1.手写数字显示
- 导入数据包
import torch import torchvision from torch.utils.data import DataLoader from torchvision import datasets, transforms import torch.nn as nn import matplotlib.pyplot as plt import numpy as np
- 加载数据集
# load minst # E:\PyCharm\workspace\GAN\data root = 'E:/PyCharm/workspace/GAN/data/' transform = transforms.Compose([ transforms.ToTensor() ]) dataset = datasets.MNIST(root=root, train=False, download=False, transform=transform) loader = DataLoader(dataset, batch_size=64, shuffle=True)
- 从其中选出batch_size张手写数字图片
for i,(real_image, real_image_label) in enumerate(loader): break
- 显示图片
real_image = torchvision.utils.make_grid(real_image) real_image = real_image.numpy() plt.imshow(np.transpose(real_image,(1,2,0)))
<matplotlib.image.AxesImage at 0x1e2762ddcc8>
2.手写数字分类
1）分类模型训练
import torch import torchvision from torch import nn from torchvision import datasets from torchvision import transforms from torch.utils.data import DataLoader from torch import optim # 设置超参数 batch_size = 128 # learning_rate = 0.01 learning_rate = 1e-3 epochsize = 30 # 定义LeNet5 结构 class LeNet5(nn.Module): def __init__(self): super(LeNet5, self).__init__() # 卷积层 self.conv_layer = nn.Sequential( nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=0), # torch.Size([128, 6, 24, 24]) nn.MaxPool2d(kernel_size=2, stride=2, padding=0), # torch.Size([128, 6, 12, 12]) nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0), # torch.Size([128, 16, 8, 8]) nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # torch.Size([128, 16, 4, 4]) ) # 全连接层 self.fullconn_layer = nn.Sequential( nn.Linear(16 * 4 * 4, 120), nn.ReLU(), nn.Linear(120, 84), nn.ReLU(), nn.Linear(84, 10), ) def forward(self, x): output = self.conv_layer(x) # output:torch.Size([batch_size, 16, 5, 5]) output = output.view(x.size(0), -1) # output:torch.Size([batch_size, 16 * 4 * 4]) output = self.fullconn_layer(output) # torch.Size([batch_size, 10]) return output # 训练集下载 mnist_traindata = datasets.MNIST('E:/学习/机器学习/数据集/MNIST', train=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.1307], std=[0.1307]) ]), download=True) mnist_train = DataLoader(mnist_traindata, batch_size=batch_size, shuffle=True) # 测试集下载 mnist_testdata = datasets.MNIST('E:/学习/机器学习/数据集/MNIST', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.1307], std=[0.1307]) ]), download=True) mnist_test = DataLoader(mnist_testdata, batch_size=batch_size, shuffle=True) # 查看相关参数 real_image, label = iter(mnist_train).next() print('real_image:', real_image.shape, 'label:', label.shape) # 利用GPU加速 device = torch.device('cuda') # 定义模型 model = LeNet5().to(device) # 一般来说，分类任务使用CrossEntropyLoss；回归任务使用MSELoss criteon = nn.CrossEntropyLoss().to(device) # 定义优化器 optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 进行迭代训练 for epoch in range(epochsize): # 训练过程 model.train() # total_num = 0 for batchidx, (image, imagelabel) in enumerate(mnist_train): # image.shape：[batch_size, 3, 32, 32] image, imagelabel = image.to(device), imagelabel.to(device) # category.shape：{batchbatch_size, 10} category = model(image) # category: [batch_size, 10] # imagelabel：[batch_size] # 计算损失 loss = criteon(category, imagelabel) # 反向更新训练 optimizer.zero_grad() loss.backward() optimizer.step() # total_num += image.size(0) # print( 'total_num:', total_num) print(epoch, 'loss:', loss.item()) # 测试过程 model.eval() # 不进行计算图构建 with torch.no_grad(): total_connect = 0 # 总的正确个数 total_num = 0 # 总的当前测试个数 for (image, imagelabel) in mnist_test: # image.shape：[batch_size, 3, 32, 32] image, imagelabel = image.to(device), imagelabel.to(device) # category.shape：{batchbatch_size, 10} category = model(image) # 得到最大值的索引 pred = category.argmax(dim=1) # _, pred = category.max(dim=1) # 计算每一次正确的个数 total_connect += torch.eq(pred, imagelabel).detach().float().sum().item() total_num += image.size(0) # 计算一次训练之后计算率 acc = total_connect / total_num print('epoch:', epoch, 'test_acc:', acc) # 保存网络结构 torch.save(model.state_dict(), 'LeNet5_mnist.mdl')
最后的正确率可以达到接近99%的效果，下面进行验证
需要注意，参数与相关设置需要与训练的时候一致
2）分类模型测试
# 训练集下载 mnist_traindata = datasets.MNIST('E:/学习/机器学习/数据集/MNIST', train=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.1307], std=[0.1307]) ]), download=True) mnist_train = DataLoader(mnist_traindata, batch_size=batch_size, shuffle=True) # 获取图像与标签 image, label = iter(mnist_train).next() # 导入参数 device = torch.device('cuda') net = LeNet5().to(device) net.load_state_dict(torch.load('E:/PyCharm/workspace/Cifar10/LeNet5_mnist.mdl'))
其中输出label的值为：
tensor([0, 6, 8, 6, 5, 1, 7, 4, 3, 6, 9, 5, 7, 1, 2, 2, 0, 4, 1, 9, 1, 6, 8, 9, 4, 9, 1, 4, 0, 2, 0, 2, 4, 6, 8, 3, 1, 6, 7, 0, 4, 8, 5, 1, 9, 7, 8, 6, 8, 1, 5, 2, 6, 8, 1, 9, 1, 1, 3, 7, 3, 1, 8, 9, 4, 5, 7, 0, 7, 3, 8, 5, 4, 8, 3, 1, 3, 6, 5, 2, 6, 9, 4, 0, 1, 3, 2, 7, 0, 6, 8, 7, 6, 4, 0, 9, 1, 5, 6, 9, 3, 3, 4, 6, 8, 6, 1, 8, 0, 5, 4, 7, 8, 6, 8, 3, 4, 9, 3, 1, 6, 9, 2, 0, 3, 3, 7, 3])
# 显示一个batch_size的照片 real_image = torchvision.utils.make_grid(image) real_image = real_image.numpy() plt.imshow(np.transpose(real_image,(1,2,0)))
# 显示第一张图片 photo = torchvision.utils.make_grid(image[0]) photo = photo.numpy() plt.imshow(np.transpose(photo,(1,2,0)))
# 选择第1张图片进行验证 test = image[0] # torch.Size([3, 32, 32]) test = test.unsqueeze(0) # torch.Size([1, 3, 32, 32]) test = test.to(device) pred = net(test) # torch.Size([1, 10]) result = F.softmax(pred) # 求概率 result.max(dim=1)
torch.return_types.max( values=tensor([1.0000], device='cuda:0', grad_fn=<MaxBackward0>), indices=tensor([0], device='cuda:0'))
由图片可以明显看出是数字“0”,网络输出正确
# 显示第三张图片 photo = torchvision.utils.make_grid(image[2]) photo = photo.numpy() plt.imshow(np.transpose(photo,(1,2,0)))
# 选择第3张图片进行验证 test = image[2] # torch.Size([3, 32, 32]) test = test.unsqueeze(0) # torch.Size([1, 3, 32, 32]) test = test.to(device) pred = net(test) # torch.Size([1, 10]) result = F.softmax(pred) # 求概率 result.max(dim=1)
torch.return_types.max( values=tensor([1.], device='cuda:0', grad_fn=<MaxBackward0>), indices=tensor([8], device='cuda:0'))
由图片可以明显看出是数字“8”,网络输出正确
