人工智能应用的实例代码
时间: 2023-07-31 21:05:24 浏览: 77
以下是人工智能应用的实例代码:
1. 图像分类
使用Keras深度学习框架进行图像分类:
```python
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten
from keras.datasets import mnist
from keras.utils import to_categorical
# 加载MNIST数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 数据预处理
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
# 构建模型
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3)
```
2. 自然语言处理
使用Python中的NLTK库进行文本处理:
```python
import nltk
from nltk.tokenize import word_tokenize
text = "Hello, how are you doing today?"
# 分词
tokens = word_tokenize(text)
# 词性标注
pos_tags = nltk.pos_tag(tokens)
print(pos_tags)
```
3. 强化学习
使用Python中的OpenAI Gym库进行强化学习:
```python
import gym
env = gym.make('CartPole-v0')
for i_episode in range(20):
observation = env.reset()
for t in range(100):
env.render()
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
if done:
print("Episode finished after {} timesteps".format(t+1))
break
env.close()
```
以上是人工智能应用的三个实例代码,分别涉及图像分类、自然语言处理和强化学习。