问题描述
我正在计算 NN 的成本函数.我对从 numpy.dot 得到的 (1,1) 答案做了一个 numpy.squeeze.然后我得到一个形状为 (0,1) 的 ndarray.
I'm calculating a cost function for a NN. I do a numpy.squeeze on a (1,1) answer that I get from numpy.dot. I then get an ndarray of shape (0,1).
什么是形状 () 的 ndarray，形状 (1,) 的 ndarray 与形状 (5) 中的一个有何不同?
What is an ndarray of shape () and how does an ndarray of shape (1,) differ from one of shape (5)?
推荐答案
- 形状为
(1, 1)的 ndarray 类似于[[3]]，就像一个 1x1 矩阵. - 形状为
(1,)的 ndarray 类似于[3]，就像大小为 1 的向量. - 形状为
()的 ndarray，又名标量，类似于3. - An ndarray of shape
(1, 1)is something like[[3]], like a 1x1 matrix. - An ndarray of shape
(1,)is something like[3], like a vector of size 1. - An ndarray of shape
(), a.k.a a scalar, is something like3.
区别很微妙，因为根据广播规则，标量和数组通常可以毫无问题地组合在一起，但是您不能索引标量，而您可以索引大小为 1 的向量或大小为 1x1 的矩阵.另一方面，标量通常可以像原始 Python 值一样使用，例如 int 或 float.如果您不想使用标量，您可以将 axis 参数传递给 np.squeeze 以确保某些维度不被挤压或使用 np.atleast_1d 以确保无论你pass 至少有一个维度.您还可以使用 检查某些内容是否为标量np.isscalar.
The difference is subtle because due to broadcasting rules scalars and arrays can usually be combined without problem, but you cannot index a scalar, while you can index a vector of size 1 or a matrix of size 1x1. On the other hand, scalars can generally be used like primitive Python values such as int or float. If you don't want to have a scalar you can either pass an axis parameter to np.squeeze to make sure that some dimension is not squeezed or use np.atleast_1d to make sure that whatever you pass has at least one dimension. You can also check if something is a scalar with np.isscalar.
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