NumPy中zeros_like()函数的使用及说明

numpy.zeros_like(a, dtype=None, order='K', subok=True, shape=None)

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.zeros_like(a)

print("原数组 a：\n", a)
print("zeros_like 生成的新数组 b：\n", b)

原数组 a：
 [[1 2 3]
  [4 5 6]]
zeros_like 生成的新数组 b：
 [[0 0 0]
  [0 0 0]]

b = np.zeros_like(a, dtype=float)
print(b)

[[0. 0. 0.]
 [0. 0. 0.]]

b = np.zeros_like(a, shape=(3, 3))
print(b)

[[0 0 0]
 [0 0 0]
 [0 0 0]]

tensor = np.arange(24).reshape(2, 3, 4)
zeros_tensor = np.zeros_like(tensor)

print("原数组形状：", tensor.shape)
print("zeros_like 结果形状：", zeros_tensor.shape)

原数组形状： (2, 3, 4)
zeros_like 结果形状： (2, 3, 4)

a = np.arange(6).reshape(2, 3)
z1 = np.zeros((2, 3))
z2 = np.zeros_like(a)

print(z1)
print(z2)

import numpy as np
import time

a = np.random.rand(1000, 1000)

# zeros
start = time.time()
z1 = np.zeros(a.shape)
print("zeros 耗时：", time.time() - start)

# zeros_like
start = time.time()
z2 = np.zeros_like(a)
print("zeros_like 耗时：", time.time() - start)

result = np.zeros_like(matrix)

grad_W = np.zeros_like(weights)

mask = np.zeros_like(image, dtype=bool)

temp = np.zeros_like(data, dtype=float)