From dc7ad67cfbde855eadfa912578ada9743fff87ae Mon Sep 17 00:00:00 2001
From: Mirko <mirko.birbaumer@hslu.ch>
Date: Thu, 7 Apr 2022 07:54:02 +0000
Subject: [PATCH] Added comments to GAN's

---
 ..., GAN's, DeepDream, and Neural Style Transfer.ipynb | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/notebooks/Block_7/Jupyter Notebook Block 7 - Generative Models - VAE, GAN's, DeepDream, and Neural Style Transfer.ipynb b/notebooks/Block_7/Jupyter Notebook Block 7 - Generative Models - VAE, GAN's, DeepDream, and Neural Style Transfer.ipynb
index 7a1ba1e..e805bea 100644
--- a/notebooks/Block_7/Jupyter Notebook Block 7 - Generative Models - VAE, GAN's, DeepDream, and Neural Style Transfer.ipynb	
+++ b/notebooks/Block_7/Jupyter Notebook Block 7 - Generative Models - VAE, GAN's, DeepDream, and Neural Style Transfer.ipynb	
@@ -1305,16 +1305,21 @@
     "    \n",
     "    \n",
     "    def train_step(self, real_images):\n",
+    "        # Samples random points in the latent space\n",
     "        batch_size = tf.shape(real_images)[0]\n",
     "        random_latent_vectors = tf.random.normal(\n",
     "        shape=(batch_size, self.latent_dim))\n",
+    "        # Decodes them to fake images\n",
     "        generated_images = self.generator(random_latent_vectors)\n",
+    "        # Combines them with real images\n",
     "        combined_images = tf.concat([generated_images, real_images], axis=0)\n",
+    "        # Assembles labels, discriminating real from fake images\n",
     "        labels = tf.concat(\n",
     "        [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0\n",
     "        )\n",
+    "        # Adds random noise to the labels — an important trick!\n",
     "        labels += 0.05 * tf.random.uniform(tf.shape(labels))\n",
-    "\n",
+    "        # Trains the discriminator\n",
     "        with tf.GradientTape() as tape:\n",
     "            predictions = self.discriminator(combined_images)\n",
     "            d_loss = self.loss_fn(labels, predictions)\n",
@@ -1322,10 +1327,13 @@
     "        self.d_optimizer.apply_gradients( \n",
     "            zip(grads, self.discriminator.trainable_weights)\n",
     "        )\n",
+    "        # Samples random points in the latent space\n",
     "        random_latent_vectors = tf.random.normal(\n",
     "        shape=(batch_size, self.latent_dim))\n",
+    "        # Assembles labels that say “these are all real images” (it’s a lie!)\n",
     "        misleading_labels = tf.zeros((batch_size, 1))\n",
     "\n",
+    "        # Trains the generator\n",
     "        with tf.GradientTape() as tape:\n",
     "            predictions = self.discriminator(\n",
     "            self.generator(random_latent_vectors))\n",
-- 
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