torch.max()用法

例子：

x = torch.rand(4,4)
print('x:\n',x)
print('torch.max(x,1):\n',torch.max(x,1))
print('torch.max(x,0):\n',torch.max(x,0))
print('torch.max(x,1)[0]:\n',torch.max(x,1)[0])
print('torch.max(x,1)[1]:\n',torch.max(x,1)[1])
print('torch.max(x,1)[1].data:\n',torch.max(x,1)[1].data)
print('torch.max(x,1)[1].data.numpy():\n',torch.max(x,1)[1].data.numpy())
print('torch.max(x,1)[1].data.numpy().squeeze():\n',torch.max(x,1)[1].data.numpy().squeeze())
print('torch.max(x,1)[0].data:\n',torch.max(x,1)[0].data)
print('torch.max(x,1)[0].data.numpy():\n',torch.max(x,1)[0].data.numpy())
print('torch.max(x,1)[0].data.numpy().squeeze():\n',torch.max(x,1)[0].data.numpy().squeeze())`

运行结果：
x:
tensor([[0.5285, 0.1247, 0.8332, 0.5485],
[0.7917, 0.6138, 0.5881, 0.3381],
[0.4226, 0.6605, 0.8571, 0.0399],
[0.1716, 0.0609, 0.9712, 0.4838]])

torch.max(x,1):
(tensor([0.8332, 0.7917, 0.8571, 0.9712]), tensor([2, 0, 2, 2]))

torch.max(x,0):
(tensor([0.7917, 0.6605, 0.9712, 0.5485]), tensor([1, 2, 3, 0]))

torch.max(x,1)[0]:
tensor([0.8332, 0.7917, 0.8571, 0.9712])

torch.max(x,1)[1]:
tensor([2, 0, 2, 2])

torch.max(x,1)[1].data:
tensor([2, 0, 2, 2])

torch.max(x,1)[1].data.numpy():
[2 0 2 2]

torch.max(x,1)[1].data.numpy().squeeze():
[2 0 2 2]

torch.max(x,1)[0].data:
tensor([0.8332, 0.7917, 0.8571, 0.9712])

torch.max(x,1)[0].data.numpy():
[0.83318216 0.7917127 0.85708565 0.9711726 ]

torch.max(x,1)[0].data.numpy().squeeze():
[0.83318216 0.7917127 0.85708565 0.9711726 ]