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First Test of WandB with CNN-GRU Model

Author

State

Finished

Start time

May 2nd, 2021 7:08:00 PM

Runtime

33m 57s

Tracked hours

17m 34s

Run path

nbvanting/thesis/2wkj2v1g

OS

Windows-10-10.0.19041-SP0

Python version

3.8.3

Git repository

git clone https://github.com/nbvanting/thesis.git

Git state

git checkout -b "lyric-gorge-1" 39d0f23b98632011202d2d7dae4261be41f61e70

Command

model/CNN-RNN.ipynb

System Hardware

CPU count

12

W&B CLI Version

0.10.28

Group

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 11 keys
- activation_cnn:
  "relu"
- activation_gru:
  "tanh"
- batch_size:
  64
- cnn_layer:
  64
- dropout:
  0.5
- epoch:
  50
- gru_layer_1:
  100
- gru_layer_2:
  50
- learning_rate:
  0.001
- loss:
  "mse"
- ▶
  metric:[] 2 items
  - 0:
    "mae"
  - 1:
    "mape"

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 10 keys
- best_epoch:
  28
- best_val_loss:
  0.08652544021606445
- epoch:
  38
- ▶
  graph:{} 4 keys
  - _type:
    "graph-file"
  - path:
    "media/graph/graph_summary_6dcff82e.graph.json"
  - sha256:
    "6dcff82ece5175ec16b299a522937e2368460a0efbbc67cb6a6cc82ce17e09ff"
  - size:
    1,136
- loss:
  0.08227711915969849
- mae:
  0.21892297267913816
- mape:
  144.59896850585938
- val_loss:
  0.10515521466732024
- val_mae:
  0.24625994265079496
- val_mape:
  127.6433563232422

This run produced these artifacts as outputs. Total: 1. Learn more

run-2wkj2v1g-history:v0

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