Dataset之MNIST:自定义函数mnist.load_mnist根据网址下载mnist数据集(四个ubyte.gz格式数据集文件)
目录
mnist.py文件
- 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()
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