diff --git a/projects/ComputerVision/em_synapses.ipynb b/projects/ComputerVision/em_synapses.ipynb
index fd209caa1..32441053b 100644
--- a/projects/ComputerVision/em_synapses.ipynb
+++ b/projects/ComputerVision/em_synapses.ipynb
@@ -1,1380 +1,1487 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2d9f0b20",
-   "metadata": {
-    "colab_type": "text",
-    "execution": {},
-    "id": "view-in-github"
-   },
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/projects/ComputerVision/em_synapses.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/projects/ComputerVision/em_synapses.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "renayVUI7b9x",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "# Knowledge Extraction from a Convolutional Neural Network\n",
-    "\n",
-    "**By Neuromatch Academy**\n",
-    "\n",
-    "__Content creators:__ Jan Funke\n",
-    "\n",
-    "__Production editors:__ Spiros Chavlis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "U6wofKujWp6X",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Objective\n",
-    "\n",
-    "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n",
-    "\n",
-    "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n",
-    "\n",
-    "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "zO4YN6W8W0Cp",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fO1IZwvkW9Me",
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Install dependencies\n",
-    "!pip install scikit-image --quiet\n",
-    "!pip install pillow --quiet\n",
-    "!pip install scikit-image --quiet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "gKkHjjTGWzUk",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import json\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from skimage.io import imread\n",
-    "from torchvision.datasets import ImageFolder\n",
-    "from torch.utils.data import DataLoader, random_split\n",
-    "from torch.utils.data.sampler import WeightedRandomSampler\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd7d427d",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Project Ideas\n",
-    "\n",
-    "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n",
-    "    * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n",
-    "    * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n",
-    "    * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n",
-    "    * (medium) Other networks:  Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n",
-    "    * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n",
-    "    \n",
-    "    \n",
-    "2. Explore the CycleGAN.\n",
-    "    * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n",
-    "    * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n",
-    "    \n",
-    "\n",
-    "3. Try on your own data!\n",
-    "    * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n",
-    "\n",
-    "### Acknowledgments\n",
-    "\n",
-    "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5642d709",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Train an Image Classifier\n",
-    "\n",
-    "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c61a11c6",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## Data Preparation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "821dc497",
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Data are downloaded.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# @title Download the data\n",
-    "import requests, os\n",
-    "from zipfile import ZipFile\n",
-    "\n",
-    "# @markdown Download the resources for this tutorial (one zip file)\n",
-    "fname = 'resources.zip'\n",
-    "url = 'https://www.dropbox.com/sh/ucpjfd3omjieu80/AAAvZynLtzvhyFx7_jwVhUK2a?dl=1'\n",
-    "\n",
-    "if not os.path.exists('data/'):\n",
-    "  print('Data downlading...')\n",
-    "  r = requests.get(url, allow_redirects=True, stream=True)\n",
-    "  with open(fname, 'wb') as fh:\n",
-    "    fh.write(r.content)\n",
-    "  print('Download is cmpleted.')\n",
-    "\n",
-    "  # @markdown Unzip the file\n",
-    "  fname = 'resources.zip'\n",
-    "\n",
-    "  # specifying the zip file name\n",
-    "  fnames = ['data.zip', 'checkpoints.zip']\n",
-    "\n",
-    "  # opening the zip file in READ mode\n",
-    "  with ZipFile(fname, 'r') as zf:\n",
-    "    # extracting all the files\n",
-    "    print('Extracting all the files now...')\n",
-    "    zf.extractall(path='.')\n",
-    "    print('Done!')\n",
-    "\n",
-    "\n",
-    "  # @markdown Extract the data\n",
-    "  fnames = ['data.zip', 'checkpoints.zip']\n",
-    "\n",
-    "  for fname in fnames:\n",
-    "    # opening the zip file in READ mode\n",
-    "    with ZipFile(fname, 'r') as zh:\n",
-    "      # extracting all the files\n",
-    "      print(f\"\\nArchive: {fname}\")\n",
-    "      print(f\"\\tExtracting data...\")\n",
-    "      zh.extractall(path='.')\n",
-    "  print('Done!')\n",
-    "\n",
-    "  # @markdown Make sure the order of classes matches the pretrained model\n",
-    "  os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n",
-    "  os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n",
-    "  os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n",
-    "  os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n",
-    "  os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n",
-    "  os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n",
-    "\n",
-    "else:\n",
-    "  print('Data are downloaded.')\n",
-    "\n",
-    "# @markdown Remove the archives\n",
-    "for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n",
-    "  if os.path.exists(i):\n",
-    "    os.remove(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b84ec7b",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## Classifier Training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a79ab567",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "### Create and Inspect Datasets\n",
-    "\n",
-    "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae50b16a",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "2d9f0b20",
+      "metadata": {
+        "execution": {},
+        "id": "2d9f0b20"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/projects/ComputerVision/em_synapses.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/projects/ComputerVision/em_synapses.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of images per class:\n",
-      "\t0_gaba:\tn=15855\tweight=6.30715862503942e-05\n",
-      "\t1_acetylcholine:\tn=4911\tweight=0.00020362451639177357\n",
-      "\t2_glutamate:\tn=3550\tweight=0.00028169014084507044\n",
-      "\t3_serotonin:\tn=2297\tweight=0.00043535045711797995\n",
-      "\t4_octopamine:\tn=951\tweight=0.0010515247108307045\n",
-      "\t5_dopamine:\tn=4649\tweight=0.00021510002151000216\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_image(filename):\n",
-    "\n",
-    "  image = imread(filename)\n",
-    "\n",
-    "  # images are grescale, we only need one of the RGB channels\n",
-    "  image = image[:, :, 0]\n",
-    "  # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n",
-    "  image = image.astype(np.float32)/255.0\n",
-    "  image = (image - 0.5)/0.5\n",
-    "\n",
-    "  return image\n",
-    "\n",
-    "\n",
-    "# create a dataset for all images of all classes\n",
-    "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n",
-    "\n",
-    "# randomly split the dataset into train, validation, and test\n",
-    "num_images = len(full_dataset)\n",
-    "# ~70% for training\n",
-    "num_training = int(0.7 * num_images)\n",
-    "# ~15% for validation\n",
-    "num_validation = int(0.15 * num_images)\n",
-    "# ~15% for testing\n",
-    "num_test = num_images - (num_training + num_validation)\n",
-    "# split the data randomly (but with a fixed random seed)\n",
-    "train_dataset, validation_dataset, test_dataset = random_split(\n",
-    "    full_dataset,\n",
-    "    [num_training, num_validation, num_test],\n",
-    "    generator=torch.Generator().manual_seed(23061912))\n",
-    "\n",
-    "# compute class weights in training dataset for uniform sampling\n",
-    "ys = np.array([y for _, y in train_dataset])\n",
-    "counts = np.bincount(ys)\n",
-    "label_weights = 1.0 / counts\n",
-    "weights = label_weights[ys]\n",
-    "\n",
-    "print(\"Number of images per class:\")\n",
-    "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n",
-    "  print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n",
-    "\n",
-    "# create a data loader with uniform sampling\n",
-    "sampler = WeightedRandomSampler(weights, len(weights))\n",
-    "# this data loader will serve 8 images in a \"mini-batch\" at a time\n",
-    "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9010bdc",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d8c6f3a",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "renayVUI7b9x",
+      "metadata": {
+        "execution": {},
+        "id": "renayVUI7b9x"
+      },
+      "source": [
+        "# Knowledge Extraction from a Convolutional Neural Network\n",
+        "\n",
+        "**By Neuromatch Academy**\n",
+        "\n",
+        "__Content creators:__ Jan Funke\n",
+        "\n",
+        "__Production editors:__ Spiros Chavlis"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x1008 with 8 Axes>"
+      "cell_type": "markdown",
+      "id": "U6wofKujWp6X",
+      "metadata": {
+        "execution": {},
+        "id": "U6wofKujWp6X"
+      },
+      "source": [
+        "---\n",
+        "# Objective\n",
+        "\n",
+        "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n",
+        "\n",
+        "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n",
+        "\n",
+        "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def show_batch(x, y):\n",
-    "  fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n",
-    "  for i in range(x.shape[0]):\n",
-    "    axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n",
-    "    axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n",
-    "  plt.show()\n",
-    "\n",
-    "# show a random batch from the data loader\n",
-    "# (run this cell repeatedly to see different batches)\n",
-    "for x, y in dataloader:\n",
-    "  show_batch(x, y)\n",
-    "  break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f882416f",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "### Create a Model, Loss, and Optimizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54f177cc",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "class Vgg2D(torch.nn.Module):\n",
-    "\n",
-    "  def __init__(\n",
-    "          self,\n",
-    "          input_size,\n",
-    "          fmaps=12,\n",
-    "          downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n",
-    "          output_classes=6):\n",
-    "\n",
-    "    super(Vgg2D, self).__init__()\n",
-    "\n",
-    "    self.input_size = input_size\n",
-    "\n",
-    "    current_fmaps = 1\n",
-    "    current_size = tuple(input_size)\n",
-    "\n",
-    "    features = []\n",
-    "    for i in range(len(downsample_factors)):\n",
-    "\n",
-    "      features += [\n",
-    "          torch.nn.Conv2d(\n",
-    "              current_fmaps,\n",
-    "              fmaps,\n",
-    "              kernel_size=3,\n",
-    "              padding=1),\n",
-    "          torch.nn.BatchNorm2d(fmaps),\n",
-    "          torch.nn.ReLU(inplace=True),\n",
-    "          torch.nn.Conv2d(\n",
-    "              fmaps,\n",
-    "              fmaps,\n",
-    "              kernel_size=3,\n",
-    "              padding=1),\n",
-    "          torch.nn.BatchNorm2d(fmaps),\n",
-    "          torch.nn.ReLU(inplace=True),\n",
-    "          torch.nn.MaxPool2d(downsample_factors[i])\n",
-    "      ]\n",
-    "\n",
-    "      current_fmaps = fmaps\n",
-    "      fmaps *= 2\n",
-    "\n",
-    "      size = tuple(\n",
-    "          int(c/d)\n",
-    "          for c, d in zip(current_size, downsample_factors[i]))\n",
-    "      check = (\n",
-    "          s*d == c\n",
-    "          for s, d, c in zip(size, downsample_factors[i], current_size))\n",
-    "      assert all(check), \\\n",
-    "          \"Can not downsample %s by chosen downsample factor\" % \\\n",
-    "          (current_size,)\n",
-    "      current_size = size\n",
-    "\n",
-    "    self.features = torch.nn.Sequential(*features)\n",
-    "\n",
-    "    classifier = [\n",
-    "        torch.nn.Linear(\n",
-    "            current_size[0] *\n",
-    "            current_size[1] *\n",
-    "            current_fmaps,\n",
-    "            4096),\n",
-    "        torch.nn.ReLU(inplace=True),\n",
-    "        torch.nn.Dropout(),\n",
-    "        torch.nn.Linear(\n",
-    "            4096,\n",
-    "            4096),\n",
-    "        torch.nn.ReLU(inplace=True),\n",
-    "        torch.nn.Dropout(),\n",
-    "        torch.nn.Linear(\n",
-    "            4096,\n",
-    "            output_classes)\n",
-    "    ]\n",
-    "\n",
-    "    self.classifier = torch.nn.Sequential(*classifier)\n",
-    "\n",
-    "  def forward(self, raw):\n",
-    "\n",
-    "    # add a channel dimension to raw\n",
-    "    shape = tuple(raw.shape)\n",
-    "    raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n",
-    "\n",
-    "    # compute features\n",
-    "    f = self.features(raw)\n",
-    "    f = f.view(f.size(0), -1)\n",
-    "\n",
-    "    # classify\n",
-    "    y = self.classifier(f)\n",
-    "\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5da43245",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# get the size of our images\n",
-    "for x, y in train_dataset:\n",
-    "  input_size = x.shape\n",
-    "  break\n",
-    "\n",
-    "# create the model to train\n",
-    "model = Vgg2D(input_size)\n",
-    "\n",
-    "# create a loss\n",
-    "loss = torch.nn.CrossEntropyLoss()\n",
-    "\n",
-    "# create an optimzer\n",
-    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01688095",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "### Train the Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fa65090d",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Will use device cuda for training\n"
-     ]
-    }
-   ],
-   "source": [
-    "# use a GPU, if it is available\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "model.to(device)\n",
-    "print(f\"Will use device {device} for training\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecbab4f7",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "The next cell merely defines some convenience functions for training, validation, and testing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a8c7fe9",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "def train(dataloader, optimizer, loss, device):\n",
-    "  '''Train the model for one epoch.'''\n",
-    "\n",
-    "  # set the model into train mode\n",
-    "  model.train()\n",
-    "\n",
-    "  epoch_loss, num_batches = 0, 0\n",
-    "  for x, y in tqdm(dataloader, 'train'):\n",
-    "\n",
-    "    x, y = x.to(device), y.to(device)\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "    y_pred = model(x)\n",
-    "    l = loss(y_pred, y)\n",
-    "    l.backward()\n",
-    "\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    epoch_loss += l\n",
-    "    num_batches += 1\n",
-    "\n",
-    "  return epoch_loss/num_batches\n",
-    "\n",
-    "\n",
-    "def evaluate(dataloader, name, device):\n",
-    "\n",
-    "  correct = 0\n",
-    "  total = 0\n",
-    "  for x, y in tqdm(dataloader, name):\n",
-    "\n",
-    "    x, y = x.to(device), y.to(device)\n",
-    "\n",
-    "    logits = model(x)\n",
-    "    probs = torch.nn.Softmax(dim=1)(logits)\n",
-    "    predictions = torch.argmax(probs, dim=1)\n",
-    "\n",
-    "    correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n",
-    "    total += len(y)\n",
-    "\n",
-    "  accuracy = correct/total\n",
-    "\n",
-    "  return accuracy\n",
-    "\n",
-    "\n",
-    "def validate(validation_dataset, device):\n",
-    "  '''Evaluate prediction accuracy on the validation dataset.'''\n",
-    "\n",
-    "  model.eval()\n",
-    "  dataloader = DataLoader(validation_dataset, batch_size=32)\n",
-    "\n",
-    "  return evaluate(dataloader, 'validate', device)\n",
-    "\n",
-    "\n",
-    "def test(test_dataset, device):\n",
-    "  '''Evaluate prediction accuracy on the test dataset.'''\n",
-    "\n",
-    "  model.eval()\n",
-    "  dataloader = DataLoader(test_dataset, batch_size=32)\n",
-    "\n",
-    "  return evaluate(dataloader, 'test', device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68bcfbbf",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d0af7638",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "def train_from_scratch(dataloader, validation_dataset,\n",
-    "                       optimizer, loss,\n",
-    "                       num_epochs=100, device=device):\n",
-    "\n",
-    "  for epoch in range(num_epochs):\n",
-    "    epoch_loss = train(dataloader, optimizer, loss, device=device)\n",
-    "    print(f\"epoch {epoch}, training loss={epoch_loss}\")\n",
-    "\n",
-    "    accuracy = validate(validation_dataset, device=device)\n",
-    "    print(f\"epoch {epoch}, validation accuracy={accuracy}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45e31b87",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n",
-    "\n",
-    "Unceck the box below and run the cell to train a model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "W5KA7zDIa3Lw",
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @markdown\n",
-    "yes_I_want_the_pretrained_model =  True # @param {type:\"boolean\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53fb8dda",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# Load a pretrained model or train the model from scratch\n",
-    "\n",
-    "# set this to True and run this cell if you want a shortcut\n",
-    "\n",
-    "if yes_I_want_the_pretrained_model:\n",
-    "  checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n",
-    "                          map_location=device)\n",
-    "  model.load_state_dict(checkpoint['model_state_dict'])\n",
-    "else:\n",
-    "  train_from_scratch(dataloader, validation_dataset,\n",
-    "                     optimizer, loss,\n",
-    "                     num_epochs=100, device=device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f6e3663",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "zO4YN6W8W0Cp",
+      "metadata": {
+        "execution": {},
+        "id": "zO4YN6W8W0Cp"
+      },
+      "source": [
+        "---\n",
+        "# Setup"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "test:   0%|          | 0/216 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
-      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
-      "test: 100%|██████████| 216/216 [00:09<00:00, 23.53it/s]"
-     ]
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "fO1IZwvkW9Me",
+      "metadata": {
+        "cellView": "form",
+        "execution": {},
+        "id": "fO1IZwvkW9Me"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Install dependencies\n",
+        "!pip install scikit-image --quiet\n",
+        "!pip install pillow --quiet\n",
+        "!pip install scikit-image --quiet"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "final test accuracy: 0.8054750869061413\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "gKkHjjTGWzUk",
+      "metadata": {
+        "execution": {},
+        "id": "gKkHjjTGWzUk"
+      },
+      "outputs": [],
+      "source": [
+        "import glob\n",
+        "import json\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "from skimage.io import imread\n",
+        "from torchvision.datasets import ImageFolder\n",
+        "from torch.utils.data import DataLoader, random_split\n",
+        "from torch.utils.data.sampler import WeightedRandomSampler\n",
+        "\n",
+        "%matplotlib inline"
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "accuracy = test(test_dataset, device=device)\n",
-    "print(f\"final test accuracy: {accuracy}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f43bba5",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n",
-    "\n",
-    "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n",
-    "\n",
-    "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72b5240c",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Train a GAN to Translate Images\n",
-    "\n",
-    "We will train a so-called CycleGAN to translate images from one class to another."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41c9e63b",
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Get the CycleGAN code and dependencies\n",
-    "\n",
-    "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n",
-    "\n",
-    "import requests, zipfile, io\n",
-    "\n",
-    "url = 'https://osf.io/vutn5/download'\n",
-    "r = requests.get(url)\n",
-    "z = zipfile.ZipFile(io.BytesIO(r.content))\n",
-    "z.extractall()\n",
-    "\n",
-    "!pip install dominate --quiet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5da5c01",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "In this example, we will translate between GABAergic and glutamatergic synapses.\n",
-    "\n",
-    "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b2519c4",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "bd7d427d",
+      "metadata": {
+        "execution": {},
+        "id": "bd7d427d"
+      },
+      "source": [
+        "---\n",
+        "# Project Ideas\n",
+        "\n",
+        "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n",
+        "    * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n",
+        "    * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n",
+        "    * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n",
+        "    * (medium) Other networks:  Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n",
+        "    * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n",
+        "    \n",
+        "    \n",
+        "2. Explore the CycleGAN.\n",
+        "    * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n",
+        "    * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n",
+        "    \n",
+        "\n",
+        "3. Try on your own data!\n",
+        "    * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n",
+        "\n",
+        "### Acknowledgments\n",
+        "\n",
+        "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 22648/22648 [00:03<00:00, 7032.03it/s]\n",
-      "100%|██████████| 5085/5085 [00:00<00:00, 7355.06it/s]\n",
-      "0it [00:00, ?it/s]\n",
-      "0it [00:00, ?it/s]\n",
-      "0it [00:00, ?it/s]\n",
-      "0it [00:00, ?it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cycle_gan\n",
-    "\n",
-    "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n",
-    "\n",
-    "## Uncomment if you want to enable the training procedure\n",
-    "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d328904",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n",
-    "\n",
-    "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a182c3bc",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "5642d709",
+      "metadata": {
+        "execution": {},
+        "id": "5642d709"
+      },
+      "source": [
+        "---\n",
+        "# Train an Image Classifier\n",
+        "\n",
+        "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------- Options ---------------\n",
-      "             aspect_ratio: 1.0                           \n",
-      "           aux_checkpoint: checkpoints/synapses/classifier/vgg_checkpoint\t[default: None]\n",
-      "   aux_downsample_factors: [(2, 2), (2, 2), (2, 2), (2, 2)]\n",
-      "             aux_input_nc: 1                             \n",
-      "           aux_input_size: 128                           \n",
-      "                  aux_net: vgg2d                         \n",
-      "       aux_output_classes: 6                             \n",
-      "               batch_size: 1                             \n",
-      "          checkpoints_dir: checkpoints/synapses/cycle_gan/gaba_glutamate\t[default: ./checkpoints]\n",
-      "                crop_size: 128                           \n",
-      "                 dataroot: data/raw/synapses/cycle_gan/0_gaba_2_glutamate\t[default: None]\n",
-      "             dataset_mode: single                        \n",
-      "                direction: AtoB                          \n",
-      "          display_winsize: 256                           \n",
-      "                    epoch: latest                        \n",
-      "                     eval: False                         \n",
-      "                  gpu_ids: 0                             \n",
-      "                init_gain: 0.02                          \n",
-      "                init_type: normal                        \n",
-      "                 input_nc: 1                             \n",
-      "                  isTrain: False                         \t[default: None]\n",
-      "                load_iter: 0                             \t[default: 0]\n",
-      "                load_size: 128                           \n",
-      "         max_dataset_size: inf                           \n",
-      "                    model: test                          \n",
-      "             model_suffix: _A                            \t[default: ]\n",
-      "               n_layers_D: 3                             \n",
-      "                     name:                               \t[default: experiment_name]\n",
-      "                      ndf: 64                            \n",
-      "                     netD: basic                         \n",
-      "                     netG: resnet_9blocks                \n",
-      "                      ngf: 64                            \n",
-      "               no_dropout: True                          \t[default: False]\n",
-      "                  no_flip: True                          \n",
-      "                     norm: instance                      \n",
-      "                    ntest: inf                           \n",
-      "                 num_test: 500                           \t[default: 50]\n",
-      "              num_threads: 1                             \t[default: 4]\n",
-      "                output_nc: 1                             \n",
-      "                    phase: test                          \n",
-      "               preprocess: none                          \n",
-      "              results_dir: data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results\t[default: ./results/]\n",
-      "           serial_batches: False                         \n",
-      "                   suffix:                               \n",
-      "                  verbose: True                          \t[default: False]\n",
-      "----------------- End -------------------\n",
-      "dataset [SingleDataset] was created\n",
-      "initialize network with normal\n",
-      "model [TestModel] was created\n",
-      "loading the model from checkpoints/synapses/cycle_gan/gaba_glutamate/latest_net_G_A.pth\n",
-      "---------- Networks initialized -------------\n",
-      "DataParallel(\n",
-      "  (module): ResnetGenerator(\n",
-      "    (model): Sequential(\n",
-      "      (0): ReflectionPad2d((3, 3, 3, 3))\n",
-      "      (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1))\n",
-      "      (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "      (3): ReLU(inplace=True)\n",
-      "      (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-      "      (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "      (6): ReLU(inplace=True)\n",
-      "      (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-      "      (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "      (9): ReLU(inplace=True)\n",
-      "      (10): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (11): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (12): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (13): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (14): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (15): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (16): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (17): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (18): ResnetBlock(\n",
-      "        (conv_block): Sequential(\n",
-      "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "          (3): ReLU(inplace=True)\n",
-      "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
-      "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
-      "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "        )\n",
-      "      )\n",
-      "      (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
-      "      (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "      (21): ReLU(inplace=True)\n",
-      "      (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
-      "      (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
-      "      (24): ReLU(inplace=True)\n",
-      "      (25): ReflectionPad2d((3, 3, 3, 3))\n",
-      "      (26): Conv2d(64, 1, kernel_size=(7, 7), stride=(1, 1))\n",
-      "      (27): Tanh()\n",
-      "    )\n",
-      "  )\n",
-      ")\n",
-      "[Network G_A] Total number of parameters : 11.366 M\n",
-      "-----------------------------------------------\n",
-      "creating web directory data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest\n",
-      "processing (0000)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/0_train.png']\n",
-      "processing (0005)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10004_train.png']\n",
-      "processing (0010)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10009_train.png']\n",
-      "processing (0015)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10013_train.png']\n",
-      "processing (0020)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10018_train.png']\n",
-      "processing (0025)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10022_train.png']\n",
-      "processing (0030)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10027_train.png']\n",
-      "processing (0035)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10031_train.png']\n",
-      "processing (0040)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10036_train.png']\n",
-      "processing (0045)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10040_train.png']\n",
-      "processing (0050)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10045_train.png']\n",
-      "processing (0055)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1004_train.png']\n",
-      "processing (0060)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10054_train.png']\n",
-      "processing (0065)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10059_train.png']\n",
-      "processing (0070)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10063_train.png']\n",
-      "processing (0075)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10068_train.png']\n",
-      "processing (0080)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10072_train.png']\n",
-      "processing (0085)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10077_train.png']\n",
-      "processing (0090)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10081_train.png']\n",
-      "processing (0095)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10086_train.png']\n",
-      "processing (0100)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10090_train.png']\n",
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-      "processing (0115)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10103_train.png']\n",
-      "processing (0120)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10108_train.png']\n",
-      "processing (0125)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10112_train.png']\n",
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-      "processing (0135)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10121_train.png']\n",
-      "processing (0140)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10126_train.png']\n",
-      "processing (0145)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10130_train.png']\n",
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-      "processing (0180)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10162_train.png']\n",
-      "processing (0185)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10167_train.png']\n",
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-      "processing (0195)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10176_train.png']\n",
-      "processing (0200)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10180_train.png']\n",
-      "processing (0205)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10185_train.png']\n",
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-      "processing (0215)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10194_train.png']\n",
-      "processing (0220)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10199_train.png']\n",
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-      "processing (0240)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10216_train.png']\n",
-      "processing (0245)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10220_train.png']\n",
-      "processing (0250)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10225_train.png']\n",
-      "processing (0255)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1022_train.png']\n",
-      "processing (0260)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10234_train.png']\n",
-      "processing (0265)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10239_train.png']\n",
-      "processing (0270)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10243_train.png']\n",
-      "processing (0275)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10248_train.png']\n",
-      "processing (0280)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10252_train.png']\n",
-      "processing (0285)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10257_train.png']\n",
-      "processing (0290)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10261_train.png']\n",
-      "processing (0295)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10266_train.png']\n",
-      "processing (0300)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10270_train.png']\n",
-      "processing (0305)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10275_train.png']\n",
-      "processing (0310)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1027_train.png']\n",
-      "processing (0315)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10284_train.png']\n",
-      "processing (0320)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10289_train.png']\n",
-      "processing (0325)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10293_train.png']\n",
-      "processing (0330)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10298_train.png']\n",
-      "processing (0335)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10301_train.png']\n",
-      "processing (0340)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10306_train.png']\n",
-      "processing (0345)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10310_train.png']\n",
-      "processing (0350)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10315_train.png']\n",
-      "processing (0355)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1031_train.png']\n",
-      "processing (0360)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10324_train.png']\n",
-      "processing (0365)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10329_train.png']\n",
-      "processing (0370)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10333_train.png']\n",
-      "processing (0375)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10338_train.png']\n",
-      "processing (0380)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10342_train.png']\n",
-      "processing (0385)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10347_train.png']\n",
-      "processing (0390)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10351_train.png']\n",
-      "processing (0395)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10356_train.png']\n",
-      "processing (0400)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10360_train.png']\n",
-      "processing (0405)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10365_train.png']\n",
-      "processing (0410)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1036_train.png']\n",
-      "processing (0415)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10374_train.png']\n",
-      "processing (0420)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10379_train.png']\n",
-      "processing (0425)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10383_train.png']\n",
-      "processing (0430)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10388_train.png']\n",
-      "processing (0435)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10392_train.png']\n",
-      "processing (0440)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10397_train.png']\n",
-      "processing (0445)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10400_train.png']\n",
-      "processing (0450)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10405_train.png']\n",
-      "processing (0455)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1040_train.png']\n",
-      "processing (0460)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10414_train.png']\n",
-      "processing (0465)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10419_train.png']\n",
-      "processing (0470)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10423_train.png']\n",
-      "processing (0475)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10428_train.png']\n",
-      "processing (0480)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10432_train.png']\n",
-      "processing (0485)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10437_train.png']\n",
-      "processing (0490)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10441_train.png']\n",
-      "processing (0495)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10446_train.png']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# translate images from class A to B, and classify each with the VGG network trained above\n",
-    "cycle_gan.test(\n",
-    "    data_dir='data/raw/synapses/',\n",
-    "    class_A='0_gaba',\n",
-    "    class_B='2_glutamate',\n",
-    "    img_size=128,\n",
-    "    checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n",
-    "    vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17fc1703",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2a582ba6",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "class_A_index = 0\n",
-    "class_B_index = 2\n",
-    "\n",
-    "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n",
-    "classification_results = []\n",
-    "for f in glob.glob(result_dir + '/*.json'):\n",
-    "  result = json.load(open(f))\n",
-    "  result['basename'] = f.replace('_aux.json', '')\n",
-    "  classification_results.append(result)\n",
-    "classification_results.sort(\n",
-    "    key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n",
-    "    reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2cc0d486",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Show the top real and fake images that make the classifier change its mind:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1567b00e",
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "c61a11c6",
+      "metadata": {
+        "execution": {},
+        "id": "c61a11c6"
+      },
+      "source": [
+        "## Data Preparation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "821dc497",
+      "metadata": {
+        "execution": {},
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "821dc497",
+        "outputId": "9871a113-d169-4d6a-b927-b00706fabd22"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data downloading...\n",
+            "Downloaded: ./part1\n",
+            "Downloaded: ./part2\n",
+            "Downloaded: ./part3\n",
+            "Download is completed.\n",
+            "Reassembling Files...\n",
+            "Downloaded files have been removed!\n",
+            "Reassembled 3 parts into resources.zip\n",
+            "Extracting all the files now...\n",
+            "Done!\n",
+            "\n",
+            "Archive: data.zip\n",
+            "\tExtracting data...\n",
+            "\n",
+            "Archive: checkpoints.zip\n",
+            "\tExtracting data...\n",
+            "Done!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# @title Download the data\n",
+        "import requests, os\n",
+        "from zipfile import ZipFile\n",
+        "\n",
+        "def download_file_parts(urls, output_directory='.'):\n",
+        "    \"\"\"\n",
+        "    Download file parts from given URLs and save them in the specified directory.\n",
+        "\n",
+        "    :param urls: List of URLs to download\n",
+        "    :param output_directory: Directory to save the downloaded parts (default is current directory)\n",
+        "    :return: List of downloaded file paths\n",
+        "    \"\"\"\n",
+        "    if not os.path.exists(output_directory):\n",
+        "        os.makedirs(output_directory)\n",
+        "\n",
+        "    downloaded_files = []\n",
+        "\n",
+        "    for i, url in enumerate(urls, 1):\n",
+        "        try:\n",
+        "            response = requests.get(url, stream=True)\n",
+        "            response.raise_for_status()  # Raises an HTTPError for bad requests\n",
+        "\n",
+        "            file_name = f\"part{i}\"\n",
+        "            file_path = os.path.join(output_directory, file_name)\n",
+        "\n",
+        "            with open(file_path, 'wb') as file:\n",
+        "                for chunk in response.iter_content(chunk_size=32768):\n",
+        "                    file.write(chunk)\n",
+        "\n",
+        "            downloaded_files.append(file_path)\n",
+        "            print(f\"Downloaded: {file_path}\")\n",
+        "\n",
+        "\n",
+        "        except requests.RequestException as e:\n",
+        "            print(f\"Error downloading {url}: {e}\")\n",
+        "\n",
+        "    return downloaded_files\n",
+        "\n",
+        "def reassemble_file(output_file):\n",
+        "    chunk_number = 1\n",
+        "    with open(output_file, 'wb') as outfile:\n",
+        "        while True:\n",
+        "            chunk_name = f'part{chunk_number}'\n",
+        "            if not os.path.exists(chunk_name):\n",
+        "                break\n",
+        "            with open(chunk_name, 'rb') as infile:\n",
+        "                outfile.write(infile.read())\n",
+        "            chunk_number += 1\n",
+        "    for i in ['part1', 'part2', 'part3']:\n",
+        "        if os.path.exists(i):\n",
+        "            os.remove(i)\n",
+        "    print(f\"Downloaded files have been removed!\")\n",
+        "    print(f\"Reassembled {chunk_number-1} parts into {output_file}\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "# @markdown Download the resources for this tutorial (one zip file)\n",
+        "fname = 'resources.zip'\n",
+        "urls = [\n",
+        "    \"https://osf.io/download/4x7p3/\",\n",
+        "    \"https://osf.io/download/fzwea/\",\n",
+        "    \"https://osf.io/download/qpbcv/\"\n",
+        "]\n",
+        "\n",
+        "if not os.path.exists('data/'):\n",
+        "    print('Data downloading...')\n",
+        "    output_dir = \".\"\n",
+        "    downloaded_parts = download_file_parts(urls, output_dir)\n",
+        "    print('Download is completed.')\n",
+        "\n",
+        "    print('Reassembling Files...')\n",
+        "    base_name = ''\n",
+        "    reassemble_file(fname)\n",
+        "\n",
+        "    # @markdown Unzip the file\n",
+        "    with ZipFile(fname, 'r') as zf:\n",
+        "        # extracting all the files\n",
+        "        print('Extracting all the files now...')\n",
+        "        zf.extractall(path='.')\n",
+        "        print('Done!')\n",
+        "\n",
+        "    # # @markdown Extract the data\n",
+        "    fnames = ['data.zip', 'checkpoints.zip']\n",
+        "\n",
+        "    for fname in fnames:\n",
+        "        with ZipFile(fname, 'r') as zh:\n",
+        "            # extracting all the files\n",
+        "            print(f\"\\nArchive: {fname}\")\n",
+        "            print(f\"\\tExtracting data...\")\n",
+        "            zh.extractall(path='.')\n",
+        "    print('Done!')\n",
+        "\n",
+        "    # @markdown Make sure the order of classes matches the pretrained model\n",
+        "    os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n",
+        "    os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n",
+        "    os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n",
+        "    os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n",
+        "    os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n",
+        "    os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n",
+        "\n",
+        "    # @markdown Remove the archives\n",
+        "    for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n",
+        "        if os.path.exists(i):\n",
+        "            os.remove(i)\n",
+        "\n",
+        "else:\n",
+        "    print('Data are already downloaded.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0b84ec7b",
+      "metadata": {
+        "execution": {},
+        "id": "0b84ec7b"
+      },
+      "source": [
+        "## Classifier Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a79ab567",
+      "metadata": {
+        "execution": {},
+        "id": "a79ab567"
+      },
+      "source": [
+        "### Create and Inspect Datasets\n",
+        "\n",
+        "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "ae50b16a",
+      "metadata": {
+        "execution": {},
+        "id": "ae50b16a",
+        "outputId": "e92b100a-6711-4369-c025-6a0e10fbbdb6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images per class:\n",
+            "\t0_gaba:\tn=15855\tweight=6.30715862503942e-05\n",
+            "\t1_acetylcholine:\tn=4911\tweight=0.00020362451639177357\n",
+            "\t2_glutamate:\tn=3550\tweight=0.00028169014084507044\n",
+            "\t3_serotonin:\tn=2297\tweight=0.00043535045711797995\n",
+            "\t4_octopamine:\tn=951\tweight=0.0010515247108307045\n",
+            "\t5_dopamine:\tn=4649\tweight=0.00021510002151000216\n"
+          ]
+        }
+      ],
+      "source": [
+        "def load_image(filename):\n",
+        "\n",
+        "  image = imread(filename)\n",
+        "\n",
+        "  # images are grescale, we only need one of the RGB channels\n",
+        "  image = image[:, :, 0]\n",
+        "  # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n",
+        "  image = image.astype(np.float32)/255.0\n",
+        "  image = (image - 0.5)/0.5\n",
+        "\n",
+        "  return image\n",
+        "\n",
+        "\n",
+        "# create a dataset for all images of all classes\n",
+        "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n",
+        "\n",
+        "# randomly split the dataset into train, validation, and test\n",
+        "num_images = len(full_dataset)\n",
+        "# ~70% for training\n",
+        "num_training = int(0.7 * num_images)\n",
+        "# ~15% for validation\n",
+        "num_validation = int(0.15 * num_images)\n",
+        "# ~15% for testing\n",
+        "num_test = num_images - (num_training + num_validation)\n",
+        "# split the data randomly (but with a fixed random seed)\n",
+        "train_dataset, validation_dataset, test_dataset = random_split(\n",
+        "    full_dataset,\n",
+        "    [num_training, num_validation, num_test],\n",
+        "    generator=torch.Generator().manual_seed(23061912))\n",
+        "\n",
+        "# compute class weights in training dataset for uniform sampling\n",
+        "ys = np.array([y for _, y in train_dataset])\n",
+        "counts = np.bincount(ys)\n",
+        "label_weights = 1.0 / counts\n",
+        "weights = label_weights[ys]\n",
+        "\n",
+        "print(\"Number of images per class:\")\n",
+        "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n",
+        "  print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n",
+        "\n",
+        "# create a data loader with uniform sampling\n",
+        "sampler = WeightedRandomSampler(weights, len(weights))\n",
+        "# this data loader will serve 8 images in a \"mini-batch\" at a time\n",
+        "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9010bdc",
+      "metadata": {
+        "execution": {},
+        "id": "e9010bdc"
+      },
+      "source": [
+        "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "3d8c6f3a",
+      "metadata": {
+        "execution": {},
+        "id": "3d8c6f3a",
+        "outputId": "5d4a01f7-aa52-4e35-b6a6-a7950779dd3d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 117
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x1400 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def show_batch(x, y):\n",
+        "  fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n",
+        "  for i in range(x.shape[0]):\n",
+        "    axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n",
+        "    axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n",
+        "  plt.show()\n",
+        "\n",
+        "# show a random batch from the data loader\n",
+        "# (run this cell repeatedly to see different batches)\n",
+        "for x, y in dataloader:\n",
+        "  show_batch(x, y)\n",
+        "  break"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f882416f",
+      "metadata": {
+        "execution": {},
+        "id": "f882416f"
+      },
+      "source": [
+        "### Create a Model, Loss, and Optimizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "54f177cc",
+      "metadata": {
+        "execution": {},
+        "id": "54f177cc"
+      },
+      "outputs": [],
+      "source": [
+        "class Vgg2D(torch.nn.Module):\n",
+        "\n",
+        "  def __init__(\n",
+        "          self,\n",
+        "          input_size,\n",
+        "          fmaps=12,\n",
+        "          downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n",
+        "          output_classes=6):\n",
+        "\n",
+        "    super(Vgg2D, self).__init__()\n",
+        "\n",
+        "    self.input_size = input_size\n",
+        "\n",
+        "    current_fmaps = 1\n",
+        "    current_size = tuple(input_size)\n",
+        "\n",
+        "    features = []\n",
+        "    for i in range(len(downsample_factors)):\n",
+        "\n",
+        "      features += [\n",
+        "          torch.nn.Conv2d(\n",
+        "              current_fmaps,\n",
+        "              fmaps,\n",
+        "              kernel_size=3,\n",
+        "              padding=1),\n",
+        "          torch.nn.BatchNorm2d(fmaps),\n",
+        "          torch.nn.ReLU(inplace=True),\n",
+        "          torch.nn.Conv2d(\n",
+        "              fmaps,\n",
+        "              fmaps,\n",
+        "              kernel_size=3,\n",
+        "              padding=1),\n",
+        "          torch.nn.BatchNorm2d(fmaps),\n",
+        "          torch.nn.ReLU(inplace=True),\n",
+        "          torch.nn.MaxPool2d(downsample_factors[i])\n",
+        "      ]\n",
+        "\n",
+        "      current_fmaps = fmaps\n",
+        "      fmaps *= 2\n",
+        "\n",
+        "      size = tuple(\n",
+        "          int(c/d)\n",
+        "          for c, d in zip(current_size, downsample_factors[i]))\n",
+        "      check = (\n",
+        "          s*d == c\n",
+        "          for s, d, c in zip(size, downsample_factors[i], current_size))\n",
+        "      assert all(check), \\\n",
+        "          \"Can not downsample %s by chosen downsample factor\" % \\\n",
+        "          (current_size,)\n",
+        "      current_size = size\n",
+        "\n",
+        "    self.features = torch.nn.Sequential(*features)\n",
+        "\n",
+        "    classifier = [\n",
+        "        torch.nn.Linear(\n",
+        "            current_size[0] *\n",
+        "            current_size[1] *\n",
+        "            current_fmaps,\n",
+        "            4096),\n",
+        "        torch.nn.ReLU(inplace=True),\n",
+        "        torch.nn.Dropout(),\n",
+        "        torch.nn.Linear(\n",
+        "            4096,\n",
+        "            4096),\n",
+        "        torch.nn.ReLU(inplace=True),\n",
+        "        torch.nn.Dropout(),\n",
+        "        torch.nn.Linear(\n",
+        "            4096,\n",
+        "            output_classes)\n",
+        "    ]\n",
+        "\n",
+        "    self.classifier = torch.nn.Sequential(*classifier)\n",
+        "\n",
+        "  def forward(self, raw):\n",
+        "\n",
+        "    # add a channel dimension to raw\n",
+        "    shape = tuple(raw.shape)\n",
+        "    raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n",
+        "\n",
+        "    # compute features\n",
+        "    f = self.features(raw)\n",
+        "    f = f.view(f.size(0), -1)\n",
+        "\n",
+        "    # classify\n",
+        "    y = self.classifier(f)\n",
+        "\n",
+        "    return y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "5da43245",
+      "metadata": {
+        "execution": {},
+        "id": "5da43245"
+      },
+      "outputs": [],
+      "source": [
+        "# get the size of our images\n",
+        "for x, y in train_dataset:\n",
+        "  input_size = x.shape\n",
+        "  break\n",
+        "\n",
+        "# create the model to train\n",
+        "model = Vgg2D(input_size)\n",
+        "\n",
+        "# create a loss\n",
+        "loss = torch.nn.CrossEntropyLoss()\n",
+        "\n",
+        "# create an optimzer\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "01688095",
+      "metadata": {
+        "execution": {},
+        "id": "01688095"
+      },
+      "source": [
+        "### Train the Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "fa65090d",
+      "metadata": {
+        "execution": {},
+        "id": "fa65090d",
+        "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Will use device cuda for training\n"
+          ]
+        }
+      ],
+      "source": [
+        "# use a GPU, if it is available\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "model.to(device)\n",
+        "print(f\"Will use device {device} for training\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ecbab4f7",
+      "metadata": {
+        "execution": {},
+        "id": "ecbab4f7"
+      },
+      "source": [
+        "The next cell merely defines some convenience functions for training, validation, and testing:"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "1a8c7fe9",
+      "metadata": {
+        "execution": {},
+        "id": "1a8c7fe9"
+      },
+      "outputs": [],
+      "source": [
+        "def train(dataloader, optimizer, loss, device):\n",
+        "  '''Train the model for one epoch.'''\n",
+        "\n",
+        "  # set the model into train mode\n",
+        "  model.train()\n",
+        "\n",
+        "  epoch_loss, num_batches = 0, 0\n",
+        "  for x, y in tqdm(dataloader, 'train'):\n",
+        "\n",
+        "    x, y = x.to(device), y.to(device)\n",
+        "    optimizer.zero_grad()\n",
+        "\n",
+        "    y_pred = model(x)\n",
+        "    l = loss(y_pred, y)\n",
+        "    l.backward()\n",
+        "\n",
+        "    optimizer.step()\n",
+        "\n",
+        "    epoch_loss += l\n",
+        "    num_batches += 1\n",
+        "\n",
+        "  return epoch_loss/num_batches\n",
+        "\n",
+        "\n",
+        "def evaluate(dataloader, name, device):\n",
+        "\n",
+        "  correct = 0\n",
+        "  total = 0\n",
+        "  for x, y in tqdm(dataloader, name):\n",
+        "\n",
+        "    x, y = x.to(device), y.to(device)\n",
+        "\n",
+        "    logits = model(x)\n",
+        "    probs = torch.nn.Softmax(dim=1)(logits)\n",
+        "    predictions = torch.argmax(probs, dim=1)\n",
+        "\n",
+        "    correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n",
+        "    total += len(y)\n",
+        "\n",
+        "  accuracy = correct/total\n",
+        "\n",
+        "  return accuracy\n",
+        "\n",
+        "\n",
+        "def validate(validation_dataset, device):\n",
+        "  '''Evaluate prediction accuracy on the validation dataset.'''\n",
+        "\n",
+        "  model.eval()\n",
+        "  dataloader = DataLoader(validation_dataset, batch_size=32)\n",
+        "\n",
+        "  return evaluate(dataloader, 'validate', device)\n",
+        "\n",
+        "\n",
+        "def test(test_dataset, device):\n",
+        "  '''Evaluate prediction accuracy on the test dataset.'''\n",
+        "\n",
+        "  model.eval()\n",
+        "  dataloader = DataLoader(test_dataset, batch_size=32)\n",
+        "\n",
+        "  return evaluate(dataloader, 'test', device)"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "markdown",
+      "id": "68bcfbbf",
+      "metadata": {
+        "execution": {},
+        "id": "68bcfbbf"
+      },
+      "source": [
+        "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "d0af7638",
+      "metadata": {
+        "execution": {},
+        "id": "d0af7638"
+      },
+      "outputs": [],
+      "source": [
+        "def train_from_scratch(dataloader, validation_dataset,\n",
+        "                       optimizer, loss,\n",
+        "                       num_epochs=100, device=device):\n",
+        "\n",
+        "  for epoch in range(num_epochs):\n",
+        "    epoch_loss = train(dataloader, optimizer, loss, device=device)\n",
+        "    print(f\"epoch {epoch}, training loss={epoch_loss}\")\n",
+        "\n",
+        "    accuracy = validate(validation_dataset, device=device)\n",
+        "    print(f\"epoch {epoch}, validation accuracy={accuracy}\")"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "markdown",
+      "id": "45e31b87",
+      "metadata": {
+        "execution": {},
+        "id": "45e31b87"
+      },
+      "source": [
+        "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n",
+        "\n",
+        "Unceck the box below and run the cell to train a model."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "W5KA7zDIa3Lw",
+      "metadata": {
+        "cellView": "form",
+        "execution": {},
+        "id": "W5KA7zDIa3Lw"
+      },
+      "outputs": [],
+      "source": [
+        "# @markdown\n",
+        "yes_I_want_the_pretrained_model =  True # @param {type:\"boolean\"}"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "53fb8dda",
+      "metadata": {
+        "execution": {},
+        "id": "53fb8dda"
+      },
+      "outputs": [],
+      "source": [
+        "# Load a pretrained model or train the model from scratch\n",
+        "\n",
+        "# set this to True and run this cell if you want a shortcut\n",
+        "\n",
+        "if yes_I_want_the_pretrained_model:\n",
+        "  checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n",
+        "                          map_location=device)\n",
+        "  model.load_state_dict(checkpoint['model_state_dict'])\n",
+        "else:\n",
+        "  train_from_scratch(dataloader, validation_dataset,\n",
+        "                     optimizer, loss,\n",
+        "                     num_epochs=100, device=device)"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "4f6e3663",
+      "metadata": {
+        "execution": {},
+        "id": "4f6e3663",
+        "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "test: 100%|██████████| 216/216 [00:12<00:00, 17.76it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "final test accuracy: 0.8054750869061413\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "accuracy = test(test_dataset, device=device)\n",
+        "print(f\"final test accuracy: {accuracy}\")"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "markdown",
+      "id": "3f43bba5",
+      "metadata": {
+        "execution": {},
+        "id": "3f43bba5"
+      },
+      "source": [
+        "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n",
+        "\n",
+        "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n",
+        "\n",
+        "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "markdown",
+      "id": "72b5240c",
+      "metadata": {
+        "execution": {},
+        "id": "72b5240c"
+      },
+      "source": [
+        "---\n",
+        "# Train a GAN to Translate Images\n",
+        "\n",
+        "We will train a so-called CycleGAN to translate images from one class to another."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "41c9e63b",
+      "metadata": {
+        "cellView": "form",
+        "execution": {},
+        "id": "41c9e63b"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Get the CycleGAN code and dependencies\n",
+        "\n",
+        "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n",
+        "\n",
+        "import requests, zipfile, io\n",
+        "\n",
+        "url = 'https://osf.io/vutn5/download'\n",
+        "r = requests.get(url)\n",
+        "z = zipfile.ZipFile(io.BytesIO(r.content))\n",
+        "z.extractall()\n",
+        "\n",
+        "!pip install dominate --quiet"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e5da5c01",
+      "metadata": {
+        "execution": {},
+        "id": "e5da5c01"
+      },
+      "source": [
+        "In this example, we will translate between GABAergic and glutamatergic synapses.\n",
+        "\n",
+        "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "2b2519c4",
+      "metadata": {
+        "execution": {},
+        "id": "2b2519c4",
+        "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 22648/22648 [00:03<00:00, 6536.44it/s]\n",
+            "100%|██████████| 5085/5085 [00:01<00:00, 3398.25it/s]\n",
+            "0it [00:00, ?it/s]\n",
+            "0it [00:00, ?it/s]\n",
+            "0it [00:00, ?it/s]\n",
+            "0it [00:00, ?it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import cycle_gan\n",
+        "\n",
+        "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n",
+        "\n",
+        "## Uncomment if you want to enable the training procedure\n",
+        "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0d328904",
+      "metadata": {
+        "execution": {},
+        "id": "0d328904"
+      },
+      "source": [
+        "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n",
+        "\n",
+        "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "a182c3bc",
+      "metadata": {
+        "execution": {},
+        "id": "a182c3bc",
+        "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "----------------- Options ---------------\n",
+            "             aspect_ratio: 1.0                           \n",
+            "           aux_checkpoint: checkpoints/synapses/classifier/vgg_checkpoint\t[default: None]\n",
+            "   aux_downsample_factors: [(2, 2), (2, 2), (2, 2), (2, 2)]\n",
+            "             aux_input_nc: 1                             \n",
+            "           aux_input_size: 128                           \n",
+            "                  aux_net: vgg2d                         \n",
+            "       aux_output_classes: 6                             \n",
+            "               batch_size: 1                             \n",
+            "          checkpoints_dir: checkpoints/synapses/cycle_gan/gaba_glutamate\t[default: ./checkpoints]\n",
+            "                crop_size: 128                           \n",
+            "                 dataroot: data/raw/synapses/cycle_gan/0_gaba_2_glutamate\t[default: None]\n",
+            "             dataset_mode: single                        \n",
+            "                direction: AtoB                          \n",
+            "          display_winsize: 256                           \n",
+            "                    epoch: latest                        \n",
+            "                     eval: False                         \n",
+            "                  gpu_ids: 0                             \n",
+            "                init_gain: 0.02                          \n",
+            "                init_type: normal                        \n",
+            "                 input_nc: 1                             \n",
+            "                  isTrain: False                         \t[default: None]\n",
+            "                load_iter: 0                             \t[default: 0]\n",
+            "                load_size: 128                           \n",
+            "         max_dataset_size: inf                           \n",
+            "                    model: test                          \n",
+            "             model_suffix: _A                            \t[default: ]\n",
+            "               n_layers_D: 3                             \n",
+            "                     name:                               \t[default: experiment_name]\n",
+            "                      ndf: 64                            \n",
+            "                     netD: basic                         \n",
+            "                     netG: resnet_9blocks                \n",
+            "                      ngf: 64                            \n",
+            "               no_dropout: True                          \t[default: False]\n",
+            "                  no_flip: True                          \n",
+            "                     norm: instance                      \n",
+            "                    ntest: inf                           \n",
+            "                 num_test: 500                           \t[default: 50]\n",
+            "              num_threads: 1                             \t[default: 4]\n",
+            "                output_nc: 1                             \n",
+            "                    phase: test                          \n",
+            "               preprocess: none                          \n",
+            "              results_dir: data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results\t[default: ./results/]\n",
+            "           serial_batches: False                         \n",
+            "                   suffix:                               \n",
+            "                  verbose: True                          \t[default: False]\n",
+            "----------------- End -------------------\n",
+            "dataset [SingleDataset] was created\n",
+            "initialize network with normal\n",
+            "model [TestModel] was created\n",
+            "loading the model from checkpoints/synapses/cycle_gan/gaba_glutamate/latest_net_G_A.pth\n",
+            "---------- Networks initialized -------------\n",
+            "DataParallel(\n",
+            "  (module): ResnetGenerator(\n",
+            "    (model): Sequential(\n",
+            "      (0): ReflectionPad2d((3, 3, 3, 3))\n",
+            "      (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1))\n",
+            "      (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "      (3): ReLU(inplace=True)\n",
+            "      (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+            "      (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "      (6): ReLU(inplace=True)\n",
+            "      (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+            "      (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "      (9): ReLU(inplace=True)\n",
+            "      (10): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (11): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (12): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (13): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (14): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (15): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (16): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (17): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (18): ResnetBlock(\n",
+            "        (conv_block): Sequential(\n",
+            "          (0): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "          (3): ReLU(inplace=True)\n",
+            "          (4): ReflectionPad2d((1, 1, 1, 1))\n",
+            "          (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n",
+            "          (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "        )\n",
+            "      )\n",
+            "      (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
+            "      (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "      (21): ReLU(inplace=True)\n",
+            "      (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
+            "      (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
+            "      (24): ReLU(inplace=True)\n",
+            "      (25): ReflectionPad2d((3, 3, 3, 3))\n",
+            "      (26): Conv2d(64, 1, kernel_size=(7, 7), stride=(1, 1))\n",
+            "      (27): Tanh()\n",
+            "    )\n",
+            "  )\n",
+            ")\n",
+            "[Network G_A] Total number of parameters : 11.366 M\n",
+            "-----------------------------------------------\n",
+            "creating web directory data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest\n",
+            "processing (0000)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/0_train.png']\n",
+            "processing (0005)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10004_train.png']\n",
+            "processing (0010)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10009_train.png']\n",
+            "processing (0015)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10013_train.png']\n",
+            "processing (0020)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10018_train.png']\n",
+            "processing (0025)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10022_train.png']\n",
+            "processing (0030)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10027_train.png']\n",
+            "processing (0035)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10031_train.png']\n",
+            "processing (0040)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10036_train.png']\n",
+            "processing (0045)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10040_train.png']\n",
+            "processing (0050)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10045_train.png']\n",
+            "processing (0055)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1004_train.png']\n",
+            "processing (0060)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10054_train.png']\n",
+            "processing (0065)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10059_train.png']\n",
+            "processing (0070)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10063_train.png']\n",
+            "processing (0075)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10068_train.png']\n",
+            "processing (0080)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10072_train.png']\n",
+            "processing (0085)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10077_train.png']\n",
+            "processing (0090)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10081_train.png']\n",
+            "processing (0095)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10086_train.png']\n",
+            "processing (0100)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10090_train.png']\n",
+            "processing (0105)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10095_train.png']\n",
+            "processing (0110)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1009_train.png']\n",
+            "processing (0115)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10103_train.png']\n",
+            "processing (0120)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10108_train.png']\n",
+            "processing (0125)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10112_train.png']\n",
+            "processing (0130)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10117_train.png']\n",
+            "processing (0135)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10121_train.png']\n",
+            "processing (0140)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10126_train.png']\n",
+            "processing (0145)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10130_train.png']\n",
+            "processing (0150)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10135_train.png']\n",
+            "processing (0155)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1013_train.png']\n",
+            "processing (0160)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10144_train.png']\n",
+            "processing (0165)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10149_train.png']\n",
+            "processing (0170)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10153_train.png']\n",
+            "processing (0175)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10158_train.png']\n",
+            "processing (0180)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10162_train.png']\n",
+            "processing (0185)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10167_train.png']\n",
+            "processing (0190)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10171_train.png']\n",
+            "processing (0195)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10176_train.png']\n",
+            "processing (0200)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10180_train.png']\n",
+            "processing (0205)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10185_train.png']\n",
+            "processing (0210)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1018_train.png']\n",
+            "processing (0215)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10194_train.png']\n",
+            "processing (0220)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10199_train.png']\n",
+            "processing (0225)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10202_train.png']\n",
+            "processing (0230)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10207_train.png']\n",
+            "processing (0235)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10211_train.png']\n",
+            "processing (0240)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10216_train.png']\n",
+            "processing (0245)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10220_train.png']\n",
+            "processing (0250)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10225_train.png']\n",
+            "processing (0255)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1022_train.png']\n",
+            "processing (0260)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10234_train.png']\n",
+            "processing (0265)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10239_train.png']\n",
+            "processing (0270)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10243_train.png']\n",
+            "processing (0275)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10248_train.png']\n",
+            "processing (0280)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10252_train.png']\n",
+            "processing (0285)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10257_train.png']\n",
+            "processing (0290)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10261_train.png']\n",
+            "processing (0295)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10266_train.png']\n",
+            "processing (0300)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10270_train.png']\n",
+            "processing (0305)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10275_train.png']\n",
+            "processing (0310)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1027_train.png']\n",
+            "processing (0315)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10284_train.png']\n",
+            "processing (0320)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10289_train.png']\n",
+            "processing (0325)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10293_train.png']\n",
+            "processing (0330)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10298_train.png']\n",
+            "processing (0335)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10301_train.png']\n",
+            "processing (0340)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10306_train.png']\n",
+            "processing (0345)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10310_train.png']\n",
+            "processing (0350)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10315_train.png']\n",
+            "processing (0355)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1031_train.png']\n",
+            "processing (0360)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10324_train.png']\n",
+            "processing (0365)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10329_train.png']\n",
+            "processing (0370)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10333_train.png']\n",
+            "processing (0375)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10338_train.png']\n",
+            "processing (0380)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10342_train.png']\n",
+            "processing (0385)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10347_train.png']\n",
+            "processing (0390)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10351_train.png']\n",
+            "processing (0395)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10356_train.png']\n",
+            "processing (0400)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10360_train.png']\n",
+            "processing (0405)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10365_train.png']\n",
+            "processing (0410)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1036_train.png']\n",
+            "processing (0415)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10374_train.png']\n",
+            "processing (0420)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10379_train.png']\n",
+            "processing (0425)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10383_train.png']\n",
+            "processing (0430)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10388_train.png']\n",
+            "processing (0435)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10392_train.png']\n",
+            "processing (0440)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10397_train.png']\n",
+            "processing (0445)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10400_train.png']\n",
+            "processing (0450)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10405_train.png']\n",
+            "processing (0455)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1040_train.png']\n",
+            "processing (0460)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10414_train.png']\n",
+            "processing (0465)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10419_train.png']\n",
+            "processing (0470)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10423_train.png']\n",
+            "processing (0475)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10428_train.png']\n",
+            "processing (0480)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10432_train.png']\n",
+            "processing (0485)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10437_train.png']\n",
+            "processing (0490)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10441_train.png']\n",
+            "processing (0495)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10446_train.png']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# translate images from class A to B, and classify each with the VGG network trained above\n",
+        "cycle_gan.test(\n",
+        "    data_dir='data/raw/synapses/',\n",
+        "    class_A='0_gaba',\n",
+        "    class_B='2_glutamate',\n",
+        "    img_size=128,\n",
+        "    checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n",
+        "    vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "17fc1703",
+      "metadata": {
+        "execution": {},
+        "id": "17fc1703"
+      },
+      "source": [
+        "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "2a582ba6",
+      "metadata": {
+        "execution": {},
+        "id": "2a582ba6"
+      },
+      "outputs": [],
+      "source": [
+        "class_A_index = 0\n",
+        "class_B_index = 2\n",
+        "\n",
+        "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n",
+        "classification_results = []\n",
+        "for f in glob.glob(result_dir + '/*.json'):\n",
+        "  result = json.load(open(f))\n",
+        "  result['basename'] = f.replace('_aux.json', '')\n",
+        "  classification_results.append(result)\n",
+        "classification_results.sort(\n",
+        "    key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n",
+        "    reverse=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2cc0d486",
+      "metadata": {
+        "execution": {},
+        "id": "2cc0d486"
+      },
+      "source": [
+        "Show the top real and fake images that make the classifier change its mind:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "1567b00e",
+      "metadata": {
+        "execution": {},
+        "id": "1567b00e",
+        "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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wcNB+nvLUpz41hoaG5B2SuxgwN3gxAmCDjj/++BgZGYnzzz8/jjrqqLjkkktmfJEfEfGTn/wk9ttvv8rPfuc734nXvva10dLSEueff368+tWvjlNOOSXuvPPOSt5Pf/rT8axnPSs+/OEPx3nnnRfNzc3xmte8ZsaXvGv94Ac/iHe+851x4oknxoc//OFYtWpVvOQlL5kRtO/nP/95/OQnP4kTTjghLrnkkjj11FPj1ltvjcMOO2zG33Fda//996/8PVZldHQ0Vq5cWdP/Nubuu++Opz/96TFv3rwZ6Wt/7dn9yam19YjQh7mOjo64++67Y2pqajqvyrf2V7bVeDztaU+L+fPnR09PT5x44omxYsWKGf/9e9/7XkQ8dsg84ogjoqOjIzo6OuKlL32p/bu6j8fagJfrvxi5++67Y7/99pv+1ee1DjzwwBgaGqr8GaqJiYlYuXJlPPjgg/Hd7343/u7v/i56enoqv2K+vlJKrFixIrbYYovptD//+c/x8MMPx7Of/exK/gMPPDDuvvvuSvq5554b3d3d0d7eHgcccEB897vf3XDDN2Bt+et//v777x+NjY0zPv+www6L//zP/4y///u/j9///vfx3//93/GRj3wkfvGLX8T73ve+6XzZ8fzTn/4UF1xwQXz84x+v6WUOAADAX4In6m7zeF199dXR1tYWz3ve8+Lqq6+Oq6++Ot7+9rdHRP5+c8YZZ8Tvfve7OPfcc+PlL395XHnllfH3f//38bKXvSwmJyfjvPPOi+c+97nxyU9+svJnhGq5m22oritWrIiDDz44vve978Xpp58en/70p2OXXXaJU045RX6Jvv/++8evf/3r6S+ynfHx8ZrvYmvvQs7dd98dXV1dsccee8xIX3s/UGf6tTZ0F+vs7IwHH3wwli9fHhER//Zv/xYjIyOxyy67xHHHHRednZ3R0dERz3nOczZ439uQtbEpv/jFL8ayZcviT3/6U/z7v/97nHzyybFw4cLKnI6IePOb3xzz5s2L9vb2eMELXhC/+MUvNvo51157bUxNTVXuYhERk5OTccYZZ8Rb3/rWeMYznrHBckopsXLlyli+fPn0nwBuamqKww47bDrP85///GhsbIyzzjor7rjjjnjggQfin//5n+NjH/tYvPKVr4zdd989InJ3sVrLVP75n/851qxZI9seEfHOd74zXvCCF1T+XPJafX198bOf/SwOOOCA+Nu//duYP39+dHd3x9Oe9jT7D+mGh4dj5cqVcd9998WXvvSluOqqq2Lp0qWVecZdDJhDT+rvqwD4i7X2Tyq9/OUvn5H+jne8o0RE+dWvflVKeezXeBsaGsq73/3uShnPeMYzynbbbVf6+/un077//e+XiCg77rjjjLxr/4TQWmNjY2Xvvfcuhx9++Iz0iCgRUX7xi19Mp/3xj38s7e3t5VWvepUtr5RSbr/99hIR5ctf/nLlv5133nklIsqKFSsq/21dV1111XQdNva/jdlrr70q7SullP/8z/8sEVGuuOIK+7OPPPJIaWhoKKeccsqM9HvuuWf689f+SaQzzjijNDY2lvvuu29G3hNOOKFERDn99NOn0y6++OJy+umnl2XLlpXrr7++nHXWWaW5ubnsuuuupbe3dzrfmWeeWSKiLF68uLzkJS8pX/3qV8snP/nJ0t3dXXbeeecyODgo6535U1oTExNlyZIl5cADD6z8t66urvKWt7ylkv6d73ynRES5+eabZ6SvHfu1/9ttt93kr6Wv7+qrry4RMePPrP385z+38+i9731viYjpP131xz/+sbzoRS8ql19+efnWt75VLr744rLDDjuUxsbGyq90r2tDf0rrtNNOK01NTfK/bbnlluWEE06Y/v8HBgbK8ccfXxoaGqbb3tnZWb75zW/O+LnseB533HHlkEMOmf7/g1/fBgAAf8Ge6LtNrPentE466aRKnnXrtS7356lqvd+sva+8+MUvnvEnd5cuXVoaGhrKqaeeOp02MTFRtttuu3LooYdu8LPc3czV9ZRTTinbbLNN5U+0nnDCCWX+/PmV8q+55poSEeWnP/1ppax1rf2zULX8b2P3jaOPPro87WlPq6QPDg6WiCh/8zd/Y392cnKyLFiwoBxxxBEz0leuXFm6urpm3FcvvPDC6XP2gQceWJYtW1Yuu+yysmTJkrJw4cLy4IMPys/Y0J/SKqWU3/3ud2W//fab0eanPe1p5Z577pmR78c//nE59thjyxe+8IVy4403lvPPP78sXry4tLe3b/RPSe2///5lm222KZOTk5X/9tnPfrbMnz+/PPzww6WUssE/pfXQQw/NqOd2221XvvrVr1byff7zny8LFiyYkfekk04q4+Pj03kyd7Fay1SOPfbY0tbWVlavXl35bzfddFNpbm4u//mf/1lKeWx9r/+ntO66667pcV+yZEm57LLLyrJly8qBBx5YGhoayr/8y79Uyj3//PNn1POII44of/rTnyr5uIsBc4fg6wA26LTTTpvx/59xxhlx2WWXxT//8z/HPvvsE48++miUUmLhwoUz8j344IPxH//xH/G3f/u3M4J4H3roofGMZzyj8q+B1v2XDqtXr47Jycl43vOeF1/5ylcqdVq6dGnsv//+0///DjvsEK94xSvi29/+dkxOTkZTU9OM8sbHx6Ovry922WWXWLBgQdx1113xxje+cUaZa+u/cuXK2GqrrWx/vPjFL45bbrnF/veM4eHhGX/iaK21v6Y8PDxsf3aLLbaI448/Pr70pS/FHnvsEa961aviz3/+c5xxxhnR0tIy/evVERFvfetb44orrojjjz8+LrrooliyZEl87WtfixtuuKHyOWedddaMzzn22GPjwAMPjDe84Q1x2WWXxd/8zd9ExGOB5iIitt566/jOd74z/Zsb2223Xbzuda+La665Jt761rc+3q6JiIhbb701VqxYEX/7t39b+W/Zvttzzz3jlltuicHBwfjJT34S3/ve96bb4Nxzzz1x2mmnxdKlS+Okk06a8dkRsdHPb2trix122CH+9V//dUaeN77xjbHnnnvGu9/97jj66KM3WAdleHi48ie/1v38ddve1tYWT3/60+O4446LV7/61TE5ORlXXnllnHjiiXHLLbfEwQcfHBG58bztttvi61//evz0pz9N1x0AAODJ9ETdbTaF7P3mlFNOmfFnug466KC4/fbb45RTTplOa2pqimc/+9mV33rJ3M3WV0qJr3/963H88cdP/6bAWi9+8Yvj2muvjbvuuiue85znTKevexfbkGc+85k138W23nrrDf732dzFGhsb4+1vf3t8/OMfj3POOSfe8pa3RF9fX7zvfe+b/rNHa39+7Tm7oaEhbr311un586xnPSuWLl0al156aXz0ox+tqU3r6unpib322iuWLl0aRxxxRCxfvjwuuOCCeOUrXxk/+tGPpn/j/ZBDDolDDjlk+ude/vKXx3HHHRf77LNPnHPOOXHzzTfL8n/729/GnXfeGWeffXblt/RXrVoVH/jAB+Lv//7vZwSZdxYtWhS33HJLjIyMxN133x3f+MY35F3sKU95Shx44IFx1FFHxY477hg/+tGP4pJLLoktttgiPvWpT0VE7i5Wa5nr6+vri+985ztx1FFHxYIFC2b8t7GxsTj77LPj1FNPjT333NO2eW37Vq1aFXfccUccdNBBEfFY/++0007x0Y9+NF7ykpfM+JnXve518exnPzseeeSRuOmmm2LFihWVechdDJhbvBgBsEG77rrrjP9/5513jsbGxsqf1ynr/V3aP/7xjxERscsuu1TK3GWXXeKuu+6akXbTTTfFRz/60fjlL38542+urnuYd3WKiHj6058eQ0ND8cgjj8TWW28dw8PDcf7558dVV10Vf/7zn2fUr7e3t/Lza/+7+rx1bbPNNtN/k3S2Ojo65N+XXft3jjf2a7Gf+9znYnh4ON7znvdM/23RE088MXbeeef4xje+MX3o3meffeKaa66JU089dfoCsvXWW8fFF18cf/3Xfz3jcqe8/vWvj3e/+93xve99b/rFyNq6HX/88TMOyq95zWvijW98Y/zkJz+Z9YuRZcuWRVNTU7z2ta+t/Lds382bNy+OPPLIiIh4xSteEddcc0284hWviLvuuiue+cxnVspZvnx5HH300TF//vzpWDDrfnZEPO6xW7RoUbz5zW+OCy64IB544AH5N2s3pKOjw8YqGRkZmfHZp59+etxxxx1x1113TY/T8ccfH3vttVecddZZ0wfqWsdzYmIizjzzzHjjG98YBxxwQKreAAAAT7Yn6m6zKWTvN+vGX4iImD9/fkREbL/99pX01atXz0jL3M3W98gjj8SaNWviyiuvjCuvvFLmefjhh2f8/7XexRYuXDh9pp+t2d7FPvzhD8fKlSvjE5/4RFxwwQUREfGiF70oTjnllLjiiium71hry3nZy14249518MEHx0477VTTn3Ne38TERBx55JFx2GGHxWc+85np9COPPDL22muv+OQnPxkf//jH7c/vsssu8YpXvCK+8Y1vTP/DwvW5P2kcEfF3f/d3sWjRojjjjDNqqm9ra+v0uB1zzDFxxBFHxHOe85zYaqut4phjjomIiB//+MdxzDHHxB133DH9Z7Je+cpXxrx58+Lcc8+Nt7zlLbHnnnum7mK1lrm+r3/96zEyMiLbftFFF8XKlSvj3HPP3WCb19Zhp512mn4pEhHR3d0dL3vZy+Kf/umfYmJiYkZ8kh133DF23HHHiHjsJcnb3va2OPLII+Pee++Njo4O7mLAJkCMEQAp6x9WFy1aFA0NDZXDdMaPfvSjePnLXx7t7e3T/2Lrlltuide//vU2+PfGnHHGGfGxj30sjj/++Pja174W3/3ud+OWW26JxYsXy783u7b+68aSUIaHh2P58uU1/W9jttlmm3jooYcq6WvTtt122w3+/Pz58+PGG2+MP/7xj/GDH/wg7rvvvrj66qvjoYcemg6yuNZxxx0XDz74YPzsZz+L22+/Pf74xz/G0572tIh47KXSxmy//fbx6KOPTv//a+u2fiC7pqamWLx48azmQ8Rj/XzDDTfEkUceWfmMiNn33dqg7esGRlyrt7c3XvrSl8aaNWvi5ptvrpS19sWY+/xFixbJf8G0rrUX0nX7tFbbbLNNTE5OVi6UY2NjsWrVqun6jo2NxRe+8IU4+uijZ7zsaGlpiZe+9KXxi1/8YvoFS63j+eUvfznuvffeePvb3x733Xff9P8iHgvmeN9998m/cQ0AAPCXaFPcbWr5nLUmJydrLiN7v1Ffdrv0de9cs72bra3L2t9QVv9b97dFImq/i42NjdV8F9tY326zzTaxfPnySptqvU+0trbG5z//+XjwwQfjhz/8Ydx7773xr//6r9Hb2xuNjY3TL9HcOTvisWDpj2eu/fCHP4xf//rX8fKXv3xG+q677hp77LFH/PjHP95oGdtvv32MjY3ZgODXXHNN7LbbbjP+UkPEY0HHr7zyyjjzzDPjwQcfnL4PjIyMxPj4eNx3330bveMccsghsc0220y/fIl47B/9LVmypBI75OUvf3mUUqZfIGXuYrWWub5ly5bF/Pnzp1/arNXb2xsf/ehH46/+6q+ir69vuu0DAwNRSon77rtv+o62sXEfHx/faDD24447Lu6///744Q9/GBHcxYBNgd8YAbBBv/vd72KnnXaa/v9///vfx9TUVDz1qU+NiIjm5ubYeeed4w9/+MOMn1v7Lx1+//vfV8pcP+3rX/96tLe3x7/+67/O+EL5qquusnVa329/+9vo7Oyc/lXe66+/Pk466aT4h3/4h+k8IyMjsWbNGlnmH/7wh9hiiy02+qvAX/3qV+PNb37zBvOstbGLw7777hu33XZb9PX1zQjAvvZf8e+77741fc4OO+ww/S/C1qxZE3feeWcce+yxlXytra0z/mXJ2oDbG/tXV2sPec961rOm09YekP/85z/PyDs2NhYrV66s6VeqN+Rb3/pW9Pf322B3++67b/zoRz+KqampGV/6//SnP43Ozs6NvuwZHR2Nqampyr+uGxkZiZe97GXx29/+Nr73ve/Jf0H0lKc8JbbccksZsPBnP/tZTeP2P//zPxERj6uf1pb/i1/8Ykawv1/84hcxNTU1/d9XrVoVExMT8lI4Pj4eU1NT0/+t1vH805/+FOPj45XLbMRjB/Uvf/nLccMNN8QrX/nKdLsAAAA2tSfibqMsXLhQ3kPW/ibKutxLlOz95vHK3M1UXbfccsvo6emJycnJmn+74w9/+EM0NjZu9Az/k5/8JF7wghfUXObacVX23Xff+PznPx+/+c1vZpz5s3exJUuWTH/5PTk5Gd///vfjoIMOmv7tEHfOjnjsT7RtKAC4s2LFiunPW9/4+HhMTExstIz/+Z//ifb2dvnXA37605/G73//+/jwhz9c+W9//vOfY2pqKs4888w488wzK/99p512irPOOisuvvjiDX7+yMjIjLvYihUrbHsiYrpNmbtYrWWu66GHHorbbrstTj755Mo/dlu9enUMDAzEJz7xifjEJz5R+dmddtopXvGKV8Q3v/nN2HbbbWPrrbe2497e3h49PT2V/7autX9Ga20/cRcD5h6/MQJggy699NIZ///aX9V96UtfOp22dOnSysFk2223jb333ju+/OUvz/j7oT/4wQ/iP/7jP2bkbWpqioaGhhmHlvvuuy+++c1vyjrdfvvtM35d/f77748bb7wxXvSiF03/C6impqbKi4nPfOYz9l8O3XnnnbF06VL539a1NsZILf/bmOOOO2465sNao6OjcdVVV8VBBx0049fc//SnP8U999yz0TLPOeecmJiYiLPPPnuD+X73u9/FFVdcEcccc8yMC8gjjzxSyXv55ZfHI488MuNvoB522GGx1VZbxbJly6Z/ZTki4otf/GJMTk7GC1/4wo3WdUOuueaa6OzsjFe96lXyvx933HGxYsWK+MY3vjGdtnLlyrjuuuviZS972fQhds2aNdMH33V9/vOfj4iY8a+HJicn47WvfW3cfvvtcd11121wPhx77LFx0003xf333z+dduutt8Zvf/vbeM1rXjOdpvrzz3/+c/zf//t/Y5999nlcf5bt8MMPj0WLFsXll18+I/3yyy+Pzs7O6bglW221VSxYsCBuuOGGGX96a2BgIL797W/H7rvvPv0r3rWO5wknnBA33HBD5X8REUcddVTccMMNM35VHAAA4C/JE3G3UXbeeefo7e2Nf//3f59Oe+ihh6bPUevq6uqSLzuy95vHK3M3U3VtamqKY489Nr7+9a/Hr3/968rPqPPxnXfeGXvttdf0n/ty1sYYqeV/G4sx8opXvCJaWlrisssum04rpcQVV1wRT3nKU2bE5XjooYfinnvukfeKdX3qU5+Khx56KN797ndPp+22227xzGc+M2688cYZMVS++93vxv333/+47k1r72/r//b7XXfdFffee++Mf9Cm+vtXv/pVfOtb34oXvehFlfghEY/dxSIe+5PK69t7773lfWCvvfaKHXbYIW644YbpODaDg4PyNxi+/vWvx+rVq2fcxZ7+9KfHihUr4vvf//6MvGvj2qzbplrvYpky17r22mtjampK/gO9rbbaSrb9BS94QbS3t8cNN9wQ55xzznT+1772tXH//ffP+G5g5cqVceONN8bhhx8+3fdqjCIivvCFL0RDQ0Pst99+EcFdDNgknshI7wA2Hx/84AdLRJRnPOMZ5WUve1m59NJLy4knnlgiorz+9a+fkff6668vEVHuvffeGenf+ta3SkNDQ9lnn33KRRddVD7wgQ+URYsWlb333rs89alPnc536623logoz3ve88rll19ezj333LLVVluVffbZp6y/TUVE2XvvvcsWW2xRPvzhD5ePf/zjZccddyzt7e3lV7/61XS+N73pTaWpqamcddZZ5XOf+1w5+eSTy3bbbVcWL15cTjrppBllrlixojQ1NZXPf/7zc9R7tXvNa15Tmpuby3vf+97yuc99rhxyyCGlubm5/OAHP5iR79BDD630xfnnn1/e8IY3lEsuuaRcdtll5UUvelGJiPLRj3608jl77LFH+cAHPlA+//nPl//zf/5PWbRoUdlxxx3LAw88MCNfR0dHOfnkk8s//MM/lEsvvbS87nWvKw0NDWXfffctg4ODM/J+6UtfKhFRDjjggHLJJZeU97znPaWlpaU873nPKxMTE9P51qxZUz7ykY+Uj3zkI+UlL3lJiYjy7ne/u3zkIx8pn/nMZyp1XbVqVWlpaSknnHCC7beJiYly8MEHl+7u7nLuueeWSy+9tOy1116lp6en3HPPPdP5brjhhrL99tuXs88+u1x22WXl4osvLscee2xpaGgoz372s8vo6Oh03rPOOqtERHnZy15Wrr766sr/1vWnP/2pLF68uOy8887lkksuKeedd15ZuHBhecYznlFGRkam85188snlec97XvnQhz5UrrzyyvK3f/u3ZfHixaW1tbXcdtttM8q87777pvvpoIMOKhEx/f9/+ctfnpH30ksvLRFRjjvuuPKP//iP5U1velOJiPKxj31sRr6PfvSjJSLKs571rHLRRReVT33qU2WPPfYoEVH+6Z/+6XGNpxIR5bTTTttgHgAAgCfLE3m3KeWxs9EHP/jB6f9/5cqVpaurqzztaU8rF198cTnvvPPK9ttvX/bbb7/KGf+oo44qXV1d5R/+4R/KV77ylXLHHXeUUmq/31x11VUlIsrPf/5z2QePPPLIjPSTTjqpdHV1Tf//mbuZq+vy5cvLjjvuWDo7O6fre/7555fXvOY1ZeHChTPKGBsbK4sWLSp/93d/V55o733ve0tElLe97W3lH//xH8vRRx9dIqIsW7ZsRr6TTjqpRET5wx/+MJ129dVXl1e+8pXlwgsvLFdeeWU5/vjjS0SUt771rZXP+X//7/+Vpqamsttuu5ULL7ywfPCDHyw9PT3l6U9/eunv75+Rd+35/4QTTigRUd7ylrdMp63rhS98YYmI8qpXvapcfvnl5QMf+EBZuHBh6erqmnEfesELXlCOOuqo8tGPfrRceeWV5Z3vfGfp7Ows8+fPL//1X/9VqevExERZsmRJOfjgg1N9eeihh5a99tprRtrdd99dFi9eXN7xjneUSy65pHz2s58tJ598cmlubi5PfepTy8qVK6fz3nPPPaWrq6t0d3eXc845p1xxxRXlda97XYmI8sIXvnBGubXexTJlrrX//vuXbbfdtkxOTtbc9vXX0FrLly8v22yzTenp6Skf/OAHy4UXXlie/vSnl46OjvLLX/5yOt9ZZ51Vnv3sZ5e/+7u/K1deeWW54IILygEHHFAiopxxxhkb/XzuYsDjx4sRANLag/N//dd/leOOO6709PSUhQsXltNPP70MDw/PyDs6Olq22GKLymGtlFKuvfbasvvuu5e2tray9957l29961vl2GOPLbvvvvuMfF/4whfKrrvuWtra2sruu+9errrqquk6rGvtQ/+f/umfpvM/61nPqnzJvHr16vLmN7+5bLHFFqW7u7u8+MUvLvfcc0/ZcccdKy9GLr/88tLZ2Vn6+voef4c9TsPDw+U973lP2XrrrUtbW1s54IADys0331zJp16M3HTTTeXAAw8sPT09pbOzsxx88MHla1/7mvycE044oWy//faltbW1bLvttuXUU08tK1asqOR761vfWvbcc8/S09NTWlpayi677FLe//732775yle+Up75zGeWtra2smTJknL66adX8v7hD38oESH/t+OOO1bKvOKKK0pElG9961uu20oppTz66KPllFNOKYsXLy6dnZ3l0EMPrVwAf//735c3velN5WlPe1rp6Ogo7e3tZa+99iof/OAHy8DAwIy8a/vY/W99v/71r8uLXvSi0tnZWRYsWFDe8IY3lOXLl8/Ic80115TnP//5ZcsttyzNzc1liy22KK961avKnXfeWSnvtttus5996KGHVvJfeeWVZbf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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def show_pair(a, b, score_a, score_b, class_a, class_b):\n",
+        "  fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n",
+        "  axs[0].imshow(a, cmap='gray')\n",
+        "  axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n",
+        "  axs[1].imshow(b, cmap='gray')\n",
+        "  axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n",
+        "  plt.show()\n",
+        "\n",
+        "\n",
+        "# show the top successful translations (according to our VGG classifier)\n",
+        "for i in range(10):\n",
+        "  basename = classification_results[i]['basename']\n",
+        "  score_A = classification_results[i]['aux_real'][class_A_index]\n",
+        "  score_B = classification_results[i]['aux_fake'][class_B_index]\n",
+        "  real_A = imread(basename + '_real.png')\n",
+        "  fake_B = imread(basename + '_fake.png')\n",
+        "  show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "m7XsnW_R7wBN"
+      },
+      "id": "m7XsnW_R7wBN",
+      "execution_count": 27,
+      "outputs": []
     }
-   ],
-   "source": [
-    "def show_pair(a, b, score_a, score_b, class_a, class_b):\n",
-    "  fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n",
-    "  axs[0].imshow(a, cmap='gray')\n",
-    "  axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n",
-    "  axs[1].imshow(b, cmap='gray')\n",
-    "  axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n",
-    "  plt.show()\n",
-    "\n",
-    "\n",
-    "# show the top successful translations (according to our VGG classifier)\n",
-    "for i in range(10):\n",
-    "  basename = classification_results[i]['basename']\n",
-    "  score_A = classification_results[i]['aux_real'][class_A_index]\n",
-    "  score_B = classification_results[i]['aux_fake'][class_B_index]\n",
-    "  real_A = imread(basename + '_real.png')\n",
-    "  fake_B = imread(basename + '_fake.png')\n",
-    "  show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "include_colab_link": true,
-   "machine_shape": "hm",
-   "name": "em_synapses",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernel": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernel": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.10"
+    },
+    "accelerator": "GPU"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb
index b02af964d..80b6eda54 100644
--- a/projects/ReinforcementLearning/lunar_lander.ipynb
+++ b/projects/ReinforcementLearning/lunar_lander.ipynb
@@ -1,1224 +1,1300 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "execution": {},
-    "id": "view-in-github"
-   },
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/projects/ReinforcementLearning/lunar_lander.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/projects/ReinforcementLearning/lunar_lander.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n",
-    "\n",
-    "**By Neuromatch Academy**\n",
-    "\n",
-    "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n",
-    "\n",
-    "__Content editors:__ Shaonan Wang, Spiros Chavlis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Objective\n",
-    "\n",
-    "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Update/Upgrade the system and install libs\n",
-    "!apt-get update > /dev/null 2>&1\n",
-    "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n",
-    "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n",
-    "!apt-get install x11-utils > /dev/null 2>&1\n",
-    "!apt-get install xvfb > /dev/null 2>&1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25h"
-     ]
-    }
-   ],
-   "source": [
-    "# @title Install dependencies\n",
-    "!pip install rarfile --quiet\n",
-    "!pip install stable-baselines3[extra] --quiet\n",
-    "!pip install ale-py --quiet\n",
-    "!pip install -q swig --quiet\n",
-    "!pip install gym[box2d] --quiet\n",
-    "!pip install pyvirtualdisplay --quiet\n",
-    "!pip install pyglet --quiet\n",
-    "!pip install pygame --quiet\n",
-    "!pip install -q gymnasium[box2d] --quiet\n",
-    "!pip install 'minigrid<=2.1.1' --quiet\n",
-    "!pip3 install box2d-py --quiet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "sgIXqXYCDBuR"
+      },
+      "source": [
+        "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n",
+        "\n",
+        "**By Neuromatch Academy**\n",
+        "\n",
+        "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n",
+        "\n",
+        "__Content editors:__ Shaonan Wang, Spiros Chavlis"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/dtypes.py:35: DeprecationWarning: ml_dtypes.float8_e4m3b11 is deprecated. Use ml_dtypes.float8_e4m3b11fnuz\n",
-      "  from tensorflow.tsl.python.lib.core import pywrap_ml_dtypes\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Imports\n",
-    "import io\n",
-    "import os\n",
-    "import glob\n",
-    "import torch\n",
-    "import base64\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import sys\n",
-    "import gymnasium\n",
-    "sys.modules[\"gym\"] = gymnasium\n",
-    "\n",
-    "import stable_baselines3\n",
-    "from stable_baselines3 import DQN\n",
-    "from stable_baselines3.common.results_plotter import ts2xy, load_results\n",
-    "from stable_baselines3.common.callbacks import EvalCallback\n",
-    "from stable_baselines3.common.env_util import make_atari_env\n",
-    "\n",
-    "import gymnasium as gym\n",
-    "from gym import spaces\n",
-    "from gym.envs.box2d.lunar_lander import *\n",
-    "from gym.wrappers.monitoring.video_recorder import VideoRecorder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "AKlrTCmFDBuS"
+      },
+      "source": [
+        "---\n",
+        "# Objective\n",
+        "\n",
+        "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# @title Play Video function\n",
-    "from IPython.display import HTML\n",
-    "from base64 import b64encode\n",
-    "from pyvirtualdisplay import Display\n",
-    "\n",
-    "# create the directory to store the video(s)\n",
-    "os.makedirs(\"./video\", exist_ok=True)\n",
-    "\n",
-    "display = Display(visible=False, size=(1400, 900))\n",
-    "_ = display.start()\n",
-    "\n",
-    "\"\"\"\n",
-    "Utility functions to enable video recording of gym environment\n",
-    "and displaying it.\n",
-    "To enable video, just do \"env = wrap_env(env)\"\"\n",
-    "\"\"\"\n",
-    "def render_mp4(videopath: str) -> str:\n",
-    "  \"\"\"\n",
-    "  Gets a string containing a b4-encoded version of the MP4 video\n",
-    "  at the specified path.\n",
-    "  \"\"\"\n",
-    "  mp4 = open(videopath, 'rb').read()\n",
-    "  base64_encoded_mp4 = b64encode(mp4).decode()\n",
-    "  return f'<video width=400 controls><source src=\"data:video/mp4;' \\\n",
-    "         f'base64,{base64_encoded_mp4}\" type=\"video/mp4\"></video>'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Introduction\n",
-    "\n",
-    "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n",
-    "\n",
-    "**Speed:** How fast the algorithm reaches the maximum possible reward.\n",
-    "\n",
-    "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n",
-    "\n",
-    "In this project, you should investigate the following question:\n",
-    "\n",
-    "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n",
-    "\n",
-    "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n",
-    "\n",
-    "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n",
-    "\n",
-    "The input state of the Lunar Lander consists of following components:\n",
-    "\n",
-    "  1. Horizontal Position\n",
-    "  2. Vertical Position\n",
-    "  3. Horizontal Velocity\n",
-    "  4. Vertical Velocity\n",
-    "  5. Angle\n",
-    "  6. Angular Velocity\n",
-    "  7. Left Leg Contact\n",
-    "  8. Right Leg Contact\n",
-    "\n",
-    "The actions of the agents are:\n",
-    "  1. Do Nothing\n",
-    "  2. Fire Main Engine\n",
-    "  3. Fire Left Engine\n",
-    "  4. Fire Right Engine\n",
-    "\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/projects/static/lunar_lander.png\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Basic DQN Implementation\n",
-    "\n",
-    "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "x5XKBDyYDBuS"
+      },
+      "source": [
+        "---\n",
+        "# Setup"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n"
-     ]
-    }
-   ],
-   "source": [
-    "nn_layers = [64, 64]  # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n",
-    "                      # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n",
-    "\n",
-    "learning_rate = 0.001  # This is the step-size with which the gradient descent is carried out.\n",
-    "                       # Tip: Use smaller step-sizes for larger networks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Now, let us setup our model and the DQN algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "log_dir = \"/tmp/gym/\"\n",
-    "os.makedirs(log_dir, exist_ok=True)\n",
-    "\n",
-    "# Create environment\n",
-    "env_name = 'LunarLander-v2'\n",
-    "env = gym.make(env_name)\n",
-    "# You can also load other environments like cartpole, MountainCar, Acrobot.\n",
-    "# Refer to https://gym.openai.com/docs/ for descriptions.\n",
-    "\n",
-    "# For example, if you would like to load Cartpole,\n",
-    "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n",
-    "\n",
-    "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n",
-    "\n",
-    "callback = EvalCallback(env, log_path=log_dir, deterministic=True)  # For evaluating the performance of the agent periodically and logging the results.\n",
-    "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n",
-    "                     net_arch=nn_layers)\n",
-    "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n",
-    "            learning_rate=learning_rate,\n",
-    "            batch_size=1,  # for simplicity, we are not doing batch update.\n",
-    "            buffer_size=1,  # size of experience of replay buffer. Set to 1 as batch update is not done\n",
-    "            learning_starts=1,  # learning starts immediately!\n",
-    "            gamma=0.99,  # discount facto. range is between 0 and 1.\n",
-    "            tau = 1,  # the soft update coefficient for updating the target network\n",
-    "            target_update_interval=1,  # update the target network immediately.\n",
-    "            train_freq=(1,\"step\"),  # train the network at every step.\n",
-    "            max_grad_norm = 10,  # the maximum value for the gradient clipping\n",
-    "            exploration_initial_eps = 1,  # initial value of random action probability\n",
-    "            exploration_fraction = 0.5,  # fraction of entire training period over which the exploration rate is reduced\n",
-    "            gradient_steps = 1,  # number of gradient steps\n",
-    "            seed = 1,  # seed for the pseudo random generators\n",
-    "            verbose=0)  # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n",
-    "\n",
-    "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n",
-    "# Refer to  https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n",
-    "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Before we train the model, let us look at an instance of Lunar Lander **before training**.  \n",
-    "\n",
-    "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "execution": {},
+        "id": "WsHayfTHDBuS"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Update/Upgrade the system and install libs\n",
+        "!apt-get update > /dev/null 2>&1\n",
+        "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n",
+        "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n",
+        "!apt-get install x11-utils > /dev/null 2>&1\n",
+        "!apt-get install xvfb > /dev/null 2>&1"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "State shape:  (8,)\n",
-      "Number of actions:  4\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "execution": {},
+        "id": "6fooEJQSDBuT",
+        "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: swig in /usr/local/lib/python3.10/dist-packages (4.2.1)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "# @title Install dependencies\n",
+        "!pip install rarfile --quiet\n",
+        "!pip install stable-baselines3[extra] --quiet\n",
+        "!pip install ale-py --quiet\n",
+        "!pip install swig\n",
+        "!pip install gym[box2d] --quiet\n",
+        "!pip install pyvirtualdisplay --quiet\n",
+        "!pip install pyglet --quiet\n",
+        "!pip install pygame --quiet\n",
+        "!pip install minigrid --quiet\n",
+        "!pip install -q swig --quiet\n",
+        "!pip install -q gymnasium[box2d] --quiet\n",
+        "!pip install 'minigrid<=2.1.1' --quiet\n",
+        "!pip3 install box2d-py --quiet"
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n"
-     ]
-    }
-   ],
-   "source": [
-    "env_name = 'LunarLander-v2'\n",
-    "env = gym.make(env_name)\n",
-    "print('State shape: ', env.observation_space.shape)\n",
-    "print('Number of actions: ', env.action_space.n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "execution": {},
+        "id": "nA2Y9HGUDBuT"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports\n",
+        "import io\n",
+        "import os\n",
+        "import glob\n",
+        "import torch\n",
+        "import base64\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import sys\n",
+        "import gymnasium\n",
+        "sys.modules[\"gym\"] = gymnasium\n",
+        "\n",
+        "import stable_baselines3\n",
+        "from stable_baselines3 import DQN\n",
+        "from stable_baselines3.common.results_plotter import ts2xy, load_results\n",
+        "from stable_baselines3.common.callbacks import EvalCallback\n",
+        "from stable_baselines3.common.env_util import make_atari_env\n",
+        "\n",
+        "import gymnasium as gym\n",
+        "from gym import spaces\n",
+        "from gym.envs.box2d.lunar_lander import *\n",
+        "from gym.wrappers.monitoring.video_recorder import VideoRecorder"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001B[33mWARN: <class 'gym.wrappers.monitoring.video_recorder.VideoRecorder'> is marked as deprecated and will be removed in the future.\u001B[0m\n",
-      "  logger.deprecation(\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "cellView": "form",
+        "execution": {},
+        "id": "_M-76WwDDBuT",
+        "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+            "  and should_run_async(code)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# @title Play Video function\n",
+        "from IPython.display import HTML\n",
+        "from base64 import b64encode\n",
+        "from pyvirtualdisplay import Display\n",
+        "\n",
+        "# create the directory to store the video(s)\n",
+        "os.makedirs(\"./video\", exist_ok=True)\n",
+        "\n",
+        "display = Display(visible=False, size=(1400, 900))\n",
+        "_ = display.start()\n",
+        "\n",
+        "\"\"\"\n",
+        "Utility functions to enable video recording of gym environment\n",
+        "and displaying it.\n",
+        "To enable video, just do \"env = wrap_env(env)\"\"\n",
+        "\"\"\"\n",
+        "def render_mp4(videopath: str) -> str:\n",
+        "  \"\"\"\n",
+        "  Gets a string containing a b4-encoded version of the MP4 video\n",
+        "  at the specified path.\n",
+        "  \"\"\"\n",
+        "  mp4 = open(videopath, 'rb').read()\n",
+        "  base64_encoded_mp4 = b64encode(mp4).decode()\n",
+        "  return f'<video width=400 controls><source src=\"data:video/mp4;' \\\n",
+        "         f'base64,{base64_encoded_mp4}\" type=\"video/mp4\"></video>'"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Total reward: -597.0358279244006\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "UJnBSc5KDBuU"
+      },
+      "source": [
+        "---\n",
+        "# Introduction\n",
+        "\n",
+        "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n",
+        "\n",
+        "**Speed:** How fast the algorithm reaches the maximum possible reward.\n",
+        "\n",
+        "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n",
+        "\n",
+        "In this project, you should investigate the following question:\n",
+        "\n",
+        "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n",
+        "\n",
+        "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n",
+        "\n",
+        "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n",
+        "\n",
+        "The input state of the Lunar Lander consists of following components:\n",
+        "\n",
+        "  1. Horizontal Position\n",
+        "  2. Vertical Position\n",
+        "  3. Horizontal Velocity\n",
+        "  4. Vertical Velocity\n",
+        "  5. Angle\n",
+        "  6. Angular Velocity\n",
+        "  7. Left Leg Contact\n",
+        "  8. Right Leg Contact\n",
+        "\n",
+        "The actions of the agents are:\n",
+        "  1. Do Nothing\n",
+        "  2. Fire Main Engine\n",
+        "  3. Fire Left Engine\n",
+        "  4. Fire Right Engine\n",
+        "\n",
+        "\n",
+        "<img src=\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/projects/static/lunar_lander.png\">"
+      ]
     },
     {
-     "data": {
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type=\"video/mp4\"></video>"
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "XuSpVWuGDBuU"
+      },
+      "source": [
+        "---\n",
+        "# Basic DQN Implementation\n",
+        "\n",
+        "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "dYwJRvx-DBuV"
+      },
+      "source": [
+        "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "execution": {},
+        "id": "MvWRAJiSDBuV",
+        "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+            "  and should_run_async(code)\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "nn_layers = [64, 64]  # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n",
+        "                      # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n",
+        "\n",
+        "learning_rate = 0.001  # This is the step-size with which the gradient descent is carried out.\n",
+        "                       # Tip: Use smaller step-sizes for larger networks."
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "env = gym.make(env_name, render_mode=\"rgb_array\")\n",
-    "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n",
-    "observation = env.reset()[0]\n",
-    "\n",
-    "total_reward = 0\n",
-    "done = False\n",
-    "while not done:\n",
-    "  frame = env.render()\n",
-    "  vid.capture_frame()\n",
-    "  action, states = model.predict(observation, deterministic=True)\n",
-    "  observation, reward, done, info, _ = env.step(action)\n",
-    "  total_reward += reward\n",
-    "vid.close()\n",
-    "env.close()\n",
-    "print(f\"\\nTotal reward: {total_reward}\")\n",
-    "\n",
-    "# show video\n",
-    "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n",
-    "HTML(html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "From the video above, we see that the lander has crashed!\n",
-    "It is now the time for training!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "EHLx2d5xDBuV"
+      },
+      "source": [
+        "Now, let us setup our model and the DQN algorithm."
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "execution": {},
+        "id": "4PzeDS2dDBuV"
+      },
+      "outputs": [],
+      "source": [
+        "log_dir = \"/tmp/gym/\"\n",
+        "os.makedirs(log_dir, exist_ok=True)\n",
+        "\n",
+        "# Create environment\n",
+        "env_name = 'LunarLander-v2'\n",
+        "env = gym.make(env_name)\n",
+        "# You can also load other environments like cartpole, MountainCar, Acrobot.\n",
+        "# Refer to https://gym.openai.com/docs/ for descriptions.\n",
+        "\n",
+        "# For example, if you would like to load Cartpole,\n",
+        "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n",
+        "\n",
+        "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n",
+        "\n",
+        "callback = EvalCallback(env, log_path=log_dir, deterministic=True)  # For evaluating the performance of the agent periodically and logging the results.\n",
+        "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n",
+        "                     net_arch=nn_layers)\n",
+        "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n",
+        "            learning_rate=learning_rate,\n",
+        "            batch_size=1,  # for simplicity, we are not doing batch update.\n",
+        "            buffer_size=1,  # size of experience of replay buffer. Set to 1 as batch update is not done\n",
+        "            learning_starts=1,  # learning starts immediately!\n",
+        "            gamma=0.99,  # discount facto. range is between 0 and 1.\n",
+        "            tau = 1,  # the soft update coefficient for updating the target network\n",
+        "            target_update_interval=1,  # update the target network immediately.\n",
+        "            train_freq=(1,\"step\"),  # train the network at every step.\n",
+        "            max_grad_norm = 10,  # the maximum value for the gradient clipping\n",
+        "            exploration_initial_eps = 1,  # initial value of random action probability\n",
+        "            exploration_fraction = 0.5,  # fraction of entire training period over which the exploration rate is reduced\n",
+        "            gradient_steps = 1,  # number of gradient steps\n",
+        "            seed = 1,  # seed for the pseudo random generators\n",
+        "            verbose=0)  # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n",
+        "\n",
+        "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n",
+        "# Refer to  https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n",
+        "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n",
-      "Episode length: 151.80 +/- 30.46\n",
-      "New best mean reward!\n",
-      "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n",
-      "Episode length: 878.80 +/- 72.71\n",
-      "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n",
-      "Episode length: 240.00 +/- 51.61\n",
-      "New best mean reward!\n",
-      "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n",
-      "Episode length: 338.20 +/- 107.08\n",
-      "New best mean reward!\n",
-      "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n",
-      "Episode length: 241.20 +/- 55.82\n",
-      "New best mean reward!\n",
-      "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n",
-      "Episode length: 646.80 +/- 65.03\n",
-      "New best mean reward!\n",
-      "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n",
-      "Episode length: 139.80 +/- 35.46\n",
-      "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n",
-      "Episode length: 321.60 +/- 31.12\n",
-      "New best mean reward!\n",
-      "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n",
-      "Episode length: 536.00 +/- 257.21\n",
-      "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n",
-      "Episode length: 339.80 +/- 19.18\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "1FIshtazDBuW"
+      },
+      "source": [
+        "Before we train the model, let us look at an instance of Lunar Lander **before training**.  \n",
+        "\n",
+        "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)."
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "<stable_baselines3.dqn.dqn.DQN at 0x7fef99c08c70>"
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "execution": {},
+        "id": "SyD6VwDhDBuW",
+        "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "State shape:  (8,)\n",
+            "Number of actions:  4\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+            "  and should_run_async(code)\n"
+          ]
+        }
+      ],
+      "source": [
+        "env_name = 'LunarLander-v2'\n",
+        "env = gym.make(env_name)\n",
+        "print('State shape: ', env.observation_space.shape)\n",
+        "print('Number of actions: ', env.action_space.n)"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n",
-    "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n",
-    "# view the performance at every training episode."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n",
-    "\n",
-    "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n",
-    "\n",
-    "```\n",
-    "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n",
-    "Episode length: 259.80 +/- 47.47\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Now, let us look at the visual performance of the lander.\n",
-    "\n",
-    "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001B[33mWARN: <class 'gym.wrappers.monitoring.video_recorder.VideoRecorder'> is marked as deprecated and will be removed in the future.\u001B[0m\n",
-      "  logger.deprecation(\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "execution": {},
+        "id": "68D-3iePDBuW",
+        "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 412
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: <class 'gym.wrappers.monitoring.video_recorder.VideoRecorder'> is marked as deprecated and will be removed in the future.\u001b[0m\n",
+            "  logger.deprecation(\n",
+            "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+            "  self.pid = _posixsubprocess.fork_exec(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Total reward: -449.2162305654916\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<video width=400 controls><source 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\" type=\"video/mp4\"></video>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "env = gym.make(env_name, render_mode=\"rgb_array\")\n",
+        "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n",
+        "observation = env.reset()[0]\n",
+        "\n",
+        "total_reward = 0\n",
+        "done = False\n",
+        "while not done:\n",
+        "  frame = env.render()\n",
+        "  vid.capture_frame()\n",
+        "  action, states = model.predict(observation, deterministic=True)\n",
+        "  observation, reward, done, info, _ = env.step(action)\n",
+        "  total_reward += reward\n",
+        "vid.close()\n",
+        "env.close()\n",
+        "print(f\"\\nTotal reward: {total_reward}\")\n",
+        "\n",
+        "# show video\n",
+        "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n",
+        "HTML(html)"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Total reward: 252.88935234615718\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "fhtq8GDLDBuW"
+      },
+      "source": [
+        "From the video above, we see that the lander has crashed!\n",
+        "It is now the time for training!\n"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<video width=400 controls><source 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type=\"video/mp4\"></video>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "execution": {},
+        "id": "Xhl3ojMwDBuW",
+        "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+            "  and should_run_async(code)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n",
+            "Episode length: 151.80 +/- 30.46\n",
+            "New best mean reward!\n",
+            "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n",
+            "Episode length: 878.80 +/- 72.71\n",
+            "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n",
+            "Episode length: 240.00 +/- 51.61\n",
+            "New best mean reward!\n",
+            "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n",
+            "Episode length: 338.20 +/- 107.08\n",
+            "New best mean reward!\n",
+            "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n",
+            "Episode length: 241.20 +/- 55.82\n",
+            "New best mean reward!\n",
+            "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n",
+            "Episode length: 646.80 +/- 65.03\n",
+            "New best mean reward!\n",
+            "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n",
+            "Episode length: 139.80 +/- 35.46\n",
+            "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n",
+            "Episode length: 321.60 +/- 31.12\n",
+            "New best mean reward!\n",
+            "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n",
+            "Episode length: 536.00 +/- 257.21\n",
+            "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n",
+            "Episode length: 339.80 +/- 19.18\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<stable_baselines3.dqn.dqn.DQN at 0x7e4cf6f2d630>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n",
+        "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n",
+        "# view the performance at every training episode."
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "env = gym.make(env_name, render_mode=\"rgb_array\")\n",
-    "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n",
-    "observation = env.reset()[0]\n",
-    "\n",
-    "total_reward = 0\n",
-    "done = False\n",
-    "while not done:\n",
-    "  frame = env.render()\n",
-    "  vid.capture_frame()\n",
-    "  action, states = model.predict(observation, deterministic=True)\n",
-    "  observation, reward, done, info, _ = env.step(action)\n",
-    "  total_reward += reward\n",
-    "vid.close()\n",
-    "env.close()\n",
-    "print(f\"\\nTotal reward: {total_reward}\")\n",
-    "\n",
-    "# show video\n",
-    "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n",
-    "HTML(html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "The lander has landed safely!!\n",
-    "\n",
-    "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "IYynM83tDBuX"
+      },
+      "source": [
+        "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n",
+        "\n",
+        "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n",
+        "\n",
+        "```\n",
+        "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n",
+        "Episode length: 259.80 +/- 47.47\n",
+        "```"
+      ]
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "UFNQVKokDBuX"
+      },
+      "source": [
+        "Now, let us look at the visual performance of the lander.\n",
+        "\n",
+        "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x, y = ts2xy(load_results(log_dir), 'timesteps')  # Organising the logged results in to a clean format for plotting.\n",
-    "plt.plot(x, y)\n",
-    "plt.ylim([-300, 300])\n",
-    "plt.xlabel('Timesteps')\n",
-    "plt.ylabel('Episode Rewards')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n",
-    "\n",
-    "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration.   \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# Additional Project Ideas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 1 Play with exploration-exploitation trade-off\n",
-    "\n",
-    "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n"
-     ]
-    }
-   ],
-   "source": [
-    "exploration_initial_eps = 1  # initial value of random action probability. Range is between 0 and 1.\n",
-    "exploration_fraction = 0.5  # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n",
-    "exploration_final_eps = 0.05  # (set by defualt) final value of random action probability. Range is between 0 and 1."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 2 Reward Shaping\n",
-    "\n",
-    "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n",
-    "class CustomEnv(gym.Env):\n",
-    "  \"\"\"Custom Environment that follows gym interface\"\"\"\n",
-    "  metadata = {'render.modes': ['human']}\n",
-    "\n",
-    "  def __init__(self, arg1, arg2):\n",
-    "    super(CustomEnv, self).__init__()\n",
-    "    # Define action and observation space\n",
-    "    # They must be gym.spaces objects\n",
-    "    # Example when using discrete actions:\n",
-    "    self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n",
-    "    # Example for using image as input (channel-first; channel-last also works):\n",
-    "    self.observation_space = spaces.Box(low=0, high=255,\n",
-    "                                        shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n",
-    "\n",
-    "  def step(self, action):\n",
-    "    ...\n",
-    "    return observation, reward, done, info\n",
-    "  def reset(self):\n",
-    "    ...\n",
-    "    return observation  # reward, done, info can't be included\n",
-    "  def render(self, mode='human'):\n",
-    "    ...\n",
-    "  def close (self):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "class Custom_LunarLander(LunarLander):\n",
-    "\n",
-    "    def step(self, action):\n",
-    "        assert self.lander is not None\n",
-    "\n",
-    "        # Update wind\n",
-    "        assert self.lander is not None, \"You forgot to call reset()\"\n",
-    "        if self.enable_wind and not (\n",
-    "            self.legs[0].ground_contact or self.legs[1].ground_contact\n",
-    "        ):\n",
-    "            # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n",
-    "            # which is proven to never be periodic, k = 0.01\n",
-    "            wind_mag = (\n",
-    "                math.tanh(\n",
-    "                    math.sin(0.02 * self.wind_idx)\n",
-    "                    + (math.sin(math.pi * 0.01 * self.wind_idx))\n",
-    "                )\n",
-    "                * self.wind_power\n",
-    "            )\n",
-    "            self.wind_idx += 1\n",
-    "            self.lander.ApplyForceToCenter(\n",
-    "                (wind_mag, 0.0),\n",
-    "                True,\n",
-    "            )\n",
-    "\n",
-    "            # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n",
-    "            # which is proven to never be periodic, k = 0.01\n",
-    "            torque_mag = math.tanh(\n",
-    "                math.sin(0.02 * self.torque_idx)\n",
-    "                + (math.sin(math.pi * 0.01 * self.torque_idx))\n",
-    "            ) * (self.turbulence_power)\n",
-    "            self.torque_idx += 1\n",
-    "            self.lander.ApplyTorque(\n",
-    "                (torque_mag),\n",
-    "                True,\n",
-    "            )\n",
-    "\n",
-    "        if self.continuous:\n",
-    "            action = np.clip(action, -1, +1).astype(np.float32)\n",
-    "        else:\n",
-    "            assert self.action_space.contains(\n",
-    "                action\n",
-    "            ), f\"{action!r} ({type(action)}) invalid \"\n",
-    "\n",
-    "        # Engines\n",
-    "        tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n",
-    "        side = (-tip[1], tip[0])\n",
-    "        dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n",
-    "\n",
-    "        m_power = 0.0\n",
-    "        if (self.continuous and action[0] > 0.0) or (\n",
-    "            not self.continuous and action == 2\n",
-    "        ):\n",
-    "            # Main engine\n",
-    "            if self.continuous:\n",
-    "                m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5  # 0.5..1.0\n",
-    "                assert m_power >= 0.5 and m_power <= 1.0\n",
-    "            else:\n",
-    "                m_power = 1.0\n",
-    "            # 4 is move a bit downwards, +-2 for randomness\n",
-    "            ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n",
-    "            oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n",
-    "            impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n",
-    "            p = self._create_particle(\n",
-    "                3.5,  # 3.5 is here to make particle speed adequate\n",
-    "                impulse_pos[0],\n",
-    "                impulse_pos[1],\n",
-    "                m_power,\n",
-    "            )  # particles are just a decoration\n",
-    "            p.ApplyLinearImpulse(\n",
-    "                (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n",
-    "                impulse_pos,\n",
-    "                True,\n",
-    "            )\n",
-    "            self.lander.ApplyLinearImpulse(\n",
-    "                (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n",
-    "                impulse_pos,\n",
-    "                True,\n",
-    "            )\n",
-    "\n",
-    "        s_power = 0.0\n",
-    "        if (self.continuous and np.abs(action[1]) > 0.5) or (\n",
-    "            not self.continuous and action in [1, 3]\n",
-    "        ):\n",
-    "            # Orientation engines\n",
-    "            if self.continuous:\n",
-    "                direction = np.sign(action[1])\n",
-    "                s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n",
-    "                assert s_power >= 0.5 and s_power <= 1.0\n",
-    "            else:\n",
-    "                direction = action - 2\n",
-    "                s_power = 1.0\n",
-    "            ox = tip[0] * dispersion[0] + side[0] * (\n",
-    "                3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n",
-    "            )\n",
-    "            oy = -tip[1] * dispersion[0] - side[1] * (\n",
-    "                3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n",
-    "            )\n",
-    "            impulse_pos = (\n",
-    "                self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n",
-    "                self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n",
-    "            )\n",
-    "            p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n",
-    "            p.ApplyLinearImpulse(\n",
-    "                (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n",
-    "                impulse_pos,\n",
-    "                True,\n",
-    "            )\n",
-    "            self.lander.ApplyLinearImpulse(\n",
-    "                (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n",
-    "                impulse_pos,\n",
-    "                True,\n",
-    "            )\n",
-    "\n",
-    "        self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n",
-    "\n",
-    "        pos = self.lander.position\n",
-    "        vel = self.lander.linearVelocity\n",
-    "        state = [\n",
-    "            (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n",
-    "            (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n",
-    "            vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n",
-    "            vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n",
-    "            self.lander.angle,\n",
-    "            20.0 * self.lander.angularVelocity / FPS,\n",
-    "            1.0 if self.legs[0].ground_contact else 0.0,\n",
-    "            1.0 if self.legs[1].ground_contact else 0.0,\n",
-    "        ]\n",
-    "        assert len(state) == 8\n",
-    "\n",
-    "        # Compare with / without shaping, referring the state description below\n",
-    "        '''\n",
-    "        state[0]: the horizontal coordinate\n",
-    "        state[1]: the vertical coordinate\n",
-    "        state[2]: the horizontal speed\n",
-    "        state[3]: the vertical speed\n",
-    "        state[4]: the angle\n",
-    "        state[5]: the angular speed\n",
-    "        state[6]: first leg contact\n",
-    "        state[7]: second leg contact\n",
-    "        '''\n",
-    "        reward = 0\n",
-    "        shaping = (\n",
-    "            -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n",
-    "            - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n",
-    "            - 100 * abs(state[4])\n",
-    "            + 10 * state[6]\n",
-    "            + 10 * state[7]\n",
-    "        )  # And ten points for legs contact, the idea is if you\n",
-    "        # lose contact again after landing, you get negative reward\n",
-    "        if self.prev_shaping is not None:\n",
-    "            reward = shaping - self.prev_shaping\n",
-    "        self.prev_shaping = shaping\n",
-    "\n",
-    "        reward -= (\n",
-    "            m_power * 0.30\n",
-    "        )  # less fuel spent is better, about -30 for heuristic landing\n",
-    "        reward -= s_power * 0.03\n",
-    "\n",
-    "        terminated = False\n",
-    "        if self.game_over or abs(state[0]) >= 1.0:\n",
-    "            terminated = True\n",
-    "            reward = -100\n",
-    "        if not self.lander.awake:\n",
-    "            terminated = True\n",
-    "            reward = +100\n",
-    "\n",
-    "        if self.render_mode == \"human\":\n",
-    "            self.render()\n",
-    "        return np.array(state, dtype=np.float32), reward, terminated, False, {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Once you have cutomized your own environment, you can execute that environment by just calling:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "## Enter the name of the custome environment you created and uncomment the line below.\n",
-    "# env = Custom_LunarLander()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 3 Identify the state information crucial to its performance.\n",
-    "\n",
-    "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n",
-    "\n",
-    "  1. Horizontal Position\n",
-    "  2. Vertical Position\n",
-    "  3. Horizontal Velocity\n",
-    "  4. Vertical Velocity\n",
-    "  5. Angle\n",
-    "  6. Angular Velocity\n",
-    "  7. Left Leg Contact\n",
-    "  8. Right Leg Contact\n",
-    "\n",
-    "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "def step(self, actions):\n",
-    "  ...\n",
-    "  ...\n",
-    "  ...\n",
-    "  state = [ # Remove one component at a time to investigate the effect on performance!\n",
-    "            (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n",
-    "            (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n",
-    "            vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n",
-    "            vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n",
-    "            self.lander.angle,\n",
-    "            20.0 * self.lander.angularVelocity / FPS,\n",
-    "            1.0 if self.legs[0].ground_contact else 0.0,\n",
-    "            1.0 if self.legs[1].ground_contact else 0.0,\n",
-    "        ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 4 Extension to Atari Games\n",
-    "\n",
-    "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n",
-    "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n",
-    "\n",
-    "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n",
-    "# env = VecFrameStack(env, n_stack=4)\n",
-    "# model = DQN('CnnPolicy', env, verbose=1)  # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n",
-    "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 5 Obstacle Avoidance and Transfer Learning\n",
-    "\n",
-    "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n",
-    "\n",
-    "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n",
-    "\n",
-    "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "## Specify the load path and uncomment below:\n",
-    "\n",
-    "# model = load(load_path,\n",
-    "#              env=gym.make('LunarLander-v2'),\n",
-    "#              custom_objects=None, **kwargs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Following are some of the resources on transfer learning that you would want to start with.\n",
-    "\n",
-    "**Research Papers**\n",
-    "\n",
-    "Surveys:\n",
-    "1. Taylor, M. E.,  et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n",
-    "   - Long, Old, Highly cited\n",
-    "\n",
-    "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n",
-    "   - Medium, Old, Good for a quick read\n",
-    "\n",
-    "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n",
-    "  - Medium, Recent, Good for a quick read\n",
-    "\n",
-    "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n",
-    "  - Specific example"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 5(b) Transfer Learning in minigrid environment\n",
-    "\n",
-    "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {}
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "execution": {},
+        "id": "hc0xXn5aDBuX",
+        "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 412
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: <class 'gym.wrappers.monitoring.video_recorder.VideoRecorder'> is marked as deprecated and will be removed in the future.\u001b[0m\n",
+            "  logger.deprecation(\n",
+            "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+            "  self.pid = _posixsubprocess.fork_exec(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Total reward: 249.47508187060953\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<video width=400 controls><source 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type=\"video/mp4\"></video>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "env = gym.make(env_name, render_mode=\"rgb_array\")\n",
+        "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n",
+        "observation = env.reset()[0]\n",
+        "\n",
+        "total_reward = 0\n",
+        "done = False\n",
+        "while not done:\n",
+        "  frame = env.render()\n",
+        "  vid.capture_frame()\n",
+        "  action, states = model.predict(observation, deterministic=True)\n",
+        "  observation, reward, done, info, _ = env.step(action)\n",
+        "  total_reward += reward\n",
+        "vid.close()\n",
+        "env.close()\n",
+        "print(f\"\\nTotal reward: {total_reward}\")\n",
+        "\n",
+        "# show video\n",
+        "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n",
+        "HTML(html)"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n",
-      "  logger.warn(\n",
-      "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n",
-      "  logger.warn(\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "cVCcx8GUDBuX"
+      },
+      "source": [
+        "The lander has landed safely!!\n",
+        "\n",
+        "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "execution": {},
+        "id": "_8ibUiTmDBuX",
+        "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 510
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+            "  and should_run_async(code)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "x, y = ts2xy(load_results(log_dir), 'timesteps')  # Organising the logged results in to a clean format for plotting.\n",
+        "plt.plot(x, y)\n",
+        "plt.ylim([-300, 300])\n",
+        "plt.xlabel('Timesteps')\n",
+        "plt.ylabel('Episode Rewards')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "2Zo8kpDUDBuX"
+      },
+      "source": [
+        "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n",
+        "\n",
+        "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration.   \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "D7JAEDEzDBuX"
+      },
+      "source": [
+        "---\n",
+        "# Additional Project Ideas"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "1m6YBf5nDBuX"
+      },
+      "source": [
+        "## 1 Play with exploration-exploitation trade-off\n",
+        "\n",
+        "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "execution": {},
+        "id": "tnbb16KUDBuY",
+        "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+            "  and should_run_async(code)\n"
+          ]
+        }
+      ],
+      "source": [
+        "exploration_initial_eps = 1  # initial value of random action probability. Range is between 0 and 1.\n",
+        "exploration_fraction = 0.5  # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n",
+        "exploration_final_eps = 0.05  # (set by defualt) final value of random action probability. Range is between 0 and 1."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "794AyGDPDBuY"
+      },
+      "source": [
+        "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "Ljf9XG5BDBuY"
+      },
+      "source": [
+        "## 2 Reward Shaping\n",
+        "\n",
+        "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "execution": {},
+        "id": "zAAhdiflDBuY"
+      },
+      "outputs": [],
+      "source": [
+        "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n",
+        "class CustomEnv(gym.Env):\n",
+        "  \"\"\"Custom Environment that follows gym interface\"\"\"\n",
+        "  metadata = {'render.modes': ['human']}\n",
+        "\n",
+        "  def __init__(self, arg1, arg2):\n",
+        "    super(CustomEnv, self).__init__()\n",
+        "    # Define action and observation space\n",
+        "    # They must be gym.spaces objects\n",
+        "    # Example when using discrete actions:\n",
+        "    self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n",
+        "    # Example for using image as input (channel-first; channel-last also works):\n",
+        "    self.observation_space = spaces.Box(low=0, high=255,\n",
+        "                                        shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n",
+        "\n",
+        "  def step(self, action):\n",
+        "    ...\n",
+        "    return observation, reward, done, info\n",
+        "  def reset(self):\n",
+        "    ...\n",
+        "    return observation  # reward, done, info can't be included\n",
+        "  def render(self, mode='human'):\n",
+        "    ...\n",
+        "  def close (self):\n",
+        "    ..."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "n7u1oEO2DBuY"
+      },
+      "source": [
+        "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "execution": {},
+        "id": "463GUtbuDBuY"
+      },
+      "outputs": [],
+      "source": [
+        "class Custom_LunarLander(LunarLander):\n",
+        "\n",
+        "    def step(self, action):\n",
+        "        assert self.lander is not None\n",
+        "\n",
+        "        # Update wind\n",
+        "        assert self.lander is not None, \"You forgot to call reset()\"\n",
+        "        if self.enable_wind and not (\n",
+        "            self.legs[0].ground_contact or self.legs[1].ground_contact\n",
+        "        ):\n",
+        "            # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n",
+        "            # which is proven to never be periodic, k = 0.01\n",
+        "            wind_mag = (\n",
+        "                math.tanh(\n",
+        "                    math.sin(0.02 * self.wind_idx)\n",
+        "                    + (math.sin(math.pi * 0.01 * self.wind_idx))\n",
+        "                )\n",
+        "                * self.wind_power\n",
+        "            )\n",
+        "            self.wind_idx += 1\n",
+        "            self.lander.ApplyForceToCenter(\n",
+        "                (wind_mag, 0.0),\n",
+        "                True,\n",
+        "            )\n",
+        "\n",
+        "            # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n",
+        "            # which is proven to never be periodic, k = 0.01\n",
+        "            torque_mag = math.tanh(\n",
+        "                math.sin(0.02 * self.torque_idx)\n",
+        "                + (math.sin(math.pi * 0.01 * self.torque_idx))\n",
+        "            ) * (self.turbulence_power)\n",
+        "            self.torque_idx += 1\n",
+        "            self.lander.ApplyTorque(\n",
+        "                (torque_mag),\n",
+        "                True,\n",
+        "            )\n",
+        "\n",
+        "        if self.continuous:\n",
+        "            action = np.clip(action, -1, +1).astype(np.float32)\n",
+        "        else:\n",
+        "            assert self.action_space.contains(\n",
+        "                action\n",
+        "            ), f\"{action!r} ({type(action)}) invalid \"\n",
+        "\n",
+        "        # Engines\n",
+        "        tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n",
+        "        side = (-tip[1], tip[0])\n",
+        "        dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n",
+        "\n",
+        "        m_power = 0.0\n",
+        "        if (self.continuous and action[0] > 0.0) or (\n",
+        "            not self.continuous and action == 2\n",
+        "        ):\n",
+        "            # Main engine\n",
+        "            if self.continuous:\n",
+        "                m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5  # 0.5..1.0\n",
+        "                assert m_power >= 0.5 and m_power <= 1.0\n",
+        "            else:\n",
+        "                m_power = 1.0\n",
+        "            # 4 is move a bit downwards, +-2 for randomness\n",
+        "            ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n",
+        "            oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n",
+        "            impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n",
+        "            p = self._create_particle(\n",
+        "                3.5,  # 3.5 is here to make particle speed adequate\n",
+        "                impulse_pos[0],\n",
+        "                impulse_pos[1],\n",
+        "                m_power,\n",
+        "            )  # particles are just a decoration\n",
+        "            p.ApplyLinearImpulse(\n",
+        "                (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n",
+        "                impulse_pos,\n",
+        "                True,\n",
+        "            )\n",
+        "            self.lander.ApplyLinearImpulse(\n",
+        "                (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n",
+        "                impulse_pos,\n",
+        "                True,\n",
+        "            )\n",
+        "\n",
+        "        s_power = 0.0\n",
+        "        if (self.continuous and np.abs(action[1]) > 0.5) or (\n",
+        "            not self.continuous and action in [1, 3]\n",
+        "        ):\n",
+        "            # Orientation engines\n",
+        "            if self.continuous:\n",
+        "                direction = np.sign(action[1])\n",
+        "                s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n",
+        "                assert s_power >= 0.5 and s_power <= 1.0\n",
+        "            else:\n",
+        "                direction = action - 2\n",
+        "                s_power = 1.0\n",
+        "            ox = tip[0] * dispersion[0] + side[0] * (\n",
+        "                3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n",
+        "            )\n",
+        "            oy = -tip[1] * dispersion[0] - side[1] * (\n",
+        "                3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n",
+        "            )\n",
+        "            impulse_pos = (\n",
+        "                self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n",
+        "                self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n",
+        "            )\n",
+        "            p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n",
+        "            p.ApplyLinearImpulse(\n",
+        "                (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n",
+        "                impulse_pos,\n",
+        "                True,\n",
+        "            )\n",
+        "            self.lander.ApplyLinearImpulse(\n",
+        "                (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n",
+        "                impulse_pos,\n",
+        "                True,\n",
+        "            )\n",
+        "\n",
+        "        self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n",
+        "\n",
+        "        pos = self.lander.position\n",
+        "        vel = self.lander.linearVelocity\n",
+        "        state = [\n",
+        "            (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n",
+        "            (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n",
+        "            vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n",
+        "            vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n",
+        "            self.lander.angle,\n",
+        "            20.0 * self.lander.angularVelocity / FPS,\n",
+        "            1.0 if self.legs[0].ground_contact else 0.0,\n",
+        "            1.0 if self.legs[1].ground_contact else 0.0,\n",
+        "        ]\n",
+        "        assert len(state) == 8\n",
+        "\n",
+        "        # Compare with / without shaping, referring the state description below\n",
+        "        '''\n",
+        "        state[0]: the horizontal coordinate\n",
+        "        state[1]: the vertical coordinate\n",
+        "        state[2]: the horizontal speed\n",
+        "        state[3]: the vertical speed\n",
+        "        state[4]: the angle\n",
+        "        state[5]: the angular speed\n",
+        "        state[6]: first leg contact\n",
+        "        state[7]: second leg contact\n",
+        "        '''\n",
+        "        reward = 0\n",
+        "        shaping = (\n",
+        "            -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n",
+        "            - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n",
+        "            - 100 * abs(state[4])\n",
+        "            + 10 * state[6]\n",
+        "            + 10 * state[7]\n",
+        "        )  # And ten points for legs contact, the idea is if you\n",
+        "        # lose contact again after landing, you get negative reward\n",
+        "        if self.prev_shaping is not None:\n",
+        "            reward = shaping - self.prev_shaping\n",
+        "        self.prev_shaping = shaping\n",
+        "\n",
+        "        reward -= (\n",
+        "            m_power * 0.30\n",
+        "        )  # less fuel spent is better, about -30 for heuristic landing\n",
+        "        reward -= s_power * 0.03\n",
+        "\n",
+        "        terminated = False\n",
+        "        if self.game_over or abs(state[0]) >= 1.0:\n",
+        "            terminated = True\n",
+        "            reward = -100\n",
+        "        if not self.lander.awake:\n",
+        "            terminated = True\n",
+        "            reward = +100\n",
+        "\n",
+        "        if self.render_mode == \"human\":\n",
+        "            self.render()\n",
+        "        return np.array(state, dtype=np.float32), reward, terminated, False, {}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "V-qZ4WYxDBuZ"
+      },
+      "source": [
+        "Once you have cutomized your own environment, you can execute that environment by just calling:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "execution": {},
+        "id": "yq4902DQDBuZ"
+      },
+      "outputs": [],
+      "source": [
+        "## Enter the name of the custome environment you created and uncomment the line below.\n",
+        "# env = Custom_LunarLander()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "EAP-DUd6DBuZ"
+      },
+      "source": [
+        "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "QTq0hmHCDBuZ"
+      },
+      "source": [
+        "## 3 Identify the state information crucial to its performance.\n",
+        "\n",
+        "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n",
+        "\n",
+        "  1. Horizontal Position\n",
+        "  2. Vertical Position\n",
+        "  3. Horizontal Velocity\n",
+        "  4. Vertical Velocity\n",
+        "  5. Angle\n",
+        "  6. Angular Velocity\n",
+        "  7. Left Leg Contact\n",
+        "  8. Right Leg Contact\n",
+        "\n",
+        "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "execution": {},
+        "id": "sz45kgEaDBuZ"
+      },
+      "outputs": [],
+      "source": [
+        "def step(self, actions):\n",
+        "  ...\n",
+        "  ...\n",
+        "  ...\n",
+        "  state = [ # Remove one component at a time to investigate the effect on performance!\n",
+        "            (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n",
+        "            (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n",
+        "            vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n",
+        "            vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n",
+        "            self.lander.angle,\n",
+        "            20.0 * self.lander.angularVelocity / FPS,\n",
+        "            1.0 if self.legs[0].ground_contact else 0.0,\n",
+        "            1.0 if self.legs[1].ground_contact else 0.0,\n",
+        "        ]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "DXy9s2ymDBuZ"
+      },
+      "source": [
+        "## 4 Extension to Atari Games\n",
+        "\n",
+        "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "execution": {},
+        "id": "4RjAt0W-DBuZ"
+      },
+      "outputs": [],
+      "source": [
+        "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n",
+        "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n",
+        "\n",
+        "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n",
+        "# env = VecFrameStack(env, n_stack=4)\n",
+        "# model = DQN('CnnPolicy', env, verbose=1)  # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n",
+        "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "E6_fFAYhDBue"
+      },
+      "source": [
+        "## 5 Obstacle Avoidance and Transfer Learning\n",
+        "\n",
+        "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n",
+        "\n",
+        "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n",
+        "\n",
+        "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "execution": {},
+        "id": "h6knZ3U8DBue"
+      },
+      "outputs": [],
+      "source": [
+        "## Specify the load path and uncomment below:\n",
+        "\n",
+        "# model = load(load_path,\n",
+        "#              env=gym.make('LunarLander-v2'),\n",
+        "#              custom_objects=None, **kwargs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "GXBIbO25DBue"
+      },
+      "source": [
+        "Following are some of the resources on transfer learning that you would want to start with.\n",
+        "\n",
+        "**Research Papers**\n",
+        "\n",
+        "Surveys:\n",
+        "1. Taylor, M. E.,  et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n",
+        "   - Long, Old, Highly cited\n",
+        "\n",
+        "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n",
+        "   - Medium, Old, Good for a quick read\n",
+        "\n",
+        "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n",
+        "  - Medium, Recent, Good for a quick read\n",
+        "\n",
+        "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n",
+        "  - Specific example"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "MlOksW3ODBue"
+      },
+      "source": [
+        "## 5(b) Transfer Learning in minigrid environment\n",
+        "\n",
+        "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "execution": {},
+        "id": "v7rB2JQlDBue"
+      },
+      "outputs": [],
+      "source": [
+        "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "OPcds7ZtDBue"
+      },
+      "source": [
+        "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "execution": {},
+        "id": "d0NiSkyeDBue",
+        "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n",
+            "  logger.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n",
+            "  logger.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "import minigrid\n",
+        "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "7J92iMqqDBue"
+      },
+      "source": [
+        "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n",
+        "\n",
+        "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "HL0W5M4uDBue"
+      },
+      "source": [
+        "## 6 Preference-Based RL (PBRL)\n",
+        "\n",
+        "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n",
+        "\n",
+        "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n",
+        "\n",
+        "**Research papers:**\n",
+        "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n",
+        "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n",
+        "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n",
+        "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n",
+        "\n",
+        "**Code Bases:**\n",
+        "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n",
+        "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {},
+        "id": "QdW-XKCMDBue"
+      },
+      "source": [
+        "---\n",
+        "# References\n",
+        "\n",
+        "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n",
+        "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n",
+        "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n",
+        "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "lunar_lander",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "gpuClass": "standard",
+    "kernel": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-   ],
-   "source": [
-    "import minigrid\n",
-    "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n",
-    "\n",
-    "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "## 6 Preference-Based RL (PBRL)\n",
-    "\n",
-    "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n",
-    "\n",
-    "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n",
-    "\n",
-    "**Research papers:**\n",
-    "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n",
-    "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n",
-    "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n",
-    "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n",
-    "\n",
-    "**Code Bases:**\n",
-    "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n",
-    "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {}
-   },
-   "source": [
-    "---\n",
-    "# References\n",
-    "\n",
-    "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n",
-    "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n",
-    "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n",
-    "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "include_colab_link": true,
-   "name": "lunar_lander",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "gpuClass": "standard",
-  "kernel": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
   },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file