AI 写作发展趋势与展望

AI 写作发展趋势与展望 | 极客日志

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.data import Field, BucketIterator
from torchtext.datasets import WikiText2

# 定义数据处理
TEXT = Field(tokenize='spacy', lower=True)
train_data, valid_data, test_data = WikiText2.splits(TEXT)
TEXT.build_vocab(train_data)

# 模型定义（简单的 Transformer 解码器结构示例）
class TransformerDecoder(nn.Module):
    def __init__(self, vocab_size, embedding_dim, heads, layers, dropout):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.transformer_layers = nn.ModuleList([
            nn.TransformerDecoderLayer(embedding_dim, heads, dropout=dropout)
            for _ in range(layers)
        ])
        self.fc = nn.Linear(embedding_dim, vocab_size)

    def forward(self, x, memory):
        x = self.embedding(x)
        for layer in self.transformer_layers:
            x = layer(x, memory)
        return self.fc(x)

# 训练参数
vocab_size = len(TEXT.vocab)
embedding_dim = 256
heads = 8
layers = 6
dropout = 0.1
learning_rate = 0.0001
epochs = 10

# 实例化模型、优化器和损失函数
model = TransformerDecoder(vocab_size, embedding_dim, heads, layers, dropout)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

# 训练数据迭代器
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
    (train_data, valid_data, test_data), batch_size=32, device='cuda' if torch.cuda.is_available() else 'cpu'
)

# 训练循环
for epoch in range(epochs):
    model.train()
    total_loss = 0
    for i, batch in enumerate(train_iterator):
        optimizer.zero_grad()
        input_seq = batch.text[:, :-1].transpose(0, 1).contiguous()
        target_seq = batch.text[:, 1:].transpose(0, 1).contiguous()
        output = model(input_seq, input_seq)
        loss = criterion(output.view(-1, vocab_size), target_seq.view(-1))
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {total_loss / len(train_iterator)}')

import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

# 示例文本数据（这里可以替换为真实的大量文本数据）
text_data = "The quick brown fox jumps over the lazy dog. The dog barked loudly at the cat."

# 数据预处理
tokenizer = Tokenizer()
tokenizer.fit_on_texts([text_data])
total_words = len(tokenizer.word_index) + 1
input_sequences = []
for line in text_data.split('.'):
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i + 1]
        input_sequences.append(n_gram_sequence)

max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len,))
xs = input_sequences[:, :-1]
ys = input_sequences[:, -1]
ys = tf.keras.utils.to_categorical(ys, num_classes=total_words)

# 构建 LSTM 模型
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(total_words, 100, input_length=max_sequence_len - 1),
    tf.keras.layers.LSTM(150),
    tf.keras.layers.Dense(total_words, activation='softmax')
])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(xs, ys, epochs=500, verbose=1)

# 生成文本
seed_text = "The quick brown"
next_words = 5
for _ in range(next_words):
    token_list = tokenizer.texts_to_sequences([seed_text])[0]
    token_list = pad_sequences([token_list], maxlen=max_sequence_len - 1,)
    predicted = model.predict(token_list, verbose=0)
    predicted_word = np.argmax(predicted)
    for word, index in tokenizer.word_index.items():
        if index == predicted_word:
            output_word = word
            break
    seed_text += " " + output_word
print(seed_text)

import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
import nltk
from torch.nn.utils.rnn import pack_padded_sequence

# 加载预训练的图像分类模型（这里以 ResNet 为例）
resnet = models.resnet152(pretrained=True)
modules = list(resnet.children())[:-1]
resnet = torch.nn.Sequential(*modules)
resnet.eval()

# 图像预处理转换
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.229, 0.229])
])

# 简单的文本处理（这里假设已经有一个词汇表和相应的索引映射）
vocab = {'<start>': 0, '<end>': 1, 'a': 2, 'dog': 3, 'runs': 4, 'in': 5, 'the': 6, 'field': 7}
word_to_idx = vocab
idx_to_word = {v: k for k, v in vocab.items()}

# 定义一个简单的基于 LSTM 的字幕生成模型（只是示例，实际可更复杂）
class CaptionGenerator(nn.Module):
    def __init__(self, embed_size, hidden_size, vocab_size):
        super().__init__()
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.lstm = nn.LSTM(embed_size + 2048, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, vocab_size)

    def forward(self, features, captions):
        embeddings = self.embed(captions)
        embeddings = torch.cat((features.unsqueeze(1).repeat(1, embeddings.size(1), 1), embeddings), dim=2)
        packed_embeddings = pack_padded_sequence(embeddings, [len(caption) for caption in captions], batch_first=True, enforce_sorted=False)
        lstm_out, _ = self.lstm(packed_embeddings)
        outputs = self.fc(lstm_out[0])
        return outputs

# 加载图像并提取特征
image_path = 'example.jpg'
image = Image.open(image_path).convert('RGB')
image_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
    image_features = resnet(image_tensor).squeeze()

# 生成字幕（这里是简单示例，假设初始字幕为<start>）
caption_generator = CaptionGenerator(256, 512, len(vocab))
caption_generator.load_state_dict(torch.load('caption_generator_model.pt'))
caption_generator.eval()
caption = ['<start>']
for _ in range(5):  # 生成 5 个单词的字幕
    caption_tensor = torch.tensor([word_to_idx[word] for word in caption]).unsqueeze(0)
    output = caption_generator(image_features.unsqueeze(0), caption_tensor)
    predicted_word_idx = output.argmax(dim=2)[-1].item()
    predicted_word = idx_to_word[predicted_word_idx]
    caption.append(predicted_word)
    if predicted_word == '<end>':
        break
print(' '.join(caption[1:]))

AI 写作发展趋势与展望

一、AI 写作的崛起之势

二、多模态生成的创新之路

（一）现状与突破

（二）工具与案例

（三）未来展望

三、产业应用的多元场景

（一）教育培训领域

（二）企业服务领域

四、伦理法规的挑战与应对

（一）伦理问题

（二）法规问题

（三）应对策略

五、经典代码案例

基于 Transformer 架构的简单文本生成代码（PyTorch 实现）

使用 TensorFlow 实现的基于 LSTM 的文本生成代码

多模态生成示例（结合图像和文本，以简单的图像字幕生成场景为例，使用 PyTorch 和预训练模型）

六、发展趋势与未来展望

（一）发展趋势

（二）未来展望

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AI 写作发展趋势与展望

一、AI 写作的崛起之势

二、多模态生成的创新之路

（一）现状与突破

（二）工具与案例

（三）未来展望

三、产业应用的多元场景

（一）教育培训领域

（二）企业服务领域

四、伦理法规的挑战与应对

（一）伦理问题

（二）法规问题

（三）应对策略

五、经典代码案例

基于 Transformer 架构的简单文本生成代码（PyTorch 实现）

使用 TensorFlow 实现的基于 LSTM 的文本生成代码

多模态生成示例（结合图像和文本，以简单的图像字幕生成场景为例，使用 PyTorch 和预训练模型）

六、发展趋势与未来展望

（一）发展趋势

（二）未来展望

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