代码载入stl10数据集的训练集和测试集
时间: 2024-05-31 08:08:50 浏览: 11
好的,关于载入stl10数据集的训练集和测试集,我可以告诉你,使用C++的STL库中的fstream,可以根据数据集的文件路径打开train_X.bin和train_y.bin,分别读取图片和标签,同理可读取test_X.bin和test_y.bin,然后将数据集划分为训练集和测试集进行训练和测试。希望能对你有所帮助。
相关问题
python代码载入stl10数据集的训练集和测试集
可以使用Python中的NumPy和SciPy库来载入STL-10数据集的训练集和测试集,具体实现可以参考以下代码:
```
import numpy as np
from scipy.io import loadmat
def load_stl10_data():
# Load the data
train_data = loadmat('train.mat')
test_data = loadmat('test.mat')
# Extract the image and label data
X_train = np.transpose(train_data['X'], (3, 0, 1, 2))
y_train = np.squeeze(train_data['y']) - 1
X_test = np.transpose(test_data['X'], (3, 0, 1, 2))
y_test = np.squeeze(test_data['y']) - 1
return X_train, y_train, X_test, y_test
# Usage
X_train, y_train, X_test, y_test = load_stl10_data()
```
这段代码会从train.mat和test.mat文件中载入STL-10数据集的训练集和测试集,然后将其转换为NumPy数组格式,并返回X_train、y_train、X_test和y_test。这些数组包含了图像数据和对应的标签,可以用于训练和测试机器学习模型。
python代码载入stl10数据集的训练集和测试集,并保存为npy文件
可以的,以下是Python代码载入stl10数据集的训练集和测试集,并保存为npy文件的示例:
```
import numpy as np
import os
import urllib.request
import tarfile
from PIL import Image
def download_data():
url = 'http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz'
if not os.path.isfile('stl10_binary.tar.gz'):
urllib.request.urlretrieve(url, 'stl10_binary.tar.gz')
if not os.path.isdir('stl10_binary'):
with tarfile.open('stl10_binary.tar.gz') as tar:
tar.extractall()
def read_data(split):
path = 'stl10_binary'
if split == 'train':
filename = os.path.join(path, 'train_X.bin')
with open(filename, 'rb') as f:
X_train = np.fromfile(f, dtype=np.uint8)
X_train = np.reshape(X_train, (-1,3,96,96))
X_train = np.transpose(X_train, (0,3,2,1))
filename = os.path.join(path, 'train_y.bin')
with open(filename, 'rb') as f:
y_train = np.fromfile(f, dtype=np.uint8)
return X_train, y_train
elif split == 'test':
filename = os.path.join(path, 'test_X.bin')
with open(filename, 'rb') as f:
X_test = np.fromfile(f, dtype=np.uint8)
X_test = np.reshape(X_test, (-1,3,96,96))
X_test = np.transpose(X_test, (0,3,2,1))
filename = os.path.join(path, 'test_y.bin')
with open(filename, 'rb') as f:
y_test = np.fromfile(f, dtype=np.uint8)
return X_test, y_test
def save_data(X, y, filename):
np.save(filename, {'X': X, 'y': y})
if __name__ == '__main__':
download_data()
X_train, y_train = read_data('train')
X_test, y_test = read_data('test')
save_data(X_train, y_train, 'train.npy')
save_data(X_test, y_test, 'test.npy')
```
这段代码会首先下载STL10数据集的压缩文件,然后解压出训练集和测试集的图像和标签,并转换为numpy数组的格式,并将其保存为npy文件。
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