diff --git a/exercise.ipynb b/exercise.ipynb
index 8f802ba..41bfda6 100644
--- a/exercise.ipynb
+++ b/exercise.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e998cbda",
+   "id": "b3ddb066",
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     "tags": []
@@ -29,7 +29,7 @@
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@@ -41,7 +41,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b0ad2695",
+   "id": "a9aaf840",
    "metadata": {},
    "source": [
     "\n",
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "774d942d",
+   "id": "3f6c5bc0",
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@@ -68,7 +68,7 @@
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    "cell_type": "markdown",
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@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8e2bfb78",
+   "id": "8c709838",
    "metadata": {},
    "outputs": [],
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@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e368025",
+   "id": "6b04f969",
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@@ -113,7 +113,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b4ba9ba1",
+   "id": "27ea9906",
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@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ecc51041",
+   "id": "3bf64cda",
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     "tags": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "358f92e4",
+   "id": "2ad014ac",
    "metadata": {},
    "source": [
     "# Part 2: Using Integrated Gradients to find what the classifier knows\n",
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23375b54",
+   "id": "e40eeba7",
    "metadata": {},
    "source": [
     "## Attributions through integrated gradients\n",
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0bc95c12",
+   "id": "e7aca710",
    "metadata": {
     "tags": []
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@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce061847",
+   "id": "44d286aa",
    "metadata": {
     "tags": []
    },
@@ -210,7 +210,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3fe7d564",
+   "id": "ec387f82",
    "metadata": {
     "tags": [
      "task"
@@ -231,7 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3b8fada",
+   "id": "8ea56240",
    "metadata": {
     "tags": []
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@@ -244,7 +244,7 @@
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   {
    "cell_type": "markdown",
-   "id": "1749ba9c",
+   "id": "bc50850e",
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     "tags": []
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b11c4963",
+   "id": "e7447933",
    "metadata": {
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@@ -284,7 +284,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4a2e4ba",
+   "id": "5cb527bf",
    "metadata": {
     "tags": []
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@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35dbc255",
+   "id": "25ecec3e",
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@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb45a3b7",
+   "id": "2b43f05d",
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@@ -323,7 +323,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9fe579e8",
+   "id": "a7b21894",
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    "outputs": [],
    "source": [
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
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+   "id": "f97eace2",
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@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68c48063",
+   "id": "ed5b7d6e",
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    "source": [
     "\n",
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4f45692",
+   "id": "bf13ae8d",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\"><h4>Task 2.3: Use random noise as a baseline</h4>\n",
@@ -399,7 +399,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "00a40c0c",
+   "id": "60d6691c",
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      "task"
@@ -419,7 +419,7 @@
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   {
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+   "id": "f24c00a3",
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@@ -433,7 +433,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01485873",
+   "id": "3835de1a",
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@@ -455,7 +455,7 @@
   },
   {
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+   "id": "10a6cfcc",
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@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b0e6145",
+   "id": "25f3d08e",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\"><h2>BONUS Task: Using different attributions.</h2>\n",
@@ -485,7 +485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f67562c",
+   "id": "65d946a8",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-success\"><h2>Checkpoint 2</h2>\n",
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "003fed33",
+   "id": "04602cf9",
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@@ -533,22 +533,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c99e326",
+   "id": "ed173d7c",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
    },
    "source": [
     "### The model\n",
-    "![cycle.png](assets/cyclegan.png)\n",
+    "![stargan.png](assets/stargan.png)\n",
     "\n",
     "In the following, we create a [StarGAN model](https://arxiv.org/abs/1711.09020).\n",
     "It is a Generative Adversarial model that is trained to turn one class of images X into a different class of images Y.\n",
     "\n",
-    "The model is made up of three networks:\n",
+    "We will not be using the random latent code (green, in the figure), so the model we use is made up of three networks:\n",
     "- The generator - this will be the bulk of the model, and will be responsible for transforming the images: we're going to use a `UNet`\n",
     "- The discriminator - this will be responsible for telling the difference between real and fake images: we're going to use a `DenseModel`\n",
-    "- The style mapping - this will be responsible for encoding the style of the image: we're going to use a `DenseModel`\n",
+    "- The style encoder - this will be responsible for encoding the style of the image: we're going to use a `DenseModel`\n",
     "\n",
     "Let's start by creating these!"
    ]
@@ -556,10 +556,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3fa2a39a",
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
+   "id": "03a51bad",
+   "metadata": {},
    "outputs": [],
    "source": [
     "from dlmbl_unet import UNet\n",
@@ -567,10 +565,11 @@
     "\n",
     "\n",
     "class Generator(nn.Module):\n",
-    "    def __init__(self, generator, style_mapping):\n",
+    "\n",
+    "    def __init__(self, generator, style_encoder):\n",
     "        super().__init__()\n",
     "        self.generator = generator\n",
-    "        self.style_mapping = style_mapping\n",
+    "        self.style_encoder = style_encoder\n",
     "\n",
     "    def forward(self, x, y):\n",
     "        \"\"\"\n",
@@ -579,7 +578,7 @@
     "        y: torch.Tensor\n",
     "            The style image\n",
     "        \"\"\"\n",
-    "        style = self.style_mapping(y)\n",
+    "        style = self.style_encoder(y)\n",
     "        # Concatenate the style vector with the input image\n",
     "        style = style.unsqueeze(-1).unsqueeze(-1)\n",
     "        style = style.expand(-1, -1, x.size(2), x.size(3))\n",
@@ -589,7 +588,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11c69ace",
+   "id": "e6c6168d",
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@@ -604,12 +603,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "734e1e36",
+   "id": "9d0ef49f",
    "metadata": {
     "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
+    "style_size = ...  # TODO choose a size for the style space\n",
+    "unet_depth = ...  # TODO Choose a depth for the UNet\n",
     "style_mapping = DenseModel(\n",
     "    input_shape=..., num_classes=...  # How big is the style space?\n",
     ")\n",
@@ -620,7 +621,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74b2fe60",
+   "id": "bd761ef3",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<div class=\"alert alert-block alert-warning\"><h3>Hyper-parameter choices</h3>\n",
+    "<ul>\n",
+    "<li>Are any of the hyperparameters you choose above constrained in some way?</li>\n",
+    "<li>What would happen if you chose a depth of 10 for the UNet?</li>\n",
+    "<li>Is there a minimum size for the style space? Why or why not?</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1220bb6",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
@@ -637,7 +653,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4416d6eb",
+   "id": "71482197",
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@@ -649,7 +665,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b20d0919",
+   "id": "709affba",
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@@ -660,7 +676,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6bc98d13",
+   "id": "7059545e",
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    "outputs": [],
    "source": [
@@ -670,24 +686,89 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2cc4a339",
+   "id": "b1a7581c",
    "metadata": {
+    "lines_to_next_cell": 0,
     "tags": []
    },
    "source": [
     "## Training a GAN\n",
     "\n",
-    "Yes, really!\n",
+    "Training an adversarial network is a bit more complicated than training a classifier.\n",
+    "For starters, we are simultaneously training two different networks that work against each other.\n",
+    "As such, we need to be careful about how and when we update the weights of each network.\n",
+    "\n",
+    "We will have two different optimizers, one for the Generator and one for the Discriminator.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7805887e",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=1e-4)\n",
+    "optimizer_g = torch.optim.Adam(generator.parameters(), lr=1e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bad28d8",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "There are also two different types of losses that we will need.\n",
+    "**Adversarial loss**\n",
+    "This loss describes how well the discriminator can tell the difference between real and generated images.\n",
+    "In our case, this will be a sort of classification loss - we will use Cross Entropy.\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "The adversarial loss will be applied differently to the generator and the discriminator! Be very careful!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a757512e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adverial_loss_fn = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c590737",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "**Cycle/reconstruction loss**\n",
+    "The cycle loss is there to make sure that the generator doesn't output an image that looks nothing like the input!\n",
+    "Indeed, by training the generator to be able to cycle back to the original image, we are making sure that it makes a minimum number of changes.\n",
+    "The cycle loss is applied only to the generator.\n",
     "\n",
-    "TODO about the losses:\n",
-    "- An adversarial loss\n",
-    "- A cycle loss\n",
-    "TODO add exercise!"
+    "cycle_loss_fn = nn.L1Loss()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0def44d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
-   "id": "87761838",
+   "id": "3a0c1d2e",
    "metadata": {
     "lines_to_next_cell": 2,
     "tags": []
@@ -702,16 +783,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcc737d6",
+   "id": "9f577571",
    "metadata": {
+    "lines_to_next_cell": 0,
     "tags": []
    },
    "source": [
     "...this time again.\n",
     "\n",
-    "<img src=\"assets/model_train.jpg\" alt=\"drawing\" width=\"500px\"/>\n",
-    "\n",
-    "TODO also turn this into a standalong script for use during the project phase\n",
+    "<img src=\"assets/model_train.jpg\" alt=\"drawing\" width=\"500px\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3077e49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO also turn this into a standalone script for use during the project phase\n",
     "from torch.utils.data import DataLoader\n",
     "from tqdm import tqdm\n",
     "\n",
@@ -788,7 +878,7 @@
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    "cell_type": "code",
    "execution_count": null,
-   "id": "86957c62",
+   "id": "b232bd07",
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@@ -803,7 +893,7 @@
   },
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    "cell_type": "markdown",
-   "id": "efd44cf5",
+   "id": "16de7380",
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@@ -814,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22c3f513",
+   "id": "856af9da",
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@@ -834,7 +924,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87b45015",
+   "id": "f7240ca5",
    "metadata": {
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@@ -850,7 +940,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4e7a929",
+   "id": "67168867",
    "metadata": {
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@@ -860,7 +950,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d02cc75",
+   "id": "c6bdbfde",
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@@ -876,7 +966,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a539070f",
+   "id": "a8543304",
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@@ -890,7 +980,7 @@
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   {
    "cell_type": "markdown",
-   "id": "d1b2507b",
+   "id": "940b48d6",
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@@ -901,7 +991,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2ab6b33",
+   "id": "8b9425d2",
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@@ -912,7 +1002,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7de66a63",
+   "id": "42f81f13",
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@@ -923,7 +1013,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6fcc912a",
+   "id": "33fbfc83",
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@@ -934,7 +1024,7 @@
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   {
    "cell_type": "markdown",
-   "id": "929e292b",
+   "id": "00ded88d",
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@@ -947,7 +1037,7 @@
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   {
    "cell_type": "markdown",
-   "id": "7abe7429",
+   "id": "f7475dc3",
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     "tags": []
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@@ -968,7 +1058,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55bb626d",
+   "id": "97a88ddb",
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@@ -993,7 +1083,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67390c1b",
+   "id": "2f82fa67",
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    "outputs": [],
    "source": [
@@ -1004,7 +1094,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2930d6cd",
+   "id": "b93db0b2",
    "metadata": {
     "lines_to_next_cell": 0,
     "title": "[markwodn]"
@@ -1017,7 +1107,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0ae9923",
+   "id": "5c7ccc7b",
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@@ -1030,7 +1120,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d2739a2",
+   "id": "d47955f7",
    "metadata": {
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@@ -1041,7 +1131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "933e724b",
+   "id": "94284732",
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@@ -1056,7 +1146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8367d7e7",
+   "id": "cb6f9edc",
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@@ -1068,7 +1158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28279f41",
+   "id": "8aba5707",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-banner alert-warning\">\n",
@@ -1083,7 +1173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db7e8748",
+   "id": "b9713122",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-success\"><h2>Checkpoint 4</h2>\n",
@@ -1096,7 +1186,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca69811f",
+   "id": "183344be",
    "metadata": {},
    "source": [
     "# Part 5: Highlighting Class-Relevant Differences"
@@ -1104,7 +1194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a84225c",
+   "id": "83417bff",
    "metadata": {},
    "source": [
     "At this point we have:\n",
@@ -1119,7 +1209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd9cd294",
+   "id": "737ae577",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\"><h3>Task 5.1 Get sucessfully converted samples</h3>\n",
@@ -1140,7 +1230,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eb49e17c",
+   "id": "84c56d18",
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@@ -1168,7 +1258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df1543ab",
+   "id": "8737c833",
    "metadata": {
     "tags": []
    },
@@ -1179,7 +1269,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31a46e04",
+   "id": "ee8f6090",
    "metadata": {
     "tags": []
    },
@@ -1191,7 +1281,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e54ae384",
+   "id": "b33a0107",
    "metadata": {
     "tags": []
    },
@@ -1212,7 +1302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccbc04c1",
+   "id": "2edae8d4",
    "metadata": {
     "tags": []
    },
@@ -1228,7 +1318,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53050f11",
+   "id": "79d46ed5",
    "metadata": {
     "tags": []
    },
@@ -1241,7 +1331,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c71cb0f8",
+   "id": "0ec9b3cf",
    "metadata": {
     "tags": []
    },
@@ -1272,7 +1362,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "76caab37",
+   "id": "c387ba61",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1280,7 +1370,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35991baf",
+   "id": "8b9e843e",
    "metadata": {
     "tags": []
    },
@@ -1361,7 +1451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a270e2d8",
+   "id": "837d2a6a",
    "metadata": {
     "tags": []
    },
@@ -1377,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34e7801c",
+   "id": "01f878a8",
    "metadata": {
     "tags": []
    },
@@ -1388,7 +1478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4009e6f",
+   "id": "28aceac4",
    "metadata": {},
    "source": [
     "HELP!!! Interactive (still!) doesn't work. No worries... uncomment the following cell and choose your index and threshold by typing them out."
@@ -1397,7 +1487,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7adaa4d4",
+   "id": "2ae84d44",
    "metadata": {
     "tags": []
    },
@@ -1413,7 +1503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33544547",
+   "id": "8ff5ceb0",
    "metadata": {
     "tags": []
    },
@@ -1431,7 +1521,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ed9c11a",
+   "id": "ca976c6b",
    "metadata": {
     "tags": []
    },
@@ -1444,7 +1534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a1577b8",
+   "id": "bd96b144",
    "metadata": {
     "tags": []
    },
diff --git a/solution.ipynb b/solution.ipynb
index ee650be..231e6f7 100644
--- a/solution.ipynb
+++ b/solution.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e998cbda",
+   "id": "b3ddb066",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3b46176",
+   "id": "7f43c1e3",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0ad2695",
+   "id": "a9aaf840",
    "metadata": {},
    "source": [
     "\n",
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "774d942d",
+   "id": "3f6c5bc0",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32c74ae3",
+   "id": "5dd19fe5",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8e2bfb78",
+   "id": "8c709838",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e368025",
+   "id": "6b04f969",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ba9ba1",
+   "id": "27ea9906",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bcfac6b2",
+   "id": "6457422b",
    "metadata": {
     "tags": [
      "solution"
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "358f92e4",
+   "id": "2ad014ac",
    "metadata": {},
    "source": [
     "# Part 2: Using Integrated Gradients to find what the classifier knows\n",
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23375b54",
+   "id": "e40eeba7",
    "metadata": {},
    "source": [
     "## Attributions through integrated gradients\n",
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0bc95c12",
+   "id": "e7aca710",
    "metadata": {
     "tags": []
    },
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce061847",
+   "id": "44d286aa",
    "metadata": {
     "tags": []
    },
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "56f04f69",
+   "id": "55d4cbcc",
    "metadata": {
     "tags": [
      "solution"
@@ -233,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3b8fada",
+   "id": "8ea56240",
    "metadata": {
     "tags": []
    },
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1749ba9c",
+   "id": "bc50850e",
    "metadata": {
     "lines_to_next_cell": 2,
     "tags": []
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b11c4963",
+   "id": "e7447933",
    "metadata": {
     "tags": []
    },
@@ -286,7 +286,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4a2e4ba",
+   "id": "5cb527bf",
    "metadata": {
     "tags": []
    },
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35dbc255",
+   "id": "25ecec3e",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb45a3b7",
+   "id": "2b43f05d",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -325,7 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9fe579e8",
+   "id": "a7b21894",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6db49a33",
+   "id": "f97eace2",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68c48063",
+   "id": "ed5b7d6e",
    "metadata": {},
    "source": [
     "\n",
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4f45692",
+   "id": "bf13ae8d",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\"><h4>Task 2.3: Use random noise as a baseline</h4>\n",
@@ -401,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c11ff6ef",
+   "id": "6e85e3e4",
    "metadata": {
     "tags": [
      "solution"
@@ -426,7 +426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24db5ea4",
+   "id": "f24c00a3",
    "metadata": {
     "tags": []
    },
@@ -440,7 +440,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "428f4870",
+   "id": "12743143",
    "metadata": {
     "tags": [
      "solution"
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "341fe9b8",
+   "id": "10a6cfcc",
    "metadata": {
     "tags": []
    },
@@ -483,7 +483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b0e6145",
+   "id": "25f3d08e",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\"><h2>BONUS Task: Using different attributions.</h2>\n",
@@ -497,7 +497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f67562c",
+   "id": "65d946a8",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-success\"><h2>Checkpoint 2</h2>\n",
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "003fed33",
+   "id": "04602cf9",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -545,22 +545,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c99e326",
+   "id": "ed173d7c",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
    },
    "source": [
     "### The model\n",
-    "![cycle.png](assets/cyclegan.png)\n",
+    "![stargan.png](assets/stargan.png)\n",
     "\n",
     "In the following, we create a [StarGAN model](https://arxiv.org/abs/1711.09020).\n",
     "It is a Generative Adversarial model that is trained to turn one class of images X into a different class of images Y.\n",
     "\n",
-    "The model is made up of three networks:\n",
+    "We will not be using the random latent code (green, in the figure), so the model we use is made up of three networks:\n",
     "- The generator - this will be the bulk of the model, and will be responsible for transforming the images: we're going to use a `UNet`\n",
     "- The discriminator - this will be responsible for telling the difference between real and fake images: we're going to use a `DenseModel`\n",
-    "- The style mapping - this will be responsible for encoding the style of the image: we're going to use a `DenseModel`\n",
+    "- The style encoder - this will be responsible for encoding the style of the image: we're going to use a `DenseModel`\n",
     "\n",
     "Let's start by creating these!"
    ]
@@ -568,10 +568,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3fa2a39a",
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
+   "id": "03a51bad",
+   "metadata": {},
    "outputs": [],
    "source": [
     "from dlmbl_unet import UNet\n",
@@ -579,10 +577,11 @@
     "\n",
     "\n",
     "class Generator(nn.Module):\n",
-    "    def __init__(self, generator, style_mapping):\n",
+    "\n",
+    "    def __init__(self, generator, style_encoder):\n",
     "        super().__init__()\n",
     "        self.generator = generator\n",
-    "        self.style_mapping = style_mapping\n",
+    "        self.style_encoder = style_encoder\n",
     "\n",
     "    def forward(self, x, y):\n",
     "        \"\"\"\n",
@@ -591,7 +590,7 @@
     "        y: torch.Tensor\n",
     "            The style image\n",
     "        \"\"\"\n",
-    "        style = self.style_mapping(y)\n",
+    "        style = self.style_encoder(y)\n",
     "        # Concatenate the style vector with the input image\n",
     "        style = style.unsqueeze(-1).unsqueeze(-1)\n",
     "        style = style.expand(-1, -1, x.size(2), x.size(3))\n",
@@ -601,7 +600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11c69ace",
+   "id": "e6c6168d",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -616,12 +615,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "734e1e36",
+   "id": "9d0ef49f",
    "metadata": {
     "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
+    "style_size = ...  # TODO choose a size for the style space\n",
+    "unet_depth = ...  # TODO Choose a depth for the UNet\n",
     "style_mapping = DenseModel(\n",
     "    input_shape=..., num_classes=...  # How big is the style space?\n",
     ")\n",
@@ -633,7 +634,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "347455b7",
+   "id": "ff22f753",
    "metadata": {
     "tags": [
      "solution"
@@ -641,15 +642,31 @@
    },
    "outputs": [],
    "source": [
-    "# Here is an example of a working exercise\n",
-    "style_mapping = DenseModel(input_shape=(3, 28, 28), num_classes=3)\n",
+    "# Here is an example of a working setup! Note that you can change the hyperparameters as you experiment.\n",
+    "# Choose your own setup to see what works for you.\n",
+    "style_encoder = DenseModel(input_shape=(3, 28, 28), num_classes=3)\n",
     "unet = UNet(depth=2, in_channels=6, out_channels=3, final_activation=nn.Sigmoid())\n",
-    "generator = Generator(unet, style_mapping=style_mapping)"
+    "generator = Generator(unet, style_encoder=style_encoder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd761ef3",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<div class=\"alert alert-block alert-warning\"><h3>Hyper-parameter choices</h3>\n",
+    "<ul>\n",
+    "<li>Are any of the hyperparameters you choose above constrained in some way?</li>\n",
+    "<li>What would happen if you chose a depth of 10 for the UNet?</li>\n",
+    "<li>Is there a minimum size for the style space? Why or why not?</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "74b2fe60",
+   "id": "d1220bb6",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
@@ -666,7 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4416d6eb",
+   "id": "71482197",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
@@ -679,7 +696,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0a3291bf",
+   "id": "7ef652d9",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": [
@@ -693,7 +710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b20d0919",
+   "id": "709affba",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -704,7 +721,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6bc98d13",
+   "id": "7059545e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -714,24 +731,89 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2cc4a339",
+   "id": "b1a7581c",
    "metadata": {
+    "lines_to_next_cell": 0,
     "tags": []
    },
    "source": [
     "## Training a GAN\n",
     "\n",
-    "Yes, really!\n",
+    "Training an adversarial network is a bit more complicated than training a classifier.\n",
+    "For starters, we are simultaneously training two different networks that work against each other.\n",
+    "As such, we need to be careful about how and when we update the weights of each network.\n",
     "\n",
-    "TODO about the losses:\n",
-    "- An adversarial loss\n",
-    "- A cycle loss\n",
-    "TODO add exercise!"
+    "We will have two different optimizers, one for the Generator and one for the Discriminator.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7805887e",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=1e-4)\n",
+    "optimizer_g = torch.optim.Adam(generator.parameters(), lr=1e-4)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "87761838",
+   "id": "1bad28d8",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "There are also two different types of losses that we will need.\n",
+    "**Adversarial loss**\n",
+    "This loss describes how well the discriminator can tell the difference between real and generated images.\n",
+    "In our case, this will be a sort of classification loss - we will use Cross Entropy.\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "The adversarial loss will be applied differently to the generator and the discriminator! Be very careful!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a757512e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adverial_loss_fn = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c590737",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "**Cycle/reconstruction loss**\n",
+    "The cycle loss is there to make sure that the generator doesn't output an image that looks nothing like the input!\n",
+    "Indeed, by training the generator to be able to cycle back to the original image, we are making sure that it makes a minimum number of changes.\n",
+    "The cycle loss is applied only to the generator.\n",
+    "\n",
+    "cycle_loss_fn = nn.L1Loss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0def44d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a0c1d2e",
    "metadata": {
     "lines_to_next_cell": 2,
     "tags": []
@@ -746,16 +828,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcc737d6",
+   "id": "9f577571",
    "metadata": {
+    "lines_to_next_cell": 0,
     "tags": []
    },
    "source": [
     "...this time again.\n",
     "\n",
-    "<img src=\"assets/model_train.jpg\" alt=\"drawing\" width=\"500px\"/>\n",
-    "\n",
-    "TODO also turn this into a standalong script for use during the project phase\n",
+    "<img src=\"assets/model_train.jpg\" alt=\"drawing\" width=\"500px\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3077e49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO also turn this into a standalone script for use during the project phase\n",
     "from torch.utils.data import DataLoader\n",
     "from tqdm import tqdm\n",
     "\n",
@@ -832,7 +923,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "86957c62",
+   "id": "b232bd07",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -847,7 +938,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efd44cf5",
+   "id": "16de7380",
    "metadata": {
     "tags": []
    },
@@ -858,7 +949,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22c3f513",
+   "id": "856af9da",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -878,7 +969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87b45015",
+   "id": "f7240ca5",
    "metadata": {
     "tags": []
    },
@@ -894,7 +985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4e7a929",
+   "id": "67168867",
    "metadata": {
     "tags": []
    },
@@ -904,7 +995,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d02cc75",
+   "id": "c6bdbfde",
    "metadata": {
     "tags": []
    },
@@ -920,7 +1011,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a539070f",
+   "id": "a8543304",
    "metadata": {
     "tags": []
    },
@@ -934,7 +1025,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1b2507b",
+   "id": "940b48d6",
    "metadata": {
     "tags": []
    },
@@ -945,7 +1036,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2ab6b33",
+   "id": "8b9425d2",
    "metadata": {
     "tags": []
    },
@@ -956,7 +1047,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7de66a63",
+   "id": "42f81f13",
    "metadata": {
     "tags": []
    },
@@ -967,7 +1058,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6fcc912a",
+   "id": "33fbfc83",
    "metadata": {
     "tags": []
    },
@@ -978,7 +1069,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "929e292b",
+   "id": "00ded88d",
    "metadata": {
     "tags": []
    },
@@ -991,7 +1082,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7abe7429",
+   "id": "f7475dc3",
    "metadata": {
     "tags": []
    },
@@ -1012,7 +1103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55bb626d",
+   "id": "97a88ddb",
    "metadata": {
     "lines_to_next_cell": 0,
     "tags": []
@@ -1037,7 +1128,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67390c1b",
+   "id": "2f82fa67",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1048,7 +1139,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2930d6cd",
+   "id": "b93db0b2",
    "metadata": {
     "lines_to_next_cell": 0,
     "title": "[markwodn]"
@@ -1061,7 +1152,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0ae9923",
+   "id": "5c7ccc7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1074,7 +1165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d2739a2",
+   "id": "d47955f7",
    "metadata": {
     "tags": []
    },
@@ -1085,7 +1176,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "933e724b",
+   "id": "94284732",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -1100,7 +1191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8367d7e7",
+   "id": "cb6f9edc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1112,7 +1203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28279f41",
+   "id": "8aba5707",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-banner alert-warning\">\n",
@@ -1127,7 +1218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db7e8748",
+   "id": "b9713122",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-success\"><h2>Checkpoint 4</h2>\n",
@@ -1140,7 +1231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca69811f",
+   "id": "183344be",
    "metadata": {},
    "source": [
     "# Part 5: Highlighting Class-Relevant Differences"
@@ -1148,7 +1239,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a84225c",
+   "id": "83417bff",
    "metadata": {},
    "source": [
     "At this point we have:\n",
@@ -1163,7 +1254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd9cd294",
+   "id": "737ae577",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-block alert-info\"><h3>Task 5.1 Get sucessfully converted samples</h3>\n",
@@ -1184,7 +1275,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eb49e17c",
+   "id": "84c56d18",
    "metadata": {
     "lines_to_next_cell": 2,
     "tags": []
@@ -1213,7 +1304,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7659deb0",
+   "id": "37413116",
    "metadata": {
     "lines_to_next_cell": 2,
     "tags": [
@@ -1245,7 +1336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df1543ab",
+   "id": "8737c833",
    "metadata": {
     "tags": []
    },
@@ -1256,7 +1347,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31a46e04",
+   "id": "ee8f6090",
    "metadata": {
     "tags": []
    },
@@ -1268,7 +1359,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e54ae384",
+   "id": "b33a0107",
    "metadata": {
     "tags": []
    },
@@ -1289,7 +1380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccbc04c1",
+   "id": "2edae8d4",
    "metadata": {
     "tags": []
    },
@@ -1305,7 +1396,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53050f11",
+   "id": "79d46ed5",
    "metadata": {
     "tags": []
    },
@@ -1318,7 +1409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c71cb0f8",
+   "id": "0ec9b3cf",
    "metadata": {
     "tags": []
    },
@@ -1349,7 +1440,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "76caab37",
+   "id": "c387ba61",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1357,7 +1448,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35991baf",
+   "id": "8b9e843e",
    "metadata": {
     "tags": []
    },
@@ -1438,7 +1529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a270e2d8",
+   "id": "837d2a6a",
    "metadata": {
     "tags": []
    },
@@ -1454,7 +1545,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34e7801c",
+   "id": "01f878a8",
    "metadata": {
     "tags": []
    },
@@ -1465,7 +1556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4009e6f",
+   "id": "28aceac4",
    "metadata": {},
    "source": [
     "HELP!!! Interactive (still!) doesn't work. No worries... uncomment the following cell and choose your index and threshold by typing them out."
@@ -1474,7 +1565,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7adaa4d4",
+   "id": "2ae84d44",
    "metadata": {
     "tags": []
    },
@@ -1490,7 +1581,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33544547",
+   "id": "8ff5ceb0",
    "metadata": {
     "tags": []
    },
@@ -1508,7 +1599,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ed9c11a",
+   "id": "ca976c6b",
    "metadata": {
     "tags": []
    },
@@ -1521,7 +1612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a1577b8",
+   "id": "bd96b144",
    "metadata": {
     "tags": []
    },