import tensorflow as tf
from tensorflow.keras import layers, models

# 构建CNN模型
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 训练模型
model.fit(train_images, train_labels, epochs=10, 
          validation_data=(test_images, test_labels))

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# 加载数据集
iris = load_iris()
X = iris.data

# 构建K-means模型
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

# 可视化聚类结果
plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='viridis')
plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.title('K-means Clustering on Iris Dataset')
plt.show()

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from scipy.stats import uniform

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 定义参数网格
param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf']}

# 网格搜索
grid_search = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)
grid_search.fit(X, y)
print("Best parameters found by grid search:", grid_search.best_params_)

# 随机搜索
param_dist = {'C': uniform(0.1, 100), 'gamma': uniform(0.001, 1), 'kernel': ['rbf']}
random_search = RandomizedSearchCV(SVC(), param_dist, n_iter=100, refit=True, verbose=2)
random_search.fit(X, y)
print("Best parameters found by random search:", random_search.best_params_)

from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical

# 加载CIFAR-10数据集
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0
train_labels, test_labels = to_categorical(train_labels), to_categorical(test_labels)

# 加载VGG16预训练模型
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))

# 冻结预训练模型的层
base_model.trainable = False

# 构建迁移学习模型
model = models.Sequential([
    base_model,
    layers.Flatten(),
    layers.Dense(256, activation='relu'),
    layers.Dense(10, activation='softmax')
])

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

# 训练模型
model.fit(train_images, train_labels, epochs=10, 
          validation_data=(test_images, test_labels))