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POST
Trains the neural-symbolic system using input-output pairs. The system learns to map inputs to outputs through both neural perception and symbolic program synthesis. Training Process
  1. Neural perception maps inputs to symbol distributions
  2. Parser constructs syntactic structures
  3. Abductive reasoning synthesizes programs that explain the examples
  4. Library learning extracts reusable program components Example
Required scope: models:manage

Request body

NSRTrainRequest
integer
Number of epochs (optional, default 1)
TrainingExampleRequest[]
required
Training examples

Response

NSRTrainResponse
number (double)
required
Final training loss
integer
required
Programs learned during training
integer
required
Successful abductions
integer
required
Total examples processed
integer (int64)
required
Training time in milliseconds

Status codes