深度学习简介：AI人工智能与大数据的完美融合

    # PyTorch示例:ResNet网络在CIFAR10数据集上进行图像分类
    import torch
    import torchvision
    
    # 载入数据集
    train_dataset = torchvision.datasets.CIFAR10(...)
    test_dataset = torchvision.datasets.CIFAR10(...)
    
    # 定义ResNet
    import torchvision.models as models
    model = models.resnet18()
    
    # 设置优化器和损失函数  
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    loss_fn = torch.nn.CrossEntropyLoss()
    
    # 训练
    for epoch in range(10):
    train(model, optimizer, loss_fn, train_dataset)
    test(model, loss_fn, test_dataset)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # Tensorflow示例:SSD在COCO数据集上进行目标检测
    import tensorflow as tf
    
    # 下载模型和权重
    model = tf.keras.applications.ssd7.... 
    
    # 加载和预处理数据
    img = tf.io.decode_image(img_path, channels=3)
    img = tf.expand_dims(img, 0)
    
    # 执行预测并可视化结果
    output = model(img)
    bboxes, scores, classes = output[:3]
    DetectorViz.draw_bboxes(img, bboxes[0], scores[0], classes[0])
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # PyTorch示例:用LSTM对IMDB数据集进行文本分类
    from torchtext import data, datasets 
    
    # 加载数据并构建词典
    TEXT = data.Field(tokenize='spacy')
    LABEL = data.LabelField(dtype=torch.float)
    train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
    TEXT.build_vocab(train_data, max_size=25000)  
    
    # 定义LSTM模型 
    import torch.nn as nn
    
    class LSTMClassifier(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, output_dim, n_layers):
        ...
    
    def forward(self, text):
        ...
    
    # 训练
    model = LSTMClassifier(vocab_size, 200, 128, 1, 2)
    optimizer = torch.optim.Adam(model.parameters())
    train_model(model, optimizer, train_iter, epoch)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # Tensorflow+HuggingFace示例:使用BERT对CoNLL2003进行NER任务
    import tensorflow as tf
    from transformers import BertTokenizer, TFBertForTokenClassification
    
    # 载入模型和标记器
    model = TFBertForTokenClassification.from_pretrained("bert-base-cased")
    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
    
    # 对句子进行预测
    text = "My name is Wolff. I live in Berlin"
    input_ids = tokenizer.encode(text, return_tensors="tf")
    output = model(input_ids)[0]
    predicted_ner = tf.math.argmax(output, axis=-1).numpy()
    
    # 解码输出
    label_list = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
    pred_tags = [label_list[i] for i in predicted_ner[0][1:-1]]
    print(pred_tags)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # Tensorflow示例:DCGAN在MNIST数据集上生成手写数字图像
    import tensorflow as tf
    
    # 定义生成器和判别器
    generator = make_generator(noise_dim=100)
    discriminator = make_discriminator()
    
    # 准备输入
    noise = tf.random.normal((1, 100))
    
    # 生成
    generated_image = generator(noise, training=False)
    
    plt.imshow(generated_image[0,...])
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # PyTorch示例:pix2pix将草图转换为真实图像
    import torch 
    from models import GeneratorUNet, PatchDiscriminator
    
    # 定义模型
    gen = GeneratorUNet(input_channels=3)    
    disc = PatchDiscriminator(input_channels=6)
    
    # 载入数据并训练gan     
    for epoch in range(opt.n_epochs):
    for i, batch in enumerate(dataloader):
        real = batch['image'].to(device)
        sketch = batch['sketch'].to(device)
    
        gen_image = gen(sketch)
        patch_real = disc(torch.cat((real, sketch), dim=1))
        patch_fake = disc(torch.cat((gen_image.detach(), sketch), dim=1))
    
        # 反向传播与优化
        optimizerD.zero_grad()
        loss_D = criterion(patch_real, valid) + criterion(patch_fake, fake)  
        loss_D.backward()
    
        # 同理更新生成器
        optimizerG.zero_grad()
        loss_G = criterion(patch_fake, valid)
        loss_G.backward()
    
        optimizerD.step()
        optimizerG.step()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    AI写代码