如何为自己的数据实现tensorflow的next_batch [英] how to implement tensorflow's next_batch for own data

问题描述

在 tensorflow MNIST教程中， mnist.train.next_batch(100)功能非常方便.我现在正在尝试自己实现一个简单的分类.我将训练数据存储在一个numpy数组中.如何为自己的数据实现类似的功能，以便给我下一批?

In the tensorflow MNIST tutorial the mnist.train.next_batch(100) function comes very handy. I am now trying to implement a simple classification myself. I have my training data in a numpy array. How could I implement a similar function for my own data to give me the next batch?

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
Xtr, Ytr = loadData()
for it in range(1000):
    batch_x = Xtr.next_batch(100)
    batch_y = Ytr.next_batch(100)

推荐答案

您发布的链接说:我们从我们的训练集中获得了100个随机数据点的批量"".在我的示例中，我使用了全局函数(而不是您示例中的方法)，因此语法上会有所不同.

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.

在我的函数中，您需要传递所需的样本数和数据数组.

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

这是正确的代码，可确保样品具有正确的标签:

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

import numpy as np

def 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)

演示运行:

[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]]

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