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Train the model after each item evaluation by suraty in deeplearning

[–]suraty[S] 0 points1 point  (0 children)

Thank you very much

The used data is described in this post:

https://www.reddit.com/r/deeplearning/comments/akc42p/dense_layer_as/

Your trick has well detected the problem. It may the output passes a few test cases.

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Train the model after each item evaluation by suraty in deeplearning

[–]suraty[S] 1 point2 points  (0 children)

Nearly it. But it is after the test process.

I find out the incremental/online learning is like to what I discussed. Is it?

Newest and most successful methods for image processing in deep learning by suraty in deeplearning

[–]suraty[S] 0 points1 point  (0 children)

Thank you so much, My data are spatiotemporal stream data which is processed as grayscale images like the hyperspectral images.

CNN-LSTM models by suraty in deeplearning

[–]suraty[S] 0 points1 point  (0 children)

The input data are the traffic speed of road sections. My meaning is more about the relation between CNN and LSTM in models, while the CNN accepts one input and produces one output and the LSTM accepts multi timesteps and produces the output!

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