从零实现
import torch
from torch import nn
from d2l import torch as d2l
def batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum):
if not torch.is_grad_enabled():
X_hat = (X - moving_mean) / torch.sqrt(moving_var + eps)
else:
assert len(X.shape) in (2, 4)
if len(X.shape) == 2:
mean = X.mean(dim=0)
var = ((X - mean) ** 2).mean(dim=0)
else:
mean = X.mean(dim=(0, 2, 3), keepdim=True)
var = ((X - mean) ** 2).mean(dim=(0, 2, 3), keepdim=True)
X_hat = (X - mean) / torch.sqrt(var + eps)
moving_mean = momentum * moving_mean + (1.0 - momentum) * mean
moving_var = momentum * moving_var + (1.0 - momentum) * var
Y = gamma * X_hat + beta
return Y, moving_mean.data, moving_var.data
创建一个正确的BatchNorm
层
class BatchNorm(nn.Module):
def __init__(self, num_features, num_dims):
super().__init__()
if num_dims == 2:
shape = (1, num_features)
else:
shape = (1, num_features, 1, 1)
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
self.moving_mean = torch.zeros(shape)
self.moving_var = torch.ones(shape)
def forward(self, X):
if self.moving_mean.device != X.device:
self.moving_mean = self.moving_mean.to(X.device)
self.moving_var = self.moving_var.to(X.device)
Y, self.moving_mean, self.moving_var = batch_norm(
X, self.gamma, self.beta, self.moving_mean,
self.moving_var, eps=1e-5, momentum=0.9)
return Y
应用BatchNorm
于LeNet模型
net = nn.Sequential(
nn.Conv2d(1, 6, kernel_size=5), BatchNorm(6, num_dims=4), nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5), BatchNorm(16, num_dims=4), nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),
nn.Linear(16*4*4, 120), BatchNorm(120, num_dims=2), nn.Sigmoid(),
nn.Linear(120, 84), BatchNorm(84, num_dims=2), nn.Sigmoid(),
nn.Linear(84, 10))
在Fashion-MNIST数据集上训练网络
lr, num_epochs, batch_size = 1.0, 10, 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
loss 0.273, train acc 0.899, test acc 0.807 32293.9 examples/sec on cuda:0
拉伸参数gamma
和偏移参数beta
net[1].gamma.reshape((-1,)), net[1].beta.reshape((-1,))
(tensor([0.4863, 2.8573, 2.3190, 4.3188, 3.8588, 1.7942], device='cuda:0', grad_fn=<ReshapeAliasBackward0>), tensor([-0.0124, 1.4839, -1.7753, 2.3564, -3.8801, -2.1589], device='cuda:0', grad_fn=<ReshapeAliasBackward0>))
简明实现
net = nn.Sequential(
nn.Conv2d(1, 6, kernel_size=5), nn.BatchNorm2d(6), nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5), nn.BatchNorm2d(16), nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),
nn.Linear(256, 120), nn.BatchNorm1d(120), nn.Sigmoid(),
nn.Linear(120, 84), nn.BatchNorm1d(84), nn.Sigmoid(),
nn.Linear(84, 10))
使用相同超参数来训练模型
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
loss 0.267, train acc 0.902, test acc 0.708 50597.3 examples/sec on cuda:0