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sweep.yaml
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sweep.yaml
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description: hyperparameter search for abnormal detection
# Training script to run
program: abnormal_detection/train_wavelet.py
# Method can be bayes, random, grid
method: bayes
# Metric to optimize
metric:
name: best_val_loss
goal: minimize
# Should we early terminate runs
early_terminate:
type: hyperband
# Parameters to search over
parameters:
sincconv_filter_length:
values: [121, 144, 100]
sincconv_nfilters:
values: [16, 32, 64]
branch_nlayers:
values: [1, 2, 3, 4, 5]
ekg_kernel_length':
values: [5, 7, 13, 21, 35]
hs_kernel_length:
values: [5, 7, 13, 21, 35]
wavelet_scale_length:
values: [7, 13, 21, 25, 35]
ekg_nfilters:
values: [1, 2, 4, 8, 16, 32]
hs_nfilters:
values: [1, 2, 4, 8, 16, 32]
final_nlayers:
values: [3, 4, 5, 6]
final_kernel_length:
values: [5, 7, 13, 21, 35]
final_nonlocal_nlayers:
values: [0]
final_nfilters:
values: [8, 16, 32]
kernel_initializer:
values: ['glorot_uniform', 'he_normal']
skip_connection:
values: [True, False]
crop_center:
values: [True, False]
remove_dirty:
values: [2]