loading configures saved architecture

tf.js

@@ -7,16 +7,15 @@ const inputShape = parseInt(args[0]);
 const outputShape = parseInt(args[1]);
 const hiddenSize = parseInt(args[2]);
 
-// Define a model for linear regression.
+
 var model = tf.sequential();
 
 
 
 model.add(tf.layers.dense({units: hiddenSize, inputShape: [inputShape]}));
 model.add(tf.layers.dense({units: outputShape}));
-model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
 
-// Generate some synthetic data for training.
+
 
 
 var xsArr = [];
@@ -24,6 +23,7 @@ var ysArr = [];
 
 
 maxApi.addHandler("train", (epochs) => {
+  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
 
   // aggregate data
   const xs = tf.tensor2d(xsArr, [xsArr.length, inputShape]);
@@ -61,26 +61,34 @@ async function getWeights() {
 maxApi.addHandler("save", (dictId, key) => {
   maxApi.getDict(dictId).then((dict) => {
     getWeights().then((weights) => {
-      dict[key] = weights;
+      dict[key] = {}
+      dict[key].weights = weights;
+      dict[key].model = model.toJSON(null, false);
       maxApi.setDict(dictId, dict);
     });
   });
 });
 
+
 maxApi.addHandler("load", (dictId, key) => {
   maxApi.getDict(dictId).then((dict) => {
-    let data = dict[key];
+    let arch = dict[key].model;
-    let tensors = [];
+    tf.models.modelFromJSON(dict[key].model).then((m) => {
-    data.forEach(item => {
+        model = m
-      let shape = item.shape;
-      let vals = [];
-      for (const [key, value] of Object.entries(item.data)) {
-        vals.push(value);
 
-      }
+        let data = dict[key].weights;
-      tensors.push(tf.tensor(vals, shape));
+        let tensors = [];
-    });
+        data.forEach(item => {
-    model.setWeights(tensors);
+          let shape = item.shape;
+          let vals = [];
+          for (const [key, value] of Object.entries(item.data)) {
+            vals.push(value);
+
+          }
+          tensors.push(tf.tensor(vals, shape));
+        });
+        model.setWeights(tensors);
+      });
   });
 });