PyTorch实战-手写数字识别-MLP模型

1 需求

包懂，40分钟掌握PyTorch深度学习框架，对应神经网络算法理论逐行讲解用PyTorch实现图像分类代码_哔哩哔哩_bilibili

10分钟入门神经网络 PyTorch 手写数字识别_哔哩哔哩_bilibili

pytorch tutorial: PyTorch 手写数字识别教程代码

从零设计并训练一个神经网络，你就能真正理解它了_哔哩哔哩_bilibili

https://github.com/xhh890921/mnist_network

2 接口

3 豆包生成代码

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms# 定义超参数
batch_size = 128
learning_rate = 0.001
num_epochs = 10# 数据预处理
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))
])# 加载 MNIST 数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform)train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)# 定义 MLP 模型
class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.fc1 = nn.Linear(784, 512)self.fc2 = nn.Linear(512, 256)self.fc3 = nn.Linear(256, 10)def forward(self, x):x = x.view(-1, 784)x = torch.relu(self.fc1(x))x = torch.relu(self.fc2(x))x = self.fc3(x)return x# 实例化模型
model = MLP()# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)# 训练模型
for epoch in range(num_epochs):for batch_idx, (data, targets) in enumerate(train_loader):# 前向传播outputs = model(data)loss = criterion(outputs, targets)# 反向传播和优化optimizer.zero_grad()loss.backward()optimizer.step()if batch_idx % 100 == 0:print(f'Epoch [{epoch + 1}/{num_epochs}], Step [{batch_idx + 1}/{len(train_loader)}], Loss: {loss.item()}')# 在测试集上评估模型
model.eval()
with torch.no_grad():correct = 0total = 0for data, targets in test_loader:outputs = model(data)_, predicted = torch.max(outputs.data, 1)total += targets.size(0)correct += (predicted == targets).sum().item()accuracy = correct / totalprint(f'Test Accuracy: {accuracy * 100:.2f}%')

3

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
import matplotlib.pyplot as pltclass Net(torch.nn.Module):def __init__(self):super().__init__()self.fc1 = torch.nn.Linear(28 * 28, 64)self.fc2 = torch.nn.Linear(64, 64)self.fc3 = torch.nn.Linear(64, 64)self.fc4 = torch.nn.Linear(64, 10)def forward(self, x):x = torch.nn.functional.relu(self.fc1(x))x = torch.nn.functional.relu(self.fc2(x))x = torch.nn.functional.relu(self.fc3(x))x = torch.nn.functional.log_softmax(self.fc4(x), dim=1)return xdef get_data_loader(is_train):to_tensor = transforms.Compose([transforms.ToTensor()])data_set = MNIST("", is_train, transform=to_tensor, download=True)return DataLoader(data_set, batch_size=15, shuffle=True)def evaluate(test_data, net):n_correct = 0n_total = 0with torch.no_grad():for (x, y) in test_data:outputs = net.forward(x.view(-1, 28 * 28))for i, output in enumerate(outputs):if torch.argmax(output) == y[i]:n_correct += 1n_total += 1return n_correct / n_totaldef main():train_data = get_data_loader(is_train=True)test_data = get_data_loader(is_train=False)net = Net()print("initial accuracy:", evaluate(test_data, net))optimizer = torch.optim.Adam(net.parameters(), lr=0.001)for epoch in range(2):for (x, y) in train_data:net.zero_grad()output = net.forward(x.view(-1, 28 * 28))loss = torch.nn.functional.nll_loss(output, y)loss.backward()optimizer.step()print("epoch", epoch, "accuracy:", evaluate(test_data, net))for (n, (x, _)) in enumerate(test_data):if n > 3:breakpredict = torch.argmax(net.forward(x[0].view(-1, 28 * 28)))plt.figure(n)plt.imshow(x[0].view(28, 28))plt.title("prediction: " + str(int(predict)))plt.show()if __name__ == "__main__":main()