combining classification from 2 different NN , on two different datasets
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we have two diffrent datasets to be utilised for 2 different NN (classification type) and decision be made on results of both,eg if one has got defect in eye (retina dataset and iris dataset)
how to implement in the code?
Antworten (1)
Abhishek Gupta
am 22 Dez. 2020
0 Stimmen
Hi,
As per my understanding, you want to train two different Neural Network on two different datasets for the classification problem. This task can be achieved by implementing and training two neural networks separately on the two datasets. After training, you can use these trained networks to make the prediction and then can compare the predictions at the same time.
The following documentation would help you in creating a simple network for classification: -
5 Kommentare
Yogini Prabhu
am 26 Dez. 2020
Abhishek Gupta
am 27 Dez. 2020
I see. One way to get a single prediction would be as follows: -
- Create a network for each dataset to convert your input into a feature vector (let's say of size 10x1 for each dataset).
- Combine the feature vectors from the above two networks into one vector (concatenate the vectors to get 20x1 column vector). This combined vector will contain the input features from the two datasets.
- Use the combined feature vector as your input and train another network to make your final predictions, whose output would be a single value.
I hope this would help!
Yogini Prabhu
am 27 Dez. 2020
Abhishek Gupta
am 27 Dez. 2020
For your problem, I would suggest you use Deep Network Designer instead, which will help you to create a customized network as described above. Here is the documentation link which might help you in getting started: -
Yogini Prabhu
am 28 Dez. 2020
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