TensorFlow dataset.shuffle、batch、repeat的使用详解
程序员文章站
2023-11-24 20:52:22
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).re...
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。
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。
上一篇: css教程 css和document
推荐阅读
-
tensorflow入门:TFRecordDataset变长数据的batch读取详解
-
使用Tensorflow将自己的数据分割成batch训练实例
-
TensorFlow dataset.shuffle、batch、repeat的使用详解
-
浅谈tensorflow中dataset.shuffle和dataset.batch dataset.repeat注意点
-
使用Tensorflow将自己的数据分割成batch训练实例
-
tensorflow中next_batch的具体使用
-
tensorflow入门:TFRecordDataset变长数据的batch读取详解
-
TensorFlow dataset.shuffle、batch、repeat的使用详解
-
Tensorflow的可视化工具Tensorboard的初步使用详解
-
详解Lua中repeat...until循环语句的使用方法