今天小编就为大家分享一篇TensorFlow dataset.shuffle、batch、repeat的使用详解，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
直接看代码例子，有详细注释！！
import tensorflow as tf import numpy as np d = np.arange(0,60).reshape([6, 10]) # 将array转化为tensor data = 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]: d Out[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。
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