TensorFlow dataset.shuffle、batch、repeat的使用详解

linux  /  管理员 发布于 7年前   209

直接看代码例子，有详细注释！！

import tensorflow as tfimport numpy as npd = np.arange(0,60).reshape([6, 10])# 将array转化为tensordata = tf.data.Dataset.from_tensor_slices(d)# 从data数据集中按顺序抽取buffer_size个样本放在buffer中，然后打乱buffer中的样本# buffer中样本个数不足buffer_size，继续从data数据集中安顺序填充至buffer_size，# 此时会再次打乱data = data.shuffle(buffer_size=3)# 每次从buffer中抽取4个样本data = data.batch(4)# 将data数据集重复，其实就是2个epoch数据集data = data.repeat(2)# 构造获取数据的迭代器iters = data.make_one_shot_iterator()# 每次从迭代器中获取一批数据batch = iters.get_next()sess = tf.Session()sess.run(batch)# 数据集完成遍历完之后，继续抽取的话会报错：OutOfRangeError

In [21]: dOut[21]: array([[ 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]])In [22]: sess.run(batch)Out[22]: array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],  [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],  [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],  [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])In [23]: sess.run(batch)Out[23]: array([[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],  [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]])

从输出结果可以看出：

shuffle是按顺序将数据放入buffer里面的；

当repeat函数在shuffle之后的话，是将一个epoch的数据集抽取完毕，再进行下一个epoch的。

那么，当repeat函数在shuffle之前会怎么样呢？如下：

data = data.repeat(2)data = data.shuffle(buffer_size=3)data = data.batch(4)

In [25]: sess.run(batch)Out[25]: array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],  [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],  [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],  [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])In [26]: sess.run(batch)Out[26]: array([[50, 51, 52, 53, 54, 55, 56, 57, 58, 59],  [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],  [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],  [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]])In [27]: sess.run(batch)Out[27]: array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],  [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],  [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],  [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])

可以看出，其实它就是先将数据集复制一遍，然后把两个epoch当成同一个新的数据集，一直shuffle和batch下去。

以上这篇TensorFlow dataset.shuffle、batch、repeat的使用详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。