学习日志|Python MINIST手写集的识别,卷积神经网络,CNN(最简单PyTorch的使用) pytorch|cnn|python

文章目录

网络结构
输出图像尺寸
网络代码
预处理代码
训练代码
结果评定

网络结构

学习日志|Python MINIST手写集的识别,卷积神经网络,CNN(最简单PyTorch的使用)

文章图片

按顺序对每个层的参数设置
Conv1

Conv2d:in_channels=1, out_channels=16, kernel_size=3, stride=1
BatchNorm2d:16
ReLU
MaxPool2d:kernel_size=2, stride=1

Conv2

Conv2d:in_channels=16, out_channels=32, kernel_size=3, stride=1
BatchNorm2d:32
ReLU
MaxPool2d:kernel_size=2, stride=1

Conv3

Conv2d:in_channels=32, out_channels=64, kernel_size=3, stride=1
BatchNorm2d:64
ReLU
AvgPool2d:kernel_size=2, stride=1

Conv4

Conv2d:in_channels=64, out_channels=128, kernel_size=3, stride=1
BatchNorm2d:128
ReLU
AvgPool2d:kernel_size=2, stride=1

MLP

Linear(128 * 16 * 16, 1000)
Linear(1000, 1000)
Linear(1000, 100)
Linear(100, 10)

输出图像尺寸

文章图片

计算公式(lambda函数化)

func = lambda size, kernel=3, stride=1, padding=0: ((size - kernel + 2 * padding) / stride + 1)

代码通过显式获取图片长宽,并且结合网络层的构造可以自动计算图像尺寸的变换

func = lambda size, kernel=3, stride=1, padding=0: ((size - kernel + 2 * padding) / stride + 1) x = func(func(self.options[1]), kernel=2) x = func(func(x), kernel=2) x = func(func(x), kernel=2) x = func(func(x), kernel=2)y = func(func(self.options[2]), kernel=2) y = func(func(y), kernel=2) y = func(func(y), kernel=2) y = func(func(y), kernel=2)x = int(x) y = int(y)

网络代码

class CNN(nn.Module): def __init__(self, channels, width, height, classes, device='cpu'): super().__init__()self.options = [channels, width, height, classes, device]self.conv1 = nn.Sequential( nn.Conv2d(in_channels=channels, out_channels=16, kernel_size=3, stride=1), nn.BatchNorm2d(16), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=1) )self.conv2 = nn.Sequential( nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=1) )self.conv3 = nn.Sequential( nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1), nn.BatchNorm2d(64), nn.ReLU(), nn.AvgPool2d(kernel_size=2, stride=1) )self.conv4 = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1), nn.BatchNorm2d(128), nn.ReLU(), nn.AvgPool2d(kernel_size=2, stride=1) )func = lambda size, kernel=3, stride=1, padding=0: ((size - kernel + 2 * padding) / stride + 1) x = func(func(self.options[1]), kernel=2) x = func(func(x), kernel=2) x = func(func(x), kernel=2) x = func(func(x), kernel=2)y = func(func(self.options[2]), kernel=2) y = func(func(y), kernel=2) y = func(func(y), kernel=2) y = func(func(y), kernel=2)x = int(x) y = int(y)self.mlp = nn.Sequential( nn.Linear(128 * x * y, 1000), nn.Linear(1000, 1000), nn.Linear(1000, 100), nn.Linear(100, self.options[3]) )self = self.to(self.options[4])def forward(self, data): data = https://www.it610.com/article/data.to(self.options[4])# 将向量转移到设备上 data = self.conv1(data) data = self.conv2(data) data = self.conv3(data) data = self.conv4(data) data = data.view(data.size(0), -1) data = self.mlp(data) return data

预处理代码

data_tf = torchvision.transforms.Compose( [ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize([0.5], [0.5]) ] )data_path = r'data/minist'# minist数据集的下载路径 # 获取数据集 train_data = https://www.it610.com/article/mnist.MNIST(data_path, train=True, transform=data_tf, download=False) test_data = mnist.MNIST(data_path, train=False, transform=data_tf, download=False) train_loader = data.DataLoader(train_data, batch_size=128, shuffle=True) test_loader = data.DataLoader(test_data, batch_size=128, shuffle=True)

训练代码

device = 'cuda:0' model = CNN(channels=1, width=28, height=28, classes=10, device=device) loss_func = torch.nn.CrossEntropyLoss() opt = torch.optim.Adam(model.parameters(), lr=0.001) loss_count = []for epoch in range(50): for i, (x, y) in enumerate(train_loader):batch_x = Variable(x) batch_y = Variable(y).to(device) out = model(batch_x)loss = loss_func(out, batch_y)opt.zero_grad() loss.backward() opt.step()if i % 20 == 0: loss = loss.cpu() loss_count.append(loss)for a, b in test_loader: test_x = Variable(a) test_y = Variable(b).to(device) out = model(test_x) test_y = test_y.cpu() out = out.cpu() accuracy = max(out, 1)[1].numpy() == test_y.numpy() print('accuracy:\t', accuracy.mean()) breakdatas = [] for i in loss_count: if i.data < 0.5: datas.append(i.data) plt.figure('LOSS') plt.plot(datas, label='Loss') plt.legend() plt.show()

因为交叉熵损失会出现大于1的值,所以显示的时候删除掉损失大于0.5的值,会更好的显示损失图
结果评定 【学习日志|Python MINIST手写集的识别,卷积神经网络,CNN(最简单PyTorch的使用)】