从零到一的突破过程与高效掌握 AI 的创作技巧

python# 示例：Python 基础语法
def greet(name):
    return f"Hello, {name}!"

print(greet("AI Learner"))

python# 示例：线性回归
from sklearn.linear_model import LinearRegression
import numpy as np

# 数据
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

# 模型训练
model = LinearRegression()
model.fit(X, y)

# 预测
print(model.predict([[5]]))  # 输出: [10]

python# 示例：使用 TensorFlow 构建神经网络
import tensorflow as tf

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 打印模型结构
model.summary()

python# 示例：使用 PyTorch 构建 CNN
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义 CNN 模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 14 * 14, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = x.view(-1, 32 * 14 * 14)
        x = self.fc1(x)
        return x

# 数据加载
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)

# 初始化模型和优化器
model = CNN()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(5):
    for images, labels in train_loader:
        outputs = model(images)
        loss = nn.CrossEntropyLoss()(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

python# 示例：使用 Hugging Face 生成文本
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# 加载预训练模型和分词器
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# 输入文本
input_text = "AI is the future of technology."

# 生成文本
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=50, num_return_sequences=1)

# 解码输出
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)