np.squeeze 从形状 (1,1) 之后返回的形状 () ndarray 是什么? [英] What is an ndarray of shape () returned after np.squeeze from shape (1,1)?

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 like 3.

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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