mnist实战
开始使用简单的全连接层进行mnist手写数字的识别，识别率最高能到95%，而使用两层卷积后再全连接，识别率能达到99%
全连接：
import torch
from torch import nn
from torch.nn import functional as F
from torch import optim
import torchvision
from matplotlib import pyplot as plt
from torch.optim.lr_scheduler import StepLR
#step 1:load dataset
def plot_image(img, label, name):
fig = plt.figure()
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.tight_layout()
plt.imshow(img[i][0] * 0.3081 + 0.1307, cmap='gray', interpolation='none')
plt.title("{}: {}".format(name, label[i].item()))
plt.xticks([])
plt.yticks([])
plt.show()
def plot_curve(data):
fig = plt.figure()
plt.plot(range(len(data)), data, color='blue')
plt.legend(['value'], loc='upper right')
plt.xlabel('step')
plt.ylabel('value')
plt.show()
batch_size=512
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('mnist_data',train=True,download=True,
transform=torchvision.transforms.Compose(
[
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.1307,),(0.3081,))#这里的两个数字分别是数据集的均值是0.1307，标准差是0.3081
]
)
),
batch_size=batch_size,shuffle=True
)
test_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('mnist_data/',train=False,download=True,#是验证集所以train=False
transform=torchvision.transforms.Compose(
[
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.1307,),(0.3081,))
]
)
),
batch_size=batch_size,shuffle=False#是验证集所以无需打乱，shuffle=False
)
# x,y = next(iter(train_loader))
# plot_image(x,y,'example')
#step2: create network
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
#wx+b
self.fc1 = nn.Linear(28*28,256)#256是自己根据经验随机设定的
self.fc2 = nn.Linear(256,64)
self.fc3 = nn.Linear(64,10)#注意这里的10是最后识别的类别数（最后一层的输出往往是识别的类别数）
def forward(self, x):
#x : [ b 1 28 28]有batch_size张图片，通道是1维灰度图像 图片大小是28*28
#h1=relu(wx+b)
x = F.relu(self.fc1(x))#使用relu非线性激活函数包裹
x = F.relu(self.fc2(x))
x = F.softmax(self.fc3(x))#由于是多类别识别，所以使用softmax函数
#x = self.fc3(x)
return x
net = Net()
optimizer = optim.Adam(net.parameters())
train_loss = []
for epoch in range(5):
for batch_idx,(x,y) in enumerate(train_loader):#enumerate表示在数据前面加上序号组成元组，默认序号从0开始
# x :[512 1 28 28] y : [512]
#由于这里的x维度为[512 1 28 28]，但是在网络中第一层就是一个全连接层，维度只能是[b,feature(784)]，所以要把x打平
#将前面多维度的tensor展平成一维
# 卷积或者池化之后的tensor的维度为(batchsize，channels，x，y)，其中x.size(0)
# 指batchsize的值，最后通过x.view(x.size(0), -1)
# 将tensor的结构转换为了(batchsize, channels * x * y)，即将（channels，x，y）拉直，然后就可以和fc层连接了
x = x.view(x.size(0),28*28)
#输出之后的维度变为[512,10]
out=net(x)
#使用交叉熵损失
loss = F.cross_entropy(out,y)
#清零梯度——计算梯度——更新梯度
#要进行梯度的清零
optimizer.zero_grad()
loss.backward()
#功能是: w` = w-lr*grad
optimizer.step()
train_loss.append(loss.item())#将loss保存在trainloss中，而loss.item()表示将tensor 的类型转换为数值类型
#打印loss
if batch_idx % 10 == 0:
print(epoch,batch_idx,loss.item())
plot_curve(train_loss)
total_correct = 0
for x, y in test_loader:
x = x.view(x.size(0),28*28)
out = net(x)
#out :[512,10]
pred = out.argmax(dim = 1)
correct = pred.eq(y).sum().float().item()#当前批次识别对的个数
total_correct+= correct
total_number = len(test_loader.dataset)
acc = total_correct / total_number
print('test acc',acc)
x,y = next(iter(test_loader))
out = net(x.view(x.size(0),28*28))
pred = out.argmax(dim=1)
plot_image(x,pred,'test')
#optimizer = optim.SGD(net.parameters(),lr=0.1,momentum=0.9)
#test acc 0.8783
#optimizer = optim.Adam(net.parameters())
#test acc 0.9574
加入卷积：
import torch
import argparse
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets,transforms
from torch.optim.lr_scheduler import StepLR
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.conv1 = nn.Conv2d(1,32,3,1)
self.conv2 = nn.Conv2d(32,64,3,1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self,x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
#print(x.shape)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
#print(x.shape)
x = torch.flatten(x,1)
#print(x.shape)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.softmax(x)
return output
#用来查看经过conv之后进入全连接层的维度
# def main():
# net = Net()
#
# tmp = torch.rand(10,1,28,28)
# out = net.forward(tmp)
#
#
# if __name__=='__main__':
# main()
# torch.Size([10, 64, 24, 24])
# torch.Size([10, 64, 12, 12])
# torch.Size([10, 9216])
def plot_image(img, label, name):
fig = plt.figure()
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.tight_layout()
plt.imshow(img[i][0] * 0.3081 + 0.1307, cmap='gray', interpolation='none')
plt.title("{}: {}".format(name, label[i].item()))
plt.xticks([])
plt.yticks([])
plt.show()
def train(args,model,device,train_loader,optimizer,epoch):
model.train()#进入训练模式来激活dropout层、正则化等的使用
for batch_idx,(data,target) in enumerate(train_loader):
data,target = data.to(device),target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output,target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval ==0:
print('train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
if args.dry_run:
break
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=14, metavar='N',
help='number of epochs to train (default: 14)')
parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=True,
help='For Saving the current Model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'batch_size': args.batch_size}
if use_cuda:
kwargs.update({'num_workers': 1,
'pin_memory': True,
'shuffle': True},
)
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
dataset1 = datasets.MNIST('', train=True, download=False,
transform=transform)
dataset2 = datasets.MNIST('', train=False,
transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
#optimizer = optim.Adam(model.parameters())
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
scheduler.step()
if args.save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
model.load_state_dict(torch.load('mnist_cnn.pt'))
#观察测试结果
for i in range(5):
x, y = next(iter(test_loader))
x,y = x.to(device),y.to(device)
out = model(x)
pred = out.argmax(dim=1)
plot_image(x.cpu(), pred.cpu(), 'test')
if __name__ == '__main__':
main()
#使用Adadelta 设置lr衰减
#Test set: Average loss: 1.4739, Accuracy: 9873/10000 (99%)
#使用SGD优化器，learning rate0.1 ，未设置lr的衰减
#Test set: Average loss: 1.4735, Accuracy: 9880/10000 (99%)
#使用Adam优化器，lr默认使用Adam的默认值0.001（使用0.1loss下不来） 未设置lr的衰减
#Test set: Average loss: 1.4749, Accuracy: 9862/10000 (99%)
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