pytorch中的squeeze函數、cat函數使用
1 squeeze(): 去除size為1的維度,包括行和列。
至於維度大於等於2時,squeeze()不起作用。
行、例:
>>> torch.rand(4, 1, 3) (0 ,.,.) = 0.5391 0.8523 0.9260 (1 ,.,.) = 0.2507 0.9512 0.6578 (2 ,.,.) = 0.7302 0.3531 0.9442 (3 ,.,.) = 0.2689 0.4367 0.6610 [torch.FloatTensor of size 4x1x3]
>>> torch.rand(4, 1, 3).squeeze() 0.0801 0.4600 0.1799 0.0236 0.7137 0.6128 0.0242 0.3847 0.4546 0.9004 0.5018 0.4021 [torch.FloatTensor of size 4x3]
列、例:
>>> torch.rand(4, 3, 1) (0 ,.,.) = 0.7013 0.9818 0.9723 (1 ,.,.) = 0.9902 0.8354 0.3864 (2 ,.,.) = 0.4620 0.0844 0.5707 (3 ,.,.) = 0.5722 0.2494 0.5815 [torch.FloatTensor of size 4x3x1]
>>> torch.rand(4, 3, 1).squeeze() 0.8784 0.6203 0.8213 0.7238 0.5447 0.8253 0.1719 0.7830 0.1046 0.0233 0.9771 0.2278 [torch.FloatTensor of size 4x3]
不變、例:
>>> torch.rand(4, 3, 2) (0 ,.,.) = 0.6618 0.1678 0.3476 0.0329 0.1865 0.4349 (1 ,.,.) = 0.7588 0.8972 0.3339 0.8376 0.6289 0.9456 (2 ,.,.) = 0.1392 0.0320 0.0033 0.0187 0.8229 0.0005 (3 ,.,.) = 0.2327 0.6264 0.4810 0.6642 0.8625 0.6334 [torch.FloatTensor of size 4x3x2]
>>> torch.rand(4, 3, 2).squeeze() (0 ,.,.) = 0.0593 0.8910 0.9779 0.1530 0.9210 0.2248 (1 ,.,.) = 0.7938 0.9362 0.1064 0.6630 0.9321 0.0453 (2 ,.,.) = 0.0189 0.9187 0.4458 0.9925 0.9928 0.7895 (3 ,.,.) = 0.5116 0.7253 0.0132 0.6673 0.9410 0.8159 [torch.FloatTensor of size 4x3x2]
2 cat函數
>>> t1=torch.FloatTensor(torch.randn(2,3)) >>> t1 -1.9405 1.2009 0.0018 0.9463 0.4409 -1.9017 [torch.FloatTensor of size 2x3]
>>> t2=torch.FloatTensor(torch.randn(2,2)) >>> t2 0.0942 0.1581 1.1621 1.2617 [torch.FloatTensor of size 2x2]
>>> torch.cat((t1, t2), 1) -1.9405 1.2009 0.0018 0.0942 0.1581 0.9463 0.4409 -1.9017 1.1621 1.2617 [torch.FloatTensor of size 2x5]
補充:pytorch中 max()、view()、 squeeze()、 unsqueeze()
查瞭好多博客都似懂非懂,後來寫瞭幾個小例子,瞬間一目瞭然。
一、torch.max()
import torch a=torch.randn(3) print("a:\n",a) print('max(a):',torch.max(a)) b=torch.randn(3,4) print("b:\n",b) print('max(b,0):',torch.max(b,0)) print('max(b,1):',torch.max(b,1))
輸出:
a:
tensor([ 0.9558, 1.1242, 1.9503])
max(a): tensor(1.9503)
b:
tensor([[ 0.2765, 0.0726, -0.7753, 1.5334],
[ 0.0201, -0.0005, 0.2616, -1.1912],
[-0.6225, 0.6477, 0.8259, 0.3526]])
max(b,0): (tensor([ 0.2765, 0.6477, 0.8259, 1.5334]), tensor([ 0, 2, 2, 0]))
max(b,1): (tensor([ 1.5334, 0.2616, 0.8259]), tensor([ 3, 2, 2]))
max(a),用於一維數據,求出最大值。
max(a,0),計算出數據中一列的最大值,並輸出最大值所在的行號。
max(a,1),計算出數據中一行的最大值,並輸出最大值所在的列號。
print('max(b,1):',torch.max(b,1)[1])
輸出:隻輸出行最大值所在的列號
max(b,1): tensor([ 3, 2, 2])
torch.max(b,1)[0], 隻返回最大值的每個數
二、view()
a.view(i,j)表示將原矩陣轉化為i行j列的形式
i為-1表示不限制行數,輸出1列
a=torch.randn(3,4) print(a)
輸出:
tensor([[-0.8146, -0.6592, 1.5100, 0.7615],
[ 1.3021, 1.8362, -0.3590, 0.3028],
[ 0.0848, 0.7700, 1.0572, 0.6383]])b=a.view(-1,1)
print(b)
輸出:
tensor([[-0.8146],
[-0.6592],
[ 1.5100],
[ 0.7615],
[ 1.3021],
[ 1.8362],
[-0.3590],
[ 0.3028],
[ 0.0848],
[ 0.7700],
[ 1.0572],
[ 0.6383]])
i為1,j為-1表示不限制列數,輸出1行
b=a.view(1,-1) print(b)
輸出:
tensor([[-0.8146, -0.6592, 1.5100, 0.7615, 1.3021, 1.8362, -0.3590,
0.3028, 0.0848, 0.7700, 1.0572, 0.6383]])
i為-1,j為2表示不限制行數,輸出2列
b=a.view(-1,2) print(b)
輸出:
tensor([[-0.8146, -0.6592],
[ 1.5100, 0.7615],
[ 1.3021, 1.8362],
[-0.3590, 0.3028],
[ 0.0848, 0.7700],
[ 1.0572, 0.6383]])
i為-1,j為3表示不限制行數,輸出3列
i為4,j為3表示輸出4行3列
b=a.view(-1,3) print(b) b=a.view(4,3) print(b)
輸出:
tensor([[-0.8146, -0.6592, 1.5100],
[ 0.7615, 1.3021, 1.8362],
[-0.3590, 0.3028, 0.0848],
[ 0.7700, 1.0572, 0.6383]])
tensor([[-0.8146, -0.6592, 1.5100],
[ 0.7615, 1.3021, 1.8362],
[-0.3590, 0.3028, 0.0848],
[ 0.7700, 1.0572, 0.6383]])
三、
1.torch.squeeze()
壓縮矩陣,我理解為降維
a.squeeze(i) 壓縮第i維,如果這一維維數是1,則這一維可有可無,便可以壓縮
import torch a=torch.randn(1,3,4) print(a) b=a.squeeze(0) print(b) c=a.squeeze(1) print(c
輸出:
tensor([[[ 0.4627, 1.6447, 0.1320, 2.0946],
[-0.0080, 0.1794, 1.1898, -1.2525],
[ 0.8281, -0.8166, 1.8846, 0.9008]]])
一頁三行4列的矩陣
第0維為1,則可以通過squeeze(0)刪掉,轉化為三行4列的矩陣
tensor([[ 0.4627, 1.6447, 0.1320, 2.0946],
[-0.0080, 0.1794, 1.1898, -1.2525],
[ 0.8281, -0.8166, 1.8846, 0.9008]])
第1維不為1,則不可以壓縮
tensor([[[ 0.4627, 1.6447, 0.1320, 2.0946],
[-0.0080, 0.1794, 1.1898, -1.2525],
[ 0.8281, -0.8166, 1.8846, 0.9008]]])
2.torch.unsqueeze()
unsqueeze(i) 表示將第i維設置為1
對壓縮為3行4列後的矩陣b進行操作,將第0維設置為1
c=b.unsqueeze(0) print(c)
輸出一個一頁三行四列的矩陣
tensor([[[ 0.0661, -0.2386, -0.6610, 1.5774],
[ 1.2210, -0.1084, -0.1166, -0.2379],
[-1.0012, -0.4363, 1.0057, -1.5180]]])
將第一維設置為1
c=b.unsqueeze(1) print(c)
輸出一個3頁,一行,4列的矩陣
tensor([[[-1.0067, -1.1477, -0.3213, -1.0633]],
[[-2.3976, 0.9857, -0.3462, -0.3648]],
[[ 1.1012, -0.4659, -0.0858, 1.6631]]])
另外,squeeze、unsqueeze操作不改變原矩陣
以上為個人經驗,希望能給大傢一個參考,也希望大傢多多支持WalkonNet。
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