Python实战小项目之Mnist手写数字识别_Python

程序流程分析图：

Python实战小项目之Mnist手写数字识别

传播过程：

Python实战小项目之Mnist手写数字识别

代码展示：

创建环境

使用<pip install+包名>来下载torch，torchvision包

准备数据集

设置一次训练所选取的样本数Batch_Sized的值为512，训练此时Epochs的值为8

BATCH_SIZE = 512
EPOCHS = 8
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

下载数据集

Normalize()数字归一化，转换使用的值0.1307和0.3081是MNIST数据集的全局平均值和标准偏差，这里我们将它们作为给定值。model

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST("data", train=True, download=True,
                   transform=transforms.Compose([.
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=BATCH_SIZE, shuffle=True)

下载测试集

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST("data", train=False,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=BATCH_SIZE, shuffle=True)

绘制图像

我们可以使用matplotlib来绘制其中的一些图像

examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
print(example_targets)
print(example_data.shape)
print(example_data)
 
import matplotlib.pyplot as plt
fig = plt.figure()
for i in range(6):
  plt.subplot(2,3,i+1)
  plt.tight_layout()
  plt.imshow(example_data[i][0], cmap="gray", interpolation="none")
  plt.title("Ground Truth: {}".format(example_targets[i]))
  plt.xticks([])
  plt.yticks([])
plt.show()

Python实战小项目之Mnist手写数字识别

搭建神经网络

这里我们构建全连接神经网络，我们使用三个全连接(或线性)层进行前向传播。

class linearNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
    def forward(self, x):
        x = x.view(-1, 784)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.fc3(x)
        x = F.log_softmax(x, dim=1)
        return x

训练模型

首先，我们需要使用optimizer.zero_grad()手动将梯度设置为零，因为PyTorch在默认情况下会累积梯度。然后，我们生成网络的输出(前向传递)，并计算输出与真值标签之间的负对数概率损失。现在，我们收集一组新的梯度，并使用optimizer.step()将其传播回每个网络参数。

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    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.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if (batch_idx) % 30 == 0:
            print("Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}".format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                       100. * batch_idx / len(train_loader), loss.item()))

测试模型

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.nll_loss(output, target, reduction="sum").item() # 将一批的损失相加
            pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标
            correct += pred.eq(target.view_as(pred)).sum().item()
 
    test_loss /= len(test_loader.dataset)
    print("
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
".format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

将训练次数进行循环

if __name__ == "__main__":
    model = linearNet()
    optimizer = optim.Adam(model.parameters())
 
    for epoch in range(1, EPOCHS + 1):
        train(model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)