用python写基于svm算法对cifar-100数据集分类的程序
时间: 2023-06-20 09:05:40 浏览: 61
好的,下面是一个基于SVM算法对CIFAR-100数据集进行分类的Python程序:
首先,需要安装必要的库:
```
pip install numpy scipy scikit-learn
```
然后,加载CIFAR-100数据集,使用sklearn中的`fetch_openml`函数:
```python
from sklearn.datasets import fetch_openml
# Load CIFAR-100 dataset
cifar100 = fetch_openml('CIFAR_100', version=1)
X = cifar100.data
y = cifar100.target
```
接下来,将数据集划分为训练集和测试集:
```python
from sklearn.model_selection import train_test_split
# Split dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
然后,使用SVM算法进行训练和预测:
```python
from sklearn.svm import SVC
# Create SVM classifier object and fit it to the training data
svm = SVC(kernel='linear', C=1, random_state=42)
svm.fit(X_train, y_train)
# Predict labels for the test set
y_pred = svm.predict(X_test)
```
最后,评估分类器的准确性:
```python
from sklearn.metrics import accuracy_score
# Calculate accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
完整的代码如下:
```python
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# Load CIFAR-100 dataset
cifar100 = fetch_openml('CIFAR_100', version=1)
X = cifar100.data
y = cifar100.target
# Split dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create SVM classifier object and fit it to the training data
svm = SVC(kernel='linear', C=1, random_state=42)
svm.fit(X_train, y_train)
# Predict labels for the test set
y_pred = svm.predict(X_test)
# Calculate accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
注意:由于CIFAR-100数据集比较大,需要一定的时间来训练分类器。您可以尝试使用不同的SVM内核和参数来提高分类器的准确性。