目录
多层感知机：
介绍：
缩写：MLP，这是一种人工神经网络，由一个输入层、一个或多个隐藏层以及一个输出层组成，每一层都由多个节点（神经元）构成。在MLP中，节点之间只有前向连接，没有循环连接，这使得它属于前馈神经网络的一种。每个节点都应用一个激活函数，如sigmoid、ReLU等，以引入非线性，从而使网络能够拟合复杂的函数和数据分布。
代码实现：
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
# Step 1: Define the MLP model
class SimpleMLP(nn.Module):
def __init__(self):
super(SimpleMLP, self).__init__()
self.fc1 = nn.Linear(784, 128) # Input layer to hidden layer
self.fc2 = nn.Linear(128, 64) # Hidden layer to another hidden layer
self.fc3 = nn.Linear(64, 10) # Hidden layer to output layer
self.relu = nn.ReLU()
def forward(self, x):
x = x.view(-1, 784) # Flatten the input from 28x28 to 784
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
# Step 2: Load MNIST dataset
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# Step 3: Define loss function and optimizer
model = SimpleMLP()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Step 4: Train the model
num_epochs = 5
for epoch in range(num_epochs):
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
# Step 5: Evaluate the model on the test set (optional)
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))运行结果：
问题答疑：
线性变换与非线性变换
在神经网络中
参数含义
在上述模型中，参数如784, 128, 64, 10并不是字节，而是神经网络层的尺寸，具体来说是神经元的数量：
