代碼：
import sys, os
sys.path.append(os.pardir) # 為了匯入父目錄的檔案而進行的設定
import numpy as np
from dataset.mnist import load_mnist ####報錯此行有問題####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) # 把影像的形狀變為原來的尺寸
print(img.shape) # (28, 28)
img_show(img)
uj5u.com熱心網友回復：
不知道你是不是已經解決了這個問題
這里的dataset不是你指的dataset1.1.2，指的是隨書（深度學習入門）代碼中給的dataset檔案目錄下的minist.py。
下載地址為https://www.ituring.com.cn/book/1921
我win7下還是報錯，我把
sys.path.append(os.pardir)做了修改才運行成功
uj5u.com熱心網友回復：
@enike，你是如何修改的uj5u.com熱心網友回復：
我知道了，是因為加載路徑的原因，如果你的mnist_show.py 與mnist.pkl 在同一級別路徑下，那么改為from mnist import load_mnistuj5u.com熱心網友回復：
呼叫這個 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):
"""讀入MNIST資料集
Parameters
----------
normalize : 將影像的像素值正規化為0.0~1.0
one_hot_label :
one_hot_label為True的情況下，標簽作為one-hot陣列回傳
one-hot陣列是指[0,0,1,0,0,0,0,0,0,0]這樣的陣列
flatten : 是否將影像展開為一維陣列
Returns
-------
(訓練影像, 訓練標簽), (測驗影像, 測驗標簽)
"""
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()
uj5u.com熱心網友回復：
請問朋友是怎么修改的？
uj5u.com熱心網友回復：
我也是遇到這個問題，大家目錄不要有中文路徑。然后修改sys.path.append(os.pardir)為：
sys.path.append(os.getcwd())
完美解決！
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