mikevoets
commented
5 years ago To create an ensemble, you will need to train a couple of models and save the models to files, for example:
For the first model: python train.py -sm=./tmp/model-1
Second model: python train.py -sm=./tmp/model-2
And so forth...
Then you can evaluate a single model, for example model-1 on Messidor-2 with: python evaluate.py -m2 -lm=./tmp/model-1.
To create an ensemble network (re integrated network) with all your trained models, use the comma delimiter in the -lm parameter: python evaluate.py -m2 -lm=./tmp/model-1,./tmp/model-2.
Then the output from the evaluate.py script should contain something like this:
Numpy version: 1.16.4
Tensorflow version: 1.13.1
Evaluating: ./data/eyepacs/bin2/test,
Saving operating thresholds metrics at: ./tmp/test_op_pts.csv,
Using operating treshold: 0.5,
Trying to load model(s):
./tmp/model-1
./tmp/model-2
...Hope this helps you!
mrzhangzizhen123
I don't understand what needs to be done to prepare for the integrated network. Could you explain it in detail?
To create an ensemble of networks and evaluate the linear average of predictions, use the -lm parameter. To specify multiple models to evaluate as an ensemble, the model paths should be comma-separated or satisfy a regular expression. For example: -lm=./tmp/model-1,./tmp/model-2,./tmp/model-3 or -lm=./tmp/model-?.