What is the best way to merge two different neural networks?

What is the best way to merge two different neural networks?

There are three ways I can think of, depending on your requirement. Have the two neural networks independent and train them separately, but combine the output just like ensemble model. Have the two networks separate until some points on the networks and make a combination layer somewhere before outfits layer.

What’s the best way to train a neural network?

A solution to the high variance of neural networks is to train multiple models and combine their predictions. The idea is to combine the predictions from multiple good but different models. A good model has skill, meaning that its predictions are better than random chance.

How are neural networks used in ensemble learning?

This stochastic nature of the learning algorithm means that each time a neural network model is trained, it may learn a slightly (or dramatically) different version of the mapping function from inputs to outputs, that in turn will have different performance on the training and holdout datasets.

How long does it take to train a deep learning neural network?

Very deep networks trained on millions of examples may take days, weeks, and sometimes months to train. Google’s baseline model […] was a deep convolutional neural network […] that had been trained for about six months using asynchronous stochastic gradient descent on a large number of cores.

How to make a neural network like ensemble model?

Have the two neural networks independent and train them separately, but combine the output just like ensemble model. Have the two networks separate until some points on the networks and make a combination layer somewhere before outfits layer. Make a brand new neural network using logics and algorithms of the two neural networks.

Which is the best way to merge models?

Don’t merge models, merge datasets and retrain : this is in my opinion the most reliable solution, models are fitted from a dataset which represent a certain distribution of data and features. If you can retrain : retrain, especially if datasets are differents.