-1在numpy reshape（np.reshape)中的意义

例子

• 例1：

  >>>import numpy as np
  >>>a = np.matrix([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>>np.reshape(a, -1)
  matrix([[1, 2, 3, 4, 5, 6, 7, 8]])
  

• 例2：

  >>>a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>>a.reshape(a.shape[0], -1)
  array([[1, 2, 3, 4,],
         [5, 6, 7, 8]])
  

解释

简单的说，-1表示自适应。
例子：
对有8个元素的numpy数组或矩阵，reshape(-1, 1)规定了列数，从而行数为8/1=8;
对有8个元素的numpy数组或矩阵，reshape(1, -1)规定了行数，从而列数为8/1=8;
引用stackoverflow上的一个例子

import numpy as np
x = np.array([[2,3,4], [5,6,7]]) 

# Convert any shape to 1D shape
x = np.reshape(x, (-1)) # Making it 1 row -> (6,)

# When you don't care about rows and just want to fix number of columns
x = np.reshape(x, (-1, 1)) # Making it 1 column -> (6, 1)
x = np.reshape(x, (-1, 2)) # Making it 2 column -> (3, 2)
x = np.reshape(x, (-1, 3)) # Making it 3 column -> (2, 3)

# When you don't care about columns and just want to fix number of rows
x = np.reshape(x, (1, -1)) # Making it 1 row -> (1, 6)
x = np.reshape(x, (2, -1)) # Making it 2 row -> (2, 3)
x = np.reshape(x, (3, -1)) # Making it 3 row -> (3, 2)

这里是官方文档中对Numpy.reshape的解释

参考

stackoverflow: What does -1 mean in numpy reshape?