我想知道哪个轴更适合用于三维数据的归一化（默认情况下是最后一个轴）。例如，我有一个形状为3 x 4 x 5（batch_size x number_of_elements x number_of_features）的Tensor。在这种情况下，我应该使用轴1来规范化每个特征还是默认轴-1（2）？：

data = tf.constant(np.arange(3 * 4 * 5).reshape(3, 4, 5) * 10, dtype=tf.float32)
print(data)

tf.Tensor(
[[[  0.  10.  20.  30.  40.]
  [ 50.  60.  70.  80.  90.]
  [100. 110. 120. 130. 140.]
  [150. 160. 170. 180. 190.]]

 [[200. 210. 220. 230. 240.]
  [250. 260. 270. 280. 290.]
  [300. 310. 320. 330. 340.]
  [350. 360. 370. 380. 390.]]

 [[400. 410. 420. 430. 440.]
  [450. 460. 470. 480. 490.]
  [500. 510. 520. 530. 540.]
  [550. 560. 570. 580. 590.]]], shape=(3, 4, 5), dtype=float32)

layer = tf.keras.layers.LayerNormalization(axis=1)
output = layer(data)
print(output)

tf.Tensor(
[[[-1.3416405  -1.3416405  -1.3416406  -1.3416405  -1.3416405 ]
  [-0.44721347 -0.44721353 -0.44721353 -0.44721353 -0.4472134 ]
  [ 0.44721353  0.44721353  0.4472134   0.44721353  0.44721365]
  [ 1.3416405   1.3416405   1.3416405   1.3416406   1.3416407 ]]

 [[-1.3416407  -1.3416407  -1.3416407  -1.3416405  -1.3416405 ]
  [-0.44721365 -0.44721365 -0.44721365 -0.44721317 -0.44721317]
  [ 0.44721317  0.44721317  0.44721317  0.44721365  0.44721365]
  [ 1.3416405   1.3416405   1.3416405   1.341641    1.3416405 ]]

 [[-1.341641   -1.341641   -1.341641   -1.341641   -1.34164   ]
  [-0.44721413 -0.44721413 -0.44721413 -0.44721413 -0.44721317]
  [ 0.44721317  0.44721317  0.44721317  0.44721317  0.44721413]
  [ 1.3416405   1.3416405   1.3416405   1.3416405   1.3416414 ]]], shape=(3, 4, 5), dtype=float32)

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