在使用机器学习以及深度学习的时，常用的示例是使用mnist数据进行分类，本文简要的实现下mnis数据的导入以及处理，问题来源stackoverflow。

直接上代码了，注释很清楚了：

import cPickle
import gzip
import numpy as np
import matplotlib.pyplot as pltdef load_data():path = '../../data/mnist.pkl.gz'f = gzip.open(path, 'rb')training_data, validation_data, test_data = cPickle.load(f)f.close()X_train, y_train = training_data[0], training_data[1]print X_train.shape, y_train.shape# (50000L, 784L) (50000L,)# get the first image and it's labelimg1_arr, img1_label = X_train[0], y_train[0]print img1_arr.shape, img1_label# (784L,) , 5# reshape first image(1 D vector) to 2D dimension imageimg1_2d = np.reshape(img1_arr, (28, 28))# show itplt.subplot(111)plt.imshow(img1_2d, cmap=plt.get_cmap('gray'))plt.show()

输出结果如下：

对label进行向量化：

def vectorized_result(label):e = np.zeros((10, 1))e[label] = 1.0return eprint vectorized_result(img1_label)
# output as below:
[[ 0.][ 0.][ 0.][ 0.][ 0.][ 1.][ 0.][ 0.][ 0.][ 0.]]

我们也可以使用简单的for循环来将上述的784为输入向量转化为28*28维向量给CNN使用：

def load_data_v2():path = '../../data/mnist.pkl.gz'f = gzip.open(path, 'rb')training_data, validation_data, test_data = cPickle.load(f)f.close()X_train, y_train = training_data[0], training_data[1]print X_train.shape, y_train.shape# (50000L, 784L) (50000L,)X_train = np.array([np.reshape(item, (28, 28)) for item in X_train])y_train = np.array([vectorized_result(item) for item in y_train])print X_train.shape, y_train.shape# (50000L, 28L, 28L) (50000L, 10L, 1L)

来源自己的stack overflow回答。