All activation functions are set to tanh, except for the output layer of the generator network (relu)

Author: generic-github-user

script.js

@@ -49,7 +49,7 @@ const generator = {
 );
 }
 ),
-"optimizer": tf.train.adam(0.0001)
+"optimizer": tf.train.adam(0.001)
 };
 
 if (logData) {
@@ -59,7 +59,12 @@ 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: "sigmoid"}));
+if (i == numLayers - 1) {
+generator.model.add(tf.layers.dense({units: layerSize, activation: "relu"}));
+}
+else {
+generator.model.add(tf.layers.dense({units: layerSize, activation: "tanh"}));
+}
 if (logData) {
 console.log(layerSize);
 }
@@ -90,7 +95,7 @@ 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: "sigmoid"}));
+discriminator.model.add(tf.layers.dense({units: layerSize, activation: "relu"}));
 if (logData) {
 console.log(layerSize);
 }