# -*- coding: utf-8 -*-
"""Untitled13.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1UmI0eZXtftAp8hd9A3cj4DhffZ7kw_x1
"""
from torch import nn
import torch
"""## 2D的BN层"""
rgb = torch.randn(1, 3, 2, 2) # (batchsize,channel,w,h)
print(rgb)
print(rgb.shape)
conv = nn.Conv2d(3, 2, 1)
x = conv(rgb)
print(x)
print(x.shape)
bn = nn.BatchNorm2d(2)
res = bn(x)
print(res)
print(res.shape)
mean = x.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)
var = ((x - mean) ** 2).mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)
x_hat = (x - mean) / torch.sqrt(var + 0.00001)
x_hat
bn2 = nn.BatchNorm1d(12)
# With Learnable Parameters
m = nn.BatchNorm1d(2,affine=False)
input = torch.randn(2, 2)
output = m(input)
input
input.shape
output
print(output.shape)
mean = input.mean(dim=0)
var = ((input - mean) ** 2).mean(dim=0)
mean
var
test = (input-mean)/ torch.sqrt(var + 1e-5)
test
m = nn.Linear(20, 30)
input = torch.randn(128, 20)
output = m(input)
print(output.size())
bn_layer = nn.BatchNorm1d(30,affine=False)
o2 = bn_layer(output)
o2.shape
