BatchNorm1d in PyTorch

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*Memos:

• My post explains Batch Normalization Layer.
• My post explains BatchNorm2d().
• My post explains BatchNorm3d().
• My post explains LayerNorm().
• My post explains requires_grad.

BatchNorm1d() can get the 2D or 3D tensor of the zero or more elements computed by 1D Batch Normalization from the 2D or 3D tensor of zero or more elements as shown below:

*Memos:

• The 1st argument for initialization is num_features(Required-Type:int). *It must be 1 <= x.
• The 2nd argument for initialization is eps(Optional-Default:1e-05-Type:float).
• The 3rd argument for initialization is momentum(Optional-Default:0.1-Type:float).
• The 4th argument for initialization is affine(Optional-Default:True-Type:bool).
• The 5th argument for initialization is track_running_stats(Optional-Default:True-Type:bool).
• The 6th argument for initialization is device(Optional-Default:None-Type:str, int or device()): *Memos:
  • If it's None, get_default_device() is used. *My post explains get_default_device() and set_default_device().
  • device= can be omitted.
  • My post explains device argument.
• The 7th argument for initialization is dtype(Optional-Default:None-Type:dtype): *Memos:
  • If it's None, get_default_dtype() is used. *My post explains get_default_dtype() and set_default_dtype().
  • dtype= can be omitted.
  • My post explains dtype argument.
• The 1st argument is input(Required-Type:tensor of float): *Memos:
  • It must be the 2D or 3D tensor of zero or more elements.
  • The number of the elements of the 2nd shallowest dimension must be same as num_features.
  • Its device and dtype must be same as BatchNorm1d()'s.
  • The tensor's requires_grad which is False by default is set to True by BatchNorm1d().
• batchnorm1d1.device and batchnorm1d1.dtype don't work.

import torch
from torch import nn

tensor1 = torch.tensor([[8., -3., 0.],
                        [1., 5., -2.]])
tensor1.requires_grad
# False

batchnorm1d1 = nn.BatchNorm1d(num_features=3)
tensor2 = batchnorm1d1(input=tensor1)
tensor2
# tensor([[1.0000, -1.0000, 1.0000],
#         [-1.0000, 1.0000, -1.0000]],
#        grad_fn=<NativeBatchNormBackward0>)

tensor2.requires_grad
# True

batchnorm1d1
# BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True,
#             track_running_stats=True)

batchnorm1d1.num_features
# 3

batchnorm1d1.eps
# 1e-05

batchnorm1d1.momentum
# 0.1

batchnorm1d1.affine
# True

batchnorm1d1.track_running_stats
# True

batchnorm1d2 = nn.BatchNorm1d(num_features=3)
batchnorm1d2(input=tensor2)
# tensor([[1.0000, -1.0000, 1.0000],
#         [-1.0000, 1.0000, -1.0000]],
#        grad_fn=<NativeBatchNormBackward0>)

batchnorm1d = nn.BatchNorm1d(num_features=3, eps=1e-05, momentum=0.1, 
                             affine=True, track_running_stats=True, 
                             device=None, dtype=None)
batchnorm1d(input=tensor1)
# tensor([[1.0000, -1.0000, 1.0000],
#         [-1.0000, 1.0000, -1.0000]],
#        grad_fn=<NativeBatchNormBackward0>)

my_tensor = torch.tensor([[8.], [-3.], [0.], [1.], [5.], [-2.]])

batchnorm1d = nn.BatchNorm1d(num_features=1)
batchnorm1d(input=my_tensor)
# tensor([[1.6830], [-1.1651], [-0.3884], [-0.1295], [0.9062], [-0.9062]], 
#        grad_fn=<NativeBatchNormBackward0>)

my_tensor = torch.tensor([[[8.], [-3.], [0.]],
                          [[1.], [5.], [-2.]]])
batchnorm1d = nn.BatchNorm1d(num_features=3)
batchnorm1d(input=my_tensor)
# tensor([[[1.0000], [-1.0000], [1.0000]],
#         [[-1.0000], [1.0000], [-1.0000]]],
#        grad_fn=<NativeBatchNormBackward0>)