numpy.random.shuffle打乱顺序函数

numpy.random.shuffle打乱顺序函数：在做将caffe模型和预训练的参数转化为tensorflow的模型和预训练的参数，以便微调，遇到如下函数：

def gen_data(source):

while True:

indices = range(len(source.images)) # indices = the number of images in the source data set

random.shuffle(indices)

for i in indices:

image = np.reshape(source.images[i], (28, 28, 1))

label = source.labels[i]

yield image, label

之前卑鄙陋寡闻，不知道这个用法，按照字面上的意思是打乱，那么这里就应该是让训练数据集中的数据打乱顺序，然后一个挨着一个地(for i in indices)生成训练数据对。下面就从docs.scipy.org中查到的random.shuffle的用法：

numpy.random.shuffle(x)

Modify a sequence in-place by shuffling its contents.

举例

python>>>

>>> arr = np.arange(10)

>>> np.random.shuffle(arr)

>>> arr

[1 7 5 2 9 4 3 6 0 8]

This function only shuffles the array along the first index of a multi-dimensional array(多维矩阵中，只对第一维(行)做打乱顺序操作):

python>>>

>>> arr = np.arange(9).reshape((3, 3))

>>> np.random.shuffle(arr)

>>> arr

array([[3, 4, 5],

[6, 7, 8],

[0, 1, 2]])This function only shuffles the array along the first index of a multi-dimensional array: