写出python使用PCA降维对cifar-10彩色图像数据集进行分类的代码
时间: 2024-05-11 21:15:52 浏览: 11
好的,以下是使用PCA降维对cifar-10彩色图像数据集进行分类的Python代码:
```python
import numpy as np
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from keras.datasets import cifar10
# Load CIFAR-10 dataset
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
# Reshape and normalize the data
X_train = X_train.reshape(X_train.shape[0], -1)
X_test = X_test.reshape(X_test.shape[0], -1)
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
# Reduce dimensionality with PCA
pca = PCA(n_components=100)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
# Train SVM classifier
clf = SVC(kernel='rbf', C=10, gamma=0.1)
clf.fit(X_train_pca, y_train)
# Evaluate classifier
score = clf.score(X_test_pca, y_test)
print('Accuracy:', score)
```
这段代码首先加载CIFAR-10数据集,然后将图像数据展开为一维向量并进行归一化。接下来,使用PCA将数据降到100维,然后使用SVM训练分类器。 最后,评估分类器的性能并输出准确度。