numpy.random.shuffle打乱顺序函数

numpy.random.shuffle

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：

1. def gen_data(source):

2. while True:

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

4. random.shuffle(indices)

5. for i in indices:

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

7. label = source.labels[i]

8. 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.

Parameters:	x : array_like The array or list to be shuffled.
Returns:	None

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:

参考：·[1] https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.shuffle.html#numpy-random-shuffle

            [2] https://github.com/ethereon/caffe-tensorflow/blob/master/examples/mnist/finetune_mnist.py

https://blog.csdn.net/jasonzzj/article/details/53932645?utm_source=copy