labels_path = os path join (path, %s-labels-idx1-ubyte % kind)
时间: 2023-06-15 19:08:18 浏览: 65
这是一行代码,使用了Python的os库中的path.join()方法,将字符串"path"和"%s-labels-idx1-ubyte % kind"拼接成一个路径。其中,%s和%kind为占位符,需要在后面传入具体的值进行替换。这行代码的作用是生成MNIST数据集中标签文件的路径。具体的解释如下:
- os.path.join()方法可以将多个路径组合成一个完整的路径。它会根据操作系统的不同自动使用不同的路径分隔符。
- "path"是一个字符串,表示MNIST数据集所在的路径。
- "%s-labels-idx1-ubyte % kind"也是一个字符串,其中%s表示占位符,表示后面需要传入一个字符串进行替换;"-labels-idx1-ubyte"是MNIST数据集中标签文件的固定部分;%kind也是占位符,表示后面需要传入一个字符串进行替换,用于表示是训练集还是测试集。
- 最后,os.path.join()方法将"path"和"%s-labels-idx1-ubyte % kind"拼接成一个完整的路径,表示MNIST数据集中标签文件的路径。
相关问题
labels_path = os.path.join(path, '{}-labels.idx1-ubyte' % (kind)) TypeError: not all arguments converted during string formatting
这个错误是因为在字符串格式化时使用了错误的占位符。正确的占位符是`{}`,而不是`%()`。请修改为以下代码:
```
labels_path = os.path.join(path, '{}-labels.idx1-ubyte'.format(kind))
```
或者使用 f-string 格式化字符串:
```
labels_path = os.path.join(path, f'{kind}-labels.idx1-ubyte')
```
import gzip import os import pickle import numpy as np def load_mnist(path, kind='train'): labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind) images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind) with gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as imgpath: images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) return images, labels def get_mnist_data(num_training=5000, num_validation=500, num_test=500): mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录 X_train, y_train = load_mnist(mnist_dir, kind='train') X_test, y_test = load_mnist(mnist_dir, kind='t10k') print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] X_train = X_train.astype('float32') / 255 X_val = X_val.astype('float32') / 255 X_test = X_test.astype('float32') / 255 return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, },这是读取mnist的函数,如何把解包时给定的值数量从两个增加到4个
在这个函数中,load_mnist函数返回了两个值,即images和labels,但是这个函数只使用了一个返回值,所以只需要将这个函数的返回值改为一个元组即可。具体来说,将return images, labels改为return (images, labels)即可。这样get_mnist_data函数就可以使用解包时给定的值数量为4了。修改后的代码如下:
```
import gzip
import os
import pickle
import numpy as np
def load_mnist(path, kind='train'):
labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)
images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)
with gzip.open(labels_path, 'rb') as lbpath:
labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)
with gzip.open(images_path, 'rb') as imgpath:
images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)
return images, labels
def get_mnist_data(num_training=5000, num_validation=500, num_test=500):
mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录
X_train, y_train = load_mnist(mnist_dir, kind='train')
X_test, y_test = load_mnist(mnist_dir, kind='t10k')
print(X_train.shape)
mask = range(num_training, num_training + num_validation)
X_val = X_train[mask]
y_val = y_train[mask]
mask = range(num_training)
X_train = X_train[mask]
y_train = y_train[mask]
mask = range(num_test)
X_test = X_test[mask]
y_test = y_test[mask]
X_train = X_train.astype('float32') / 255
X_val = X_val.astype('float32') / 255
X_test = X_test.astype('float32') / 255
return X_train, y_train, X_val, y_val, X_test, y_test
```
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