15.1 BatchNorm操作手动实现
import torch
from torch import nn
def batch_norm(X,gamma,beta,moving_mean,moving_var,eps,momentum):
if not torch.is_grad_enabled():#这个是推理模式
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)
# 更新移动平均的均值和方差
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
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)
#这是两个需要更新的参数
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)#这个不能为0，应该是/sqrt(var)
def forward(self,X):
#计算设备对齐
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
model=nn.Sequential(
nn.Conv2d(1,6,kernel_size=5),BatchNorm(6,num_dims=4),nn.Sigmoid(),nn.MaxPool2d(kernel_size=2,stride=2),
nn.Conv2d(6,16,kernel_size=5),BatchNorm(16,num_dims=4),nn.Sigmoid(),nn.MaxPool2d(kernel_size=2,stride=2),
nn.Flatten(),#Flatten()之后就是[batch_size,features] 2维度的向量矩阵
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))
15.2 BatchNorm实验效果
################################################################################################################
"""BatchNorm"""
################################################################################################################
import torch
import torchvision
from torch import nn
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn.metrics import accuracy_score
from torch.nn import functional as F
plt.rcParams['font.family']=['Times New Roman']
class Reshape(torch.nn.Module):
def forward(self,x):
return x.view(-1,1,28,28)#[bs,1,28,28]
def plot_metrics(train_loss_list, train_acc_list, test_acc_list, title='Training Curve'):
epochs = range(1, len(train_loss_list) + 1)
plt.figure(figsize=(4, 3))
plt.plot(epochs, train_loss_list, label='Train Loss')
plt.plot(epochs, train_acc_list, label='Train Acc',linestyle='--')
plt.plot(epochs, test_acc_list, label='Test Acc', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('Value')
plt.title(title)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
def train_model(model,train_data,test_data,num_epochs):
train_loss_list = []
train_acc_list = []
test_acc_list = []
for epoch in range(num_epochs):
total_loss=0
total_acc_sample=0
total_samples=0
loop=tqdm(train_data,desc=f"EPOCHS[{epoch+1}/{num_epochs}]")
for X,y in loop:
#X=X.reshape(X.shape[0],-1)
#print(X.shape)
X=X.to(device)
y=y.to(device)
y_hat=model(X)
loss=CEloss(y_hat,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#loss累加
total_loss+=loss.item()*X.shape[0]
y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
y_true=y.detach().cpu().numpy()
total_acc_sample+=accuracy_score(y_pred,y_true)*X.shape[0]#保存样本数
total_samples+=X.shape[0]
test_acc_samples=0
test_samples=0
for X,y in test_data:
X=X.to(device)
y=y.to(device)
#X=X.reshape(X.shape[0],-1)
y_hat=model(X)
y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
y_true=y.detach().cpu().numpy()
test_acc_samples+=accuracy_score(y_pred,y_true)*X.shape[0]#保存样本数
test_samples+=X.shape[0]
avg_train_loss=total_loss/total_samples
avg_train_acc=total_acc_sample/total_samples
avg_test_acc=test_acc_samples/test_samples
train_loss_list.append(avg_train_loss)
train_acc_list.append(avg_train_acc)
test_acc_list.append(avg_test_acc)
print(f"Epoch {epoch+1}: Loss: {avg_train_loss:.4f},Trian Accuracy: {avg_train_acc:.4f},test Accuracy: {avg_test_acc:.4f}")
plot_metrics(train_loss_list, train_acc_list, test_acc_list)
return model
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
def batch_norm(X,gamma,beta,moving_mean,moving_var,eps,momentum):
if not torch.is_grad_enabled():#这个是推理模式
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)
# 更新移动平均的均值和方差
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
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)
#这是两个需要更新的参数
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)#这个不能为0，应该是/sqrt(var)
def forward(self,X):
#计算设备对齐
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
################################################################################################################
transforms=transforms.Compose([transforms.Resize(28),transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,))])
train_img=torchvision.datasets.FashionMNIST(root="./data",train=True,transform=transforms,download=True)
test_img=torchvision.datasets.FashionMNIST(root="./data",train=False,transform=transforms,download=True)
train_data=DataLoader(train_img,batch_size=128,num_workers=4,shuffle=True)
test_data=DataLoader(test_img,batch_size=128,num_workers=4,shuffle=False)
################################################################################################################
device=torch.device("cuda:1" if torch.cuda.is_available() else 'cpu')
model=nn.Sequential(
nn.Conv2d(1,6,kernel_size=5),BatchNorm(6,num_dims=4),nn.Sigmoid(),nn.MaxPool2d(kernel_size=2,stride=2),
nn.Conv2d(6,16,kernel_size=5),BatchNorm(16,num_dims=4),nn.Sigmoid(),nn.MaxPool2d(kernel_size=2,stride=2),
nn.Flatten(),#Flatten()之后就是[batch_size,features] 2维度的向量矩阵
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)).to(device)
model.apply(init_weights)
optimizer=torch.optim.SGD(model.parameters(),lr=0.01,momentum=0.9)
CEloss=nn.CrossEntropyLoss()
model=train_model(model,train_data,test_data,num_epochs=15)
################################################################################################################
print("BatchNorm算法学习参数效果:")
print("gamma:",model[1].gamma.reshape((-1,)))
print("beta:",model[1].beta.reshape((-1,)))
