What it does
Given a pre-built keras deep learning model and labeled training dataset, this tool works in two modes.
In both modes, besides the performance scores, the true labels and predicted values are outputted, which could be used in generating plots in other tools, machine learning visualization extensions, for example.
Note that since all training and model parameters are accessible and changeable in the Hyperparameter Swapping section, the training and evaluation processes are flexible and transparent.
For metrics, there are two sets of metrics for deep learning training and evaluation, one from the keras model builder and the other from scikit-learn. Keras metrics, if selected, are always evaluated, while the sklearn metrics could be ignored when default is the selection. Please be aware that not every sklearn metric works with deep learning model at current moment. Feel free to file a ticket if an issue is found and contibuting with PRs is always welcomed.
Input
Output