深度学习应用

import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data import DataLoader # 定义简单的CNN模型 class SimpleCNN(nn.Module): def __init__(self, num_classes=10): super(SimpleCNN, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), ) self.classifier = nn.Sequential( nn.Flatten(), nn.Linear(128 * 4 * 4, 256), nn.ReLU(), nn.Dropout(0.5), nn.Linear(256, num_classes) ) def forward(self, x): x = self.features(x) x = self.classifier(x) return x # 数据预处理 transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # 加载CIFAR-10数据集 train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) # 初始化模型、损失函数和优化器 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = SimpleCNN().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练循环 for epoch in range(10): model.train() total_loss = 0 for images, labels in train_loader: images, labels = images.to(device), labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() total_loss += loss.item() print(f'Epoch [{epoch+1}/10], Loss: {total_loss/len(train_loader):.4f}')

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification import torch # 方法1：使用pipeline快速实现 classifier = pipeline("sentiment-analysis", model="bert-base-chinese") texts = [ "这个产品非常好用，我很满意！", "质量太差了，完全不值这个价格。", "一般般吧，没什么特别的感觉。" ] results = classifier(texts) for text, result in zip(texts, results): print(f"文本: {text}") print(f"结果: {result['label']}, 置信度: {result['score']:.4f}\n") # 方法2：自定义模型使用 tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") model = AutoModelForSequenceClassification.from_pretrained( "bert-base-chinese", num_labels=3 # 正面、负面、中性 ) # 编码输入 inputs = tokenizer( "这款手机的拍照效果很棒！", return_tensors="pt", padding=True, truncation=True, max_length=128 ) # 推理 with torch.no_grad(): outputs = model(**inputs) predictions = torch.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(predictions, dim=-1) print(f"预测类别: {predicted_class.item()}") print(f"各类别概率: {predictions[0].tolist()}")

# 使用OpenAI API示例 from openai import OpenAI client = OpenAI(api_key="your-api-key") # 简单对话 response = client.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "你是一个专业的AI助手。"}, {"role": "user", "content": "请解释什么是深度学习？"} ], temperature=0.7, max_tokens=500 ) print(response.choices[0].message.content) # 代码生成示例 code_response = client.chat.completions.create( model="gpt-4", messages=[ {"role": "user", "content": "用Python写一个快速排序算法"} ] ) print(code_response.choices[0].message.content)