数组中的分号使用
output[:2]=output[0]+output[1]
output[2:]=output[2]+…+output[n]
import torch
if __name__ == '__main__':
intent_logits = torch.tensor([1,2,3],dtype=torch.float)
slot_logits = torch.tensor([4,5,6],dtype=torch.float)
outputs = torch.tensor([0.2,0.3,0.1,0.5,0.4],dtype=torch.float)
outputs = tuple(outputs)
total_loss = torch.tensor([1.003],dtype=torch.float)
outputs = ((intent_logits, slot_logits),) + outputs[1:] # add hidden states and attention if they are here
print(outputs)
outputs = (total_loss,) + outputs
print(outputs)
print(outputs[:2])
print(outputs[2:])
((tensor([1., 2., 3.]), tensor([4., 5., 6.])), tensor(0.3000), tensor(0.1000), tensor(0.5000), tensor(0.4000))
(tensor([1.0030]), (tensor([1., 2., 3.]), tensor([4., 5., 6.])), tensor(0.3000), tensor(0.1000), tensor(0.5000), tensor(0.4000))
(tensor([1.0030]), (tensor([1., 2., 3.]), tensor([4., 5., 6.])))
(tensor(0.3000), tensor(0.1000), tensor(0.5000), tensor(0.4000))
本文通过一个具体的例子展示了如何使用PyTorch进行张量操作,包括创建张量、进行数学运算以及如何将多个张量组合成输出元组。此外,还介绍了如何将损失值、意图和槽位的logits以及隐藏状态和注意力机制添加到输出中。
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