numpy.random.shuffle打亂順序函式的實現
阿新 • • 發佈:2020-01-09
numpy.random.shuffle
在做將caffe模型和預訓練的引數轉化為tensorflow的模型和預訓練的引數,以便微調,遇到如下函式:
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
之前卑鄙陋寡聞,不知道這個用法,按照字面上的意思是打亂,那麼這裡就應該是讓訓練資料集中的資料打亂順序,然後一個挨著一個地(for i in indices)生成訓練資料對。下面就從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
|
---|---|
Returns: |
None |
舉例
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(多維矩陣中,只對第一維(行)做打亂順序操作):
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:
參考:
[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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