Guava Enabled Recurrent Sequence Classification Networks in Java. bluewave is intended to simplify the configuration and use of neural networks.
blueweave uses a builder pattern to setup the netowrk:
//get the config from the classpath
Config conf = ConfigFactory.load();
//build the network
ClassifierNetwork network = new TimeseriesClassifierNetwork.TimeseriesClassifierNetworkBuilder()
.setNetworkClasses(trainingModel.getNetworkClasses())
.setTrainClassifications(trainingModel.getNetworkClassifications())
.setTrainTable(trainingTable)
.setTestTable(testingTable)
.setConfig(conf,"TimeseriesClassifierNetwork")
.build();
//the classification results as a table
Table<Integer, String, Object> result = network.evaluate();
blueweave uses typesafe to make setting up multi layer networks easier.
{
"TimeseriesClassifierNetwork": {
"layers": [{
"number":0,
"type":"GravesLSTM",
"activation": "tanh",
"nIn": 1,
"nOut": 10
}, {
"number":1,
"type":"RnnOutputLayer",
"lossFunction": "MCXENT",
"activation": "softmax",
"nIn": 10,
"nOut": 5
}],
"optimizationAlgo": {
"type": "STOCHASTIC_GRADIENT_DESCENT",
"iterations": 1
},
"seed": 123,
"learningRate": 0.02,
"gradientNormalization": {
"threshold": 0.45,
"type": "ClipElementWiseAbsoluteValue"
},
"updater": {
"type": "NESTEROVS",
"momentum": 0.85
},
"weightInit": "XAVIER"
}
}
Currently implementations are limited to recurrent series, but more table types and network classifiers are intended. The data for both trainging and testing can be represented.
| Date | Series1 | Series1 |
|---|---|---|
| 2016-04-01 | 0.12 | 0.22 |
| 2016-04-02 | 0.23 | 0.12 |
| 2016-04-03 | 0.46 | 0.10 |
| 2016-04-04 | 0.51 | 0.10 |
| 2016-04-05 | 1.46 | 0.11 |
The training model is used to define the mapping of each series to the possible classification types that will be used to train the network. This is an example of training a network to classify a set of number series.
{
"startDate":"2016-04-01",
"endDate":"2016-08-01",
"networkClasses":[
{"id":0, "name":"cyclic"},
{"id":1, "name":"upward-trend"},
{"id":2, "name":"downward-trend"},
{"id":3, "name":"upward-shift"},
{"id":4, "name":"downward-shift"}
],
"networkClassifications":[
{"name":"PACW", "classId":0},
{"name":"PAG", "classId":0},
{"name":"PAHC", "classId":4},
{"name":"PANW", "classId":2},
{"name":"PATK", "classId":3},
{"name":"PATR", "classId":3},
{"name":"PAY", "classId":4},
{"name":"PAYC", "classId":1},
{"name":"PAYX", "classId":3}
]
}
And to deserialize that model for use by the builder:
//make the training model
SequenceNetworkModel trainingModel = GsonFactory.fromJson(
IOUtils.toString(
TimeseriesClassifierTest.class.getResourceAsStream("/data/01/train/trainModel01.json"), "UTF-8"),
SequenceNetworkModel.class, GsonFactory.Type.DEFAULT);
The data model for both training and testing is simple tables. An example of a serialized model for training could be:
{
"columns": [
"DATE",
"PACW",
"PAG",
"PAHC",
"PANW",
"PATK",
"PAY",
"PAYC",
"PAYX"
],
"data": [
[
"2016-04-01",
37.07,
36.7,
27.39,
161.12,
45.92,
28.2,
35.43,
54.17
],
[
"2016-04-04",
37.11,
35.82,
27.29,
161.59,
45.37,
27.78,
35.8,
53.45
],
[
"2016-04-05",
36.25,
35.68,
26.66,
151.92,
46.05,
27.76,
34.43,
53.11
],
[
"2016-04-06",
36.76,
35.74,
27.26,
158.22,
46.64,
28.23,
35.52,
53.64
]
]
}
And deserializing it is as simple as using the provided factory:
Table<Date, String, Double> trainingTable = GsonFactory.fromJson(
IOUtils.toString( TimeseriesClassifierTest.class.getResourceAsStream(
"/data/01/train/trainTable01.json"), "UTF-8"),
TreeBasedTable.class, GsonFactory.Type.DEFAULT);