nnsum.json

[
  {
    "modelParams": {
      "name": "Main",
      "description": "",
      "steps": 1,
      "method": "optimize"
    },
    "methodParams": {
      "samples": "1",
      "optMethod": "adam",
      "steps": "10000",
      "stepSize": "0.2"
    },
    "blocks": [
      {
        "type": "Data",
        "name": "x",
        "show": false,
        "typeCode": 2,
        "useAsParameter": true,
        "dims": "",
        "value": "[ [2,7], [6,6], [12,8] ]"
      },
      {
        "type": "Data",
        "name": "y",
        "show": false,
        "typeCode": 2,
        "useAsParameter": true,
        "dims": "",
        "value": "9, 12, 20"
      },
      {
        "type": "Data",
        "name": "xpred",
        "show": false,
        "typeCode": 2,
        "useAsParameter": true,
        "dims": "",
        "value": "21, 13"
      },
      {
        "type": "Neural Net",
        "initialValue": 0,
        "history": false,
        "name": "net",
        "typeCode": 6,
        "layers": [
          {
            "type": "affine",
            "name": "layer1",
            "in": "2",
            "out": "1"
          }
        ],
        "convert": true
      },
      {
        "type": "Observer",
        "distribution": "Gaussian",
        "params": {
          "sigma": "0.3",
          "mu": "net(x)"
        },
        "typeCode": 4,
        "value": "y"
      },
      {
        "type": "Expression",
        "name": "ypred",
        "history": false,
        "show": true,
        "typeCode": 1,
        "value": "net(xpred)"
      }
    ]
  }
]