向张量添加维度并沿新轴复制值

Question

假设我有一个 2D ndarray X

X.shape == (m, n)

我想向 X 添加两个维度，同时沿这些新轴复制值。即我想要

new_X.shape == (m, n, k, l)

并且对于所有 i,j

new_X[i, j, :, :] = X[i, j]

实现此目标的最佳方法是什么？

Answer 1

您可以使用 np.broadcast_to -

简单地获取张量视图

np.broadcast_to(a[...,None,None],a.shape+(k,l)) # a is input array

好处是没有额外的内存开销，因此实际上是免费的 rumtime。

如果你需要一个有自己内存的数组输出space，附加.copy()。

样本运行-

In [9]: a =  np.random.rand(2,3)

In [10]: k,l = 4,5

In [11]: np.broadcast_to(a[...,None,None],a.shape+(k,l)).shape
Out[11]: (2, 3, 4, 5)

# Verify memory space sharing
In [12]: np.shares_memory(a,np.broadcast_to(a[...,None,None],a.shape+(k,l)))
Out[12]: True

# Verify virtually free runtime
In [17]: a =  np.random.rand(100,100)
    ...: k,l = 100,100
    ...: %timeit np.broadcast_to(a[...,None,None],a.shape+(k,l))
100000 loops, best of 3: 3.41 µs per loop

向张量添加维度并沿新轴复制值

Add dimensions to a tensor and duplicate values along the new axes

python

numpy

multidimensional-array

array-broadcasting