np.mean(data, axis=dim)与np.std(data, axis=dim)中axis的解释

np.mean(data, axis=0)
假如data为2维数组，形如（3, 4），此时axis= 0的操作其实等价为 在轴0处求平均，然后执行np.squeeze操作
图形解释：

data = np.arange(12).reshape(3, 4)
mean = np.mean(data, axis=0)
print("data:", data)
print("data.shape:", data.shape)
print("mean:", mean)
print("mean.shape:", mean.shape)
输出：
data: [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
data.shape: (3, 4)
mean: [4. 5. 6. 7.]
mean.shape: (4,)

假如data是3维数组，形如（2，3，4）,此时axis= 0的操作其实等价为 在轴0处求平均，然后执行np.squeeze操作

data = np.arange(24).reshape(2, 3, 4)
mean = np.mean(data, axis=0)
print("data:", data)
print("data.shape:", data.shape)
print("mean:", mean)
print("mean.shape:", mean.shape)
输出
data: [[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]
data.shape: (2, 3, 4)
mean: [[ 6.  7.  8.  9.]
 [10. 11. 12. 13.]
 [14. 15. 16. 17.]]
mean.shape: (3, 4)