如何在 PyTorch 中访问和修改张量的值？
我们使用索引和切片来访问张量的值。索引用于访问张量的单个元素的值，而切片用于访问元素序列的值。
我们使用赋值运算符来修改张量的值。使用赋值运算符分配新值/秒将使用新值/秒修改张量。
脚步
导入所需的库。在这里，所需的库是torch。
定义PyTorch张量。
使用索引访问特定索引处的单个元素的值或使用slicing访问元素序列的值。
使用赋值运算符用新值修改访问的值。
最后，打印张量以检查是否使用新值修改了张量。
示例 1
# Python program to access and modify values of a tensor in PyTorch# Import the librariesimport torch# Define PyTorch Tensora = torch.Tensor([[3, 5],[1, 2],[5, 7]])
print("a:\n",a)# Access a value at index [1,0]-> 2nd row, 1st Col using indexingb = a[1,0]
print("a[1,0]:\n", b)# Other indexing method to access valuec = a[1][0]
print("a[1][0]:\n",c)# Modifying the value 1 with new value 9# assignment operator is used to modify with new valuea[1,0] = 9
print("tensor 'a' after modifying value at a[1,0]:")
print("a:\n",a)输出结果
a: tensor([[3., 5.], [1., 2.], [5., 7.]]) a[1,0]: tensor(1.) a[1][0]: tensor(1.) tensor 'a' after modifying value at a[1,0]: a: tensor([[3., 5.], [9., 2.], [5., 7.]])
示例 2
# Python program to access and modify values of a tensor in PyTorch# Import necessary librariesimport torch# Define PyTorch Tensora = torch.Tensor([[3, 5],[1, 2],[5, 7]])print("a:\n", a)# Access all values of 2nd row using slicingb = a[1]print("a[1]:\n", a[1])# Access all values of 1st and 2nd rowsb = a[0:2]print("a[0:2]:\n" , a[0:2])# Access all values of 2nd colc = a[:,1]print("a[:,1]:\n", a[:,1])# Access values from first two rows but 2nd colprint("a[0:2, 1]:\n", a[0:2, 1])# assignment operator is used to modify with new value# Modifying the values of 2nd rowa[1] = torch.Tensor([9, 9])
print("After modifying a[1]:\n", a)# Modify values of first two rows but 2nd cola[0:2, 1] = torch.Tensor([4, 4])print("After modifying a[0:2, 1]:\n", a)输出结果
a:tensor([[3., 5.], [1., 2.], [5., 7.]])a[1]: tensor([1., 2.])a[0:2]: tensor([[3., 5.], [1., 2.]])a[:,1]: tensor([5., 2., 7.])a[0:2, 1]: tensor([5., 2.])After modifying a[1]: tensor([[3., 5.], [9., 9.], [5., 7.]])After modifying a[0:2, 1]:tensor([[3., 4.], [9., 4.], [5., 7.]])