numpy.random.shuffle打乱顺序函数

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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：

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.

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)>>> arrarray([[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

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