Comments (2)
是一样的, pytorch也好, tensorflow也好, 用的是label编码的方式作为输入的原因之一是,one-hot会有大量内存消耗, 而输入label encode + 查表得方式计算得embedding 与 one-hot后与embedding得weights相乘得到得结果会是一样的。
eg :
x = torch.LongTensor([0,1,2,3])
x_one_hot = nn.functional.one_hot(x)
embedding = nn.Embedding(4, 2)
embedding(x)
tensor([[-0.5853, -0.4129],
[ 1.4309, 2.0298],
[ 0.8416, 0.2533],
[ 0.6935, 1.2494]], grad_fn=)
torch.matmul(x_one_hot.float(), embedding.weight.data)
tensor([[-0.5853, -0.4129],
[ 1.4309, 2.0298],
[ 0.8416, 0.2533],
[ 0.6935, 1.2494]])
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这下终于明白了,感谢大佬的回复。
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Related Issues (7)
- 模型中offset的问题 HOT 2
- 模型中关于输入的feature_fields的疑惑 HOT 6
- 关于DIN的疑问
- 关于DIN模型的疑惑 HOT 4
- 关于DIEN的PyTorch实现 HOT 1
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