用户提问: ai代码大全
Ai回答: AI代码大全是一个非常广泛的话题，因为人工智能（AI）涵盖了众多领域和技术。以下是一些常见的AI技术及其代码示例，包括机器学习、深度学习、自然语言处理（NLP）、计算机视觉等。这些示例使用Python和流行的库如TensorFlow、PyTorch、scikit-learn等。
1、机器学习：线性回归
python
from sklearn.linear_model import LinearRegression
import numpy as np
创建数据集
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 2, 3, 4, 5])
创建模型
model = LinearRegression()
训练模型
model.fit(X, y)
预测
predictions = model.predict(np.array([[6]]))
print(predictions)
2、深度学习：简单的神经网络
python
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
创建数据集
X = [[0, 0], [0, 1], [1, 0], [1, 1]]
y = [[0], [1], [1], [0]]
创建模型
model = Sequential()
model.add(Dense(2, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
训练模型
model.fit(X, y, epochs=1000, verbose=0)
预测
predictions = model.predict(X)
print(predictions)
3、自然语言处理（NLP）：情感分析
python
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
示例文本数据
sentences = [
I love machine learning,
This is a great project,
I hate this,
This is terrible
]
labels = [1, 1, 0, 0] 1 表示正面情感，0 表示负面情感
文本预处理
tokenizer = Tokenizer(num_words=100, oov_token=<OOV>)
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
padded_sequences = pad_sequences(sequences, maxlen=10)
构建模型
model = Sequential([
Embedding(100, 16, input_length=10),
LSTM(32),
Dense(1, activation='sigmoid')
])
编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
训练模型
model.fit(padded_sequences, np.array(labels), epochs=100, verbose=0)
测试
test_sentence = [I really love this]
test_seq = tokenizer.texts_to_sequences(test_sentence)
test_padded = pad_sequences(test_seq, maxlen=10)
prediction = model.predict(test_padded)
print(prediction)
4、计算机视觉：图像分类
python
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
假设我们有一个简单的图像分类任务
使用CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
归一化数据
x_train = x_train / 255.0
x_test = x_test / 255.0
创建模型
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
编译模型
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
训练模型
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))
评估模型
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(fTest accuracy: {test_acc})
5、强化学习：Q-Learning
python
import numpy as np
定义环境
class SimpleEnv:
def __init__(self):
self.state = 0
self.actions = [0, 1]
def step(self, action):
if action == 0:
reward = 1
next_state = self.state + 1
else:
reward = -1
next_state = self.state 1
self.state = next_state
return next_state, reward
def reset(self):
self.state = 0
return self.state
Q-Learning
class QLearningAgent:
def __init__(self, actions, epsilon=0.1, alpha=0.1, gamma=0.9):
self.q_table = {}
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
self.actions = actions
def get_q_value(self, state, action):
if state not in self.q_table:
self.q_table[state] = {a: 0 for a in self.actions}
return self.q_table[state][action]
def choose_action(self, state):
if np.random.uniform(0, 1) < self.epsilon:
action = np.random.choice(self.actions)
else:
q_values = [self.get_q_value(state, a) for a in self.actions]
action = self.actions[np.argmax(q_values)]
return action
def learn(self, state, action, reward, next_state):
q_value = self.get_q_value(state, action)
next_q_value = max([self.get_q_value(next_state, a) for a in self.actions])
new_q_value = q_value + self.alpha * (reward + self.gamma * next_q_value q_value)
self.q_table[state][action] = new_q_value
运行Q-Learning
env = SimpleEnv()
agent = QLearningAgent(actions=[0, 1])
for episode in range(100):
state = env.reset()
done = False
while not done:
action = agent.choose_action(str(state))
next_state, reward = env.step(action)
agent.learn(str(state), action, reward, str(next_state))
state = next_state
if abs(next_state) > 5:
done = True
总结
以上是几种常见AI技术的代码示例。每个示例都展示了如何使用Python和相关库来实现不同的AI任务。根据你的具体需求，可以选择适合的技术和工具。如果你有特定的需求或问题，欢迎进一步提问！
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提问时间: 2025-04-22 00:06:58
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