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algorithm_config.json
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{
"no sequences": {
},
"triple sentences": {
},
"graph walk sentences": {
"depth": 4,
"max walks": 100
},
"no sgns": {
"embeddings path": ""
},
"triple sentence sgns": {
"embedding size": 300,
"window size": 2,
"initial learning rate": 0.25,
"min count": 5,
"subsampling": 0.00001,
"negative sampling": 15,
"iterations": 5,
"max vocab": 5000000,
"embeddings path": "/home/alex/PycharmProjects/thesis/data/algorithm_io/simple_sentence_class_embeddings-20161107.csv"
},
"graph walk sentence sgns": {
"embedding size": 300,
"window size": 3,
"initial learning rate": 0.25,
"min count": 5,
"subsampling": 0.00001,
"negative sampling": 50,
"iterations": 5,
"max vocab": 5000000,
"embeddings path": "/home/alex/PycharmProjects/thesis/data/algorithm_io/graph_walk_class_embeddings-20161107.csv"
},
"most-common": {
},
"distance-knn (k=5)": {
"neighbors": 5
},
"distance-knn (k=10)": {
"neighbors": 10
},
"distance-knn (k=15)": {
"neighbors": 15
},
"distance-knn (k=20)": {
"neighbors": 20
},
"linear projection (c=1)": {
"clusters": 1,
"sgd iterations": 15
},
"linear projection (c=10)": {
"clusters": 10,
"sgd iterations": 15
},
"linear projection (c=25)": {
"clusters": 25,
"sgd iterations": 15
},
"linear projection (c=50)": {
"clusters": 50,
"sgd iterations": 15
},
"deep neural network (h=4, n=3600)": {
"hidden layers": 4,
"hidden neurons": 3600,
"epochs": 5,
"batch size": 32,
"model path": "data/algorithm_io/dnn_4_3600.h5"
},
"deep neural network (h=8, n=2400)": {
"hidden layers": 8,
"hidden neurons": 2400,
"epochs": 8,
"batch size": 128,
"model path": "data/algorithm_io/dnn_8_2400.h5"
},
"concat neural network (act=relu, net=20, h=2, n=2400)": {
"networks": 20,
"hidden layers": 2,
"hidden neurons": 2400,
"epochs": 8,
"batch size": 64,
"activation": "relu",
"model path": "data/algorithm_io/concatnn_relu_20_2400.h5"
},
"concat neural network (act=linear, net=30, h=2, n=2400)": {
"networks": 30,
"hidden layers": 2,
"hidden neurons": 2400,
"epochs": 8,
"batch size": 64,
"activation": "linear",
"model path": "data/algorithm_io/concatnn_linear_30_2400.h5"
},
"concat neural network (act=relu, net=1, h=3, n=1200)": {
"networks": 10,
"hidden layers": 3,
"hidden neurons": 1200,
"epochs": 8,
"batch size": 64,
"activation": "relu",
"model path": "data/algorithm_io/concatnn_relu_1_3_1200.h5"
},
"concat neural network (act=relu, net=10, h=3, n=1200)": {
"networks": 10,
"hidden layers": 3,
"hidden neurons": 1200,
"epochs": 8,
"batch size": 64,
"activation": "relu",
"model path": "data/algorithm_io/concatnn_relu_10_3_1200.h5"
},
"concat neural network (act=relu, net=20, h=3, n=1200)": {
"networks": 20,
"hidden layers": 3,
"hidden neurons": 1200,
"epochs": 8,
"batch size": 64,
"activation": "relu",
"model path": "data/algorithm_io/concatnn_relu_20_3_1200.h5"
},
"concat neural network (act=relu, net=30, h=3, n=600)": {
"networks": 30,
"hidden layers": 3,
"hidden neurons": 600,
"epochs": 8,
"batch size": 64,
"activation": "relu",
"model path": "data/algorithm_io/concatnn_relu_30_3_600.h5"
},
"concat neural network (act=relu, net=20, h=4, n=1200)": {
"networks": 20,
"hidden layers": 4,
"hidden neurons": 1200,
"epochs": 8,
"batch size": 64,
"activation": "relu",
"model path": "data/algorithm_io/concatnn_relu_20_4_1200.h5"
},
"combinations": {
"baseline": {
"sequences": "no sequences",
"sgns": "no sgns",
"classification": "most-common"
},
"ts+distknn(k=5)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "distance-knn (k=5)"
},
"ts+distknn(k=10)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "distance-knn (k=10)"
},
"ts+distknn(k=15)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "distance-knn (k=15)"
},
"ts+distknn(k=20)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "distance-knn (k=20)"
},
"ts+linproj(c=1)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "linear projection (c=1)"
},
"ts+linproj(c=10)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "linear projection (c=10)"
},
"ts+linproj(c=25)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "linear projection (c=25)"
},
"ts+linproj(c=50)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "linear projection (c=50)"
},
"ts+dnn(h=4,n=3600)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "deep neural network (h=4, n=3600)"
},
"ts+dnn(h=8,n=2400)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "deep neural network (h=8, n=2400)"
},
"ts+concatnn(act=relu,net=20,n=2400)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "concat neural network (act=relu, net=20, h=2, n=2400)"
},
"ts+concatnn(act=relu,net=1,h=3,n=1200)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "concat neural network (act=relu, net=1, h=3, n=1200)"
},
"ts+concatnn(act=relu,net=10,h=3,n=1200)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "concat neural network (act=relu, net=10, h=3, n=1200)"
},
"ts+concatnn(act=relu,net=20,h=3,n=1200)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "concat neural network (act=relu, net=20, h=3, n=1200)"
},
"ts+concatnn(act=relu,net=30,h=3,n=600)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "concat neural network (act=relu, net=30, h=3, n=600)"
},
"ts+concatnn(act=relu,net=20,h=4,n=1200)": {
"sequences": "triple sentences",
"sgns": "triple sentence sgns",
"classification": "concat neural network (act=relu, net=20, h=4, n=1200)"
}
}
}