1. 分析

论文：LeNet-5论文，论文中的image是1X32X32的照片，MINST的image是1X28X28，因此代码：self.hidden1 = nn.Linear(256, 120)中第一个维度是256。否则如果image是1X32X32时，代码应该是：self.hidden1 = nn.Linear(400, 120)
python代码——训练模型：

import torch
import torchvision
import torch.nn as nn
import torch.utils.data as DataEPOCH = 5
BATCH_SIZE = 50
LR = 0.002
DOWNLOAD_MINST = Truetrain_data = torchvision.datasets.MNIST(root='./mnist',train=True,transform=torchvision.transforms.ToTensor(),download=DOWNLOAD_MINST
)test_data = torchvision.datasets.MNIST(root='./mnist',train=False,transform=torchvision.transforms.ToTensor(),download=DOWNLOAD_MINST,
)train_loader = Data.DataLoader(dataset=train_data,batch_size=BATCH_SIZE,shuffle=True,num_workers=4,
)test_loader = Data.DataLoader(dataset=test_data,batch_size=BATCH_SIZE,shuffle=True,num_workers=4,
)class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1,out_channels=6,kernel_size=(5, 5),stride=(1, 1),padding=0,),nn.ReLU(),nn.AvgPool2d(kernel_size=2, stride=2,))self.conv2 = nn.Sequential(nn.Conv2d(in_channels=6,out_channels=16,kernel_size=(5, 5),stride=(1, 1),padding=0,),nn.ReLU(),nn.AvgPool2d(kernel_size=2, stride=2,))self.flat = nn.Flatten()self.hidden1 = nn.Linear(256, 120)self.hidden2 = nn.Linear(120, 84)self.predict = nn.Linear(84, 10)def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = self.flat(x)x = torch.relu(self.hidden1(x))x = torch.relu(self.hidden2(x))out = self.predict(x)return outnet = CNN()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=LR)
loss_function = torch.nn.CrossEntropyLoss()def train(data_loader, model, loss_function, optimizer):size = len(data_loader.dataset)model.train()for batch, (X, y) in enumerate(data_loader):X, y = X.to(device), y.to(device)# Compute prediction errorprediction = model(X)loss = loss_function(prediction, y)# Back propagationoptimizer.zero_grad()loss.backward()optimizer.step()if batch % 100 == 0:loss, current = loss.item(), batch * len(X)print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")def test(data_loader, model, loss_fn):size = len(data_loader.dataset)num_batches = len(data_loader)model.eval()test_loss, correct = 0, 0with torch.no_grad():for X, y in data_loader:X, y = X.to(device), y.to(device)prediction = model(X)test_loss += loss_fn(prediction, y).item()correct += (prediction.argmax(1) == y).type(torch.float).sum().item()test_loss /= num_batchescorrect /= sizeprint(f"Test Error:\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")def call_model():for t in range(EPOCH):print(f"Epoch {t + 1}\n-------------------------------")train(train_loader, net, loss_function, optimizer)test(test_loader, net, loss_function)torch.save(net.state_dict(), 'net.pkl')print("Saved PyTorch Model State to model.pth")if __name__ == '__main__':call_model()

LeNet-5模型中激活函数使用的是sigmoid或者tanh函数，而在本文的代码中用的是relu，也就是针对每一个卷积层都是使用：nn.ReLU(),但如果如果一定要参照原文，则使用：nn.Tanh(),或者nn.Sigmoid()
运行结果：

Epoch 1
-------------------------------
loss: 2.306098  [    0/60000]
loss: 0.412156  [ 5000/60000]
loss: 0.412486  [10000/60000]
loss: 0.152816  [15000/60000]
loss: 0.309540  [20000/60000]
loss: 0.100331  [25000/60000]
loss: 0.130987  [30000/60000]
loss: 0.114419  [35000/60000]
loss: 0.151787  [40000/60000]
loss: 0.164818  [45000/60000]
loss: 0.046076  [50000/60000]
loss: 0.023506  [55000/60000]
Test Error:
Accuracy: 97.9%, Avg loss: 0.065859 Epoch 2
-------------------------------
loss: 0.085328  [    0/60000]
loss: 0.045330  [ 5000/60000]
loss: 0.114169  [10000/60000]
loss: 0.186654  [15000/60000]
loss: 0.104414  [20000/60000]
loss: 0.014921  [25000/60000]
loss: 0.102247  [30000/60000]
loss: 0.136939  [35000/60000]
loss: 0.111537  [40000/60000]
loss: 0.004142  [45000/60000]
loss: 0.025103  [50000/60000]
loss: 0.085847  [55000/60000]
Test Error:
Accuracy: 98.3%, Avg loss: 0.063019 Epoch 3
-------------------------------
loss: 0.062379  [    0/60000]
loss: 0.004411  [ 5000/60000]
loss: 0.028583  [10000/60000]
loss: 0.045782  [15000/60000]
loss: 0.230601  [20000/60000]
loss: 0.026126  [25000/60000]
loss: 0.044285  [30000/60000]
loss: 0.016973  [35000/60000]
loss: 0.015370  [40000/60000]
loss: 0.006403  [45000/60000]
loss: 0.018655  [50000/60000]
loss: 0.017848  [55000/60000]
Test Error:
Accuracy: 98.7%, Avg loss: 0.042296 Epoch 4
-------------------------------
loss: 0.045252  [    0/60000]
loss: 0.102186  [ 5000/60000]
loss: 0.030442  [10000/60000]
loss: 0.055944  [15000/60000]
loss: 0.056801  [20000/60000]
loss: 0.057802  [25000/60000]
loss: 0.060436  [30000/60000]
loss: 0.010024  [35000/60000]
loss: 0.012362  [40000/60000]
loss: 0.021393  [45000/60000]
loss: 0.004517  [50000/60000]
loss: 0.002426  [55000/60000]
Test Error:
Accuracy: 98.7%, Avg loss: 0.040705 Epoch 5
-------------------------------

2. 加载模型，测试所有数据集

import torch
import torchvision
import torch.nn as nn
import torch.utils.data as DataEPOCH = 5
BATCH_SIZE = 50
LR = 0.002
DOWNLOAD_MINST = Truetrain_data = torchvision.datasets.MNIST(root='./mnist',train=True,transform=torchvision.transforms.ToTensor(),download=DOWNLOAD_MINST
)test_data = torchvision.datasets.MNIST(root='./mnist',train=False,transform=torchvision.transforms.ToTensor(),download=DOWNLOAD_MINST,
)train_loader = Data.DataLoader(dataset=train_data,batch_size=BATCH_SIZE,shuffle=True,num_workers=4,
)test_loader = Data.DataLoader(dataset=test_data,batch_size=BATCH_SIZE,shuffle=True,num_workers=4,
)class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1,out_channels=6,kernel_size=(5, 5),stride=(1, 1),padding=0,),nn.ReLU(),nn.AvgPool2d(kernel_size=2, stride=2,))self.conv2 = nn.Sequential(nn.Conv2d(in_channels=6,out_channels=16,kernel_size=(5, 5),stride=(1, 1),padding=0,),nn.ReLU(),nn.AvgPool2d(kernel_size=2, stride=2,))self.flat = nn.Flatten()self.hidden1 = nn.Linear(256, 120)self.hidden2 = nn.Linear(120, 84)self.predict = nn.Linear(84, 10)def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = self.flat(x)x = torch.relu(self.hidden1(x))x = torch.relu(self.hidden2(x))out = self.predict(x)return outnet = CNN()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=LR)
loss_function = torch.nn.CrossEntropyLoss()def train(data_loader, model, loss_function, optimizer):size = len(data_loader.dataset)model.train()for batch, (X, y) in enumerate(data_loader):X, y = X.to(device), y.to(device)# Compute prediction errorprediction = model(X)loss = loss_function(prediction, y)# Back propagationoptimizer.zero_grad()loss.backward()optimizer.step()if batch % 100 == 0:loss, current = loss.item(), batch * len(X)print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")def test(data_loader, model, loss_fn):size = len(data_loader.dataset)num_batches = len(data_loader)model.eval()test_loss, correct = 0, 0with torch.no_grad():for X, y in data_loader:X, y = X.to(device), y.to(device)prediction = model(X)test_loss += loss_fn(prediction, y).item()correct += (prediction.argmax(1) == y).type(torch.float).sum().item()test_loss /= num_batchescorrect /= sizeprint(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")def call_model():for t in range(EPOCH):print(f"Epoch {t + 1}\n-------------------------------")train(train_loader, net, loss_function, optimizer)test(test_loader, net, loss_function)torch.save(net.state_dict(), 'net.pkl')print("Saved PyTorch Model State to model.pth")def load_model():model = CNN()model.load_state_dict(torch.load("model.pth"))classes = ["T-shirt/top","Trouser","Pullover","Dress","Coat","Sandal","Shirt","Sneaker","Bag","Ankle boot",]model.eval()with torch.no_grad():accuracy = 0for (images, labels) in test_loader:predict = model(images)for i in range(len(predict)):predicted, actual = classes[predict[i].argmax(0)], classes[labels[i]]print(f'Predicted: "{predicted}", Actual: "{actual}"')if predicted == actual:accuracy += 1print("accuracy:%.4f" % (accuracy / len(test_data.data)))print('num:%d' % accuracy)if __name__ == '__main__':load_model()

运行结果：

...
accuracy:0.9885
num:9885

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LeNet-5实现分类MINST数据集（学习笔记四）

1. 分析

2. 加载模型，测试所有数据集

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