一、普通用法 (手动调整size)
view()相当于reshape、resize,重新调整Tensor的形状。
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import torch a1 = torch.arange( 0 , 16 ) print (a1) # tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) |
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a2 = a1.view( 8 , 2 ) a3 = a1.view( 2 , 8 ) a4 = a1.view( 4 , 4 ) print (a2) #tensor([[ 0, 1], # [ 2, 3], # [ 4, 5], # [ 6, 7], # [ 8, 9], # [10, 11], # [12, 13], # [14, 15]]) print (a3) #tensor([[ 0, 1, 2, 3, 4, 5, 6, 7], # [ 8, 9, 10, 11, 12, 13, 14, 15]]) print (a4) #tensor([[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11], # [12, 13, 14, 15]]) |
二、特殊用法:参数-1 (自动调整size)
view中一个参数定为-1,代表自动调整这个维度上的元素个数,以保证元素的总数不变。
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v1 = torch.arange( 0 , 16 ) print (v1) # tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) v2 = v1.view( - 1 , 16 ) v2 # tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]]) v2 = v1.view( - 1 , 8 ) v2 # tensor([[ 0, 1, 2, 3, 4, 5, 6, 7], # [ 8, 9, 10, 11, 12, 13, 14, 15]]) v2 = v1.view( - 1 , 4 ) v2 #tensor([[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11], # [12, 13, 14, 15]]) v2 = v1.view( - 1 , 2 ) v2 #tensor([[ 0, 1], # [ 2, 3], # [ 4, 5], # [ 6, 7], # [ 8, 9], # [10, 11], # [12, 13], # [14, 15]]) |
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v3 = v1.view( 4 * 4 , - 1 ) v3 # tensor([[ 0], # [ 1], # [ 2], # [ 3], # [ 4], # [ 5], # [ 6], # [ 7], # [ 8], # [ 9], # [10], # [11], # [12], # [13], # [14], # [15]]) |
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原文链接:https://blog.csdn.net/qq_36998053/article/details/123528858