Train the NSR Machine on examples
Trains the neural-symbolic system using input-output pairs. The system learns to map inputs to outputs through both neural perception and symbolic program…
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
Required scope:
- Neural perception maps inputs to symbol distributions
- Parser constructs syntactic structures
- Abductive reasoning synthesizes programs that explain the examples
- Library learning extracts reusable program components Example
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