CODEWITHSUNDEEP
machine-learningkerasneural-networkdeep-learning
Mithun Sarker
Mithun Sarker

Reputation: 4023

How to merge two saved keras model?

Let's say I have a dataset of 2 million. At first, I used only 1 million, trained those and saved the model in h5 format like first.h5. Later I used another 1 million data, trained those using the same algorithm and saved as second.h5. Training requires more than a day , hence I can't use all two million data at once. Is there any way , I can merge those two saved model like first.h5 + second.h5 = merged.h5

Upvotes: 4

Views: 2398

Answers (2)

mujjiga
mujjiga

Reputation: 16916

There is no way you can do that (merge models). Let me put it in simple terms. You train a child named first using some 1 million data to identify if an image is a cat or a dog. Then you trained a second child named second using the other 1 million data to identify if an image is a cat or a dog. Now what you are asking for is to combine the first and second.

However, assume that the training data is IID (independent and identically distributed) then what you can do is create an ensemble of both the models for making predictions.

The simple way to ensemble two models is are

  • Max Voting
  • Averaging
  • Weighted Averaging

Follow this link on how to the ensemble.

Or a simple strategy is to average the final score of both the models and use the averaged score to make the predictions.

A more powerful strategy is to use the validation set to find the weights for the classes and then use these weights for making the final predictions on unseen data.

Upvotes: 3

Ioannis Nasios
Ioannis Nasios

Reputation: 8537

You could merge - average weights - but this will not be the same as training with full dataset.

Usually training with more data leads to better results, to better model.

If you don' t want to train with full dataset i would recommend not to average weights but to use both models for inference and average predictions.

Upvotes: 2

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