在python中高效地计算三阶张量

import numpy as np

# 创建一个三阶张量
tensor = np.array([
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]
])

# 计算张量的形状
shape = tensor.shape
print("张量的形状：", shape)

# 计算张量的元素个数
size = tensor.size
print("张量的元素个数：", size)

# 计算张量的和
sum_tensor = np.sum(tensor)
print("张量的和：", sum_tensor)

# 计算张量的平均值
mean_tensor = np.mean(tensor)
print("张量的平均值：", mean_tensor)

# 计算张量的最大值
max_tensor = np.max(tensor)
print("张量的最大值：", max_tensor)

# 计算张量的最小值
min_tensor = np.min(tensor)
print("张量的最小值：", min_tensor)

# 对张量进行转置
transpose_tensor = np.transpose(tensor)
print("转置后的张量：", transpose_tensor)

# 对张量进行切片操作
slice_tensor = tensor[:, :, 1]
print("切片后的张量：", slice_tensor)