Sigmoid activation function

Author: generic-github-user

script.js

@@ -59,7 +59,7 @@ if (logData) {
 generator.model.add(tf.layers.dense({units: numParameters, inputShape: [numParameters]}));
 for (var i = 0; i < numLayers; i ++) {
 const layerSize = Math.round(imageVolume / (2 ** ((numLayers - 1) - i)));
-generator.model.add(tf.layers.dense({units: layerSize, activation: "tanh"}));
+generator.model.add(tf.layers.dense({units: layerSize, activation: "sigmoid"}));
 if (logData) {
 console.log(layerSize);
 }
@@ -90,7 +90,7 @@ if (logData) {
 discriminator.model.add(tf.layers.dense({units: imageVolume, inputShape: [imageVolume]}));
 for (var i = 0; i < numLayers; i ++) {
 const layerSize = Math.round(imageVolume / (2 ** (i + 1)));
-discriminator.model.add(tf.layers.dense({units: layerSize, activation: "tanh"}));
+discriminator.model.add(tf.layers.dense({units: layerSize, activation: "sigmoid"}));
 if (logData) {
 console.log(layerSize);
 }
@@ -240,7 +240,7 @@ trainingData.images[trainingData.images.length - 1].onload = function () {
 generatorLoss.dispose();
 discriminatorLoss.dispose();
 
-// generator.optimizer.minimize(generator.calculateLoss);
+generator.optimizer.minimize(generator.calculateLoss);
 discriminator.optimizer.minimize(discriminator.calculateLoss);
 
 // All this is just display code