这几天又在玩树莓派，先是搞了个物联网，又在尝试在树莓派上搞一些简单的神经网络，这次搞得是mlp识别mnist手写数字识别
训练代码在电脑上，cpu就能训练，很快的：
1 import torch
2 import torch.nn as nn
3 import torch.optim as optim
4 from torchvision import datasets, transforms
5
6 # 设置随机种子
7 torch.manual_seed(42)
8
9 # 定义MLP模型
10 class MLP(nn.Module):
11 def __init__(self):
12 super(MLP, self).__init__()
13 self.fc1 = nn.Linear(784, 256)
14 self.fc2 = nn.Linear(256, 128)
15 self.fc3 = nn.Linear(128, 10)
16
17 def forward(self, x):
18 x = x.view(-1, 784)
19 x = torch.relu(self.fc1(x))
20 x = torch.relu(self.fc2(x))
21 x = self.fc3(x)
22 return x
23
24 # 加载MNIST数据集
25 transform = transforms.Compose([
26 transforms.ToTensor(),
27 # transforms.Normalize((0.1307,), (0.3081,))
28 ])
29
30 train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
31 test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
32
33 train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
34 test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)
35
36 # 创建模型实例
37 model = MLP()
38
39 # 定义损失函数和优化器
40 criterion = nn.CrossEntropyLoss()
41 optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
42
43 # 训练模型
44 def train(model, train_loader, optimizer, criterion, epochs):
45 model.train()
46 for epoch in range(1, epochs + 1):
47 for batch_idx, (data, target) in enumerate(train_loader):
48 optimizer.zero_grad()
49 output = model(data)
50 loss = criterion(output, target)
51 loss.backward()
52 optimizer.step()
53
54 if batch_idx % 100 == 0:
55 print('Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
56 epoch, batch_idx * len(data), len(train_loader.dataset),
57 100. * batch_idx / len(train_loader), loss.item()))
58
59 # 训练模型
60 train(model, train_loader, optimizer, criterion, epochs=5)
61
62 # 保存模型为NumPy格式
63 numpy_model = {}
64 numpy_model['fc1.weight'] = model.fc1.weight.detach().numpy()
65 numpy_model['fc1.bias'] = model.fc1.bias.detach().numpy()
66 numpy_model['fc2.weight'] = model.fc2.weight.detach().numpy()
67 numpy_model['fc2.bias'] = model.fc2.bias.detach().numpy()
68 numpy_model['fc3.weight'] = model.fc3.weight.detach().numpy()
69 numpy_model['fc3.bias'] = model.fc3.bias.detach().numpy()
70
71 # 保存为NumPy格式的数据
72 import numpy as np
73 np.savez('mnist_model.npz', **numpy_model)然后需要自己倒出一些图片在dataset里：我保存在了mnist_pi文件夹下,“_”后面的是标签，主要是在pc端导出保存到树莓派下
树莓派推理端的代码，需要numpy手动重新搭建网络，然后加载那些保存的矩阵参数，做矩阵乘法和加法
1 import numpy as np
2 import os
3 from PIL import Image
4
5 # 加载模型
6 model_data = np.load('mnist_model.npz')
7 weights1 = model_data['fc1.weight']
8 biases1 = model_data['fc1.bias']
9 weights2 = model_data['fc2.weight']
10 biases2 = model_data['fc2.bias']
11 weights3 = model_data['fc3.weight']
12 biases3 = model_data['fc3.bias']
13
14 # 进行推理
15 def predict(image, weights1, biases1,weights2, biases2,weights3, biases3):
16 image = image.flatten()/255 # 将输入图像展平并进行归一化
17 output = np.dot(weights1, image) + biases1
18 output = np.dot(weights2, output) + biases2
19 output = np.dot(weights3, output) + biases3
20 predicted_class = np.argmax(output)
21 return predicted_class
22
23
24
25
26 folder_path = './mnist_pi' # 替换为图片所在的文件夹路径
27 def infer_images_in_folder(folder_path):
28 for file_name in os.listdir(folder_path):
29 file_path = os.path.join(folder_path, file_name)
30 if os.path.isfile(file_path) and file_name.endswith(('.jpg', '.jpeg', '.png')):
31 image = Image.open(file_path)
32 label = file_name.split(".")[0].split("_")[1]
33 image = np.array(image)
34 print("file_path:",file_path,"img size:",image.shape,"label:",label)
35 predicted_class = predict(image, weights1, biases1,weights2, biases2,weights3, biases3)
36 print('Predicted class:', predicted_class)
37
38 infer_images_in_folder(folder_path)
结果:
效果还不错：
这次内容就到这里了，下次争取做一个卷积的神经网络在树莓派上推理，然后争取做一个目标检测的模型在树莓派上
多思考也是一种努力，做出正确的分析和选择，因为我们的时间和精力都有限，所以把时间花在更有价值的地方。
