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第一步：准备数据

mnist开源数据集

第二步：搭建模型

我们这里搭建了一个LeNet5网络

参考代码如下：

import torch from torch import nn class Reshape(nn.Module): def forward(self, x): return x.view(-1, 1, 28, 28) class LeNet5(nn.Module): def __init__(self): super(LeNet5, self).__init__() self.net = nn.Sequential( Reshape(), # CONV1, ReLU1, POOL1 nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2), # nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), # CONV2, ReLU2, POOL2 nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Flatten(), # FC1 nn.Linear(in_features=16 * 5 * 5, out_features=120), nn.ReLU(), # FC2 nn.Linear(in_features=120, out_features=84), nn.ReLU(), # FC3 nn.Linear(in_features=84, out_features=10) ) # 添加softmax层 self.softmax = nn.Softmax() def forward(self, x): logits = self.net(x) # 将logits转为概率 prob = self.softmax(logits) return prob if __name__ == '__main__': model = LeNet5() X = torch.rand(size=(256, 1, 28, 28), dtype=torch.float32) for layer in model.net: X = layer(X) print(layer.__class__.__name__, '\toutput shape: \t', X.shape) X = torch.rand(size=(1, 1, 28, 28), dtype=torch.float32) print(model(X))

第三步：训练代码

import torch from torch import nn from torchvision import datasets from torchvision.transforms import ToTensor from torch.utils.data import DataLoader from model import LeNet5 # DATASET train_data = datasets.MNIST( root='./data', train=False, download=True, transform=ToTensor() ) test_data = datasets.MNIST( root='./data', train=False, download=True, transform=ToTensor() ) # PREPROCESS batch_size = 256 train_dataloader = DataLoader(dataset=train_data, batch_size=batch_size) test_dataloader = DataLoader(dataset=test_data, batch_size=batch_size) for X, y in train_dataloader: print(X.shape) # torch.Size([256, 1, 28, 28]) print(y.shape) # torch.Size([256]) break # MODEL device = 'cuda' if torch.cuda.is_available() else 'cpu' model = LeNet5().to(device) # TRAIN MODEL loss_func = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(params=model.parameters()) def train(dataloader, model, loss_func, optimizer, epoch): model.train() data_size = len(dataloader.dataset) for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) y_hat = model(X) loss = loss_func(y_hat, y) optimizer.zero_grad() loss.backward() optimizer.step() loss, current = loss.item(), batch * len(X) print(f'EPOCH{epoch+1}\tloss: {loss:>7f}', end='\t') # Test model def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print(f'Test Error: Accuracy: {(100 * correct):>0.1f}%, Average loss: {test_loss:>8f}\n') if __name__ == '__main__': epoches = 80 for epoch in range(epoches): train(train_dataloader, model, loss_func, optimizer, epoch) test(test_dataloader, model, loss_func) # Save models torch.save(model.state_dict(), 'model.pth') print('Saved PyTorch LeNet5 State to model.pth')

第四步：统计训练过程

EPOCH1 loss: 1.908403 Test Error: Accuracy: 58.3%, Average loss: 1.943602 EPOCH2 loss: 1.776060 Test Error: Accuracy: 72.2%, Average loss: 1.750917 EPOCH3 loss: 1.717706 Test Error: Accuracy: 73.6%, Average loss: 1.730332 EPOCH4 loss: 1.719344 Test Error: Accuracy: 76.0%, Average loss: 1.703456 EPOCH5 loss: 1.659312 Test Error: Accuracy: 76.6%, Average loss: 1.694500 EPOCH6 loss: 1.647946 Test Error: Accuracy: 76.9%, Average loss: 1.691286 EPOCH7 loss: 1.653712 Test Error: Accuracy: 77.0%, Average loss: 1.690819 EPOCH8 loss: 1.653270 Test Error: Accuracy: 76.8%, Average loss: 1.692459 EPOCH9 loss: 1.649021 Test Error: Accuracy: 77.5%, Average loss: 1.686158 EPOCH10 loss: 1.648204 Test Error: Accuracy: 78.3%, Average loss: 1.678802 EPOCH11 loss: 1.647159 Test Error: Accuracy: 78.4%, Average loss: 1.676133 EPOCH12 loss: 1.647390 Test Error: Accuracy: 78.6%, Average loss: 1.674455 EPOCH13 loss: 1.646807 Test Error: Accuracy: 78.4%, Average loss: 1.675752 EPOCH14 loss: 1.630824 Test Error: Accuracy: 79.1%, Average loss: 1.668470 EPOCH15 loss: 1.524222 Test Error: Accuracy: 86.3%, Average loss: 1.599240 EPOCH16 loss: 1.524022 Test Error: Accuracy: 86.7%, Average loss: 1.594947 EPOCH17 loss: 1.524296 Test Error: Accuracy: 87.1%, Average loss: 1.588946 EPOCH18 loss: 1.523599 Test Error: Accuracy: 87.3%, Average loss: 1.588275 EPOCH19 loss: 1.523655 Test Error: Accuracy: 87.5%, Average loss: 1.586576 EPOCH20 loss: 1.523659 Test Error: Accuracy: 88.2%, Average loss: 1.579286 EPOCH21 loss: 1.523733 Test Error: Accuracy: 87.9%, Average loss: 1.582472 EPOCH22 loss: 1.523748 Test Error: Accuracy: 88.2%, Average loss: 1.578699 EPOCH23 loss: 1.523788 Test Error: Accuracy: 88.0%, Average loss: 1.579700 EPOCH24 loss: 1.523708 Test Error: Accuracy: 88.1%, Average loss: 1.579758 EPOCH25 loss: 1.523683 Test Error: Accuracy: 88.4%, Average loss: 1.575913 EPOCH26 loss: 1.523646 Test Error: Accuracy: 88.7%, Average loss: 1.572831 EPOCH27 loss: 1.523654 Test Error: Accuracy: 88.9%, Average loss: 1.570528 EPOCH28 loss: 1.523642 Test Error: Accuracy: 89.0%, Average loss: 1.570223 EPOCH29 loss: 1.523663 Test Error: Accuracy: 89.0%, Average loss: 1.570385 EPOCH30 loss: 1.523658 Test Error: Accuracy: 88.9%, Average loss: 1.571195 EPOCH31 loss: 1.523653 Test Error: Accuracy: 88.4%, Average loss: 1.575981 EPOCH32 loss: 1.523653 Test Error: Accuracy: 89.0%, Average loss: 1.570087 EPOCH33 loss: 1.523642 Test Error: Accuracy: 88.9%, Average loss: 1.571018 EPOCH34 loss: 1.523649 Test Error: Accuracy: 89.0%, Average loss: 1.570439 EPOCH35 loss: 1.523629 Test Error: Accuracy: 90.4%, Average loss: 1.555473 EPOCH36 loss: 1.461187 Test Error: Accuracy: 97.1%, Average loss: 1.491042 EPOCH37 loss: 1.461230 Test Error: Accuracy: 97.7%, Average loss: 1.485049 EPOCH38 loss: 1.461184 Test Error: Accuracy: 97.7%, Average loss: 1.485653 EPOCH39 loss: 1.461156 Test Error: Accuracy: 98.2%, Average loss: 1.479966 EPOCH40 loss: 1.461335 Test Error: Accuracy: 98.2%, Average loss: 1.479197 EPOCH41 loss: 1.461152 Test Error: Accuracy: 98.7%, Average loss: 1.475477 EPOCH42 loss: 1.461153 Test Error: Accuracy: 98.7%, Average loss: 1.475124 EPOCH43 loss: 1.461153 Test Error: Accuracy: 98.9%, Average loss: 1.472885 EPOCH44 loss: 1.461151 Test Error: Accuracy: 99.1%, Average loss: 1.470957 EPOCH45 loss: 1.461156 Test Error: Accuracy: 99.1%, Average loss: 1.471141 EPOCH46 loss: 1.461152 Test Error: Accuracy: 99.1%, Average loss: 1.470793 EPOCH47 loss: 1.461151 Test Error: Accuracy: 98.8%, Average loss: 1.474548 EPOCH48 loss: 1.461151 Test Error: Accuracy: 99.1%, Average loss: 1.470666 EPOCH49 loss: 1.461151 Test Error: Accuracy: 99.1%, Average loss: 1.471546 EPOCH50 loss: 1.461151 Test Error: Accuracy: 99.0%, Average loss: 1.471407 EPOCH51 loss: 1.461151 Test Error: Accuracy: 98.8%, Average loss: 1.473795 EPOCH52 loss: 1.461164 Test Error: Accuracy: 98.2%, Average loss: 1.480009 EPOCH53 loss: 1.461151 Test Error: Accuracy: 99.2%, Average loss: 1.469931 EPOCH54 loss: 1.461152 Test Error: Accuracy: 99.2%, Average loss: 1.469916 EPOCH55 loss: 1.461151 Test Error: Accuracy: 98.9%, Average loss: 1.472574 EPOCH56 loss: 1.461151 Test Error: Accuracy: 98.6%, Average loss: 1.476035 EPOCH57 loss: 1.461151 Test Error: Accuracy: 98.2%, Average loss: 1.478933 EPOCH58 loss: 1.461150 Test Error: Accuracy: 99.4%, Average loss: 1.468186 EPOCH59 loss: 1.461151 Test Error: Accuracy: 99.4%, Average loss: 1.467602 EPOCH60 loss: 1.461151 Test Error: Accuracy: 99.1%, Average loss: 1.471206 EPOCH61 loss: 1.461151 Test Error: Accuracy: 98.8%, Average loss: 1.473356 EPOCH62 loss: 1.461151 Test Error: Accuracy: 99.2%, Average loss: 1.470242 EPOCH63 loss: 1.461150 Test Error: Accuracy: 99.1%, Average loss: 1.470826 EPOCH64 loss: 1.461151 Test Error: Accuracy: 98.7%, Average loss: 1.474476 EPOCH65 loss: 1.461150 Test Error: Accuracy: 99.3%, Average loss: 1.469116 EPOCH66 loss: 1.461150 Test Error: Accuracy: 99.4%, Average loss: 1.467823 EPOCH67 loss: 1.461150 Test Error: Accuracy: 99.5%, Average loss: 1.466486 EPOCH68 loss: 1.461152 Test Error: Accuracy: 99.3%, Average loss: 1.468688 EPOCH69 loss: 1.461150 Test Error: Accuracy: 99.5%, Average loss: 1.466256 EPOCH70 loss: 1.461150 Test Error: Accuracy: 99.5%, Average loss: 1.466588 EPOCH71 loss: 1.461150 Test Error: Accuracy: 99.6%, Average loss: 1.465280 EPOCH72 loss: 1.461150 Test Error: Accuracy: 99.4%, Average loss: 1.467110 EPOCH73 loss: 1.461151 Test Error: Accuracy: 99.6%, Average loss: 1.465245 EPOCH74 loss: 1.461150 Test Error: Accuracy: 99.5%, Average loss: 1.466551 EPOCH75 loss: 1.461150 Test Error: Accuracy: 99.5%, Average loss: 1.466001 EPOCH76 loss: 1.461150 Test Error: Accuracy: 99.3%, Average loss: 1.468074 EPOCH77 loss: 1.461151 Test Error: Accuracy: 99.6%, Average loss: 1.465709 EPOCH78 loss: 1.461150 Test Error: Accuracy: 99.5%, Average loss: 1.466567 EPOCH79 loss: 1.461150 Test Error: Accuracy: 99.6%, Average loss: 1.464922 EPOCH80 loss: 1.461150 Test Error: Accuracy: 99.6%, Average loss: 1.465109

第五步：搭建GUI界面

第六步：整个工程的内容

有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码，主要使用方法可以参考里面的“文档说明_必看.docx”

视频演示和项目源码下载：

https://www.bilibili.com/video/BV1pQBxYmE5R/