how to implement tensorflow's next_batch for own data

The link you posted says: "we get a "batch" of one hundred random data points from our training set". In my example I use a global function (not a method like in your example) so there will be a difference in syntax.

In my function you'll need to pass the number of samples wanted and the data array.

Here is the correct code, which ensures samples have correct labels:

import numpy as npdef next_batch(num, data, labels):    '''    Return a total of `num` random samples and labels.     '''    idx = np.arange(0 , len(data))    np.random.shuffle(idx)    idx = idx[:num]    data_shuffle = [data[ i] for i in idx]    labels_shuffle = [labels[ i] for i in idx]    return np.asarray(data_shuffle), np.asarray(labels_shuffle)Xtr, Ytr = np.arange(0, 10), np.arange(0, 100).reshape(10, 10)print(Xtr)print(Ytr)Xtr, Ytr = next_batch(5, Xtr, Ytr)print('\n5 random samples')print(Xtr)print(Ytr)

And a demo run:

[0 1 2 3 4 5 6 7 8 9][[ 0  1  2  3  4  5  6  7  8  9] [10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24 25 26 27 28 29] [30 31 32 33 34 35 36 37 38 39] [40 41 42 43 44 45 46 47 48 49] [50 51 52 53 54 55 56 57 58 59] [60 61 62 63 64 65 66 67 68 69] [70 71 72 73 74 75 76 77 78 79] [80 81 82 83 84 85 86 87 88 89] [90 91 92 93 94 95 96 97 98 99]]5 random samples[9 1 5 6 7][[90 91 92 93 94 95 96 97 98 99] [10 11 12 13 14 15 16 17 18 19] [50 51 52 53 54 55 56 57 58 59] [60 61 62 63 64 65 66 67 68 69] [70 71 72 73 74 75 76 77 78 79]]

In order to shuffle and sampling each mini-batch, the state whether a sample has been selected inside the current epoch should also be considered. Here is an implementation which use the data in the above answer.

import numpy as np class Dataset:def __init__(self,data):    self._index_in_epoch = 0    self._epochs_completed = 0    self._data = data    self._num_examples = data.shape[0]    pass@propertydef data(self):    return self._datadef next_batch(self,batch_size,shuffle = True):    start = self._index_in_epoch    if start == 0 and self._epochs_completed == 0:        idx = np.arange(0, self._num_examples)  # get all possible indexes        np.random.shuffle(idx)  # shuffle indexe        self._data = self.data[idx]  # get list of `num` random samples    # go to the next batch    if start + batch_size > self._num_examples:        self._epochs_completed += 1        rest_num_examples = self._num_examples - start        data_rest_part = self.data[start:self._num_examples]        idx0 = np.arange(0, self._num_examples)  # get all possible indexes        np.random.shuffle(idx0)  # shuffle indexes        self._data = self.data[idx0]  # get list of `num` random samples        start = 0        self._index_in_epoch = batch_size - rest_num_examples #avoid the case where the #sample != integar times of batch_size        end =  self._index_in_epoch          data_new_part =  self._data[start:end]          return np.concatenate((data_rest_part, data_new_part), axis=0)    else:        self._index_in_epoch += batch_size        end = self._index_in_epoch        return self._data[start:end]dataset = Dataset(np.arange(0, 10))for i in range(10):    print(dataset.next_batch(5))

the output is:

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

the first and second (3rd and 4th,...) mini-batch correspond to one whole epoch..

I use Anaconda and Jupyter.In Jupyter if you run ?mnist you get:File: c:\programdata\anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\base.pyDocstring: Datasets(train, validation, test)

In folder datesets you shall find mnist.py which contains all methods including next_batch.