fix: wrong scaling on polynomial activation

ora-io · Feb 1, 2023 · 25e5420 · 25e5420
1 parent 4ccaa54
commit 25e5420
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Showing 2 changed files with 38 additions and 26 deletions.
diff --git a/keras2circom/circom.py b/keras2circom/circom.py
@@ -265,8 +265,8 @@ def inject_component(self) -> str:
             for i in range(len(self.outputs[0].shape)):
                 inject_str += '{}for (var i{} = 0; i{} < {}; i{}++) {{\n'.format(
                             ' '*i*4, i, i, self.outputs[0].shape[i], i)
-            inject_str += '{}{}{} = Poly({});\n'.format(' '*(i+1)*4,
-                        self.name, parse_index(self.outputs[0].shape), int(self.weight_scale))
+            inject_str += '{}{}{} = Poly({:.0f});\n'.format(' '*(i+1)*4,
+                        self.name, parse_index(self.outputs[0].shape), round(self.bias_scale**.5))
             inject_str += '}'*len(self.outputs[0].shape)+'\n'
             return inject_str
 
@@ -298,13 +298,13 @@ def to_json(self, weight_scale: float, current_scale: float) -> typing.Dict[str,
         '''convert the component params to json format'''
         self.weight_scale = weight_scale
         self.bias_scale = self.calc_bias_scale(weight_scale, current_scale)
-        print(self.name, self.weight_scale, self.bias_scale)
+        # print(self.name, current_scale, self.weight_scale, self.bias_scale)
 
         json_dict = {}
         for signal in self.inputs:
             if signal.value is not None:
                 if signal.name == 'bias' or signal.name == 'b':
-                    print(signal.value)
+                    # print(signal.value)
                     json_dict.update({f'{self.name}_{signal.name}': list(map('{:.0f}'.format, (signal.value*self.bias_scale).round().flatten().tolist()))})
                 else:
                     json_dict.update({f'{self.name}_{signal.name}': list(map('{:.0f}'.format, (signal.value*self.weight_scale).round().flatten().tolist()))})
@@ -386,7 +386,7 @@ def to_json(self) -> str:
         for component in self.components:
             json_dict.update(component.to_json(weight_scale, current_scale))
             current_scale = component.calc_bias_scale(weight_scale, current_scale)
-            print(component.name, current_scale)
+            # print(component.name, current_scale)
         return json.dumps(json_dict)
 
     def calculate_scale(self) -> float:

diff --git a/test/test.js b/test/test.js
@@ -15,6 +15,12 @@ const exec = require('await-exec');
 const best_practice = require('../models/best_practice.json');
 const alt_model = require('../models/alt_model.json');
 
+function softmax(arr) {
+    return arr.map(function(value,index) { 
+      return Math.exp(value) / arr.map( function(y /*value*/){ return Math.exp(y) } ).reduce( function(a,b){ return a+b })
+    })
+}
+
 describe('keras2circom test', function () {
     this.timeout(100000000);
 
@@ -81,22 +87,24 @@ describe('keras2circom test', function () {
 
             assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
 
-            console.log(best_practice['y']);
-            console.log(witness.slice(1, 11));
-
+            const scale = 1E-51;
 
+            let predicted = [];
+            for (var i=0; i<best_practice['y'].length; i++) {
+                predicted.push(parseFloat(Fr.toString(Fr.e(witness[i+1]))) * scale);
+            }
 
-            // let ape = 0;
-
-            // for (var i=0; i<OUTPUT.out.length; i++) {
-            //     console.log('actual', OUTPUT.out[i], 'predicted', Fr.toString(witness[i+2])*OUTPUT.scale);
-            //     ape += Math.abs((OUTPUT.out[i]-parseInt(Fr.toString(witness[i+2]))*OUTPUT.scale)/OUTPUT.out[i]);
-            // }
+            let ape = 0;
 
-            // const mape = ape/OUTPUT.out.length;
+            for (var i=0; i<best_practice['y'].length; i++) {
+                const actual = best_practice['y'][i];
+                console.log('actual', actual, 'predicted', predicted[i]);
+                ape += Math.abs((predicted[i]-actual)/actual);
+            }
 
-            // console.log('mean absolute % error', mape);
+            const mape = ape/best_practice['y'].length;
 
+            console.log('mean absolute % error', mape);
         });
     });
 
@@ -163,22 +171,26 @@ describe('keras2circom test', function () {
 
             assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
 
-            console.log(alt_model['y']);
-            console.log(witness.slice(1, 11));
-
+            const scale = 1E-60;
 
+            let predicted_raw = [];
+            for (var i=0; i<alt_model['y'].length; i++) {
+                predicted_raw.push(parseFloat(Fr.toString(Fr.e(witness[i+1]))) * scale);
+            }
 
-            // let ape = 0;
+            const predicted = softmax(predicted_raw);
 
-            // for (var i=0; i<OUTPUT.out.length; i++) {
-            //     console.log('actual', OUTPUT.out[i], 'predicted', Fr.toString(witness[i+2])*OUTPUT.scale);
-            //     ape += Math.abs((OUTPUT.out[i]-parseInt(Fr.toString(witness[i+2]))*OUTPUT.scale)/OUTPUT.out[i]);
-            // }
+            let ape = 0;
 
-            // const mape = ape/OUTPUT.out.length;
+            for (var i=0; i<alt_model['y'].length; i++) {
+                const actual = alt_model['y'][i]
+                console.log('actual', actual, 'predicted', predicted[i]);
+                ape += Math.abs((predicted[i]-actual)/actual);
+            }
 
-            // console.log('mean absolute % error', mape);
+            const mape = ape/alt_model['y'].length;
 
+            console.log('mean absolute % error', mape);
         });
     });
 });