Python：为什么报错No module named 'dataset. mnist'。已经安装了dataset 1.1.2

[Jack Song]

代码：
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import numpy as np
from dataset.mnist import load_mnist ####报错此行有问题####No module named 'dataset. mnist'
from PIL import Image


def img_show(img):
pil_img = Image.fromarray(np.uint8(img))
pil_img.show()

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)

img = x_train[0]
label = t_train[0]
print(label) # 5

print(img.shape) # (784,)
img = img.reshape(28, 28) # 把图像的形状变为原来的尺寸
print(img.shape) # (28, 28)

img_show(img)

[enike]

不知道你是不是已经解决了这个问题
这里的dataset不是你指的dataset1.1.2，指的是随书（深度学习入门）代码中给的dataset文件目录下的minist.py。
下载地址为https://www.ituring.com.cn/book/1921
我win7下还是报错，我把
sys.path.append(os.pardir)做了修改才运行成功

[歼十出击lt]

@enike，你是如何修改的

[weixin_42323494]

我知道了，是因为加载路径的原因，如果你的mnist_show.py 与mnist.pkl 在同一级别路径下，那么改为from mnist import load_mnist

[身在被窝_心在编程]

调用这个 mnist.py load_mnist 方法

# coding: utf-8
try:
    import urllib.request
except ImportError:
    raise ImportError('You should use Python 3.x')
import os.path
import gzip
import pickle
import os
import numpy as np

url_base = 'http://yann.lecun.com/exdb/mnist/'
key_file = {
    'train_img': 'train-images-idx3-ubyte.gz',
    'train_label': 'train-labels-idx1-ubyte.gz',
    'test_img': 't10k-images-idx3-ubyte.gz',
    'test_label': 't10k-labels-idx1-ubyte.gz'
}

dataset_dir = os.path.dirname(os.path.abspath(__file__))
save_file = dataset_dir + "/mnist.pkl"

train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784


def _download(file_name):
    file_path = dataset_dir + "/" + file_name

    if os.path.exists(file_path):
        return

    print("Downloading " + file_name + " ... ")
    urllib.request.urlretrieve(url_base + file_name, file_path)
    print("Done")


def download_mnist():
    for v in key_file.values():
        _download(v)


def _load_label(file_name):
    file_path = dataset_dir + "/" + file_name

    print("Converting " + file_name + " to NumPy Array ...")
    with gzip.open(file_path, 'rb') as f:
        labels = np.frombuffer(f.read(), np.uint8, offset=8)
    print("Done")

    return labels


def _load_img(file_name):
    file_path = dataset_dir + "/" + file_name

    print("Converting " + file_name + " to NumPy Array ...")
    with gzip.open(file_path, 'rb') as f:
        data = np.frombuffer(f.read(), np.uint8, offset=16)
    data = data.reshape(-1, img_size)
    print("Done")

    return data


def _convert_numpy():
    dataset = {}
    dataset['train_img'] = _load_img(key_file['train_img'])
    dataset['train_label'] = _load_label(key_file['train_label'])
    dataset['test_img'] = _load_img(key_file['test_img'])
    dataset['test_label'] = _load_label(key_file['test_label'])

    return dataset


def init_mnist():
    download_mnist()
    dataset = _convert_numpy()
    print("Creating pickle file ...")
    with open(save_file, 'wb') as f:
        pickle.dump(dataset, f, -1)
    print("Done!")


def _change_one_hot_label(X):
    T = np.zeros((X.size, 10))
    for idx, row in enumerate(T):
        row[X[idx]] = 1

    return T


def load_mnist(normalize=True, flatten=True, one_hot_label=False):
    """读入MNIST数据集

    Parameters
    ----------
    normalize : 将图像的像素值正规化为0.0~1.0
    one_hot_label :
        one_hot_label为True的情况下，标签作为one-hot数组返回
        one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组
    flatten : 是否将图像展开为一维数组

    Returns
    -------
    (训练图像, 训练标签), (测试图像, 测试标签)
    """
    if not os.path.exists(save_file):
        init_mnist()

    with open(save_file, 'rb') as f:
        dataset = pickle.load(f)

    if normalize:
        for key in ('train_img', 'test_img'):
            dataset[key] = dataset[key].astype(np.float32)
            dataset[key] /= 255.0

    if one_hot_label:
        dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
        dataset['test_label'] = _change_one_hot_label(dataset['test_label'])

    if not flatten:
        for key in ('train_img', 'test_img'):
            dataset[key] = dataset[key].reshape(-1, 1, 28, 28)

    return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label'])


if __name__ == '__main__':
    init_mnist()

[YSK111]

引用 1 楼 enike 的回复:

不知道你是不是已经解决了这个问题
这里的dataset不是你指的dataset1.1.2，指的是随书（深度学习入门）代码中给的dataset文件目录下的minist.py。
下载地址为https://www.ituring.com.cn/book/1921
我win7下还是报错，我把
sys.path.append(os.pardir)做了修改才运行成功

请问朋友是怎么修改的？

[不堪沉沦]

我也是遇到这个问题，大家目录不要有中文路径。 然后修改sys.path.append(os.pardir)为： sys.path.append(os.getcwd()) 完美解决！

[Deepsdu]

把mnist和之前的文件和放到新建的dataset文件夹下面，参考https://zhidao.baidu.com/question/695018183155472284.html

[qq_44935207]

你改好了吗请问

[surper_patrick]

直接把common和dataset文件夹拷贝到python的安装路径下面的Lib/site_packages下，就可以了