Created
June 18, 2018 15:54
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from __future__ import print_function | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.autograd import Variable | |
def sample_gumbel(shape, eps=1e-20): | |
U = torch.rand(shape).cuda() | |
return -Variable(torch.log(-torch.log(U + eps) + eps)) | |
def gumbel_softmax_sample(logits, temperature): | |
y = logits + sample_gumbel(logits.size()) | |
return F.softmax(y / temperature, dim=-1) | |
def gumbel_softmax(logits, temperature): | |
""" | |
input: [*, n_class] | |
return: [*, n_class] an one-hot vector | |
""" | |
y = gumbel_softmax_sample(logits, temperature) | |
shape = y.size() | |
_, ind = y.max(dim=-1) | |
y_hard = torch.zeros_like(y).view(-1, shape[-1]) | |
y_hard.scatter_(1, ind.view(-1, 1), 1) | |
y_hard = y_hard.view(*shape) | |
return (y_hard - y).detach() + y | |
if __name__ == '__main__': | |
import math | |
data = Variable(torch.cuda.FloatTensor([[math.log(0.1), math.log(0.4), math.log(0.3), math.log(0.2)]] * 20000)) | |
one_hot = gumbel_softmax(data, 0.8).sum(dim=0) | |
print(one_hot) |
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