Caffe 需要打乱数据吗? [英] Does Caffe need data to be shuffled?

问题描述

我使用 C++ 将我的图像数据转换为 caffe db 格式(leveldb、lmdb)作为示例我将此代码用于 imagenet.

I convert my image data to caffe db format (leveldb, lmdb) using C++ as example I use this code for imagenet.

是否需要对数据进行混洗，我可以将我所有的正样本写入数据库，然后将所有负样本写入数据库，例如 00000000111111111，还是需要对数据进行混洗并且标签看起来应该像 010101010110101011010?

Is data need to be shuffled, can I write to db all my positives and then all my negatives like 00000000111111111, or data need to be shuffled and labels should look like 010101010110101011010?

caffe 如何从 DB 中采样数据，它使用大小 = batch_size 的所有数据的随机子集是真的吗?

How caffe sample data from DB, is it true that it use random subset of all data with size = batch_size?

推荐答案

你应该洗牌样本吗? 想想不洗牌的学习过程；caffe 只能看到 0 个样本 - 您希望算法推断出什么?一直简单地预测 0 一切都很酷.如果您在点击第一个 1 之前有足够的 0，caffe 将非常有信心预测总是 0.从这一点开始移动模型将非常困难.
另一方面，如果它不断看到 0 和 1 的混合，它会从一开始学习用于分离示例的有意义的特征.
底线:打乱训练样本非常有利，尤其是在使用基于 SGD 的方法时.

Should you shuffle the samples? Think about the learning process if you don't shuffle; caffe sees only 0 samples - what do you expect the algorithm to deduce? simply predict 0 all the time and everything is cool. If you have plenty of 0 before you hit the first 1 caffe will be very confident in predicting always 0. It will be very difficult to move the model from this point.
On the other hand, if it constantly sees a mix of 0 and 1 it learns from the beginning meaningful features for separating the examples.
Bottom line: it is very advantageous to shuffle the training samples, especially when using SGD-based approaches.

AFAIK，caffe 不会随机采样 batch_size 样本，而是在 batch_size 样本之后顺序遍历输入 DB batch_size.

AFAIK, caffe does not randomly sample batch_size samples, but rather goes sequentially over the input DB batch_size after batch_size samples.

TL;DR
洗牌.

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