Question

您好我正在尝试测试以下Seq2Seq模型以获取Chatbot，我正在关注本教程：

http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/

这是主要代码：

https://github.com/suriyadeepan/easy_seq2seq

我遇到的问题是在trainnig时间，在下载适当的语料库后，我运行以下代码进行训练：

python execute.py

按照存储库说明，模型开始训练，这是输出，主要问题是我的计算机已经计算了大约2天9小时的结果，使用所有处理器，我的计算机的规格如下：

 Processors: Intel® Core™ i7-6600U CPU @ 2.60GHz × 4 

 Ram: 15.3 GiB

考虑到这些事实，我想感谢那些训练过这个模型的人的反馈，如果我有一种错误，或者它是否正常，因为这是一个非常复杂的模型，除了知道我的计算机是否能够为了计算这些数据，下面是我得到的输出，非常感谢支持，

python3 execute.py

>> Mode : train

Preparing data in working_dir/
Creating 3 layers of 256 units.
Created model with fresh parameters.

Reading development and training data (limit: 0).
global step 300 learning rate 0.5000 step-time 2.58 perplexity 64.59
  eval: bucket 0 perplexity 75.38
  eval: bucket 1 perplexity 56.04
  eval: bucket 2 perplexity 110.91
  eval: bucket 3 perplexity 92.75
global step 600 learning rate 0.5000 step-time 2.22 perplexity 8.04
  eval: bucket 0 perplexity 186.31
  eval: bucket 1 perplexity 348.11
  eval: bucket 2 perplexity 286.05
  eval: bucket 3 perplexity 246.06
global step 900 learning rate 0.5000 step-time 2.43 perplexity 2.22
  eval: bucket 0 perplexity 353.47
  eval: bucket 1 perplexity 851.75
  eval: bucket 2 perplexity 1252.18
  eval: bucket 3 perplexity 1092.34
global step 1200 learning rate 0.5000 step-time 2.51 perplexity 1.27
  eval: bucket 0 perplexity 2439.94
  eval: bucket 1 perplexity 4914.90
  eval: bucket 2 perplexity 4302.70
  eval: bucket 3 perplexity 4757.61
global step 1500 learning rate 0.5000 step-time 2.81 perplexity 1.11
  eval: bucket 0 perplexity 8616.23
  eval: bucket 1 perplexity 5605.63
  eval: bucket 2 perplexity 7266.88
  eval: bucket 3 perplexity 18350.05
global step 1800 learning rate 0.5000 step-time 2.77 perplexity 1.10
  eval: bucket 0 perplexity 5445.96
  eval: bucket 1 perplexity 23896.49
  eval: bucket 2 perplexity 34089.69
  eval: bucket 3 perplexity 18601.78
global step 2100 learning rate 0.5000 step-time 2.66 perplexity 1.01
  eval: bucket 0 perplexity 13390.66
  eval: bucket 1 perplexity 14239.79
  eval: bucket 2 perplexity 62781.50
  eval: bucket 3 perplexity 66383.43
global step 2400 learning rate 0.5000 step-time 2.75 perplexity 1.01
  eval: bucket 0 perplexity 16025.10
  eval: bucket 1 perplexity 19353.18
  eval: bucket 2 perplexity 50499.01
  eval: bucket 3 perplexity 22968.12
global step 2700 learning rate 0.5000 step-time 2.75 perplexity 1.15
  eval: bucket 0 perplexity 9214.54
  eval: bucket 1 perplexity 9529.81
  eval: bucket 2 perplexity 19052.16
  eval: bucket 3 perplexity 12740.78
global step 3000 learning rate 0.4950 step-time 2.54 perplexity 1.03
  eval: bucket 0 perplexity 18002.15
  eval: bucket 1 perplexity 48698.23
  eval: bucket 2 perplexity 56023.42
  eval: bucket 3 perplexity 43504.27
global step 3300 learning rate 0.4950 step-time 2.77 perplexity 1.01
  eval: bucket 0 perplexity 11827.19
  eval: bucket 1 perplexity 37759.41
  eval: bucket 2 perplexity 54461.78
  eval: bucket 3 perplexity 25944.24
global step 3600 learning rate 0.4950 step-time 2.43 perplexity 1.01
  eval: bucket 0 perplexity 16221.68
  eval: bucket 1 perplexity 73671.18
  eval: bucket 2 perplexity 284799.78
  eval: bucket 3 perplexity 119904.67
global step 3900 learning rate 0.4950 step-time 1.88 perplexity 1.01
  eval: bucket 0 perplexity 24126.39
  eval: bucket 1 perplexity 65459.55
  eval: bucket 2 perplexity 42027.96
  eval: bucket 3 perplexity 73571.20
global step 4200 learning rate 0.4950 step-time 2.36 perplexity 1.01
  eval: bucket 0 perplexity 69183.19
  eval: bucket 1 perplexity 69995.42
  eval: bucket 2 perplexity 102648.55
  eval: bucket 3 perplexity 139732.95
global step 4500 learning rate 0.4950 step-time 2.34 perplexity 1.01
  eval: bucket 0 perplexity 23524.59
  eval: bucket 1 perplexity 63201.23
  eval: bucket 2 perplexity 143448.13
  eval: bucket 3 perplexity 215924.14
global step 4800 learning rate 0.4950 step-time 2.32 perplexity 1.21
  eval: bucket 0 perplexity 14127.02
  eval: bucket 1 perplexity 22433.28
  eval: bucket 2 perplexity 56531.84
  eval: bucket 3 perplexity 24848.56
global step 5100 learning rate 0.4901 step-time 2.36 perplexity 1.02
  eval: bucket 0 perplexity 17618.08
  eval: bucket 1 perplexity 40156.18
  eval: bucket 2 perplexity 43300.34
  eval: bucket 3 perplexity 58052.43
global step 5400 learning rate 0.4901 step-time 3.02 perplexity 1.00
  eval: bucket 0 perplexity 22818.83
  eval: bucket 1 perplexity 23717.10
  eval: bucket 2 perplexity 170402.32
  eval: bucket 3 perplexity 59760.11
^Z
[1]+  Stopped                 python3 execute.py
adolfo@adolfo-Latitude-E5570:~/Downloads/easy_seq2seq-master$ fg
python3 execute.py
^Z
[1]+  Stopped                 python3 execute.py
adolfo@adolfo-Latitude-E5570:~/Downloads/easy_seq2seq-master$ fg
python3 execute.py
global step 5700 learning rate 0.4901 step-time 13.76 perplexity 1.00
  eval: bucket 0 perplexity 19748.73
  eval: bucket 1 perplexity 62520.70
  eval: bucket 2 perplexity 49733.03
  eval: bucket 3 perplexity 97241.32
global step 6000 learning rate 0.4901 step-time 2.40 perplexity 1.00
  eval: bucket 0 perplexity 22433.97
  eval: bucket 1 perplexity 37075.54
  eval: bucket 2 perplexity 129078.26
  eval: bucket 3 perplexity 115380.06
global step 6300 learning rate 0.4901 step-time 2.15 perplexity 1.00
  eval: bucket 0 perplexity 17475.21
  eval: bucket 1 perplexity 68835.76
  eval: bucket 2 perplexity 67453.78

Answer 1

在CPU上训练深层模型需要永远。如果你计划实际使用深度学习技术，你将不得不获得一个gpu或使用预训练的技术，即便如此我也会推荐一个gpu，因为预测速度要快得多。

以下Seq2Seq模型的预计培训时间是多少？

1 个答案: