

Run path

moritzm00/UTKFace-Age-Regression/b1qcuo2l

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 "floral-dew-116" 09b3e1a06a101211ee9034f980a88d2c5b376e8e

Command

/usr/local/bin/train model=senet50 train.batch_size=64 train.target_size=[200,200] model.finetune_base=true

System Hardware

CPU count	8
Logical CPU count	8
GPU count	1
GPU type	NVIDIA RTX A4000

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_41_ebbcb34f25ab8c7b1d1f.plotly.json"
  - sha256:
    "ebbcb34f25ab8c7b1d1f4a3071cb15149cf5b03dfb14dd1479104447c4908f9e"
  - size:
    147,586
- ▶
  empirical_cdfs:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/empirical_cdfs_41_9951b81988d004ea7fb1.plotly.json"
  - sha256:
    "9951b81988d004ea7fb1b3e4cbed4892a12fb6dafd14be13780103ffe7fc61d0"
  - size:
    303,226
- epoch/epoch:
  40
- epoch/learning_rate:
  0.0000499581619806122
- epoch/loss:
  80.96212768554688
- epoch/mae:
  6.722951412200928
- epoch/val_loss:
  72.48835754394531
- epoch/val_mae:
  6.253450393676758
- ▶
  predictions:{} 7 keys
- ▶
  residuals:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/residuals_41_b32cdfb778902f9f6af9.plotly.json"
  - sha256:
    "b32cdfb778902f9f6af9088e8ce799c8dc551b66944f4acda5e4d7f7baa1f47e"
  - size:
    113,933
- test_mae:
  5.717088539054353

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

run_b1qcuo2l_model:v0

run-b1qcuo2l-history:v0

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