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Simple Abstractor

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Tags

train size = 3000; trial = 9

Notes
Author
State
Finished
Start time
May 3rd, 2023 3:30:45 AM
Runtime
1m 3s
Tracked hours
58s
Run path
abstractor/object_argsort_autoregressive/4dm3zzq8
OS
Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.10
Python version
3.8.16
Git repository
git clone https://github.com/jdlafferty/relational
Git state
git checkout -b "train-size-=-3000;-trial-=-9" f397b06952b125c267dcbb3ea837aa1fcf1d84fd
Command
evaluate_argsort_model_learning_curves.py --model simple-abstractor --pretraining_mode none --init_trainable True --pretraining_task_type "reshuffled attr" --pretraining_task_data_path object_sorting_datasets/product_structure_reshuffled_object_sort_dataset.npy --eval_task_data_path object_sorting_datasets/product_structure_object_sort_dataset.npy --n_epochs 400 --early_stopping True --min_train_size 100 --max_train_size 3000 --train_size_step 100 --num_trials 10 --start_trial 0 --pretraining_train_size 1000 --wandb_project_name object_argsort_autoregressive
System Hardware
CPU count36
Logical CPU count 36
GPU count1
GPU typeNVIDIA GeForce RTX 3090
W&B CLI Version
0.13.9
Config

Config parameters are your model's inputs. Learn more

  • {} 3 keys
    • "Simple Abstractor"
    • 3,000
    • 9
Summary

Summary metrics are your model's outputs. Learn more

  • {} 10 keys
    • "table-file"
    • 0.99949
    • 191
    • 0.0010000000474974513
    • 0.04021574929356575
    • 0.9865333437919616
    • 0.0005366668337956071
    • 0.999970018863678
    • 0.99935
    • 0.999946653842926
Artifact Inputs

This run consumed these artifacts as inputs. Learn more

Artifact Outputs

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