### Python深度学习代码示例 #### 使用PyTorch创建简单的神经网络 通过构建一个基本的多层感知器（MLP），可以展示如何利用Python编写深度学习应用。下面是一个基于PyTorch框架实现简单全连接神经网络的例子： ```python import torch from torch import nn, optim import torchvision.transforms as transforms from torchvision.datasets import MNIST from torch.utils.data import DataLoader # 定义超参数 batch_size = 64 learning_rate = 0.01 epochs = 5 # 数据预处理 transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) train_dataset = MNIST(root='./data', train=True, download=True, transform=transform) test_dataset = MNIST(root='./data', train=False, download=True, transform=transform) train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size * 2, shuffle=False) # 构建模型 class MLP(nn.Module): def __init__(self): super().__init__() self.model = nn.Sequential( nn.Linear(28*28, 512), nn.ReLU(), nn.Dropout(p=0.2), nn.Linear(512, 10) ) def forward(self, x): x = x.view(-1, 28*28) # 将输入展平成一维向量 output = self.model(x) return output device = 'cuda' if torch.cuda.is_available() else 'cpu' model = MLP().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) for epoch in range(epochs): model.train() running_loss = 0.0 for data, target in train_loader: data, target = data.to(device), target.to(device) optimizer.zero_grad() outputs = model(data) loss = criterion(outputs, target) loss.backward() optimizer.step() running_loss += loss.item() print(f"Epoch {epoch+1}/{epochs}, Loss: {(running_loss/len(train_loader)):.4f}") print('Finished Training') ``` 此段代码展示了如何加载MNIST手写数字识别数据集并训练一个多层感知机来进行图像分类任务[^3]。 #### 利用Keras库快速搭建卷积神经网络(CNN) 对于计算机视觉领域中的图片分类问题，CNN是非常有效的解决方案之一。这里给出一段使用Keras API建立简易版LeNet架构的实例: ```python from keras.models import Sequential from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D # 创建顺序模型 model = Sequential([ # 添加第一个卷积层 Conv2D(filters=6, kernel_size=(5, 5), activation='relu', input_shape=(28, 28, 1)), MaxPooling2D(pool_size=(2, 2)), # 第二个卷积层 Conv2D(filters=16, kernel_size=(5, 5), activation='relu'), MaxPooling2D(pool_size=(2, 2)), # 展开层 Flatten(), # 全连接层 Dense(units=120, activation='relu'), Dense(units=84, activation='relu'), # 输出层 Dense(units=10, activation='softmax') ]) ``` 这段代码定义了一个小型化的LeNet结构用于解决CIFAR-10或者Fashion-MNIST这类彩色小图象的数据集上的分类挑战[^4]。