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Number of epochs #11
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Hi, there are two questions.
And...em...how do you like the name |
Thank you very much for the answer! So I need to add more varient data. Understood! Also I think your name is very cute! But I don't know the meaning precisely in Chinese so I can't answer. However in English I guess Holmeyoung = Young Holmes = the young/small detective (The movie) and which I find cute ❤️ |
Hi Mariem, I just have a question as you was able to train your model successfully. Does the test loss starts small and then get bigger and reduced again. The number of epoch is 180 now, and the accuracy doesn't change 0.0000. Actually, I am having hard time training the model. |
Hello, that did happen to me when the text length (text inside the images of the dataset) was variable. The accuracy increased and the loss decreased when I cha'ged it to a fixed length. Holmeyoung said that it has nothing to do with the length, but just try it haha 😅 It may work for you. However, this is just a suggestion, can you tell me the steps you followed? |
Thank you for your suggestions. I generates 20,000 images with text of different length. Then, I created txt file contains images paths and labels. Then, I converted the files to train.lmdb and val.lmdb. Finally, I train the model without making any changes in params.py file, but the accuracy doesn't printed out. So I change the valInterval to 300, and the accuracy start to show. However, the accuracy is 0.0000 most of the times. Now, the number of epoch is 240, and the accuracy still 0.0000 :( |
Now the number of epoch is 310, and the accuracy just changed from 0.00000 to 0.000977. |
I see, change the display interval to 10 and valInterval to 100. Check out issue #3 "cuda gpu" for more explanation by Holmeyoung. Also try my suggestion about generating text with fixed length. But don't worry because it's able to recognize variable lengths later on after the train. Since we work on RNNs it's able to recognize anything with variable lengths. Just try fixed in the train phase. |
Thank you very much. I will make the changes, and hopefully it will work fine. |
Do we have to add more epochs so it's able to recognize better in the demo phase?
I reached 260 and it gave good results but when the number of epochs increases it deviates from the right result and makes wrong guesses. But after that it gets better and later it deviates again.
Does it have to reach 1000 so that it can give the best results and never guesses wrong?
What do you think?
P.S: the program is good. It reached 95% accuracy, but when I want it to learn on noisy images it just takes sometimes to guess good. I shall be patient as you said ^_^
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