以下 Seq2Seq 模型的预期训练时间是多少？
What is the expected time of training for the following Seq2Seq model?
您好，我正在尝试测试以下 Seq2Seq 模型以获得聊天机器人，我正在学习本教程：
http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/
这是主要代码：
https://github.com/suriyadeepan/easy_seq2seq
我遇到的问题是在训练时间，下载合适的语料库后我运行使用以下代码进行训练：
python execute.py
按照存储库说明，模型开始训练，这是输出，主要问题是我的电脑已经计算了大约2天9小时的结果，使用了所有处理器，规格我的电脑如下：
Processors: Intel® Core™ i7-6600U CPU @ 2.60GHz × 4
Ram: 15.3 GiB
根据这个事实，我想感谢训练过这个模型的人的反馈，如果我有某种错误或者它是否正常，因为它是一个非常复杂的模型，除了知道我的计算机是否会能够计算这些数据，下面是我得到的输出，非常感谢您的支持，
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
在 CPU 上训练深度模型需要很长时间。如果你打算实际使用深度学习技术，你将必须获得一个 gpu 或使用一个预训练的，即使那样我也会推荐一个 gpu，因为预测速度要快得多。
您好，我正在尝试测试以下 Seq2Seq 模型以获得聊天机器人，我正在学习本教程：
http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/
这是主要代码：
https://github.com/suriyadeepan/easy_seq2seq
我遇到的问题是在训练时间，下载合适的语料库后我运行使用以下代码进行训练：
python execute.py
按照存储库说明，模型开始训练，这是输出，主要问题是我的电脑已经计算了大约2天9小时的结果，使用了所有处理器，规格我的电脑如下：
Processors: Intel® Core™ i7-6600U CPU @ 2.60GHz × 4
Ram: 15.3 GiB
根据这个事实，我想感谢训练过这个模型的人的反馈，如果我有某种错误或者它是否正常，因为它是一个非常复杂的模型，除了知道我的计算机是否会能够计算这些数据，下面是我得到的输出，非常感谢您的支持，
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
在 CPU 上训练深度模型需要很长时间。如果你打算实际使用深度学习技术，你将必须获得一个 gpu 或使用一个预训练的，即使那样我也会推荐一个 gpu，因为预测速度要快得多。