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I couldn't get the weights-improvement-48.hdf5 #22
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@anuragmishracse I‘m sorry to disturb you. After testing, the captions seem to be composed of similar fragments. |
Can you please upload the weights-improvement-48.hdf5 file? The captions generated from the weights-improvement-04.gdf5 is not good. |
I am also Facing same problem. After Training network , just generate |
Sorry, I only get the weights-improvement-8.hdf5 file and I cannot figure out why.
At2018-03-20 20:12:48,Abhishek [email protected]:
I am also Facing same problem. After Training network , just generate
weights-improvement-01.hdf5
weights-improvement-02.hdf5
weights-improvement-03.hdf5
weights-improvement-04.hdf5
weights-improvement-05.hdf5
not further improvement of weights. Can anyone share solution for same?
or Can you please upload the weights-improvement-48.hdf5 file
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@anuragmishracse Can you please see this issue ? |
@suikammd The reason may be because your loss is not reducing and it is not saving further weight files (we can set this in keras during training) |
@sudhakar-sah After changing 1024 batch size, the loss of network is reducing. But the best weight is not good. For example: |
@ShixiangWan I tried this repo few days ago and my loss was not reducing after 2-3 epochs. I am planning to work on this repo. There are few things that we can do for example, we can train a word2vec model on captions and initialize the embedding layer with these weights. are you still working on it ? if you like, we can work on it together ? |
Do you solve this problem? I have same problem and the loss was not reducing. And only saving the model after 3 epochs. How should i do to reduce the loss? |
Do you work with the repo now? I meet many problems , please help me. |
After training, I noticed I just generate weights as follows:
weights-improvement-01.hdf5
weights-improvement-02.hdf5
weights-improvement-03.hdf5
weights-improvement-04.hdf5
Apart from mentioned above, I get no other hdf5 file generated, can anyone tell me what's the problem?
I had checked the address of my training dataset is right and the epoch is 50.
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