Merge pull request #257 from BrainJS/256-fix-to-function

fix: Resolve `toFunction`

Author: robertleeplummerjr

src/neural-network.js

@@ -885,11 +885,11 @@ export default class NeuralNetwork {
         case 'sigmoid':
           return `1/(1+1/Math.exp(${result.join('')}))`;
         case 'relu':
-          return `var sum = ${result.join('')};(sum < 0 ? 0 : sum);`;
+          return `(${result.join('')} < 0 ? 0 : ${result.join('')})`;
         case 'leaky-relu':
-          return `var sum = ${result.join('')};(sum < 0 ? 0 : 0.01 * sum);`;
+          return `(${result.join('')} < 0 ? 0 : 0.01 * ${result.join('')})`;
         case 'tanh':
-          return `Math.tanh(${result.join('')});`;
+          return `Math.tanh(${result.join('')})`;
         default:
           throw new Error('unknown activation type ' + activation);
       }

test/base/to-function.js

@@ -2,18 +2,68 @@ import assert from 'assert';
 import NeuralNetwork from '../../src/neural-network';
 
 describe('.toFunction()', () => {
-  const originalNet = new NeuralNetwork();
-  const xorTrainingData = [
-    {input: [0, 0], output: [0]},
-    {input: [0, 1], output: [1]},
-    {input: [1, 0], output: [1]},
-    {input: [1, 1], output: [0]}];
-  originalNet.train(xorTrainingData);
-  const xor = originalNet.toFunction();
-  it('runs same as original network', () => {
-    assert.deepEqual(xor([0, 0])[0].toFixed(6), originalNet.run([0, 0])[0].toFixed(6));
-    assert.deepEqual(xor([0, 1])[0].toFixed(6), originalNet.run([0, 1])[0].toFixed(6));
-    assert.deepEqual(xor([1, 0])[0].toFixed(6), originalNet.run([1, 0])[0].toFixed(6));
-    assert.deepEqual(xor([1, 1])[0].toFixed(6), originalNet.run([1, 1])[0].toFixed(6));
+  describe('sigmoid activation', () => {
+    const originalNet = new NeuralNetwork();
+    const xorTrainingData = [
+      {input: [0, 0], output: [0]},
+      {input: [0, 1], output: [1]},
+      {input: [1, 0], output: [1]},
+      {input: [1, 1], output: [0]}];
+    originalNet.train(xorTrainingData);
+    const xor = originalNet.toFunction();
+    it('runs same as original network', () => {
+      assert.deepEqual(xor([0, 0])[0].toFixed(6), originalNet.run([0, 0])[0].toFixed(6));
+      assert.deepEqual(xor([0, 1])[0].toFixed(6), originalNet.run([0, 1])[0].toFixed(6));
+      assert.deepEqual(xor([1, 0])[0].toFixed(6), originalNet.run([1, 0])[0].toFixed(6));
+      assert.deepEqual(xor([1, 1])[0].toFixed(6), originalNet.run([1, 1])[0].toFixed(6));
+    });
+  });
+  describe('relu activation', () => {
+    const originalNet = new NeuralNetwork({ activation: 'relu' });
+    const xorTrainingData = [
+      {input: [0, 0], output: [0]},
+      {input: [0, 1], output: [1]},
+      {input: [1, 0], output: [1]},
+      {input: [1, 1], output: [0]}];
+    originalNet.train(xorTrainingData);
+    const xor = originalNet.toFunction();
+    it('runs same as original network', () => {
+      assert.deepEqual(xor([0, 0])[0].toFixed(6), originalNet.run([0, 0])[0].toFixed(6));
+      assert.deepEqual(xor([0, 1])[0].toFixed(6), originalNet.run([0, 1])[0].toFixed(6));
+      assert.deepEqual(xor([1, 0])[0].toFixed(6), originalNet.run([1, 0])[0].toFixed(6));
+      assert.deepEqual(xor([1, 1])[0].toFixed(6), originalNet.run([1, 1])[0].toFixed(6));
+    });
+  });
+  describe('leaky-relu activation', () => {
+    const originalNet = new NeuralNetwork({ activation: 'leaky-relu' });
+    const xorTrainingData = [
+      {input: [0, 0], output: [0]},
+      {input: [0, 1], output: [1]},
+      {input: [1, 0], output: [1]},
+      {input: [1, 1], output: [0]}];
+    originalNet.train(xorTrainingData);
+    const xor = originalNet.toFunction();
+    it('runs same as original network', () => {
+      assert.deepEqual(xor([0, 0])[0].toFixed(6), originalNet.run([0, 0])[0].toFixed(6));
+      assert.deepEqual(xor([0, 1])[0].toFixed(6), originalNet.run([0, 1])[0].toFixed(6));
+      assert.deepEqual(xor([1, 0])[0].toFixed(6), originalNet.run([1, 0])[0].toFixed(6));
+      assert.deepEqual(xor([1, 1])[0].toFixed(6), originalNet.run([1, 1])[0].toFixed(6));
+    });
+  });
+  describe('tanh activation', () => {
+    const originalNet = new NeuralNetwork({ activation: 'tanh' });
+    const xorTrainingData = [
+      {input: [0, 0], output: [0]},
+      {input: [0, 1], output: [1]},
+      {input: [1, 0], output: [1]},
+      {input: [1, 1], output: [0]}];
+    originalNet.train(xorTrainingData);
+    const xor = originalNet.toFunction();
+    it('runs same as original network', () => {
+      assert.deepEqual(xor([0, 0])[0].toFixed(6), originalNet.run([0, 0])[0].toFixed(6));
+      assert.deepEqual(xor([0, 1])[0].toFixed(6), originalNet.run([0, 1])[0].toFixed(6));
+      assert.deepEqual(xor([1, 0])[0].toFixed(6), originalNet.run([1, 0])[0].toFixed(6));
+      assert.deepEqual(xor([1, 1])[0].toFixed(6), originalNet.run([1, 1])[0].toFixed(6));
+    });
   });
 });