Comments (2)
Hi @Rane90,
We used weights and biases (W&B) to run sweeps over the hyperparameters of our optimization scheme (the 4 hyperparameters are the leraning rate, the scale of the initialized x_i's, the "alpha" of the modified Relu and the minimal lambda value). That's why we call it a sweep.. In order to run your own sweep you should get your W&B user and configure a sweep there, and run it on your own cluster..
If you find it helpful, I can share with you the configuration that we used for example for one of our sweeps in W&B:
parameters:
data_per_class_train:
value: 250
extraction_data_amount_per_class:
value: 500
extraction_epochs:
value: 50000
extraction_evaluate_rate:
value: 1000
extraction_init_scale:
distribution: log_uniform_values
max: 0.1
min: 1e-06
extraction_lr:
distribution: log_uniform_values
max: 1
min: 1e-05
extraction_min_lambda:
distribution: uniform
max: 0.5
min: 0.01
extraction_model_relu_alpha:
distribution: uniform
max: 500
min: 10
model_hidden_list:
value: '[1000,1000]'
model_init_list:
value: '[0.001,0.001]'
pretrained_model_path:
value: path_to_trained_model.pth
problem:
value: cifar10_vehicles_animals
proj_name:
value: some_name_example_sweep
run_mode:
value: reconstruct
wandb_active:
value: true
program: Main.py
Then, after the sweep has finished (we usually did about 100-300 runs per sweep), then you can use our code to download the results.
from dataset_reconstruction.
@nivha Wonderful. Thank you!
from dataset_reconstruction.
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from dataset_reconstruction.