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преди 4 години
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  1. from icosagon.dropout import dropout_sparse, \
  2. dropout_dense
  3. import torch
  4. import numpy as np
  5. def test_dropout_01():
  6. for i in range(11):
  7. torch.random.manual_seed(i)
  8. a = torch.rand((5, 10))
  9. a[a < .5] = 0
  10. keep_prob=i/10. + np.finfo(np.float32).eps
  11. torch.random.manual_seed(i)
  12. b = dropout_dense(a, keep_prob=keep_prob)
  13. torch.random.manual_seed(i)
  14. c = dropout_sparse(a.to_sparse(), keep_prob=keep_prob)
  15. print('keep_prob:', keep_prob)
  16. print('a:', a.detach().cpu().numpy())
  17. print('b:', b.detach().cpu().numpy())
  18. print('c:', c, c.to_dense().detach().cpu().numpy())
  19. assert torch.all(b == c.to_dense())