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manifest.json
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{
"title": "TAnoGan",
"description": "Implementation of http://arxiv.org/abs/2008.09567",
"inputDimensionality": "multivariate",
"version": "0.3.0",
"authors": "Md Abul Bashar, Richi Nayak",
"language": "Python",
"type": "Detector",
"mainFile": "algorithm.py",
"learningType": "semi-supervised",
"trainingStep": {
"parameters": [
{
"name": "epochs",
"type": "int",
"defaultValue": 1,
"optional": "true",
"description": "Number of training iterations over entire dataset"
},
{
"name": "cuda",
"type": "boolean",
"defaultValue": "false",
"optional": "true",
"description": "Set to `true`, if the GPU-backend (using CUDA) should be used. Otherwise, the algorithm is executed on the CPU."
},
{
"name": "window_size",
"type": "int",
"defaultValue": 30,
"optional": "true",
"description": "Size of the sliding windows"
},
{
"name": "learning_rate",
"type": "float",
"defaultValue": 0.0002,
"optional": "true",
"description": "Learning rate for Adam optimizer"
},
{
"name": "batch_size",
"type": "int",
"defaultValue": 32,
"optional": "true",
"description": "Number of instances trained at the same time"
},
{
"name": "n_jobs",
"type": "int",
"defaultValue": 1,
"optional": "true",
"description": "Number of workers (processes) used to load and preprocess the data"
},
{
"name": "random_state",
"type": "int",
"defaultValue": 42,
"optional": "true",
"description": "Seed for random number generation."
},
{
"name": "early_stopping_patience",
"type": "int",
"defaultValue": 10,
"optional": "true",
"description": "If 1 - (loss / last_loss) is less than `delta` for `patience` epochs, stop"
},
{
"name": "early_stopping_delta",
"type": "float",
"defaultValue": 0.05,
"optional": "true",
"description": "If 1 - (loss / last_loss) is less than `delta` for `patience` epochs, stop"
},
{
"name": "split",
"type": "float",
"defaultValue": 0.8,
"optional": "true",
"description": "Train-validation split for early stopping"
}
],
"modelInput": "none"
},
"executionStep": {
"parameters": [
{
"name": "cuda",
"type": "boolean",
"defaultValue": "false",
"optional": "true",
"description": "Set to `true`, if the GPU-backend (using CUDA) should be used. Otherwise, the algorithm is executed on the CPU."
},
{
"name": "window_size",
"type": "int",
"defaultValue": 30,
"optional": "true",
"description": "Size of the sliding windows"
},
{
"name": "batch_size",
"type": "int",
"defaultValue": 32,
"optional": "true",
"description": "Number of instances trained at the same time"
},
{
"name": "n_jobs",
"type": "int",
"defaultValue": 1,
"optional": "true",
"description": "Number of workers (processes) used to load and preprocess the data"
},
{
"name": "iterations",
"type": "int",
"defaultValue": 25,
"optional": "true",
"description": "Number of test iterations per window"
},
{
"name": "random_state",
"type": "int",
"defaultValue": 42,
"optional": "true",
"description": "Seed for random number generation."
}
],
"modelInput": "required"
}
}