stl10 数据集载入并保存为npy文件
时间: 2024-05-03 17:23:36 浏览: 16
好的,为您解答。您可以使用Python中的numpy库来载入和保存stl10数据集为npy文件。下面是示例代码:
```
import numpy as np
import os
from torchvision import datasets
# 载入stl10数据集
data_path = "/your/stl10/data/path/"
train_dataset = datasets.STL10(data_path, split='train', download=True)
# 将数据集保存为npy文件
train_data = train_dataset.data
train_labels = train_dataset.labels
save_path = "/your/save/path/"
if not os.path.exists(save_path):
os.makedirs(save_path)
np.save(os.path.join(save_path, "train_data.npy"), train_data)
np.save(os.path.join(save_path, "train_labels.npy"), train_labels)
print("数据集已保存为npy文件。")
```
希望能帮到您。
相关问题
将stl10数据集保存为npy文件
可以使用Python中的NumPy库直接将STL10数据集保存为npy文件。可以按照以下步骤操作:
1. 下载STL10数据集,并导入需要的模块和库:
```python
import numpy as np
import os
from skimage import io
```
2. 通过以下代码读取数据:
```python
def read_stl10_data(path):
data = {}
data['X_train'] = read_single_file(os.path.join(path, 'train_X.bin'), np.uint8, (5000, 3, 96, 96))
data['X_test'] = read_single_file(os.path.join(path, 'test_X.bin'), np.uint8, (8000, 3, 96, 96))
data['y_train'] = read_labels(os.path.join(path, 'train_y.bin')) - 1
data['y_test'] = read_labels(os.path.join(path, 'test_y.bin')) - 1
return data
def read_single_file(filename, dtype, shape):
with open(filename, 'rb') as f:
return np.fromfile(f, dtype=dtype).reshape(shape)
def read_labels(filename):
with open(filename, 'rb') as f:
return np.fromfile(f, dtype=np.uint8)
```
3. 将数据集保存为npy文件:
```python
data_path = '/path/to/stl10_data/'
data = read_stl10_data(data_path)
np.save('stl10_data.npy', data)
```
这样,STL10数据集就会被保存为名为“stl10_data.npy”的文件。
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。