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# coding: utf-8
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try:
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import urllib.request
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except ImportError:
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raise ImportError('You should use Python 3.x')
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import os.path
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import gzip
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import pickle
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import os
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import numpy as np
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url_base = 'http://yann.lecun.com/exdb/mnist/'
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key_file = {
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'train_img':'train-images-idx3-ubyte.gz',
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'train_label':'train-labels-idx1-ubyte.gz',
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'test_img':'t10k-images-idx3-ubyte.gz',
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'test_label':'t10k-labels-idx1-ubyte.gz'
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}
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dataset_dir = os.path.dirname(os.path.abspath(__file__))
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save_file = dataset_dir + "/mnist.pkl"
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train_num = 60000
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test_num = 10000
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img_dim = (1, 28, 28)
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img_size = 784
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def _download(file_name):
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file_path = dataset_dir + "/" + file_name
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if os.path.exists(file_path):
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return
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print("Downloading " + file_name + " ... ")
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urllib.request.urlretrieve(url_base + file_name, file_path)
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print("Done")
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def download_mnist():
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for v in key_file.values():
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_download(v)
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def _load_label(file_name):
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file_path = dataset_dir + "/" + file_name
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print("Converting " + file_name + " to NumPy Array ...")
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with gzip.open(file_path, 'rb') as f:
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labels = np.frombuffer(f.read(), np.uint8, offset=8)
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print("Done")
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return labels
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def _load_img(file_name):
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file_path = dataset_dir + "/" + file_name
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print("Converting " + file_name + " to NumPy Array ...")
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with gzip.open(file_path, 'rb') as f:
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data = np.frombuffer(f.read(), np.uint8, offset=16)
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data = data.reshape(-1, img_size)
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print("Done")
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return data
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def _convert_numpy():
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dataset = {}
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dataset['train_img'] = _load_img(key_file['train_img'])
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dataset['train_label'] = _load_label(key_file['train_label'])
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dataset['test_img'] = _load_img(key_file['test_img'])
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dataset['test_label'] = _load_label(key_file['test_label'])
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return dataset
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def init_mnist():
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'''
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Note：已将数据集下载至本地，第一次加载会将数据集保存成pickle
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'''
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# download_mnist()
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dataset = _convert_numpy()
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print("Creating pickle file ...")
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with open(save_file, 'wb') as f:
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pickle.dump(dataset, f, -1)
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print("Done!")
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def _change_one_hot_label(X):
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T = np.zeros((X.size, 10))
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for idx, row in enumerate(T):
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row[X[idx]] = 1
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return T
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def load_mnist(normalize=True, flatten=True, one_hot_label=False):
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"""读入MNIST数据集
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Parameters
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----------
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normalize : 将图像的像素值正规化为0.0~1.0
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one_hot_label :
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one_hot_label为True的情况下，标签作为one-hot数组返回
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one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组
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flatten : 是否将图像展开为一维数组
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Returns
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-------
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(训练图像, 训练标签), (测试图像, 测试标签)
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"""
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if not os.path.exists(save_file):
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init_mnist()
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with open(save_file, 'rb') as f:
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dataset = pickle.load(f)
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if normalize:
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for key in ('train_img', 'test_img'):
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dataset[key] = dataset[key].astype(np.float32)
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dataset[key] /= 255.0
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if one_hot_label:
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dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
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dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
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if not flatten:
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for key in ('train_img', 'test_img'):
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dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
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return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label'])
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if __name__ == '__main__':
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init_mnist()
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