{"cells": [{"cell_type": "markdown", "id": "57b870be", "metadata": {"papermill": {"duration": 0.009484, "end_time": "2025-04-03T19:24:25.582651", "exception": false, "start_time": "2025-04-03T19:24:25.573167", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 8: Deep Autoencoders\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2025-04-03T19:24:19.069528\n", "\n", "In this tutorial, we will take a closer look at autoencoders (AE).\n", "Autoencoders are trained on encoding input data such as images into a smaller feature vector,\n", "and afterward, reconstruct it by a second neural network, called a decoder.\n", "The feature vector is called the \"bottleneck\" of the network as we aim to compress the input data into a smaller amount of features.\n", "This property is useful in many applications, in particular in compressing data or comparing images on a metric beyond pixel-level comparisons.\n", "Besides learning about the autoencoder framework, we will also see the \"deconvolution\"\n", "(or transposed convolution) operator in action for scaling up feature maps in height and width.\n", "Such deconvolution networks are necessary wherever we start from a small feature vector\n", "and need to output an image of full size (e.g. in VAE, GANs, or super-resolution applications).\n", "This notebook is part of a lecture series on Deep Learning at the University of Amsterdam.\n", "The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.\n", "\n", "\n", "---\n", "Open in [{height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/08-deep-autoencoders.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://lightning.ai/docs/)\n", "| Join us [on Discord](https://discord.com/invite/tfXFetEZxv)"]}, {"cell_type": "markdown", "id": "c32021b8", "metadata": {"papermill": {"duration": 0.008778, "end_time": "2025-04-03T19:24:25.599804", "exception": false, "start_time": "2025-04-03T19:24:25.591026", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "93bc076c", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2025-04-03T19:24:25.617872Z", "iopub.status.busy": "2025-04-03T19:24:25.617123Z", "iopub.status.idle": "2025-04-03T19:24:26.819076Z", "shell.execute_reply": "2025-04-03T19:24:26.817595Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 1.214152, "end_time": "2025-04-03T19:24:26.822107", "exception": false, "start_time": "2025-04-03T19:24:25.607955", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}, {"name": "stdout", "output_type": "stream", "text": ["\r\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\r\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\r\n"]}], "source": ["! pip install --quiet \"seaborn\" \"numpy <3.0\" \"pytorch-lightning >=2.0,<2.6\" \"matplotlib\" \"torchvision\" \"torch >=1.8.1,<2.7\" \"tensorboard\" \"torchmetrics >=1.0,<1.8\""]}, {"cell_type": "markdown", "id": "6e7f448e", "metadata": {"papermill": {"duration": 0.013412, "end_time": "2025-04-03T19:24:26.849894", "exception": false, "start_time": "2025-04-03T19:24:26.836482", "status": "completed"}, "tags": []}, "source": ["
"]}, {"cell_type": "code", "execution_count": 2, "id": "4522b293", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:26.877874Z", "iopub.status.busy": "2025-04-03T19:24:26.877493Z", "iopub.status.idle": "2025-04-03T19:24:30.509356Z", "shell.execute_reply": "2025-04-03T19:24:30.508037Z"}, "papermill": {"duration": 3.648449, "end_time": "2025-04-03T19:24:30.511744", "exception": false, "start_time": "2025-04-03T19:24:26.863295", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Device: cuda:0\n"]}], "source": ["import os\n", "import urllib.request\n", "from urllib.error import HTTPError\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib_inline.backend_inline\n", "import pytorch_lightning as pl\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import torch.utils.data as data\n", "import torchvision\n", "from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint\n", "from torch.utils.tensorboard import SummaryWriter\n", "from torchvision import transforms\n", "from torchvision.datasets import CIFAR10\n", "from tqdm.notebook import tqdm\n", "\n", "%matplotlib inline\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "matplotlib.rcParams[\"lines.linewidth\"] = 2.0\n", "sns.reset_orig()\n", "sns.set()\n", "\n", "# Tensorboard extension (for visualization purposes later)\n", "%load_ext tensorboard\n", "\n", "# Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)\n", "DATASET_PATH = os.environ.get(\"PATH_DATASETS\", \"data\")\n", "# Path to the folder where the pretrained models are saved\n", "CHECKPOINT_PATH = os.environ.get(\"PATH_CHECKPOINT\", \"saved_models/tutorial9\")\n", "\n", "# Setting the seed\n", "pl.seed_everything(42)\n", "\n", "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.deterministic = True\n", "torch.backends.cudnn.benchmark = False\n", "\n", "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "print(\"Device:\", device)"]}, {"cell_type": "markdown", "id": "80ceb200", "metadata": {"papermill": {"duration": 0.013269, "end_time": "2025-04-03T19:24:30.538889", "exception": false, "start_time": "2025-04-03T19:24:30.525620", "status": "completed"}, "tags": []}, "source": ["We have 4 pretrained models that we have to download.\n", "Remember the adjust the variables `DATASET_PATH` and `CHECKPOINT_PATH` if needed."]}, {"cell_type": "code", "execution_count": 3, "id": "44e64ee9", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:30.566417Z", "iopub.status.busy": "2025-04-03T19:24:30.565761Z", "iopub.status.idle": "2025-04-03T19:24:31.674156Z", "shell.execute_reply": "2025-04-03T19:24:31.672691Z"}, "papermill": {"duration": 1.124291, "end_time": "2025-04-03T19:24:31.676495", "exception": false, "start_time": "2025-04-03T19:24:30.552204", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_64.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_128.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_256.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/cifar10_384.ckpt...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial9/\"\n", "# Files to download\n", "pretrained_files = [\"cifar10_64.ckpt\", \"cifar10_128.ckpt\", \"cifar10_256.ckpt\", \"cifar10_384.ckpt\"]\n", "# Create checkpoint path if it doesn't exist yet\n", "os.makedirs(CHECKPOINT_PATH, exist_ok=True)\n", "\n", "# For each file, check whether it already exists. If not, try downloading it.\n", "for file_name in pretrained_files:\n", " file_path = os.path.join(CHECKPOINT_PATH, file_name)\n", " if not os.path.isfile(file_path):\n", " file_url = base_url + file_name\n", " print(f\"Downloading {file_url}...\")\n", " try:\n", " urllib.request.urlretrieve(file_url, file_path)\n", " except HTTPError as e:\n", " print(\n", " \"Something went wrong. Please try to download the files manually,\"\n", " \" or contact the author with the full output including the following error:\\n\",\n", " e,\n", " )"]}, {"cell_type": "markdown", "id": "6daa4518", "metadata": {"papermill": {"duration": 0.014044, "end_time": "2025-04-03T19:24:31.704815", "exception": false, "start_time": "2025-04-03T19:24:31.690771", "status": "completed"}, "tags": []}, "source": ["In this tutorial, we work with the CIFAR10 dataset.\n", "In CIFAR10, each image has 3 color channels and is 32x32 pixels large.\n", "As autoencoders do not have the constrain of modeling images probabilistic, we can work on more complex image data\n", "(i.e. 3 color channels instead of black-and-white) much easier than for VAEs.\n", "In case you have downloaded CIFAR10 already in a different directory, make sure to set DATASET_PATH\n", "accordingly to prevent another download.\n", "\n", "In contrast to previous tutorials on CIFAR10 like\n", "[Tutorial 5](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial5/Inception_ResNet_DenseNet.html)\n", "(CNN classification), we do not normalize the data explicitly with a mean of 0 and std of 1,\n", "but roughly estimate it scaling the data between -1 and 1.\n", "This is because limiting the range will make our task of predicting/reconstructing images easier."]}, {"cell_type": "code", "execution_count": 4, "id": "c9e9747c", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:31.732072Z", "iopub.status.busy": "2025-04-03T19:24:31.731690Z", "iopub.status.idle": "2025-04-03T19:24:41.363711Z", "shell.execute_reply": "2025-04-03T19:24:41.362286Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 9.647558, "end_time": "2025-04-03T19:24:41.366285", "exception": false, "start_time": "2025-04-03T19:24:31.718727", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /__w/11/s/.datasets/cifar-10-python.tar.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 0%| | 0/170498071 [00:00 only make them a tensor\n", "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n", "\n", "# Loading the training dataset. We need to split it into a training and validation part\n", "train_dataset = CIFAR10(root=DATASET_PATH, train=True, transform=transform, download=True)\n", "pl.seed_everything(42)\n", "train_set, val_set = torch.utils.data.random_split(train_dataset, [45000, 5000])\n", "\n", "# Loading the test set\n", "test_set = CIFAR10(root=DATASET_PATH, train=False, transform=transform, download=True)\n", "\n", "# We define a set of data loaders that we can use for various purposes later.\n", "train_loader = data.DataLoader(train_set, batch_size=256, shuffle=True, drop_last=True, pin_memory=True, num_workers=4)\n", "val_loader = data.DataLoader(val_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)\n", "test_loader = data.DataLoader(test_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)\n", "\n", "\n", "def get_train_images(num):\n", " return torch.stack([train_dataset[i][0] for i in range(num)], dim=0)"]}, {"cell_type": "markdown", "id": "4730ef44", "metadata": {"papermill": {"duration": 0.019394, "end_time": "2025-04-03T19:24:41.406570", "exception": false, "start_time": "2025-04-03T19:24:41.387176", "status": "completed"}, "tags": []}, "source": ["## Building the autoencoder\n", "\n", "In general, an autoencoder consists of an **encoder** that maps the input $x$ to a lower-dimensional feature vector $z$,\n", "and a **decoder** that reconstructs the input $\\hat{x}$ from $z$.\n", "We train the model by comparing $x$ to $\\hat{x}$ and optimizing the parameters to increase the similarity between $x$ and $\\hat{x}$.\n", "See below for a small illustration of the autoencoder framework."]}, {"cell_type": "markdown", "id": "74ea2de7", "metadata": {"papermill": {"duration": 0.012046, "end_time": "2025-04-03T19:24:41.435244", "exception": false, "start_time": "2025-04-03T19:24:41.423198", "status": "completed"}, "tags": []}, "source": ["
"]}, {"cell_type": "markdown", "id": "283eaeb8", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.00935, "end_time": "2025-04-03T19:24:41.459716", "exception": false, "start_time": "2025-04-03T19:24:41.450366", "status": "completed"}, "tags": []}, "source": ["We first start by implementing the encoder.\n", "The encoder effectively consists of a deep convolutional network, where we scale down the image layer-by-layer using strided convolutions.\n", "After downscaling the image three times, we flatten the features and apply linear layers.\n", "The latent representation $z$ is therefore a vector of size *d* which can be flexibly selected."]}, {"cell_type": "code", "execution_count": 5, "id": "d65ba268", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:41.479861Z", "iopub.status.busy": "2025-04-03T19:24:41.479614Z", "iopub.status.idle": "2025-04-03T19:24:41.487293Z", "shell.execute_reply": "2025-04-03T19:24:41.486321Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.019995, "end_time": "2025-04-03T19:24:41.488645", "exception": false, "start_time": "2025-04-03T19:24:41.468650", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Encoder(nn.Module):\n", " def __init__(self, num_input_channels: int, base_channel_size: int, latent_dim: int, act_fn: object = nn.GELU):\n", " \"\"\"Encoder.\n", "\n", " Args:\n", " num_input_channels : Number of input channels of the image. For CIFAR, this parameter is 3\n", " base_channel_size : Number of channels we use in the first convolutional layers. Deeper layers might use a duplicate of it.\n", " latent_dim : Dimensionality of latent representation z\n", " act_fn : Activation function used throughout the encoder network\n", "\n", " \"\"\"\n", " super().__init__()\n", " c_hid = base_channel_size\n", " self.net = nn.Sequential(\n", " nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2), # 32x32 => 16x16\n", " act_fn(),\n", " nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.Conv2d(c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2), # 16x16 => 8x8\n", " act_fn(),\n", " nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2), # 8x8 => 4x4\n", " act_fn(),\n", " nn.Flatten(), # Image grid to single feature vector\n", " nn.Linear(2 * 16 * c_hid, latent_dim),\n", " )\n", "\n", " def forward(self, x):\n", " return self.net(x)"]}, {"cell_type": "markdown", "id": "a50e06f7", "metadata": {"papermill": {"duration": 0.008817, "end_time": "2025-04-03T19:24:41.506536", "exception": false, "start_time": "2025-04-03T19:24:41.497719", "status": "completed"}, "tags": []}, "source": ["Note that we do not apply Batch Normalization here.\n", "This is because we want the encoding of each image to be independent of all the other images.\n", "Otherwise, we might introduce correlations into the encoding or decoding that we do not want to have.\n", "In some implementations, you still can see Batch Normalization being used, because it can also serve as a form of regularization.\n", "Nevertheless, the better practice is to go with other normalization techniques if necessary like Instance Normalization or Layer Normalization.\n", "Given the small size of the model, we can neglect normalization for now."]}, {"cell_type": "markdown", "id": "6ec032ec", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008791, "end_time": "2025-04-03T19:24:41.524177", "exception": false, "start_time": "2025-04-03T19:24:41.515386", "status": "completed"}, "tags": []}, "source": ["The decoder is a mirrored, flipped version of the encoder.\n", "The only difference is that we replace strided convolutions by transposed convolutions\n", "(i.e. deconvolutions) to upscale the features.\n", "Transposed convolutions can be imagined as adding the stride to the input instead of the output,\n", "and can thus upscale the input.\n", "For an illustration of a `nn.ConvTranspose2d` layer with kernel size 3, stride 2, and padding 1,\n", "see below (figure credit - [Vincent Dumoulin and Francesco Visin](https://arxiv.org/abs/1603.07285)):\n", "\n", "
\n", "\n", "You see that for an input of size $3\\times3$, we obtain an output of $5\\times5$.\n", "However, to truly have a reverse operation of the convolution,\n", "we need to ensure that the layer scales the input shape by a factor of 2 (e.g. $4\\times4\\to8\\times8$).\n", "For this, we can specify the parameter `output_padding` which adds additional values to the output shape.\n", "Note that we do not perform zero-padding with this, but rather increase the output shape for calculation.\n", "\n", "Overall, the decoder can be implemented as follows:"]}, {"cell_type": "code", "execution_count": 6, "id": "0b32cf90", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:41.542928Z", "iopub.status.busy": "2025-04-03T19:24:41.542692Z", "iopub.status.idle": "2025-04-03T19:24:41.551833Z", "shell.execute_reply": "2025-04-03T19:24:41.550823Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020166, "end_time": "2025-04-03T19:24:41.553176", "exception": false, "start_time": "2025-04-03T19:24:41.533010", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Decoder(nn.Module):\n", " def __init__(self, num_input_channels: int, base_channel_size: int, latent_dim: int, act_fn: object = nn.GELU):\n", " \"\"\"Decoder.\n", "\n", " Args:\n", " num_input_channels : Number of channels of the image to reconstruct. For CIFAR, this parameter is 3\n", " base_channel_size : Number of channels we use in the last convolutional layers. Early layers might use a duplicate of it.\n", " latent_dim : Dimensionality of latent representation z\n", " act_fn : Activation function used throughout the decoder network\n", "\n", " \"\"\"\n", " super().__init__()\n", " c_hid = base_channel_size\n", " self.linear = nn.Sequential(nn.Linear(latent_dim, 2 * 16 * c_hid), act_fn())\n", " self.net = nn.Sequential(\n", " nn.ConvTranspose2d(\n", " 2 * c_hid, 2 * c_hid, kernel_size=3, output_padding=1, padding=1, stride=2\n", " ), # 4x4 => 8x8\n", " act_fn(),\n", " nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), # 8x8 => 16x16\n", " act_fn(),\n", " nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),\n", " act_fn(),\n", " nn.ConvTranspose2d(\n", " c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2\n", " ), # 16x16 => 32x32\n", " nn.Tanh(), # The input images is scaled between -1 and 1, hence the output has to be bounded as well\n", " )\n", "\n", " def forward(self, x):\n", " x = self.linear(x)\n", " x = x.reshape(x.shape[0], -1, 4, 4)\n", " x = self.net(x)\n", " return x"]}, {"cell_type": "markdown", "id": "11d1134f", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008807, "end_time": "2025-04-03T19:24:41.570864", "exception": false, "start_time": "2025-04-03T19:24:41.562057", "status": "completed"}, "tags": []}, "source": ["The encoder and decoder networks we chose here are relatively simple.\n", "Usually, more complex networks are applied, especially when using a ResNet-based architecture.\n", "For example, see [VQ-VAE](https://arxiv.org/abs/1711.00937) and\n", "[NVAE](https://arxiv.org/abs/2007.03898) (although the papers discuss architectures for VAEs,\n", "they can equally be applied to standard autoencoders).\n", "\n", "In a final step, we add the encoder and decoder together into the autoencoder architecture.\n", "We define the autoencoder as PyTorch Lightning Module to simplify the needed training code:"]}, {"cell_type": "code", "execution_count": 7, "id": "a00458da", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:41.589788Z", "iopub.status.busy": "2025-04-03T19:24:41.589543Z", "iopub.status.idle": "2025-04-03T19:24:41.599883Z", "shell.execute_reply": "2025-04-03T19:24:41.598848Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.021509, "end_time": "2025-04-03T19:24:41.601211", "exception": false, "start_time": "2025-04-03T19:24:41.579702", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Autoencoder(pl.LightningModule):\n", " def __init__(\n", " self,\n", " base_channel_size: int,\n", " latent_dim: int,\n", " encoder_class: object = Encoder,\n", " decoder_class: object = Decoder,\n", " num_input_channels: int = 3,\n", " width: int = 32,\n", " height: int = 32,\n", " ):\n", " super().__init__()\n", " # Saving hyperparameters of autoencoder\n", " self.save_hyperparameters()\n", " # Creating encoder and decoder\n", " self.encoder = encoder_class(num_input_channels, base_channel_size, latent_dim)\n", " self.decoder = decoder_class(num_input_channels, base_channel_size, latent_dim)\n", " # Example input array needed for visualizing the graph of the network\n", " self.example_input_array = torch.zeros(2, num_input_channels, width, height)\n", "\n", " def forward(self, x):\n", " \"\"\"The forward function takes in an image and returns the reconstructed image.\"\"\"\n", " z = self.encoder(x)\n", " x_hat = self.decoder(z)\n", " return x_hat\n", "\n", " def _get_reconstruction_loss(self, batch):\n", " \"\"\"Given a batch of images, this function returns the reconstruction loss (MSE in our case).\"\"\"\n", " x, _ = batch # We do not need the labels\n", " x_hat = self.forward(x)\n", " loss = F.mse_loss(x, x_hat, reduction=\"none\")\n", " loss = loss.sum(dim=[1, 2, 3]).mean(dim=[0])\n", " return loss\n", "\n", " def configure_optimizers(self):\n", " optimizer = optim.Adam(self.parameters(), lr=1e-3)\n", " # Using a scheduler is optional but can be helpful.\n", " # The scheduler reduces the LR if the validation performance hasn't improved for the last N epochs\n", " scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=0.2, patience=20, min_lr=5e-5)\n", " return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler, \"monitor\": \"val_loss\"}\n", "\n", " def training_step(self, batch, batch_idx):\n", " loss = self._get_reconstruction_loss(batch)\n", " self.log(\"train_loss\", loss)\n", " return loss\n", "\n", " def validation_step(self, batch, batch_idx):\n", " loss = self._get_reconstruction_loss(batch)\n", " self.log(\"val_loss\", loss)\n", "\n", " def test_step(self, batch, batch_idx):\n", " loss = self._get_reconstruction_loss(batch)\n", " self.log(\"test_loss\", loss)"]}, {"cell_type": "markdown", "id": "82571f1a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008801, "end_time": "2025-04-03T19:24:41.619030", "exception": false, "start_time": "2025-04-03T19:24:41.610229", "status": "completed"}, "tags": []}, "source": ["For the loss function, we use the mean squared error (MSE).\n", "The mean squared error pushes the network to pay special attention to those pixel values its estimate is far away.\n", "Predicting 127 instead of 128 is not important when reconstructing, but confusing 0 with 128 is much worse.\n", "Note that in contrast to VAEs, we do not predict the probability per pixel value, but instead use a distance measure.\n", "This saves a lot of parameters and simplifies training.\n", "To get a better intuition per pixel, we report the summed squared error averaged over the batch dimension\n", "(any other mean/sum leads to the same result/parameters).\n", "\n", "However, MSE has also some considerable disadvantages.\n", "Usually, MSE leads to blurry images where small noise/high-frequent patterns are removed as those cause a very low error.\n", "To ensure realistic images to be reconstructed, one could combine Generative Adversarial Networks\n", "(lecture 10) with autoencoders as done in several works (e.g. see [here](https://arxiv.org/abs/1704.02304),\n", "[here](https://arxiv.org/abs/1511.05644) or these [slides](http://elarosca.net/slides/iccv_autoencoder_gans.pdf)).\n", "Additionally, comparing two images using MSE does not necessarily reflect their visual similarity.\n", "For instance, suppose the autoencoder reconstructs an image shifted by one pixel to the right and bottom.\n", "Although the images are almost identical, we can get a higher loss than predicting a constant pixel value for half of the image (see code below).\n", "An example solution for this issue includes using a separate, pre-trained CNN,\n", "and use a distance of visual features in lower layers as a distance measure instead of the original pixel-level comparison."]}, {"cell_type": "code", "execution_count": 8, "id": "4a6bda99", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:41.638368Z", "iopub.status.busy": "2025-04-03T19:24:41.637641Z", "iopub.status.idle": "2025-04-03T19:24:42.193082Z", "shell.execute_reply": "2025-04-03T19:24:42.191967Z"}, "papermill": {"duration": 0.56755, "end_time": "2025-04-03T19:24:42.195488", "exception": false, "start_time": "2025-04-03T19:24:41.627938", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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", 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", 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["def compare_imgs(img1, img2, title_prefix=\"\"):\n", " # Calculate MSE loss between both images\n", " loss = F.mse_loss(img1, img2, reduction=\"sum\")\n", " # Plot images for visual comparison\n", " grid = torchvision.utils.make_grid(torch.stack([img1, img2], dim=0), nrow=2, normalize=True, value_range=(-1, 1))\n", " grid = grid.permute(1, 2, 0)\n", " plt.figure(figsize=(4, 2))\n", " plt.title(f\"{title_prefix} Loss: {loss.item():4.2f}\")\n", " plt.imshow(grid)\n", " plt.axis(\"off\")\n", " plt.show()\n", "\n", "\n", "for i in range(2):\n", " # Load example image\n", " img, _ = train_dataset[i]\n", " img_mean = img.mean(dim=[1, 2], keepdims=True)\n", "\n", " # Shift image by one pixel\n", " SHIFT = 1\n", " img_shifted = torch.roll(img, shifts=SHIFT, dims=1)\n", " img_shifted = torch.roll(img_shifted, shifts=SHIFT, dims=2)\n", " img_shifted[:, :1, :] = img_mean\n", " img_shifted[:, :, :1] = img_mean\n", " compare_imgs(img, img_shifted, \"Shifted -\")\n", "\n", " # Set half of the image to zero\n", " img_masked = img.clone()\n", " img_masked[:, : img_masked.shape[1] // 2, :] = img_mean\n", " compare_imgs(img, img_masked, \"Masked -\")"]}, {"cell_type": "markdown", "id": "f2cdf2d5", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.015751, "end_time": "2025-04-03T19:24:42.275870", "exception": false, "start_time": "2025-04-03T19:24:42.260119", "status": "completed"}, "tags": []}, "source": ["### Training the model\n", "\n", "During the training, we want to keep track of the learning progress by seeing reconstructions made by our model.\n", "For this, we implement a callback object in PyTorch Lightning which will add reconstructions every $N$ epochs to our tensorboard:"]}, {"cell_type": "code", "execution_count": 9, "id": "9443c70d", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:42.300635Z", "iopub.status.busy": "2025-04-03T19:24:42.300399Z", "iopub.status.idle": "2025-04-03T19:24:42.306619Z", "shell.execute_reply": "2025-04-03T19:24:42.305639Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020109, "end_time": "2025-04-03T19:24:42.307804", "exception": false, "start_time": "2025-04-03T19:24:42.287695", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GenerateCallback(Callback):\n", " def __init__(self, input_imgs, every_n_epochs=1):\n", " super().__init__()\n", " self.input_imgs = input_imgs # Images to reconstruct during training\n", " # Only save those images every N epochs (otherwise tensorboard gets quite large)\n", " self.every_n_epochs = every_n_epochs\n", "\n", " def on_train_epoch_end(self, trainer, pl_module):\n", " if trainer.current_epoch % self.every_n_epochs == 0:\n", " # Reconstruct images\n", " input_imgs = self.input_imgs.to(pl_module.device)\n", " with torch.no_grad():\n", " pl_module.eval()\n", " reconst_imgs = pl_module(input_imgs)\n", " pl_module.train()\n", " # Plot and add to tensorboard\n", " imgs = torch.stack([input_imgs, reconst_imgs], dim=1).flatten(0, 1)\n", " grid = torchvision.utils.make_grid(imgs, nrow=2, normalize=True, value_range=(-1, 1))\n", " trainer.logger.experiment.add_image(\"Reconstructions\", grid, global_step=trainer.global_step)"]}, {"cell_type": "markdown", "id": "3c3364a5", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011635, "end_time": "2025-04-03T19:24:42.331171", "exception": false, "start_time": "2025-04-03T19:24:42.319536", "status": "completed"}, "tags": []}, "source": ["We will now write a training function that allows us to train the autoencoder with different latent dimensionality\n", "and returns both the test and validation score.\n", "We provide pre-trained models and recommend you using those, especially when you work on a computer without GPU.\n", "Of course, feel free to train your own models on Lisa."]}, {"cell_type": "code", "execution_count": 10, "id": "ccededbf", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:42.355625Z", "iopub.status.busy": "2025-04-03T19:24:42.355386Z", "iopub.status.idle": "2025-04-03T19:24:42.362761Z", "shell.execute_reply": "2025-04-03T19:24:42.361748Z"}, "papermill": {"duration": 0.021191, "end_time": "2025-04-03T19:24:42.364062", "exception": false, "start_time": "2025-04-03T19:24:42.342871", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_cifar(latent_dim):\n", " # Create a PyTorch Lightning trainer with the generation callback\n", " trainer = pl.Trainer(\n", " default_root_dir=os.path.join(CHECKPOINT_PATH, f\"cifar10_{latent_dim}\"),\n", " accelerator=\"auto\",\n", " devices=1,\n", " max_epochs=500,\n", " callbacks=[\n", " ModelCheckpoint(save_weights_only=True),\n", " GenerateCallback(get_train_images(8), every_n_epochs=10),\n", " LearningRateMonitor(\"epoch\"),\n", " ],\n", " )\n", " trainer.logger._log_graph = True # If True, we plot the computation graph in tensorboard\n", " trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n", "\n", " # Check whether pretrained model exists. If yes, load it and skip training\n", " pretrained_filename = os.path.join(CHECKPOINT_PATH, f\"cifar10_{latent_dim}.ckpt\")\n", " if os.path.isfile(pretrained_filename):\n", " print(\"Found pretrained model, loading...\")\n", " model = Autoencoder.load_from_checkpoint(pretrained_filename)\n", " else:\n", " model = Autoencoder(base_channel_size=32, latent_dim=latent_dim)\n", " trainer.fit(model, train_loader, val_loader)\n", " # Test best model on validation and test set\n", " val_result = trainer.test(model, dataloaders=val_loader, verbose=False)\n", " test_result = trainer.test(model, dataloaders=test_loader, verbose=False)\n", " result = {\"test\": test_result, \"val\": val_result}\n", " return model, result"]}, {"cell_type": "markdown", "id": "ae7afaef", "metadata": {"papermill": {"duration": 0.011675, "end_time": "2025-04-03T19:24:42.387445", "exception": false, "start_time": "2025-04-03T19:24:42.375770", "status": "completed"}, "tags": []}, "source": ["### Comparing latent dimensionality\n", "\n", "
\n", "\n", "When training an autoencoder, we need to choose a dimensionality for the latent representation $z$.\n", "The higher the latent dimensionality, the better we expect the reconstruction to be.\n", "However, the idea of autoencoders is to *compress* data.\n", "Hence, we are also interested in keeping the dimensionality low.\n", "To find the best tradeoff, we can train multiple models with different latent dimensionalities.\n", "The original input has $32\\times 32\\times 3 = 3072$ pixels.\n", "Keeping this in mind, a reasonable choice for the latent dimensionality might be between 64 and 384:"]}, {"cell_type": "code", "execution_count": 11, "id": "dc66ffff", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:42.411917Z", "iopub.status.busy": "2025-04-03T19:24:42.411683Z", "iopub.status.idle": "2025-04-03T19:24:50.570445Z", "shell.execute_reply": "2025-04-03T19:24:50.569266Z"}, "papermill": {"duration": 8.172662, "end_time": "2025-04-03T19:24:50.571887", "exception": false, "start_time": "2025-04-03T19:24:42.399225", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Lightning automatically upgraded your loaded checkpoint from v0.9.0 to v2.4.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint saved_models/tutorial9/cifar10_64.ckpt`\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "eb548b0a058942608353a5f2edc41420", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: | | 0/? [00:00\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["latent_dims = sorted(k for k in model_dict)\n", "val_scores = [model_dict[k][\"result\"][\"val\"][0][\"test_loss\"] for k in latent_dims]\n", "\n", "fig = plt.figure(figsize=(6, 4))\n", "plt.plot(\n", " latent_dims, val_scores, \"--\", color=\"#000\", marker=\"*\", markeredgecolor=\"#000\", markerfacecolor=\"y\", markersize=16\n", ")\n", "plt.xscale(\"log\")\n", "plt.xticks(latent_dims, labels=latent_dims)\n", "plt.title(\"Reconstruction error over latent dimensionality\", fontsize=14)\n", "plt.xlabel(\"Latent dimensionality\")\n", "plt.ylabel(\"Reconstruction error\")\n", "plt.minorticks_off()\n", "plt.ylim(0, 100)\n", "plt.show()"]}, {"cell_type": "markdown", "id": "4542ba4f", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.014333, "end_time": "2025-04-03T19:24:50.892925", "exception": false, "start_time": "2025-04-03T19:24:50.878592", "status": "completed"}, "tags": []}, "source": ["As we initially expected, the reconstruction loss goes down with increasing latent dimensionality.\n", "For our model and setup, the two properties seem to be exponentially (or double exponentially) correlated.\n", "To understand what these differences in reconstruction error mean, we can visualize example reconstructions of the four models:"]}, {"cell_type": "code", "execution_count": 13, "id": "d4c51ca3", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:50.922754Z", "iopub.status.busy": "2025-04-03T19:24:50.922556Z", "iopub.status.idle": "2025-04-03T19:24:50.928473Z", "shell.execute_reply": "2025-04-03T19:24:50.927494Z"}, "papermill": {"duration": 0.022531, "end_time": "2025-04-03T19:24:50.929819", "exception": false, "start_time": "2025-04-03T19:24:50.907288", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def visualize_reconstructions(model, input_imgs):\n", " # Reconstruct images\n", " model.eval()\n", " with torch.no_grad():\n", " reconst_imgs = model(input_imgs.to(model.device))\n", " reconst_imgs = reconst_imgs.cpu()\n", "\n", " # Plotting\n", " imgs = torch.stack([input_imgs, reconst_imgs], dim=1).flatten(0, 1)\n", " grid = torchvision.utils.make_grid(imgs, nrow=4, normalize=True, value_range=(-1, 1))\n", " grid = grid.permute(1, 2, 0)\n", " plt.figure(figsize=(7, 4.5))\n", " plt.title(f\"Reconstructed from {model.hparams.latent_dim} latents\")\n", " plt.imshow(grid)\n", " plt.axis(\"off\")\n", " plt.show()"]}, {"cell_type": "code", "execution_count": 14, "id": "f10b06ed", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:50.960614Z", "iopub.status.busy": "2025-04-03T19:24:50.960253Z", "iopub.status.idle": "2025-04-03T19:24:51.618386Z", "shell.execute_reply": "2025-04-03T19:24:51.617259Z"}, "papermill": {"duration": 0.676342, "end_time": "2025-04-03T19:24:51.620709", "exception": false, "start_time": "2025-04-03T19:24:50.944367", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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", 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", 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["input_imgs = get_train_images(4)\n", "for latent_dim in model_dict:\n", " visualize_reconstructions(model_dict[latent_dim][\"model\"], input_imgs)"]}, {"cell_type": "markdown", "id": "0a901d77", "metadata": {"papermill": {"duration": 0.029019, "end_time": "2025-04-03T19:24:51.697376", "exception": false, "start_time": "2025-04-03T19:24:51.668357", "status": "completed"}, "tags": []}, "source": ["Clearly, the smallest latent dimensionality can only save information about the rough shape and color of the object,\n", "but the reconstructed image is extremely blurry and it is hard to recognize the original object in the reconstruction.\n", "With 128 features, we can recognize some shapes again although the picture remains blurry.\n", "The models with the highest two dimensionalities reconstruct the images quite well.\n", "The difference between 256 and 384 is marginal at first sight but can be noticed when comparing, for instance,\n", "the backgrounds of the first image (the 384 features model more of the pattern than 256)."]}, {"cell_type": "markdown", "id": "197c240d", "metadata": {"papermill": {"duration": 0.020404, "end_time": "2025-04-03T19:24:51.738725", "exception": false, "start_time": "2025-04-03T19:24:51.718321", "status": "completed"}, "tags": []}, "source": ["### Out-of-distribution images\n", "\n", "Before continuing with the applications of autoencoder, we can actually explore some limitations of our autoencoder.\n", "For example, what happens if we try to reconstruct an image that is clearly out of the distribution of our dataset?\n", "We expect the decoder to have learned some common patterns in the dataset,\n", "and thus might in particular fail to reconstruct images that do not follow these patterns.\n", "\n", "The first experiment we can try is to reconstruct noise.\n", "We, therefore, create two images whose pixels are randomly sampled from a uniform distribution over pixel values,\n", "and visualize the reconstruction of the model (feel free to test different latent dimensionalities):"]}, {"cell_type": "code", "execution_count": 15, "id": "a17525de", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:51.780880Z", "iopub.status.busy": "2025-04-03T19:24:51.780673Z", "iopub.status.idle": "2025-04-03T19:24:51.945352Z", "shell.execute_reply": "2025-04-03T19:24:51.944304Z"}, "papermill": {"duration": 0.187565, "end_time": "2025-04-03T19:24:51.946770", "exception": false, "start_time": "2025-04-03T19:24:51.759205", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["rand_imgs = torch.rand(2, 3, 32, 32) * 2 - 1\n", "visualize_reconstructions(model_dict[256][\"model\"], rand_imgs)"]}, {"cell_type": "markdown", "id": "2ff7e41f", "metadata": {"papermill": {"duration": 0.030978, "end_time": "2025-04-03T19:24:52.016819", "exception": false, "start_time": "2025-04-03T19:24:51.985841", "status": "completed"}, "tags": []}, "source": ["The reconstruction of the noise is quite poor, and seems to introduce some rough patterns.\n", "As the input does not follow the patterns of the CIFAR dataset, the model has issues reconstructing it accurately.\n", "\n", "We can also check how well the model can reconstruct other manually-coded patterns:"]}, {"cell_type": "code", "execution_count": 16, "id": "38a8b16a", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:52.062127Z", "iopub.status.busy": "2025-04-03T19:24:52.061924Z", "iopub.status.idle": "2025-04-03T19:24:52.219451Z", "shell.execute_reply": "2025-04-03T19:24:52.218389Z"}, "papermill": {"duration": 0.181649, "end_time": "2025-04-03T19:24:52.220785", "exception": false, "start_time": "2025-04-03T19:24:52.039136", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"]}, {"data": {"application/pdf": 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["plain_imgs = torch.zeros(4, 3, 32, 32)\n", "\n", "# Single color channel\n", "plain_imgs[1, 0] = 1\n", "# Checkboard pattern\n", "plain_imgs[2, :, :16, :16] = 1\n", "plain_imgs[2, :, 16:, 16:] = -1\n", "# Color progression\n", "xx, yy = torch.meshgrid(torch.linspace(-1, 1, 32), torch.linspace(-1, 1, 32))\n", "plain_imgs[3, 0, :, :] = xx\n", "plain_imgs[3, 1, :, :] = yy\n", "\n", "visualize_reconstructions(model_dict[256][\"model\"], plain_imgs)"]}, {"cell_type": "markdown", "id": "5fc62876", "metadata": {"papermill": {"duration": 0.03093, "end_time": "2025-04-03T19:24:52.284084", "exception": false, "start_time": "2025-04-03T19:24:52.253154", "status": "completed"}, "tags": []}, "source": ["The plain, constant images are reconstructed relatively good although the single color channel contains some noticeable noise.\n", "The hard borders of the checkboard pattern are not as sharp as intended, as well as the color progression,\n", "both because such patterns never occur in the real-world pictures of CIFAR.\n", "\n", "In general, autoencoders tend to fail reconstructing high-frequent noise (i.e. sudden, big changes across few pixels)\n", "due to the choice of MSE as loss function (see our previous discussion about loss functions in autoencoders).\n", "Small misalignments in the decoder can lead to huge losses so that the model settles for the expected value/mean in these regions.\n", "For low-frequent noise, a misalignment of a few pixels does not result in a big difference to the original image.\n", "However, the larger the latent dimensionality becomes, the more of this high-frequent noise can be accurately reconstructed."]}, {"cell_type": "markdown", "id": "50776d5f", "metadata": {"papermill": {"duration": 0.022697, "end_time": "2025-04-03T19:24:52.329523", "exception": false, "start_time": "2025-04-03T19:24:52.306826", "status": "completed"}, "tags": []}, "source": ["### Generating new images\n", "\n", "Variational autoencoders are a generative version of the autoencoders because we regularize the latent space to follow a Gaussian distribution.\n", "However, in vanilla autoencoders, we do not have any restrictions on the latent vector.\n", "So what happens if we would actually input a randomly sampled latent vector into the decoder?\n", "Let's find it out below:"]}, {"cell_type": "code", "execution_count": 17, "id": "40fac9a4", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:52.376910Z", "iopub.status.busy": "2025-04-03T19:24:52.376537Z", "iopub.status.idle": "2025-04-03T19:24:52.525861Z", "shell.execute_reply": "2025-04-03T19:24:52.524772Z"}, "papermill": {"duration": 0.174798, "end_time": "2025-04-03T19:24:52.527201", "exception": false, "start_time": "2025-04-03T19:24:52.352403", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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IwNL4eU17USMWSymwdf1n533OlX7NqRBCckSM+m9Ro18f3pxcl14O0VhY4MgNd2j7/tYLuXT6dXrbBObkcgluShEmu/eHeo/aEiLZPdvhtAJ4lb2cRO+Q5PC/swXJh60CmHvEQ1dDGV24ksrcpfWagU/0RFWKqwjWhkiCwC56Ll05EVwKLs8Op7PyTVQQBEEQHCKTqCAIgiA4RCZRQRAEQXCITKKCIAiC4JBwA4siykPIiWtKOjWMcHfyT0td/B1WqeDSn+dAuXpc22VrQH2PiIY1qOGOVHa44D8Mczkt2O/6KNRG31DCIyaycS900qu2lOPCqi089gE+Na0e1RJ57GwlLvHIh/JmuAECoa4824d59CJIo+gaGllQxcLsOoQaW3Zy6e03KChgwv50LpWtOFDbV69CRE+wmC+9r8glXxlafH7urbBCk/4GJv0InHK0E+LsW6/zZplmq7U9eOh8Lt0eSUFMpfg/a5qbAhzquim64Yr9ceuWd/IM3Tz6Pt6c8cxcbRsYIjK8GiWvSEq1f64dBaac+eZIuJuyR++iD3KUZPkpRqZhDD682aX/2lZKsWpCmQuXcmVtxerUw0SoW9LIBWEmSIgwm7A4VXKAN1cWrtT2Ee8ZLp25vIOOtA/Gf/Cb09SwhE/yDvoxsMgZIc6S7X7dwxA+2fkVqhJWptxwLnVx0dV+PQhRd4d3TdR225IsLnnSqH6OqzKmCnE113Z8SUsuRbHAvcJKEFjkiqGpLdYPgXu+GizfCL4aYQ+2iiAIgiAIIZFJVBAEQRAcIpOoIAiCIDgkXHemy3MtliEbYXl0THtXX97OLbw58e5HtN2i8Y1cum0Krcn11oRs1MrDfxO3HMvW1eG6o7O2X3p2JpeGXyWnnU8151JBIJYkF/hEDZM8CaVBWABuZs7SdmHgLS7FsJzb1RKuhaujdD/ZRYVcCTJvlPkEZNso9NOC/dHYi1VuGlT9ez/DpQ++ourw/eeBT3T71Pe1XW76RC69OZb+1Ru/vyuXLvVjrhQDvRaX2NJ7D2QbUCbzifq/Vv+Y3Hv3avuLCW24dHYa+dWm3vsIl6rGPKntyswLuMH+QGWrrOHNE6d7ajvV3rm/eDG43wwjSduxMa249Oh4S0kDB8BTU2F+HRsFeOEy+CmfTaEKAZaXRnkvPQAl6C6NTEyiRh5mHvDQ5U0cCtUD6pehgIllOZlcaphCvvm/vbkcuYUZfswyHxFBR2jgBVdfm4rVtD00CRJWjO9CgSCZX+JzyHscC5n6Va7/X29maUbgpwx2yj48fZYdwjW0J1fik+k97HbFcmknyzJ/wAVRCwlN6KXnCcKxPB56oEwDpKAij+bI6Fu4tDiSUsqcToH3cKSbgk56uiBHR8PudDnOTHtJ2SDfRAVBEATBITKJCoIgCIJDZBIVBEEQBIfIJCoIgiAIDgk3sCgEBnr+Q4QFhZBwO2gNqjVA26vPQNWO+AiqpjJ7IVRUT2oI9eudYOltLEUZdNkM3TjLUyCkgPNcBdjFgZXcqugq2XHld4FmUGTBmSJQ4thq4JjisAqvq9BREHVpVfLxEtiQn8kajFrghRWOg6Ji2HWbMvNhLvki6Z+2oxdgybOKHqnNpiPgf7uWbMn7ohbwoT/PM81flUvfnjqo7d39IbuCeXySCoewa5VwAjnQ/PUxupWXq77PpZUDB2s7WdVTYVB0BaLnKkY30naV6jxbhXr9Zrq8h3c8zSUzeEnbxT6I1Dt3frG2m5+rz6Wgl0aALxoG3pd7f9T21GNzuZQxm6VAiYM7/vJZyioxMQHiSvgrxXrVmeTFJ/S+vVQX56Oa8LC521Ak4MmecF6FbB8DF53g0pFd67T9wfhbueQpZ/v1I5gRVhmrQZ0hvunmiP7ajldweQNeOuXoSIyyefgBbd/+BQQnKpM+FTWwFleKvznKuosXOJpF1jRNA6lte2264+HtGj+YUq+cb9aWSzxdheVt5bKxlVIqoqr6z3GxwTLEfQOXenmpI3CPlWrMnnN8d0NmG9uwIvkmKgiCIAiOkUlUEARBEBwik6ggCIIgOOQa+ETD9XQqFWQ/irvsp++PHgM/zU/pP/N9cKm4iNbal27bBHtpOFj9Q6wuMbZCuXq5kJuGhZf8uco0D4FWTIv326bu4kpEMsvekJ3BpaYThyg7Mr8ke9s7XAmcJV/XN/ghnhP6fZSYP9fqtSoM0A7vnwy7bH7vndouXQ6nbOZ9rO1dK+BonmF0eavhlWZ5IlQt9LHm1qARtqvwPGiBn1Q4JOHBctmJhp/lgg3YY6/mcyXnpsnaLu9arJxA/p3C7JNcaNOR3KV9Dn3EpZU/03J4oxTS3Z//jO7ChY0budRj83ZtZ3jRaVeOhrL3AUhskjCEnFvBU3zUqFHxtukhwnyl5KOT7ZO+lLxCRYBPtMo0yoqOkQkqnt3ksW0bcmnaGPKDfjb3Wy4NKkcJzVXODi71yiKX84DnpigbWnq78GYeexUvMCFT/6J8cs3evHszl9699wdqWPIfRNLldZ+HrBSwpWUGaEVvNqNLClc8vZtru3JsTS4Nq9NA25abWsLstdjFbGb3x1coT3kQYooKBMH9vOkSXZwNebu5NKruQ9rujjv5598j5ZuoIAiCIDhEJlFBEARBcIhMooIgCILgEJlEBUEQBMEh4QYWmQrW/Bsq2m5L+BQGCBTlkTc+JgGrxrMYgUffnaDsNGuvaP9174HV0Nm3sLCYmPDjp+wjGgy+kxCRRLiHbKq6ULIcCtbH38MDfGwXaJtFEGZVcs5uQ6WUfWDRovFkL8zmCve/f4Yf4tkrMOVDuDUszu//kzdTSukepTXI49Lpi5QbwdwLxWRKh1Is1Tq88Avse7j3F1Yl5tJ4FH0qHDD1xD+u26GKt0Fz4LL12t494JJ1639FtGcWb6bEUXDKjmUtuVS2HdnLBrTjUvHvFIK0/7amXKoZpECQ01u2cumEj4Kp3BhXNfgRSoExeuyrXCrDSuvsPHAfl66UTtV2QuQ0LhUqqsGyJvAhl968QvFNp//qxKXACRY/VQLD4fxTy7V9acOdXEph3yOG+CA4Lc5sru0zeWu51DKPgqSweJDiMTwDlC09DcgvEcPecuVMeBuc3kRRga/dAmMoI9/2veEvokHu+x3ePCrA3qhRUKol6ha6ldEta3Cp9ARlmDmfDtWEPulN9l0t4KS7nKMX1pXXnoduRNGlN87DYHOVUgieax103n+FzsvjgdeBwU4lphlUyWm0jrJS3BQiqNUR8k1UEARBEBwik6ggCIIgOEQmUUEQBEFwSLjOQkN5bSUjRKJukGIT0Q/KCLJc6kX+v8LsFafAxAXg08jB5541KuzdGDZ2+FjWPJNPNG9aX9jOn+1o/444x/Pgw39OVVnigMr4Ie5WKlbhwsfDbdfBWv7Zg2k33psh5frZjeSMWRqDXhA/ZQzvgcOwGzstyDGvVJ9K5Ksq9KInOTyXaNyb4C4qvUwDrPSZEL5zy17INPEiHp9MOwkMsHT/XxP0Qx5sT+4yOuzLX8KmH5cnOxF6G1WfSga0fAF8okYMpe9IWgzZG1KWZWv7Xsx58dJjPKVGeQXQoVu0tKT+5uvywcsarzZo+0YDzqteFI2onNqXufTETXS/tn4P6QWq7hyp7c11ZnCplpGs7Y2nwOO4iK3lb4xd5yMorHzzf6O8Ap+oi42bkS64KTf2oivQrgwWKoDsHYBpsksasE0OkjQ4lTd/HUOBGnkKUpTc9QuFXGT8Cpf3+maZ2k5tBsfyvT6dGvMhYYXhobHhToAdJt1EyRxcrZO4lPkbDdGYWHiVtXuymbZnPwg+7MYh/KBhx7fYId9EBUEQBMEhMokKgiAIgkNkEhUEQRAEh8gkKgiCIAgOCT8LQfhbckdt2AVe/Dzdv212hRBYjlT8FqUKiJ05HDSDIhpCOpUtuzT+hfX3Lc0C0ALkBjczIAwglX0Ki4yETdiRT2tnkuc/Hj+VwP6PKsQLv4zZ4WcaCPIMG/nHuBTpTaeGG8p9VO1SR9t35u7l0lZfbW3Xj4BxyCN/qipgSzOKTWh2Sz8uBT6nWiUhTqxpRwj9KKlCKSC2v1YCm2Yz25KGpDaz4bTgyJGKL2yH9BrQBwUZKk4GKZdFyk9wAYxydJsbYFTFp5S7QjVJhfOPWEPhXUZjWK5ejgUWPTHGcpIpyhY+2qJst4I7qQxFUSEr9xzg0vnfyb5uA1QxepwVOPkcR3JPVkDqaDqUD+LPpCXZCI8eao8P2ySjjLbTzCz1n+Oy/wJj4rGWsS3PnnUSxmQ5Uo1IGgGbXoLiVyneStouDUKtqjq7SSq+CK+yN1oM03aSC57Qp6MoH8iHpUu4NDHtOm3f/TMUboqqXpEaGDG6Z+dSbZcrB1FRFatAUhFbQr02jRAtO+SbqCAIgiA4RCZRQRAEQXCITKKCIAiC4JDwPZ1IeD8Wh8jCYO1HAvkpU2MrcelSAeUrcGGHI9nv78VBSDl9XTF5NPaf2wUHq9zWrhvYQ8sK5QDbDK+bSR5H5cdczwue0mZOLizyH8xsyLGNOaxdbjiWi3mjjLAdlT0C/zh7OhKm33v1ZshyYAY+pz24H8Y9kpOpOBeux8eHKUdHz54D7fphGWwN2R+emQHFCaZ9xXyisMgb/qs0q4LHpU4UuTd3bVnOpeBBugZeWCivfFPYDtEnGjuWbA/4Tq8B3E9/IAgj+TrmV/0pEkZXv8q0HN68MpFL+xWlaHhkCbiEP32SJz2wJFvgt8VyrfnA2cmF7MNztf3kjfCZAAs5qOWDobefPV7gOoaMF2oGJodJMeiWt8ULxWsCNPfCDYr9nQo3HH0il0tP7bxXhUERPjcTVI625+e/ziVfZ+YvdPQcu1yQo2TtITqzlCoxdp+KwE+t+I68p198fZBLSUG6pMECGBv7P39D2w1dcKyRb82hY3EnqAUDvuk1bXkTa9l+CbSkAILLZtpfxPBfqeF0QhAEQRCE0MgkKgiCIAgOkUlUEARBEBwik6ggCIIgOMRpYBHPLoBeW+7Q/Q+8tMx7fOriaa7MnEMu9/tujudShRr0qUoJsOL7oC9b2+Nrd+LSnEskmYm2fnXlh0QBBbWoREAEpoPwdKbIF/MA/F+SvovCnQ7j5Xg6gtaer/GDdpiVjwgE4Fgmc9u73c7qzDghEg+VFkvtE/m29/loDkRq5Ocf1nZ8mQ247T5tnQrAivL1Sz7RtomBRRAHYF9N6KixD5QQpXrYA3FCQZGQ7uoGbdet3ppLVavv0PYkV00u3Rl5VNsXMFLtlmEV/vWB/4fha/lrPzwFNFd1bd4ydhd+ivh8M5zJgacoEnBCDXighgxL03bQ3wiOVUJZFNyJZbjy/Bja/xlIqAC36wKOuxC1eXaxdBPHykJNp4J46uHeU79x6YEApW+o6YFcFitYfoWqv0Is1duXftL2e59Ms+tSbzOHN//cMo8avT6FTQtY8avKv4B0mQUxlWLKFhZ02fUFqE6TVs3+pWeP10PX/p67GoLG4hb9K3g8lmpZSG/s1X6IVCpYTM9XRM9qeDTbb3clWRSDFhED3TCiKOrSmiqHJ4DBHR5jWjMTXgdhvl7lm6ggCIIgOEQmUUEQBEFwiEyigiAIguCQ0J6YYvstXTZ2qJWsYeKNhh1WrkW/dMdVgUTVbuapW7bkDy617ddc22+XwirvG1Jrabvv2Y1w7GTmfnDHciV2/4PUuLCES8GZlFLg493gmjHYj+wjkiChufelV7W9NxFcAg0fHqrt47mwlDvoIxepaVm8fq0pU44WmB/b/SOXuvVoRY2DfJwAllTaRw9TJormbbZyyeWhIgGtqt7BpdeeOavtC0G4vJXYenATR16QuTpiEq7nkvEcuc/NL9DnxoaeN6IBV/KN3tqub0D2/OdZ1vma6lUuqVSW2QN7uOxHlqNjtPrvYdBDVGfKMxZNW5uGPQnK9rHKhu20nl4N9xeC9iC5hJV5lCtu5iAb/kIkl2p3px4m7gH/q4e5y2tHcEXtY0fOQQfpCXZew7PhkS++Su7NggA8az6WHSKzCBK1Vx9Ob5t1pyEfxH7Yhy1zjCTe7ND2CerS9BGw6U8scf8n8A5RPOnB65g/36DLcfVO8B0eZ8OwMvr9QpQICIEvjy7U+pfhPbw4srK2/7z6A5fqfjZG25feXscllzuRGiZESPToRBntL1yAeJFDl69q2+uBEcVZiBEtx9mLoqm1EEJYyDdRQRAEQXCITKKCIAiC4BCZRAVBEATBITKJCoIgCIJDQgUWBbMe13ZpFiRAyPuLvMdrL4M3OuXm5trOMCE3f+HFDtq+XFSPS8Ob0qdGvbiWS2vema7tKTUf5dKFQ3dqu02fJlzaMe+ItluNrsOlQaW0HDipfC0ufRpFF6TPqYe4pMrNJLvOTK643hpPO1/yMZeSoqmoh3fdIthhRbo4nmiIkThyOy1DLl23mku3jHhA29su4/JqexI9dF5RkclccgUpfupSyRUuXc2m/adUa8Ml02cbTASbYbP4Cq3K9+XDTYlM6qpttwv+t7u1IgUC/O7P5NJzF2dre8956NL3ragUxrxYKHk/7xm6vA2efJtLh+c+pu2AC+I77lJ0K08oCFuophpre2Hgdy5deMU20C5jLEW/lN4XXjhKKCBfgTJeJtvEsCW+otyASApOZkrATlIu+5ALS2Bh0GYzpQIsbchnk6H0h4u9UXD5u4pgb6wRn0B4V6M6FI7XocUbXCry07kUBaECSUj4OITLG3RT/pZUvIRPf0+2bbkopSzJBfh4Km6F0UM+1g1LLOGr7LV8BWofGW7KB7K98Sou1StHr0p3TQifrHs7vYg+bAMvpdb16dKvf+Ykl3q/xl7mBswpidGUHSWoIOgss3C/trtPe4xLv79A55J5ZQyXNh+hnQRwhN44hk75t/lYMolxN3515FW3LFc3zMBN+SYqCIIgCA6RSVQQBEEQHCKTqCAIgiA4JGSyhfxd2rz83QGuzJhLWXzTk4u49MVD92u7TERvLhVUpmWzPlzX62O/bu+4eIJLJsu5np0PS56VcaeyofkocnZG3x/NpeIgOc8y0G03qJh+Bj/d4wMupe7ux44L+cdVLPkpy34Eiss1kRoVsHp7BKRjxo/RfYno2ocrP52iixO8AhnSX3z3fbv9HRlFDsiYu5/g0uQ7X9H2O+lwK1WQJXb4T6oJaDweuPJ7M1pSl76FNe8jx3D/BvxvZ7BmN085LjWpQG7LaeprLlU2cCk+7lFzwPMwV8o/8Ke2q6sOXDqkKG33p+ZZLj22gxIRnBuA3mJYKW6LL5gX1nb2eKMn8mbVifRoHH/xYdyWfLFpO+G4B1pQunB/tVZcUiNZBowTO0A6kk72FTx9e79qCIL2DnfuO53wDIz/C3tf0vbrJx7g0pNdJ2jbfRkeXr9vibZ9JeDqU+DDgxwa5e6lwXABvbY799CT0rapsqMypkop3pFOjbGQIl81flqbUcMgsUNCR3pCk5ec49LlABVduFrwCZeMAvLABk+Dd//IWrrAzyZA3Y5h1akCx4O74Wr42cshNrYZl+o9NkvbG1/8TgHU+XUvLeDC9JEU0ZKxYz+XIB0Cjq6ti8nbbc7HsijsmQf3u1IjfBQic6QEQi7iPZQp4i5li3wTFQRBEASHyCQqCIIgCA6RSVQQBEEQHCKTqCAIgiA4JFRgUam7u7ZHfL+bS39cZI3L4N79jcUB3YRJ8U0WZuLCFPkxbPm2tzcEj6ifKUTIFwE12QtKyDkfGwnnkpVJ3fDGNeaSP++CtktNCKzws04t7gcxQuPMFtQwYIWyMmmtcU7tyVxJqkXpBYwYSySRfagOV6zr2qntKpdquwck5hwtyr48DapzfHkm62+b6wOw4K9gke1m9pRLhSwHS5fFaXvl0hVcmj6HQlr27/6USxHeW+32H+miALe0ss3sNrPCLq/l6r4YQau8vw2egh4qKjRxaDXcZXMgi4RxQaRGqy8oPUjeKYiY++J+SgcR6077990Oia8Ydn7iFR6CZ5sb4VL3l3izwjqKxzFLl8Gm9/ck+1dL/RS2YN0+u8I1J+8gxIhcyabB8Gjl6lx69Nhiu50U+yhsp2s3GORbtx7WticFYoQa3UD257tgh1l/UDeUfWBR8WW8vJOfI/vCGZCG0nvj00S4lTd0rqrtkh8giC9OUTcCLghpdMfRmMxoDtcw7TGKR3MPwEBIRpmWv/DmHya9Dyev/o1L+yJoqPR5qRKXFI/Ow3fhc6O2abvR7aB567IPYRxYxX40BfgCECLkc1GGmaZ7fudS+u0sYi4LpoOM7jQDqnoYTMqQb6KCIAiC4BCZRAVBEATBITKJCoIgCIJDQvlEo9LoZ/rer0Fq8j96zqNGEH6zvr0erX/3dB3LpajeVK+8VtvOXHo44T5tl6sCjs/MHvSbdfl64BLIuUJeq+gKVbmUUUAryrv0f5pLaxZO03ZpEDNfe8hpdyKtPpdMlaRtA7NiXyqi5tbiClzqH2ufUSFE4XR+RQPoLrDkIQiP6ARyWlQ4BW6rW1y0/8W496iKZbVdnA/Jo7NzKGF60LRN1LxtLzg+i9l1q1EW1msfP0RZCRLK3cGljKwG2o5zwU2JU9TDcRGQviPUpeEaXt1aLOl07eARLnV0k+NzBlwM5X6YfF+/zNrKpW6ukPlMriHml7zlctHjEHA1wS1pVb7RAC5acjR5mXKaw2AYV26wthe37sKlUx99Sw2Lh92wT3ZvslgKa3gA91rDnTSYZPnQKz+v1PbcO+9Q4RHFHtAlq8GtOPT75tpudB18qh3z7u27intMtM3pD1wCt6IqpUANZRSA1Iq8mzdAB1V0Gr2Wow14DF0sE0GBgkoV3jgKcCnTCcanuze9AEM8Qjdv+4s3+5aSL9kfATvcls1esIk3wV5yF5IdAads1iZPqtEZzmvSYBqW12dCOE6XWjSnBEsh9Ur3DynAxd8MUgClrnpL24Vu6LzXU5Ma7/6obJBvooIgCILgEJlEBUEQBMEhMokKgiAIgkNkEhUEQRAEh4SOeiCf+8Qu73HhuY60otb353EumZfJ3+/7ERYv+xaSh3xX5DdcengQhWPc8TIk+7/1U1qHPjwaYo7KeSgswmXAPwS1KpN7v00DOM2fg6zDJji0A75sbb8+AcKRkjvReu0RteFYg1j1lIxVEO7QH6MHwoW59E0MnwheJTd7MOyF7a6utIg+6tvtXLo9uFzb7TH0Y8RCKv7gat2RSyYru1O92YtcuqLIG18+EeKqeKSCJ3UIl/wXKTilNBfOuVYDythw6TAEuBmKll4nhAglskj2WS5u+oiCmKIaxnHpj+vXarvmwKNcih5ICRa6GeHFlVxrXK4WvDn9Ubrjeyo+wqWNp+gxLNcvn0tDo3Zpu2HyJS71NuhWznBX4VL0jWwgQukjpRr012buPgjN4CEyGOumXr+Qo22vC4K4Fj5FUSEXv9jLpXdHUKWNStdDDMvTNeNVGFTAsJ1fhpMdZT+6gjBM1Lb3WCaWTcoOo8IF3jRvYJf0r1zYdBJlHni2Wi9QcmgnCVENuHShlI4d0/oZLl0dSSd2rtdcLrVy0xu7nIIT2xKkeLTr29fkkrmPxf60xFDKvSz2B+Ol4P1Vil/nNtLlPv8IZC+pHkexhB3xruZmUiRgg1qPcimLTUTNN97CpZ8r36ZtzKEDRXzeVxJYJAiCIAjXGplEBUEQBMEhMokKgiAIgkPCXQnuRrfS4B9oZfe3VTrApo0p1a86heuQc9jv4yXgmCr8/Ji2lwyBbNGHb3pe22WMcGd9t5u2vOgq5pLf5L91Y3F01jT9kCH6mRaUgPiZiD7wId+vZLse4orJKqKHmxnBghs+F4ylW5Z9rvRvW9vwV7Y2S9b/wZXpAfJMXMGq7716kEOr8u+zuGQ0G6Dtb0Y05NJnDUkKccr3rYQs8+83/9ZuyytH6fbd1gVy7k//hnr422lYlH1jqznaruiBVNq/Fe3Sdr/kNnAwdgFK3ZDD+p3cj6m30eAG/i/+KwoP7OR+67X90g94IsyXPu7uGVw5v2S2trN+gMew19q22m7kGYeHpviDSPQq1xhHvTpp8Yke/VmbMaj4Wa76ufs2cunTJ0Zru3ArZBk3c9mYt8YEUK9e6gb5JZ4+la5ssU29EWXYbQZY7v68nrSTL+x9opEpH/KmbzL54wKb28Kme1Zp88NZcJfjBpCD8NEbHuRSzHmqOXHlHvC/lg4g52SlsuBKT/QksRac8pCiMdo2d2J+Ff4S3ehTDrDEdxymRz5zBryUXp1HMQdfz8nm0prnaSemfdzDkdWLeDO63bPattzjSBUW8k1UEARBEBwik6ggCIIgOEQmUUEQBEFwiEyigiAIguAQhyUmmsVX0/a31WBNrnsWFXgJfDYFPvYJLI4GmB84YzBEbaT1pTrvaz8HqXUiFVNwY7WHjEwKQPjs9S/wYJZgojBhnS/db7vDACxs/30TBRZ1a+/osOjs9kRSOy853HX9/i8pbULjUljKfcL+U1WLyaUf2e4xLr1uTND2eBP+FfMZFIGy41OIzVk9nIozHLsYdqoIxqI/Yen9ojTbcCSlFttL4YHD5KO46dpePQvKYgwZSEvgJ9fsxiW3QQv4MzDYoaKbQm3ijbCexMILcO+iUqPstuRBN6uWQ2BRaQlFbfTsXoZLjSu/oW1DQTQWDkS4C7/VpaCYGgqyNxisVMvIsz249OX1FOBmng17MIQXnucvLvz3G/3PUDO8LybFZ3+Atqs72T0gOFGl79Smv7AGV3YkUM2ovN5w5Xd/Qo/elC2fcKl8O0qb8Fbq21yKVBHKhvdiKS3DIHdt0Jy9UEPAhwNGVnqiKVXC1Tgo1WKaeeHsu/CNfbz58BQaKiON6L9t/u+Rb6KCIAiC4BCZRAVBEATBITKJCoIgCIJDHPpETxevo0YG5BU2osnjYnSoxSUzhE8UtoNW8fIF2m5f7nMuRZQjN6MLPXMlGQepG0YSHoD/8B3Cd2LaN0O4cCCxw5NUNV2tbYm7Y9c+1t65GcIHlBjmYmCl/D7yFpRD6aSyhad3borSTraY2WdxibCc/lvvBqdd0t2WBfdhwa9ATXSQHA/Yr6m+5rBDlb4D5RPem0I+pzdxGbqXjbWIOBiiC1ZSkvSBzb8PpwuHf/+TNxsN6KJtdwTs3HDRkDqyeQ2X/LvHazt62GY8Qojhxk/scS5U81LOcV86JPF2V/2Vb8ilj04+pe0lf4Bve+sxOpcxA3pyqWIieeO+WPwOlyZMIBd+h6cHc4mv/8f86CpEOQL+kIf/bcP/7zdRSik19HlonmXhI2WwSwWNqBv94Y3argO9Ak4UQVWAO3dka7tkD+zwZF2q9hFoGlZqfqXUjYq5Y9NwnKSz/Yd4IsOuA8Hnpcj5kHP/efdz2o5/uB2Xes+ndBDBffb3wQMH/ukSOVkrVviMSw1UWC5S+SYqCIIgCA6RSVQQBEEQHCKTqCAIgiA4RCZRQRAEQXBIqMCiEH71MVHk3v8gAHElgUWT6VOdoBxBgEeZWPz7ARZaY4kW4VEBmOy/9OIG1rL1U5sGVDvxRJKj3l+yHbd1kgEAgRXlb7KlzF48ZZ+9Mz7Mf2227d3/7zdSSinlZ3FAn6K0h9kD3BD4E/HYRGoM6c4lteh2bX77xiWu5JohrqGTJfD84pQxIASrr4tiFX4OOiofETbThpbX9pSvIdmCCpwle/d9qNAFXjv+CpfOfEJBNxtuvpcp1e360OL2G8PrrHq0ZXNtz3hqDJfcB3kGjPAqlShlqle07e8zj0tFLJYufu1sBVxnt8MIN1XFua07FH+5rTsfQrbhc2OHP82bN3Wfpu0Zn8NgeHQPJSIYWR+Sw1xk9pZTsP80uuFqeCI8ownMtjy8b58ML9gtE3IjqGx2ylEJINWop82YLvAS6cVaZz76kkslftYNS82VffQYZuCLmHfdMhi87A/GMAgYNN8oogYEVuIM0xzjJ7fZ52joRzkfpkZARoXr2bW/7B7KpVl7qLjW02OhOo1/Pr2jYlaN5VJKHJXn+jTIX4eqBht7vW37Kt9EBUEQBMEpMokKgiAIgkNkEhUEQRAEh4TyibLfuVUsSs3dzbXd9+gxLq35kTwTZdrcxaXCvbQMPym+JpdyLnXWtn8rpGYuns0WmOeCq8NjVNV2IA8cTmZpjrZrNgZPUs2m1PltP0GGdE8k/T4e9MMOs7JoWXowaOvb80SA77BtMtku/I/FNtNzSApL6Qo8eucgLt0+fKTdp/YwZ0czzNRftwrLM37sOHzMw4cHenpaUjb2hRsgv3mfzTAeri3b/ODeeY05ccd5wLvziJ/uUTruJMwEDV50Ck35dDRroeZm69xbrgKlJTl+mpxI5lKfxykDhvkOuaqffRaX4TtiwW5KbPLBrQ9wiTutRrwEg/KTfWz01oN8BarXM9p8fy0oG5n91RXwU6pyD7PGH1wJZNAi983vYUb7HXR5x78ND29c5fnUMOFTaRWmanvO+BwubUyn1CtnD1Xn0uE8ctTt/gLy+5+rR2vti3tBkpK0CLqtv/wICSu+fIHGyVMThyk7CnAIpbBn7YVErhgNadh0d5/mUu28y9q+svN9+BSzTXzXJI5J0/YS/zYuJXvI/9oa87JsU3RxzLK4x7EsluIivhsHM/9uxasgdbb1iRqzaf/t0aNrsNQulRV6vlmH/3z7dS4tPThS26YbxmHLKMpfUehK4VIlM6zM+vJNVBAEQRAcIpOoIAiCIDhEJlFBEARBcIhMooIgCILgkFCBRTwAwZLNnnutf0qqzqWf7iLP/zYfxHBsju6o7cYYttE8mdz7rRoM4tLpIeRYru+GHVaJof77/LDGnzuj47HARUmQci9M+gqWclevPkTbNzcowyUzj5zMba4bwaVoD8WVbD80gUvGP/4vxcQwmCEd79f2qfQLYe7kGWaPxz32vUIhGF4fDyZTysOXpeOZuPtps8eXrbnS/HoqTrInC0IJgn5eev4aFGCp4aKb0rMs1KM4kE9xRudLYGH7JS996g484/psOMyujAfzUZEQ5TmEWhKZrgoo0cAe/AbksgiY7GqEdzEs69/HViir7XdOYP6HKDr9wimjuBI3Y4G2P8XjvtDoN21XfRMqxqiH6EQeKgsBFw/xlfdJliwqH5Hpe44LXRpTmOHGywqhbk1fuoILu6dR5EvdJzHZiJtiWLwRkK+gS12ocIKHomMF2sIjzzO77LsIr5cZz1L42IofH+KSr+ic7bE42ZABRsWy64YJYMzPqHTVmu1vcmlpCr3nNlyAmxLDxoq7M7znU8rSuSzKgNwgi03q1Yupd3LpkfOLqNERBrmrEaUKMaNgAEQZFEBadAR2qBS7AjgRmcsoOqnHeHi9RLoGaLsqpMpQi9hgW2U+Anv8i45V1OwAfKr3E9pOWwpRbLGuG0hStsg3UUEQBEFwiEyigiAIguAQmUQFQRAEwSGhfKLcrWjJKR5i7q3AfotflQef2zVhjbb/uAyrhmOatNd24HQWl4qWP0ndKIYU0QbLP56Q2opLp49RxgYDPZNRbnJ8zbztVi5NmEvrcNuPeo9LtaqRF3DcLTxduHp+DmUndtumy3YKeq2uZlEqg/D/A+IevLdRyisl3/edZ/eCVud61rD4RMl75KkCHvON+9drOzeI0p6/tH3vHeCMuZqR8a86rpRSZRLJ2fliCnhcqrOV3emZ4H+tzHJbVEqAzNdVWtM1PYjX130DNd2tMF92kI3YLMyHsDub7K6LQGKOusffeoErtw6GRNjh4D+9mzeNyk3Itv9UzEuQ3v3lmQu0/TOOroq8MbwvHCv5M2oMXsIlU51h3WiIUjtqeH/m0nfMrVhnPLwo2pSns5n/KLjfqo2kqAXlTlU2WK5GKI8zyz3ixhtustIatZNh/M98jrJSNGoByWHemwkeaFsqgf9V1WL+8nKQ5kXtoiwH/gKswMGehkHojI5nu/+hE1yACwcpp0rRWUxlUI2uxvQmr4KUTVcnqRlkfq8S0UDbDY02XJrPEiBk1odKFVUXU8hF3A3gwy4b3VLbF9URBZDb/inVgQv1Fe1wdgS40sdXYb06gftbTRfnai6kESmM5zEcPZUN8k1UEARBEBwik6ggCIIgOEQmUUEQBEFwiEyigiAIguCQUIFF3FFt8czzFeqf4eL96T/Rwunz942Hj2XymAtYGpwLpS/CxWRe9pxzELYQ6Q7x/wE5z2MjoVLBUwOpYML8p9Zz6eLxddresR7Chx6fRuvcE8pCBQYfC60yMedDZHSIWBDWVzyPybeO1Pb9b+4KZw9KsfoLSm1G6TCLnripTy8uJe5gNeXj+3FJGTRyTBdcDW88XdIUqAOjbupKi5d/++t3Lt0wiBZiGwUQIrRoDAWdtR3UjUv5P27Q9oIXPuPS6WKKmGicA0O02h905V1YjiKJqn2o+k9DboTMfSu13W0ilPs4y/KSfDsZVnkPYJkN+veBHB1K/ceBRUalxtAM81PwKKuJW6lizMRuD8Kmfrb+3fsESIrnsoDiJIZiaSjwVWHAp+AJrTjmDW3nj6rFJRU9QIUF5itQlL3BVFGgBOiNVbAP3jX+HOp83r76XMooouJVTcfW4VKt6hQx99wDzbjUK3aettecX2bb9yIetKKMhs217U6E0lL+AA1Kv59fahVdje5snwVwyh3TkmizKlAya5NJ55VXAKGaqbFUF+uTOLgLnhpNqRsGJCIpy662B8tT8bdXBazHktaXnusjEb9xKZJNTGcVXI3X1e/avgWDfVzsaA+pJlwa/yN7VpribMYejrIGvK/asPI3IWLT5JuoIAiCIDhEJlFBEARBcIhMooIgCILgkFA+0RiWYsHA6faEST/Nzz8Lv/uX7tyq7aj6sGq4eCP7YfkapB8Hrm8AP4JHeNx2WyJwXtVqtNV240qQ0HzfGUrUHgyAM2bT2w9ou+1dT3Hp28lUbj6mRl0uDZ/1MHXCE+5/MwOnkV8tKxtyjp9VtnXYo5hHoBCvPF//3PkknNfSO+m80t7/kkuuMuQi8pWAb87w0pJnjxeuoctFN6VZDXAynd9JHmgDc3u4uKPCgGOVmXCHtu+rAT6hW4ZThoGvfXBlUgJ0CTJ5mQWlejKX0ytu6PwzH5Gz/yR+ivP2lq28yd1KkRHgPI6IIb9aaSEmwrfDFaYbNBRGy7uokTMcNB9biO61ZNLn48by3vDYbBYSD63QV97udluFSpsQ2MNbJzp30fbYXTCS/yymAWDJG/NAmSRtv/jYt1xKu5dcpEYMdoS1DBd06rp2NA7XLFZ2GB0gKUGtiTRcr2RB6v/cZq9oO/405Khp9ALFcJgtIbGJn5U3uD2uOfTQpGzqRlx5LrV2Ua8qGuioVvyNarmKHNOuma0gl0vmZfL1FlSGEhERiiJLjimYRLYoyjeSDm8v1UDR+8qFcQBlG9CznFUbAhrYoVSCAed13HdS2zVVZ2WDfBMVBEEQBIfIJCoIgiAIDpFJVBAEQRAcIpOoIAiCIDgkVGAR9x4Xoyf5++Id2vbl7uPSdY9Q2MLQyZO59PK8Gdo+MGkul8wS2/XaCMz6056ape0p0yfafyoEcF6GQQEI990IK5Sf/XI/9S8Aq4aPb92i7XbDwPPf6zZaoRxdGRz1hqMYEZeX3Psj5zzJpZdmzfjb5v+H9cMopunyPghHeuwgufRPYWRS+xXkt+91HdT0ePsnCnaIqQ+VcFyRFOBgGGGGdym3JetBuB+j/cffAiPqm440DuvX5OVo1LFiGmCWWKy1QborG/dCIo5DGRTgYCn3EcXSaNxwVyfcJR/McMt/2k/pO/rWgx7+F8F/o721//EObYe1aXmuT31INiaRUBG0lj9UnJILntCqnatoe9aRdC7dUEyPeYIbQk5efvcbbXsHQyoPxYMTMaWMCrL3RlEBVzy/QVEXO1zVGvCmUUzXLW9BLJfM83SaZe4ZyKUdcRRlUyVjG5d8LJdLlbI4uoIXtel1NeJKOYMX8gnxFcsimXaSyV6wz+dM4lJgMUlRj0BUoMFG0asKyiLtNqk8yw9GSy7VZU+zpX+tjZu0vabv11wKbqfOFx2Fz20tpXcjDDVEvokKgiAIgkNkEhUEQRAEh8gkKgiCIAgOCeUTzQvSotTLwUIuZeTS0vhKnstcqs/K3Dd3JXPpq+G3a/vVEvjUkpeWaLuwABbe8nm+cx3ICf7kY/yXdEjNrCDZseV/BebPCGZwIVBI6387tIPS80MPlaUeZkIPqzPfmcfIBmkAlYB3RcZxSbn+6X8w3D8amnpzKTl7vQCsUP55wfPafuOdQ1z6/hKtWK9aHTIPxDUfrW3Dhcm+/3dQpgLlzVi1bhyXOrZ7V9sWV1cpy3qRF5HEpc/mkTvKgGGoqjYh339ULCT9COEj7F6F3FHj3qTrqS7+i43/Dz4YeKaXnGdGuOnowyeEO9JyrKCNbfGDwiNv5rLU5x5ICm9UHhVWBw0o9uCZTj7mlk9s4tLFnTupUbkrfKpmK9YC76YKsNejid5zXhei6CRXAkHerKpsCC6F+hbHj1GQQTC7BmxahhzVOSqNK9+cp+QYcVnwoaiq1MOyKQu5dJ7VnGgRDT7RYoMGX4QLjuWCZAuWN49pYyufoomjsxumg62DUrWdxVIoKKWuKEpL4jXXcik+QKPoN3Mpl1p5KXFEI9WFS2VMqmlR7hZwOec1prdcRAD85RFXwnpFyzdRQRAEQXCITKKCIAiC4BCZRAVBEATBITKJCoIgCIJDjOeff/7fbyUIgiAIwt+Qb6KCIAiC4BCZRAVBEATBITKJCoIgCIJDZBIVBEEQBIfIJCoIgiAIDpFJVBAEQRAcIpOoIAiCIDhEJlFBEARBcEioKi7PsjwMISZb075pqfUQoszEvOBZbY8dXotLzV8eoO0tVaAcgfsf/xNQVATFGcwS2mFkJGx54TXq4bnd57nU5msqGOIKdUUBM4fqXRgXsARNGVZMIBELJvAyAwHo/Aszp9sdy1lKjRC3Ev4Q4r6aISqB/M/CD3ylqJhLO09v13YGryWi1O7LVNJh/fm2XNr2Od0IT/0qXIqtz4pORMDliE2mZmIC9LAGq/BxH9vfX2+9oGx47ccVvFmaSeMwUAiDIchKIRmRUJvCU4uqgqj2vUBq2VTbMZcPcKlKhWhtT+s+nEs3Vq6kbbcBp7/iRLq2+9etwyUVxLoo1xh4MzQfOUPbK999gktZrOrMQRzKn1wkbc2Uc1wyIiO0HZkGNX0Sa9I4GXnmFWXD52Z/3rz0w6fafvCHWVyaUZP2b3nfQYkcPzxr53KpjFX1SDivSFZpp+jUFS4l16Nb6XKHWxdox3Eq1dK1VUMuFeTTa1N54OXojqcyWQ9/9guX7mlNz0P1+AguxUbSODSM/96XwBdesH0q5ZuoIAiCIDhEJlFBEARBcIhMooIgCILgkFAevLNBctT5UCpmv6r/bILY0x2l7RYKfs4OwXBXBW2PjQpyqSj2krb/uRPUQulVcEbGlWPuB7w25e+vrO1KCeASC98PyjHi6FzMaNiFcYldUg+6FeOZo6KoVIUH9z75cH8b8mknH/y+i0vZf1A5+Jd71+BS8551qfH/zO+JmH7e2rvmMW33H/EplwpK6Rr2aFOZS/f06qrtRpFVuXQ4ks4zCA4ylf3NKm0HCn/ikif2G23H1I7j0inm0t5Thbp0R2tlx+xffuDN9z/9WNsHZr7NJTNAt9VbriKXyg4YpO28CnBbC2aTW73w+D4uZQTp9G/ygH+o2SJyaL3VtQ2Xbh32IDX+Z52gCLpmnx09WNvJ6LS+mk9vm5axIK1MoWYgFhyf3iZ0K9214OGNq8x2csa2g/fc34Q3v427VdtPVYdj4SvPsGuk4lsoNRkc4XbE1q8EbSNcPyinJfPa/v7MXC61f2qQtv1+mCmiSnO0PaAsuGZXfU5PzU19m3Gpdl26lcoId37hWM7wn7+95JuoIAiCIDhEJlFBEARBcIhMooIgCILgEJlEBUEQBMEhoUJi6o5srm3fGliTbuazhg/igIyPyeX+2h17uTResQAHdO9Gm9STxBc6cemWpDtDdDI8wHlsBqjDm9fs4VKPO9pRB7GHbhZl4ArlfQ+VegD3yOzycCPMM7m0uwj8lCdem6XrMb7FnulHaMlzID+TSwNqlNN2ytH5XNoxj+JxNn0J51WrTV9tx//4HRyMXTifL48r36pntT3U+xqXPApzSvznHBsBAQgtvz6obUtEi8tFMRfVL8RyqdO+Y9qOnQE7vPuBstouxkC7+uWf0/aZ4Bou+fNStJ270zamIXMPO/3WU5QNYzFE6L6Jz2h74cghXHrp8y+13bkpZFRY8Su7MtlRXDILaCW7CtinUfEVcWH3LTdou3u5mlwKnIeMDf81DBcEhfVsRL0KBuG8Xl1CCSs6DKrApQOF9B0jaELuleiOFNISWw5uawJcUVvuTYWgmOpDW2g73vrVhvYfIu7HNJ1EzBQ/+hVvlhy/qO2o2jCiIiexsVcBT5Jd0hZ92nHlxmfpNFf6IOdJST417x0wgEsnr9AAe/pJONSaNyhyreNDm9X/AuSbqCAIgiA4RCZRQRAEQXCITKKCIAiC4JBQPtHSDczzeRm9QEHuB0WP4/20Qn9CSj0uPXADraiNUOBj4D/nb6i6jEv1FCw9BvjH7F0AhT7o/IEfyAsy7pldXNrHfKIW14ZTP2iIzajHBi4ANxsnkR2AHAKqlK78gXXgqA5xnaa1odzfsZVhu4f/+l3b00f25NKL/SljetlKnfFYbEuLo8YkT23gUB+unPQd1ran5cvYR3IKWq8m3z9mtDdN8rnOX3mYS01a0dguKYLB1i2TEixMPr2LS5GH6GobbcEnqsaQuzTaCyvZjwyjJyX+Q/gQ3rwQbqqgvURYroyX/eHOFEj8fcdjlDZhWvoFLl2e9DkdtUU/Lrnu/ICkt0fBwYr3s85ivQTm6wpkXFT/C3BFJPPmN7/SmLziAe/7L6uow7UHQOaB1Fj6jhE9Cp6aOvXpytfArAYt2U3KV7aUx+8vHVnIxfZ1kKOhVSdK7XJtvvQsZ47qzyFvRrQ3UdvGvgz41CbKjaCa9QapMXt4WyRx5Yu6tMObD4NP9DAb8tfHQdhC1lUWV4GP/IoVR7Xd8SHlgGueG0a+iQqCIAiCQ2QSFQRBEASHyCQqCIIgCA6RSVQQBEEQHBKy/shNbcletA2kS1RZxbqUfRTVK1e7IVyiW+dp2t4QNV3Z0CDMSKKQcO/xR2fhY2vfp1Xz0WXu4VIEizmw+p9Z21Gpg5BYKjXEUT/MUjhYYRYFSU3fC476xm2VHcF8igTJOwxRIQ8seVHbbw66n0sptadSN/CUsaY8FpMxJmkzNxbW2g8ow1uw2NwI75pabsql1rTHb3JgsK1f+5G2Xf6WcOAOt2j77HkI/fma2c+PO88lzzgalj7s7AHWrbKgKBaMYS2FFGB33TDS1DWFX4v9hTC84kY/pW2zDWRvSKtHqScuj4JHvvBPGjZFsz+BY52mIjYNn4OCOQenUc0Ns+S3cHruHBd13khuzpVHH52q7Tb3PcylT95rpe1aWMUllV3Eiy3hGrZkwUR1cDDwwCLIu2HpLDarRVPn3x8JMWJt0ynO6Je3tnKp51ga2B2qjufSbdkbtP1os2+4ZDxKkUpRw/mQVyqPDeVAAkiFR8heBDs0P/tV28EE+FREAsX0LZ0Eg83VtaO2PZ0gpcLVvfTwTrl7AZdGjH1U/VMwODGYRbaBdZaubAlnd/JNVBAEQRAcIpOoIAiCIDhEJlFBEARBcEhIn+holgi7H+YeeHwe2aW42HgyS1vcoCtXNk19Vdv1C8Aneih8b6ct8Ev3VJYi+5XHwAsYf4acWw3HYN5z1gvL6ndY8W+RHPXQz1pncFmz9yq5mZOToCO5eeSALI4Kefvsjoy9/+W5H7V9175VXPKl0pnFlYBHz7hC3di8GNyKecxdXmrxlrKRkp0Py/zjPctps1CZtKFx12FqpjWN51LF6BHa9uN6bdfL32s7sy9kVPiJ2ZgnQbERrwrCXrDNnw3LaRnsXFwqV/0zMDBBnWGnPLFeeS7NbpCqbTf2qRyzA3VAMuuQc+vKPWO59FIJNefie+LiZHIL1qmUCtqlK+qfAr3v9CMlaTn43SYuZS56W9sN7/qAS/XjaCf78bZeYV8xovAl14xJltsaXv75UMw8foI3Z7speqDKAHCXGh46+NxZE7l07nFy9RkfQVUA1ZD1vnoTkDbQp9SlLJD+olsZyFvMlVdMCs54JesSl0rYPrq/Avt7Yhv5yFs16cGlnOxsbV/Jg7tyfO92bde9STnCEt9BL9izl8HZP/pZ6nGntLvtdiffRAVBEATBITKJCoIgCIJDZBIVBEEQBIfIJCoIgiAIDgkZmbL/NNnF6C+vzeIg9pwEaQZbk5uH1R5YsMORxMe5ciHnNW1XtK92Yl3zz6TLKLw0kAJhzE1XuVTCUhRsegNW1/pZpyyH4rEpkfi/R0EWaRuw1npaBbIX/AHS++9RLEjpuf1cKhtHZzO2XQqXUiLpU+m7oTpHayw6EibxZSk9wIZP4CqWZIRVWiR8TBZnlBS9gksBH11D03qXOSD9wJJ+RMem2G3nxVAlszcFU5wsl8ilg1coO8JB+06EDy8fg2vXVQEbvaVmjvrP4cFEfwQLuOQ2orXdyg3jld9Uyz/RPHomAiV+rHi8Pw+wd4OlhsthtmXcj99xqWTdEm0bMZ24VDp1NDXysrEfdNHafwfBiXNvoGwj10+DJCpmCQ3sXcUQMTbXTyddiJ2/zDq/C0PkvOy+VsCrEf3PQyQNiHZct51eX/WrRv9t6/9Dy7urYPNzuy2BZRAwaH7P3l/pT3OpxEfhTg1MeLGzeSJUjZRleA2X/5Kp7QpNH+RSVhYN0lI/7HLpsxSBWPQEhNO5vBglGh4Ge1VMnjWNS+u+oGGD2SAA+SYqCIIgCA6RSVQQBEEQHCKTqCAIgiA4JKRPNJcl4D4J7jdVJ5tJ6AfZdYzsED+Q573BW2nTyb1x1zPgfz3JdtIT93gfcz+swLXxkQPo1MwmkN275G0qOF96ERI6N7q/jbZrNIUdVqYf8FXnwSB9Tzkk1Orv8Zz5eu2a4C0pyaTf/QNFS7h0uSRb2+9sguXVUa5z2s4qxBwNKkWFgTsWbvrpbbTDX3I2cKlfmc7h7DDkwbDJrk1aF4t3x4krKTY2xCnzHZp2Qtej++BDSeBY+ufUZvZAlPjS8zCTN1g228py2v8VBI/T/R5KaI55AuAfZw/mFDEU93VBMgSPSSlK4vyHuNTEINdUpg8iJH54j3xdJk+AolQ0c8b1bwoJEO4cTZEKjTtU5tLpzeQFT6nGE2CoggOLtB157hSXCtk47FoJRlpn9jSsxRiAQtaMBUVdYZ1vgd7jY5a0F/+Yji0S/v1GIQmVvGQqnJn/+2RtHys5xqVyXnotRwfRk2rNTBMWvBsXr8BVM+2fBx/PIvLgHC69/uHj1q3DwaAkLRHJcKkCvrDOS76JCoIgCIJDZBIVBEEQBIfIJCoIgiAIDpFJVBAEQRAcEjKw6Deq76F+34laHpnZ6Ekv5c2wq13cRt75HajsP0E7Wf89rCiPnEgBCBPxVEY9RC5iAz3rh2eRo75hWisupa8m+8x62GEERSOp5ZhRYcgEsoPLQAoUU6RGhYchYGpoGwr4mN/zKfhYMV2DQCQ4t/MLKXDDb4ZdxYVlG+gx9SuuuNjF6ZPYkUsZPgrW6voLVJbwRv+l7Wkdb+VSNw/dymjMchBm7FBhAIZNtIvdylBpGOw1+xwdEXEQtWEbjBQ2kdhcwuwk1N5jQTwQp2GP5TQqMbsRFmThOURCBBYpVYziDDIL5nChdD6FBW2dDdemRkWyz+GQ7MUyVuzOB4nlZFFrt0LMUQXW5Av5lVKl7CwXT4cgpm1BahbilXLF0El3iIXb0JZtWQ3j4L5iV6oGXsSWzI7GgfL8KbqvQ5UTLAOvKEAXLhqLxIwdNEnb28rCeW1fMJO1LI8hS2yC98v1EtV7ib27ApdSfqdkMds98M6Pa0UVf5w9NSEiiUIw/7MpvPn6e2Oo4Y63bm0L3fWlC6BwjRlejJh8ExUEQRAEh8gkKgiCIAgOkUlUEARBEBwS0qlmsoXn2b+D5GP+HIdZyuFn+u11aDpvgdt1ZTkK1v3yBZfOjKUfwd0Jln8IjH9pKqXqeekH+KOX4FPp7Kf5cQfgU8WbyK7UBaQEWqCsAnno+QmSv/Dcx5By3T2QutXyD3C5NDtJqbQv/AS5njdtoN/6Pfm5ClitbDAi6UY/Paq33WYWt2IyS1y+p29t3NTS/KdcYK70tNhKoAV4Tgm4X5NPk0tvRmX0aBlh+TfXb71OhbNd2FSyNFlKiRJ82qLYjXWSOVupKszd6TaglMJh5uy0VHQoy7IouJRlCFGsg3kC/JQjnqRr8we6cLc/Sna7h2Akt2fOrhv7Qz6UV38n23LZr2f2nyjtZptuQ6mYP/Howmy9hMIWOnvgavCUH9m4Q37+E1Di3sJF2Pv0pZagjn+Nid5ogyX895t5XDqU+ZG221acxKUA9yUa8GiY4BMFDP5ooDfSPZjy0qRN+hU+1rq6Ni1J9oe266vtb/762e64ITDsm6Z9rogyEeAG9mdShQNP+VFhH5x2mHO+NMR2dsg3UUEQBEFwiEyigiAIguAQmUQFQRAEwSEyiQqCIAiCQ0IGFm3+nuwSCG+5BgEY6C6ua7/dGb70fv1ELr1bfo62B2dCIFCPWPb/gXWtPfFYDh6LhT6cvQpSxV5ke8uCtJ8HZ5jr8AjUDGZmc+GzhWW0HQFBIeqzG8jecARiJLbsrKFtoxi7aI8rlq5hQmyIhAXh3lceTGbpBE/KEIUSr0dj+fetaZs7qBG4omyB9c+zqlI4xjNBkGLtT8Vkp3n24HEuuWAz4NXZZDc+DNIj88m2JC/YySIVpkOkjuLFj5w9T/xGpuGzHGRhMa2+gGinhNUk7fsEMm8oFyX9mNDxba4stJwY4/s+lKPjoei1KNIVTfwVEif0qkLr+h8/DxeAB+BZki3ksnMOYB0gVz1WZuQ5SAByazeKsrHEcPFqLzfkQi6Xi1kU4LOjKmQe6MS6MWvW91zyz3qYGo/fr2woMNN502vQPXIbcCublB+n7aB6wm6HRlQMNEE0sWH7BjBNerKDTSHzgDvIdoKZPRasWKLtI82rcsnFBsDo5jCiavjo0Ws7tisciwVJFc6GeMwDRxZo+2AJlLFyX2K3r7wKE7+P3ljBEicPonwTFQRBEASHyCQqCIIgCA6RSVQQBEEQHBLSJ5rE/DkW18Q/94l6Yf7+hdkNccOT17Gs6L5CFCmL9Q1J4BLw+8iHY2B3J036Tds/v/0cl4IBloDbBb/7pwfJhXkuriKXGoxNZC2L04K64bl4Oxfqr16sbe92cCxW/J2crrd5YIm6O4WaK65cUuFh+slf+E76i1yaV3cW2y7M/YFbMUGBvzzfRy7i1T5Yy58QkaTtqp4yXHpv3n3avq3ddypcqBtRls4b1PajxFfbD72rF5emPUgOmPFwk9WYJ7gLLolLBydf0Pa99eFT05j3eD92kDvnHCYssSdV0eNw/hFw9Z1lcQBZb4IL3+2lVCFvwor/UEwtKafth0LkjTCq8Vbek7Suf9+jZ7nER7wlngGaKfAOcd3fRNs1er3MpWYu6pXFtzvkNMVSpLdrA1ptClVY/8JrXPkzky5U8BvwiVreG3Zs8kMaiR7eYbQDFWv3qanTeCIK9VfZgdpeOtbWXWolRLp3XuyhKvgVS5Yf0nZElzpcMi/QRd2y+yjsL8H2XMIkJq0yb9Yeu1LbZeLgUpvR9Bga1neZ7U05ce5LthVsFpmYYN36XyHfRAVBEATBITKJCoIgCIJDZBIVBEEQBIfIJCoIgiAIDgkVWLR9R7a2Pw6A5//9gQ20HfzZfhl2CCBcRt2S3I4a+RiB4YewCDvMACTgf+Q28tu/9XUnLr36Ko8lsfif+yobTBb84cuuwaVawd3adnfbwKXtv1ElnG97wFrjgd9S6IdxHtMSrGDxONfBkvKRhdQccRJyVEw7add3FSyg05zfDWIu7jnTTNvXu4YpWyB6yBWkrBeGbyuXGuZQEMc7U/dxaeUJctSv+X4Zl25uQ5Vx4lJTuZR/yTZ+KqUnFZrA9d+qmK0NP3wIEnE0rVedGhd2c+kky4dQfAYP5meaB6s9HCQzHgfUSta0VIiIYf/BesIKRvkPOMqSLQQxowgf8kvNu7gye+X28HYP3c26leqplB6HpzXC4HElkA1j0g+0ZSkGdED0kOUB5Uv+L4MWOEuPRl0jgkuZLJRmC/RCnX77M20bltsQy57KIhz/yTREY997lUsFr3+twiDOBY98iGAizjPPLMdPJdptGaqKkWknKGXS1XG3gLQJ7qb0AgzshjwMy++geMyGNbpxqf53/akRZ6mzZGMrpYqoX+e6PMyVwfn0tvluGrzYXdVvZi1LrB4/NKRoOPTzPG3fcXdrLn06/y9tvzRtmrJBvokKgiAIgkNkEhUEQRAEh8gkKgiCIAgOCeUTbcF+R57rhoXSc5dR0oMWG/pwaXcnnjjBHhOdotlbwvpU2Py0kVxubxnw03lEBDk+S0uhDrthtKeGuRkk5tKKUuAu63yOvJE7D4Crj7sSP/p1PBcG5iyhRhW8ESNZQvpI/EeH+YuM1uhHAV+nLSa4M1T3x4Zre8srD3KpiYevNQZ/RrCY/JSGF9JjlE0mH8ZNt7zJpXfnkvv8wolNXEqrQwm4574wi0sjx/Is3uBYzFjTQ9sut2Uwc6cIuH5+nNhC2999dJ5L3PH1rA92N+55sr2TwLFmfkz2EQVwD6HF78O9e9H2KQrCxMRzvOPK47abRlJH+peB9AIdh1yn7WZjYdV84XdrqJF7DnZ4kZyucX3acqXl7FHavlwIXsBT55KoUQYc/wmT7tV2/tKFXAruYJ7/QWlcin90rrbbRsIy+Tx26ZdZChWMqKVt485+XPKWb6rtKuUGcKm1i57Q+iYMvBfu70mNTRAhwWnoao9/oMfcsOSX4BuZ9k5QC/YZFYJsl1PXLOXSEx0oNCE+Go/loh66m5XjypJcugKvrp3Cpc7VKGphaCUoH9JsUgrtuw+MjSuNHtN2vTxIvWKwx6jyY/O5pDxVWMP29P0ln/JmVJ3a2v7igT//tvm/R76JCoIgCIJDZBIVBEEQBIfIJCoIgiAIDpFJVBAEQRAcErKKiz1+liuhYDlUIHHHRWo7kA8rlP+buHy0AP7EKQgl8MRTpghfFiQKcLspSCroh5wPQRYjUqqgmMzUryg646oJdUs4PxdAiXZ3Kl2oWbGVuDTpURZM8QCWBUnmxU/CrrrCK9xEgmI2Jkf970FYoVzLpFM2ghCpEQy20rbXgMXmxT6KF9iXCQkgso7+oO3uPaB+TvOIOdq+GoSV8oaiCJ9QJ2wJVbPnjtd2abvYvn6KpYpJlZlk750D0hEWFvQHdpEnIrEED7H4HuV2FFjkY+kLuh3kK83V3i5sVT7+q5z0EsXLlEWtLOtSxrtvcKnqVLoeGe1ugT0G6A1gDniAK9t3eGmr6e/Dp67SbfW0v4ErtW4Zoe3THaHMTt4+CnfqdM9wLs3w0rCxXM4FJt3mDSastS+pREFSHjfc86SIy9qub0C+jn0llHxmyVl4vRSvYYEwsbB4nxNlpNhJFkJknjgYpMQOF7CyUF1FCWESXVCQaHvpYW1f9UIknCcCboRtl7wQ+zSsWwdtr/sZ3vlxrgraHncIhuj8B1pqu0Hfp7l0f+aH2rbU7YpRFILkjkpTttimL/G7buLNDl3G2O8kLOSbqCAIgiA4RCZRQRAEQXCITKKCIAiC4JBQPlH++3u+mc+lfs9TtfELb4EX8NUhtER34IQ5XBowdLq267a8j0vPzKYV+n3veoFLl/6cy1q2LqwYA3x96wY9pO0KOeC0GFOeVpR/WQAZFQKKzqU0yL2Pyhckf0mnCEgv0CGNHAkvnAQHISbat+bS1tbTBeCnSX3rLW3f/Asku4/pN1jbxu3oLg0BP5UYUDwsYcOyYqgl0MNPmQ3OZsLlTUp8TduNXE25tPfCaW2/ce8cLgULmPvchJuSYdCC/Z/bQPqOx7OpYP2WYkwVEQr+DyIMmyBrfnE9/B/54EGS5rTgiurH9pFwHUhtyO+jvv4RpKiB5JvpPq4Bl3zBG7Vdmr+XjvuussOSUeHLC+Rl3DEak5ywO2nEe7lyyx3vsR3aYnErjilD74qXJ0OS8TIdqXrEpvqQQ8DLEgc0bgnZK/IfIKd48DCkKNnZnOVwd+E7Ko6axt2DuNLKC650zp0GPYYHXNu4dC6S3gA+D5Q6qOWmHTYydnDpVOESbWeNgR0Gj7KBPdrWJxpQlroaSXZbhmBUNr1ttn0NpRTcR+nefjW7MpcGRVKHr+8EbkW3YR8jYE/PV+gF2PuB3lxyxdCVf7w3+F/9+ZTopnQlXN6lyjaYpoinW3E5+RIY5YW6HX77OSVM5JuoIAiCIDhEJlFBEARBcIhMooIgCILgEJlEBUEQBMEhoQKLkt4Zpu28R78Fzd4XW60LeaqTa3fhUkmAEvy/99EkLpVjReQv/DEHDmVSs1PdcbDDi9u1ve0ihAipWNvFtq/vpqXikyd05lLfD2nN70kXpBfwRN2q7SWnPuZSTDIri7EUcgj0v3e1ti9fhU8pF6WDsPT14SCF0rx2HMqMDF1I0RljWo1VYcLDwiA3hip5kw6+Jbcsl9ZHU5BI69ZQI6ZltZratnS+Q02qijDkjQtc+n4sldMxA1C3p4AVnSh7DgJwvh84Q9tVF0K0j6lsEyy8OoUKQUx86TUuFbHRO2RRdS4NrcBjHyw7Z2MvuIALBlsQPmD8nfipT5UN/NmLhiomLyhb4MFrFENBfLGWtftNKGDMayRxpb5JS+8LsKRJJIvUMPDGPuulyLIHRkFUYEUPxa0Z1oAkIqfZSN70/0HRedkmFPGoPYwCl3KX4HhlUYy/1oYUJXvSd2l7s4IkKs+upScl3w2FgKo3o0A7XyTc8QoGRWdd7+rKpXmH92k7sL4IehheBpQ+VyC9wB/laJyE2IElzcuOK9QNsxKMDfd1dPt6RMID5WJxhhEu2KHf5KFVENLIsVSImd2Nnq8pj0KpFtWWgrOi/5wH0mUWaFYVvs6dbUCxP5VYXgsVIoeCU3qsXqTtexvDhDW8Ymo4e5BvooIgCILgEJlEBUEQBMEhMokKgiAIgkNC+UTBDxr2gtSP11HO8ZFTm3Ep9xz9CH7HqNu4tHrhT9o2sLK7i/kINhx9TzkA/D7K3E2ujryD4DuJdmdqu8SErARBk5xM7+4Et0qrADUffQXS1pspbG14fjJILmqaJqwTL3FX0/ZJXHe8MEDOyG75mEs+BCnskubC5Y2+jRZiDxy+nkv3xJEUEcoXYdg1vr0XPGTpIzdqu1E1WHldcv56bR++vItLvWMpb8ZDaU9x6e1zL2k7JbUml2rf2JYaLyk7zp46w5s1KqSzVhxuu0lbxdvBKbSG1QvohwMAxrLlGoZdPsCOpvHdqXMfgi8qECS34tFtF7lUr4SGTSwuVzfsXU4e9g93JU9ZFJ2cicdDiQhS8LiXF52gHraD5OmBbfTwmufh0WgZxzx/lvNgry8P+qwjm1LSg0AJfCwlkvyFs3Pf4dKp3sxTi6+XMFl/w1e8+da2Wtp+xP3c3zb/v8dV+3izRe3y2o6uc5lLDxg0ehNUD9wN7zEMco8Rb99lotbNq3nz7AkKmGjyOKSe6B9HvnrPGzO5pO4ijyO+8lVqESVsCVw6yqWIyo21Hd2tI5fS3vpI2081rMOlW1iFh7anoSrGkb53aftPDBEY15+qKUxqaRnzhHwTFQRBEASHyCQqCIIgCA6RSVQQBEEQHCKTqCAIgiA4JFRgkf2yaYwkQJ9w7nBaAZ371XG7Hfy66Gc76R/HWyillGILoEufg24c+5Zc+h8VgLQ3QD7nEgUL4F1uSg4wddQTXCo+/yXbDpaNR9WbpW1PErim/bnZrIUFKAwqC+KJrsqVNj0oX0GZ5ujrhkgd3F81FoKU0opL9z+6TtsveeBe2hbFQCylRTA4BXZYxU1bdvoQails7E+FIJb5IWzhukUjtf14MtSj4PV98i9DYMWEJ+azFvyzGJ1GI7ti80cUwOMsLE/H/dqKqP8YF3Yxu0E2FNOoxevn/OORfaYYhutvB9/Q9srP/uTSlqVU4OXyqTwu+V1U1GXi2me59FR7uhqxBoxk3vvfLk3nQvkSCvBpXHUafipEQJrtOIkwKInE+b/WcanncQpIjI+APAy7v6HTLJgFkZAGGzUj3oGnpoW3urb9xae59M2BbG3/1QZqVdkn+QgXcz/EsDz+Gl23eyeN5pJLUbBPoYIYsRfdNA6vqJ+4ZCo6zUxM0RDPmpFQ4CkUJ0065/QfbwKNScN9v3KlYjbdiKr3LOTSqj9GaNvzEX+UIdAotXZbrvgDFJ3q/2Mjl040o/Qdo2PLc2nc83Rx/LN+h2MF+I2AR7RozVJqtBypbJBvooIgCILgEJlEBUEQBMEhMokKgiAIgkNC+kQT2RTrRn9ODjWN8uDP8NYnj4t19/DjMzgtStnuI66FV9RfSvs3h0Ny7urdKX12m4UbuLR8FXl3CqMga/OsaWO0PehmSEx87ijVlz//KzgtmtxGW+b64bzmraek2N/MWcWl4CW6pLUS4CJ2K8/WUBeHmwIjbmZTbTdu9AOXZjA/qOVfKp5Rwqts+dvdYmMDBe5kX94HpM/jKNnCoXyodrA373faQyH0MZIdK2iC529MAaXRaDMPUjR0HkHr7d2ecthHvv/LKNGWrhjwl/cKkAf+ptbg6/ppdxVt147bhTu0Xb5tR72hPXnTuESOOu8FdNNl0diwxDaYbEX5vrc+59KFujROaqXgsdhuGidCGQB3IFE5wT6wgpFs1ObNH2rSs1bR1Qa6wUpafPoQpMj/pZAyYLwZNZtLhWa2treaH3Bp6yCWN+MfO0Gt4MMWpJQParN6g0tr2ZW/VUH6muZqgLY9Cu7XevUd7VxlcqlYUUp3q0+U3wd8sE+bzLHqAc1bq4m274gawKVf0ult80f2IS5FLaCCHPFff8mlfB89UIGAo0ufCp5UV2MWWRKZg5uaNrZSCZaXw79GvokKgiAIgkNkEhUEQRAEh8gkKgiCIAgOkUlUEARBEBwSMrCIB0+40fPPAosiGsKi7DsCtLD5Nx94kkMw/6fF2h47cEiYnwqBGU//H0Q0S+CS0ZziIFIrX8+lM98/qO3ii99xacx95O0f1IfXf1d1m0YwG3IjcCxhJDOqVNf2DaeGcWnmLCq2Xh298anrrtN2fIcQ6TCAH1pQ4FJ9BVeDB89API9SJ1iIzFN4KB5JYlmTzweKy16yDKg/oyj2YXk+1khhLbcfogyuY3YlrAQx8bkbqRuDoCyGadJ67fdG3cylV5f/ru3nGkA3Rvw8nBqRUIPoutEsfcde6OGCPue1XfP6NC51fZDGueHny+EhbIdTsuIstNkpl9aHgVf1QSrq0rUkCT60g3JZ3N2/mEvRpbRDwxqQRFL5KIwKc/S/uMk+FTIjA9QVCfgossYTCZk3Cljg4uR3LnAp7+sr2t7+zRIu1Uq7XdvrD0GEVNASWHZtwXIpEZ3o0B3VFC6tUMe03VA15ZKf3aONClJPjGevb8jdoFQnFkzUIez+Zhksf001GBvGE7TDev3g7b1rP3X4bO9RXDLZmM8pgajAcENL8ZFP+4uetY1tIPZzMnsov76abb9HCKB0deMBiXvsPiPfRAVBEATBITKJCoIgCIJDZBIVBEEQBIeE9Il2Zt6uWvgj+K8shfHgSlzKimvItlsTZj8euoMcTnXOgMcxLpJ+3G8VC79Zh+j92SL6Wb16rK3P5bg/iTcD/ljWusKlolJaotuhMvifvh5PtdfTUmCtfcI45mWJwW6wf2A6PQIekq+mkx90l285l+6Lp6zN0U0iuaQ2KTuGHaRy8DlXwVsQ2EqJ4IPr6nPJqEcp1xc8CC7dKZVoPEzG0+IJIP4oBufGL4fINfFBj0Fcyrm6krXgUzzzd3081h2sOQAvhqtKDWr48rl0fTLl999c7FM23AP3X93+PmWjjngMFuwbDchddP3qjnAstY+1WnIpaJK7KCcLE3DbYV13znJN7D/FhQs/U474ql2Gc6lt32RtV2tVk0upFTqzVghP5TX45zvE3vFI8OapGVnLrhO3HSfH/9Un9ykbejRbzJv9Ru3QdlTdZC6ZpeG55rAf7tjwIhVS4AIEN9EQvdjzNy4tK6Z8BbOiPuGSwQbAXPM1Lp0rpQIMKyO6cmm4AcnZbcE7lMCSjagI0Eon06tn0se3c8k7gXLUGLfAODR/ZDEYJvjmVTFrmljfokI32uo8zC8hEsLwshKq+FP7DeFeGrWqsZb4RAVBEAThWiOTqCAIgiA4RCZRQRAEQXCITKKCIAiC4JCQgUW9WYxMGnptgzT7urtAyEmXsuQ9jnoLPOTFD9p6+40uFJ00pwQKoazqSUEcse3ANf3Xq121/favsA590SNU9Hzlclj/26A+5UYY0iSGSyMLT1CXMKog4KO21w/lU+on08L24763uBT3FB06pxSyN3DcceCon9CAdjj5UEUuNZlDsT/e1HD/B8pozy5OIS7YD25nNiim8QJ9aA6sQ3923BZtP/haLS7lsnCM7jGWAeakPg8POdiFO4hgzXalIFX3Uv6Ks/e8yqUQwUQh8EbeRQ3jFhR5MFFNlHiYCfTeZdDlLlOWR4W876B7lntXso4eomnrIOQkykPDZsgQiHV6dwElkUiIgko1/0vgI94SmrT1/rCCs/wYLrTxZ7pQX94znkvfuXdqOxjA68v6Mf04RJndlEqP/Pezv7DrRuRg3F8+vWyHnJ3MpeNP0wP7E9YjeuEMbbn3TYhAVJ2pi0U3R3ClosFe5mHGdynVjZ1zwsq3uZQ7hl2BPee45Pt8gbZjH4C3d8ExFpF6iIcPKtd1FI4UmdaYS3H9qXRPiNdfEYYjzYvgkZuFypYS3gi8TpGValJ/u8/IN1FBEARBcIhMooIgCILgEJlEBUEQBMEhoXyirrrkL4yoASv0r2tDP6UviQVXX5KiLNuZ45ZyKe5JWv9uwvJ39er89toemQIpzSsd/lrbhXs/51L7D2nLgiAs1zXY0tshzadyadmf5Oqo1RocP17uIUBvgSeStSNhPXW6jzwfJvq98vzfa7vkILhVIhuw/2BwEX3pZcolnRZdGz5Vg30qbH9Gvd60afrv0EMfS1sdtLosWYeLIb11YE4dbVeaB+7Szb+tYC0nTtDw4T72psnQDaMhJZGoOALy+xsLZ2o7/P4ZA7uxlmVBfRlmo/NM2Q8p+Bf2qvpvUcLcexvS07m0MusPbd9aCfy+But8sXmcS6dK1mq7dtTdXHIr8Mb9c/j9slzNZ7//RtsTk8ugSJ9zJ8Hr5Z53KT17hxb3cKnB4/Rm2z9rPZeMaDr4oEp9uVTVA2nx7TDA+6bKNKAd1jIzuXSmKr0dCi/O4NLe5j9p2yxSwEJysj50ay+ueEK8OOyfBz5YsytDKvnSn6l5WxBiE46WZmv78UiInpndnIbK8W7PcimyD1U48K2CLAdXxpBv0vs6DDZ1Yh7ZhQex+45eRMV8J+ITFQRBEIRrjUyigiAIguAQmUQFQRAEwSEyiQqCIAiCQ0IFFkXGUbELTwEvb6J8UbRc/bPAai4NdTfQttfcxaWaT9Kcnf4ixF88VJ4c5m4D0gvcGSQP+RcBiMBxKQpPsgQSRLMy96X+o1wa2j5J23MHnudS5x/A9W2HCZValF9lh/Op9o3q8Ob6WyksJu07iNsqZMuB47HkwivFzL/tDTey6MD7FCQSXPonl3wv02LzyOsgzMpoRFkUrkyDWJL6uRTGkJufA1K9VtpO9ECwT44ftrTjJmzykIM0lFI8FD7mjcPkAFuWadO98whXvkmsru0xeRe4lMvCIgyMv8ifQnVm4mbsgmN5NpDNSsQopZRnEmtUA4nt/+r+V9jfq6priwEnUr8S1bR49laoOfP9lx9oe1ryTC4Nu4HWvB/MgSibn9+ihyhwcTyX4rtS6ok3xv3KpVujqSpIiHFsCQgJseWExARtt78Ki/fvf4MiUBY+/RGX6nnpzWHg0cZOnqXth2fDheJvgIWFO7n0SALkHrEjDuPPKuSRXeUyBB091J+SEgyrOo9Lz3Zaou30XyBW02DxPbcryDbDMU3oxzxWq6QCpg0ZoOjyWkZUBLtuS1wYShZlWzFmVDl66R3d8wGXXrzK3vkvvggfC7Bown2TQFJOkqiEIrJ6OFvJN1FBEARBcIhMooIgCILgEJlEBUEQBMEhoXyiQR85tPK/OMSlTe+Tc2tTxn1cGs+SG0d0gJ/Ohz5NjtUV6a245HbZ+oHe3065zm9s2ppLmez3/HW4yP0A+5n+NO7wNPvp/J0VbbnUSR3Tdgjvi4FL7auX36zt9MutrVv/X44qSM086IfrtZ2jwJ/hYv/c3F7zdS75mddm3XHMuW6PkfSutt23b+eSuy9bYF7mMfiYi6Ry42Dx8q4KtHz7Iczn7IqnEXDoHNQSqJjKK8zD/drF0hc0XTwW9riVvJsqGf/t20GmuRLc2/7HnqDGdXdxafCSVWTXSOHSK7c9pO1ntnzNpYTP6Bq6PoNeRLCx0jkCBs6Tt/2o7Sb9wHF/JJeuwP0vXdL2Lfc8qexw2yrWHA+sG0+NhTHZsQz5s0/EZnBp0Xhy7pqYqP05Yxc18LVhsOwFZj6Myfxl5MEa9jR4iz059KwNiayhbAg7oQjQPgFiOHY+v0jbbvtdWi6h6WL5W6LgU0Y0jUPTC644n3U3/5qiM9BMv0xX+08v+EQnXE+BIC50+1UwSErH/ZvMKRw5lqdfVzEzqVkYBW8e8yR1I7JeFeiwmx8BxgaPHjCtXuwQ6TGIOi6QHkmiy/uFuR+3ZYMvtTsoGczpHoDcO2FSpmYT3ty1kt4A87+0LR8i30QFQRAEwSEyiQqCIAiCQ2QSFQRBEASHyCQqCIIgCA4JFVhk7mVBB6vQT3uF2ZboFuYUL/0FnMxdX9+m7doV6qrwMBo00/agmd9wyTePYo5uObuWSyeKKWyhv4I1/sXM152uIrnEy5i4ww5p2LajobbrVINLmh+gVcOPdJvLpe/3/kWNDKhUUKUy5UZ4ftutXCpl2SYeGw8u9zvb2Xfx6G/aLLgOKjDMzCnQ9otln+aScaQnNUxYQ53M4hv2BCDYIRigi1i3B1TdwdANGBv9WCGbM79s5pKqwjJgVMbR9hNLARGEG1aYQed1eOMlLpXtR/doy5cHuDRl+yrWsl3obwkdKTZpy9VYnWP1FzT2PF/COExidYEKsIyPLY1ioHmaHSwfYz1YZE3lTRDE8uYBenrXm/ApSzARwBVLbwM2m1nAIkFFBi9cYxtYdE1Ya1KAWxsDwrtOBCnu8GAxDLwXP5pmu0f2lG9T27iyzWxl3fhfUYTFlAqLqLljMYyv56pQxNzVx+Ah/+uXsMJnzA/hWAUfsqCjBOvGmpL3YdicG0b5INJUPG5re9eHHqPAva7V5nBptHe4tgvxkbr5LMteEkzHXVJwnWviLC5sHE95frKCmMznFcqUotZmc+nDEfR66Xbr9VwyIsKqQSTfRAVBEATBITKJCoIgCIJDZBIVBEEQBIeE8omm3XizthNvqselY5fIh1fw4AIumeuyqYEZ0u9uEK4fFGD7cD1xM1ciJ1Bi5cirkBW+UTfyiT5xfDSXVgVoiX5RWcj8vquYftyvjKvm41jrYgkcq3Fj+iW9JGDr3fqpJlzDfC813esgAcLADxdQIwYW2G86TPu/uHs5HMDeJ9qx4R3a3hgI2G02MxM67+9B9eWN7ZCyIvbTN7UdNewRLn2/hK58wekf8Ah8vMGxLnG3Sjbmqe/MstjXiwNpAK3zNw/BsvHHLpFHf+G5T7nkv/kXsn2QosE00aUJsDQiBiS7j0xsqu0O/bty6fAhOlYVN6SeiC4hh1ZBie1NAephkn0+2LLBLxXXgTx/7ZtCRpFVJyn/Q3FmuPk6OO5kaHqqk12yBzdlnXLB8Fdpbu6AbOmgGyHwY1KCvb6vtH3KBf68OcfeoEZRFJdytrCxEUDncQ7dr9WLICn80T605H+4ut2uh0FLLQbmwrMMhfR00n796jCXoll/i4qUEyyRH9WZUg5Oeb0iB+RQc3qonTCWT6bz3Dr9Cy7dXm+otk+Y3EGurizkWV8saeWpGZzUjAs3Rjyg7WV3wUspeRNJ2Ruh5sTuLXR5m74LE2LZ13kK/mHKBvkmKgiCIAgOkUlUEARBEBwik6ggCIIgOEQmUUEQBEFwSKjAov6J/bTd29OcS38mDNT2muW9ubR/G63ld+2HtcABtrLbbTgrzwCYbB9ZuVBMZFTNdG3vNPpwKTf+KW0nJHm59N1J6vCe9blc2jbnI21nHofKKqYPvOIIdbHBoMZcmNmLQj8+PQXrtbuUo2Aii1f9h0O0Q3+R5fbZBqeECCbiWJZMJ+++rO2ruHh5zisntH0UE1Z8m0FxQOXuglCCS/M/pEbRC1zyK7ryZjyscTaSWfhMKi4Ov50yUZjvQSKOLzIozshv4pr0wCnlBIrBMXBteFwsXbmFLz/IJW8iFZOJ9treL9Oknc+c8ZZdD4w65aFdoY42k4rrcOW+WymkJcYPN6hts5PaXr52B5eC9hVI6nQk++2l8NQUsNF111DYQzEVcVEuvHV/ljyj7aYxvbiUpGpp27BGrQSZZPsdwI31bnq4KbQqx0znkq+QAnLMhEQuRYymKLbStRi3w554//MQInfy1DEVDtaSMbYb+tnuO9SBj736AT0pW13QjQ0+ujg//gmPf/pTdDADI7pcD9LVjmoBV76pukPZgH2HHpp5NOYfTJvKpURFnW9pQIBn3iQali/e+i2XptVjFZn8cMo5j1Nql57vQISg/zS9zGsG4LwaxtFQSZqIz1dFbNog30QFQRAEwSEyiQqCIAiCQ2QSFQRBEASHhPKJLjhJ5eA/j/uZSwU5S7Tt/+EEl8zP2PLts7DDlGPkLs2c9YldPyzegRKTftCfeBQyRM9rTVkU/PmH1H9OLnpcXl4Rabcl/sNRaLsV4qkzSduf9oPM1/xIE2qDC+cEv4TozSyKYP6MCIvD9BqTw+7EyhnzuTRh+zusBZ6DpZ9Q7wOJmBxAca9wIkp0LksXn+PCgA4dqNEFsuer0jRtup6FTyWMXqHtLEsCDIN75/ARMDOVLXQ5TBP2mJdNKQbc6PlLiI5lrRBxALH2EhHdDtIcJMTX13a3HEhD0SaJnEBnt8MT+stpcgmb9r44A4akWvoD5SiolgD/fAdMehwGTAeX2IoFZPsgGYb6Zmu2trPbgmuue8RN2u7mAg9xpKL8Ai4F6fi599TiLm3ibq9tU7Xn0tYW5I51YW76I7Wph20fTOOSfwZ7LLO5ooJr2BXormwJkakfh0nFJLJb94XharKXSKVY+Fhtqr+ght0APuz3VtMTWlDAFVWmDO2kewyEJpjqF9bCtDmml20GAyCpGZUWyAlUUOHBh97UGrdxqXcR5dvpdN/X0Itd5ElN7gVpLno3pk/NbQtjI7ome6a8PLuCUiZ/z0NKDY58ExUEQRAEh8gkKgiCIAgOkUlUEARBEBwik6ggCIIgOCRUYFH+j1SU3CyFFcRGs6ok7YU6GCqbR8KAkzn/A4pUSuwFKRqqVyIndkEAil2ce28L7e7HDVxShby0SAhPfQgsn+JLqi0XhwfCWArK81OGeIyFu2dq2xJjwy8NFoxRHtYswKCYEhbtEyjchbtsqmzhBwj/QtGW/V58EhUekJDOldIt17GWJVCLfyrL7liDsrK5EH8PBbV9Nm4lly6aFFoyxwcxEllB+9OEeAG3RbP9lD2lBRTFUymtOpe+mUtxMQOG34uf+4/zjUTtPcKbgSxq/noJwyX60AObcvIyl/ZlUW9DnS3+g/3ubor8eqAzVDtJclGzb0MYr7+3pSNUgGwQqoCFcC3ZAvfuy6sLte3dDHe8Q8ckbd9UthOXbqzTWdvbj2Zwac8xqia0pyLUtGmZRp1/sN4MLtVW9AL09KrKJf8sSlihGiog1DuVYf/9JToeNB7t1PUuiCV08cFrOS57v0RB3RpVkkd2BJyW+rE9RQy1jvqcSy6FVXgYJnsBlgZhAORuoTfAy71f5JLvR6oE9WJ56CI/LUuamKsmXZzENhAjlpdBIUIxLSpy6dUhzbUdHRnmHVLKiPv328g3UUEQBEFwjEyigiAIguAQmUQFQRAEwSGhfh2OGdRF24FIcCREJtHa8JLWf3EpuH89feoirH83TtKC5bi68Jv14AottF2KnpoFE2jFcskdeVzyfnFc21fnP8Al5eOV423Tav8N7qaKQimJ2SUoMY+OGzIPtGTrlS3+J/5bfxFqx7jjAxMqVEqic4lPa6LCw8VWzgfNEMnobX1kph9XZcOFsvwrlmGzmWVLy7FsD53HpMHFlluZq5xQ+u83cUpxDvjLB901RttTD//KpeemwVLxcIjHQgJGNF2NqFRw7pbmk7v08kX+LCi3QV4rrxtukM9gdwGdxYvm0UWLrg93vCrLL/EDj1JQKmc/2dm/g+RjUvAq3v181szN5sp3r1Pq9299JxXwmQoLSNjyJbsA09et51JSDfLiljxgX7TgCrQMr81mFuzd0UV5MMjP8TEeIte7vYc935Ibht29RvdCd1tGva1tD6RGwY9ZsswrGlGZfsipUe3uIdo+MmcXl94aRR7opTcP5FKCix4idzK4S4/so8FctPUSlzwx9MYuOnycSycLKSKlTASmeTH4edlWOwiBfBMVBEEQBIfIJCoIgiAIDpFJVBAEQRAcIpOoIAiCIDjEeP755/9f90EQBEEQ/n+JfBMVBEEQBIfIJCoIgiAIDpFJVBAEQRAcIpOoIAiCIDhEJlFBEARBcIhMooIgCILgEJlEBUEQBMEhMokKgiAIgkP+PxSuPAQKZW5kc3RyZWFtCmVuZG9iagoxNCAwIG9iagozMDUyMQplbmRvYmoKMiAwIG9iago8PCAvVHlwZSAvUGFnZXMgL0tpZHMgWyAxMSAwIFIgXSAvQ291bnQgMSA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL0NyZWF0b3IgKE1hdHBsb3RsaWIgdjMuOS4yLCBodHRwczovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKE1hdHBsb3RsaWIgcGRmIGJhY2tlbmQgdjMuOS4yKSAvQ3JlYXRpb25EYXRlIChEOjIwMjUwNDAzMTkyNDUyWikKPj4KZW5kb2JqCnhyZWYKMCAxNgowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAzMTUzMCAwMDAwMCBuIAowMDAwMDAwNjAzIDAwMDAwIG4gCjAwMDAwMDA2MjQgMDAwMDAgbiAKMDAwMDAwMDY4NCAwMDAwMCBuIAowMDAwMDAwNzA1IDAwMDAwIG4gCjAwMDAwMDA3MjYgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzQzIDAwMDAwIG4gCjAwMDAwMDA1ODMgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAwNTYzIDAwMDAwIG4gCjAwMDAwMDA3NTggMDAwMDAgbiAKMDAwMDAzMTUwOCAwMDAwMCBuIAowMDAwMDMxNTkwIDAwMDAwIG4gCnRyYWlsZXIKPDwgL1NpemUgMTYgL1Jvb3QgMSAwIFIgL0luZm8gMTUgMCBSID4+CnN0YXJ0eHJlZgozMTc0MQolJUVPRgo=", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["model = model_dict[256][\"model\"]\n", "latent_vectors = torch.randn(8, model.hparams.latent_dim, device=model.device)\n", "with torch.no_grad():\n", " imgs = model.decoder(latent_vectors)\n", " imgs = imgs.cpu()\n", "\n", "grid = torchvision.utils.make_grid(imgs, nrow=4, normalize=True, value_range=(-1, 1), pad_value=0.5)\n", "grid = grid.permute(1, 2, 0)\n", "plt.figure(figsize=(8, 5))\n", "plt.imshow(grid)\n", "plt.axis(\"off\")\n", "plt.show()"]}, {"cell_type": "markdown", "id": "361cedea", "metadata": {"papermill": {"duration": 0.02481, "end_time": "2025-04-03T19:24:52.577580", "exception": false, "start_time": "2025-04-03T19:24:52.552770", "status": "completed"}, "tags": []}, "source": ["As we can see, the generated images more look like art than realistic images.\n", "As the autoencoder was allowed to structure the latent space in whichever way it suits the reconstruction best,\n", "there is no incentive to map every possible latent vector to realistic images.\n", "Furthermore, the distribution in latent space is unknown to us and doesn't necessarily follow a multivariate normal distribution.\n", "Thus, we can conclude that vanilla autoencoders are indeed not generative."]}, {"cell_type": "markdown", "id": "78f69b7b", "metadata": {"papermill": {"duration": 0.024074, "end_time": "2025-04-03T19:24:52.625873", "exception": false, "start_time": "2025-04-03T19:24:52.601799", "status": "completed"}, "tags": []}, "source": ["## Finding visually similar images\n", "\n", "One application of autoencoders is to build an image-based search engine to retrieve visually similar images.\n", "This can be done by representing all images as their latent dimensionality, and find the closest $K$ images in this domain.\n", "The first step to such a search engine is to encode all images into $z$.\n", "In the following, we will use the training set as a search corpus, and the test set as queries to the system.\n", "\n", "(Warning: the following cells can be computationally heavy for a weak CPU-only system.\n", "If you do not have a strong computer and are not on Google Colab,\n", "you might want to skip the execution of the following cells and rely on the results shown in the filled notebook)"]}, {"cell_type": "code", "execution_count": 18, "id": "dab65b46", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:52.675758Z", "iopub.status.busy": "2025-04-03T19:24:52.675338Z", "iopub.status.idle": "2025-04-03T19:24:52.679639Z", "shell.execute_reply": "2025-04-03T19:24:52.678660Z"}, "papermill": {"duration": 0.031046, "end_time": "2025-04-03T19:24:52.680991", "exception": false, "start_time": "2025-04-03T19:24:52.649945", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# We use the following model throughout this section.\n", "# If you want to try a different latent dimensionality, change it here!\n", "model = model_dict[128][\"model\"]"]}, {"cell_type": "code", "execution_count": 19, "id": "1188b743", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:52.731623Z", "iopub.status.busy": "2025-04-03T19:24:52.730795Z", "iopub.status.idle": "2025-04-03T19:24:56.752225Z", "shell.execute_reply": "2025-04-03T19:24:56.751236Z"}, "papermill": {"duration": 4.049118, "end_time": "2025-04-03T19:24:56.754486", "exception": false, "start_time": "2025-04-03T19:24:52.705368", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "6c40d2ebdcb0406d895409c6bc6f15df", "version_major": 2, "version_minor": 0}, "text/plain": ["Encoding images: 0%| | 0/175 [00:00\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/pdf": 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", 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", 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", 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", 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", 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", 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Plot the closest images for the first N test images as example\n", "for i in range(8):\n", " find_similar_images(test_img_embeds[0][i], test_img_embeds[1][i], key_embeds=train_img_embeds)"]}, {"cell_type": "markdown", "id": "517053d6", "metadata": {"papermill": {"duration": 0.043458, "end_time": "2025-04-03T19:24:58.041741", "exception": false, "start_time": "2025-04-03T19:24:57.998283", "status": "completed"}, "tags": []}, "source": ["Based on our autoencoder, we see that we are able to retrieve many similar images to the test input.\n", "In particular, in row 4, we can spot that some test images might not be that different\n", "from the training set as we thought (same poster, just different scaling/color scaling).\n", "We also see that although we haven't given the model any labels,\n", "it can cluster different classes in different parts of the latent space (airplane + ship, animals, etc.).\n", "This is why autoencoders can also be used as a pre-training strategy for deep networks,\n", "especially when we have a large set of unlabeled images (often the case).\n", "However, it should be noted that the background still plays a big role in autoencoders while it doesn't for classification.\n", "Hence, we don't get \"perfect\" clusters and need to finetune such models for classification."]}, {"cell_type": "markdown", "id": "8ea479ca", "metadata": {"papermill": {"duration": 0.033694, "end_time": "2025-04-03T19:24:58.108807", "exception": false, "start_time": "2025-04-03T19:24:58.075113", "status": "completed"}, "tags": []}, "source": ["### Tensorboard clustering\n", "\n", "Another way of exploring the similarity of images in the latent space is by dimensionality-reduction methods like PCA or T-SNE.\n", "Luckily, Tensorboard provides a nice interface for this and we can make use of it in the following:"]}, {"cell_type": "code", "execution_count": 22, "id": "bc1dcdb8", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:58.177265Z", "iopub.status.busy": "2025-04-03T19:24:58.177056Z", "iopub.status.idle": "2025-04-03T19:24:58.181966Z", "shell.execute_reply": "2025-04-03T19:24:58.181021Z"}, "papermill": {"duration": 0.040936, "end_time": "2025-04-03T19:24:58.183306", "exception": false, "start_time": "2025-04-03T19:24:58.142370", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# We use the following model throughout this section.\n", "# If you want to try a different latent dimensionality, change it here!\n", "model = model_dict[128][\"model\"]"]}, {"cell_type": "code", "execution_count": 23, "id": "fc84e5a1", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:58.252231Z", "iopub.status.busy": "2025-04-03T19:24:58.251380Z", "iopub.status.idle": "2025-04-03T19:24:58.259368Z", "shell.execute_reply": "2025-04-03T19:24:58.258171Z"}, "papermill": {"duration": 0.044024, "end_time": "2025-04-03T19:24:58.260838", "exception": false, "start_time": "2025-04-03T19:24:58.216814", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Create a summary writer\n", "writer = SummaryWriter(\"tensorboard/\")"]}, {"cell_type": "markdown", "id": "6c2ea5bb", "metadata": {"papermill": {"duration": 0.033368, "end_time": "2025-04-03T19:24:58.327797", "exception": false, "start_time": "2025-04-03T19:24:58.294429", "status": "completed"}, "tags": []}, "source": ["The function `add_embedding` allows us to add high-dimensional feature vectors to TensorBoard on which we can perform clustering.\n", "What we have to provide in the function are the feature vectors, additional metadata such as the labels,\n", "and the original images so that we can identify a specific image in the clustering."]}, {"cell_type": "code", "execution_count": 24, "id": "dc565a3f", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:58.396663Z", "iopub.status.busy": "2025-04-03T19:24:58.395766Z", "iopub.status.idle": "2025-04-03T19:24:58.400968Z", "shell.execute_reply": "2025-04-03T19:24:58.399785Z"}, "papermill": {"duration": 0.041701, "end_time": "2025-04-03T19:24:58.402880", "exception": false, "start_time": "2025-04-03T19:24:58.361179", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# In case you obtain the following error in the next cell, execute the import statements and last line in this cell\n", "# AttributeError: module 'tensorflow._api.v2.io.gfile' has no attribute 'get_filesystem'\n", "\n", "# import tensorflow as tf\n", "# import tensorboard as tb\n", "# tf.io.gfile = tb.compat.tensorflow_stub.io.gfile"]}, {"cell_type": "code", "execution_count": 25, "id": "342753c5", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:24:58.472035Z", "iopub.status.busy": "2025-04-03T19:24:58.471193Z", "iopub.status.idle": "2025-04-03T19:25:05.123408Z", "shell.execute_reply": "2025-04-03T19:25:05.121669Z"}, "papermill": {"duration": 6.689521, "end_time": "2025-04-03T19:25:05.125955", "exception": false, "start_time": "2025-04-03T19:24:58.436434", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Note: the embedding projector in tensorboard is computationally heavy.\n", "# Reduce the image amount below if your computer struggles with visualizing all 10k points\n", "NUM_IMGS = len(test_set)\n", "\n", "writer.add_embedding(\n", " test_img_embeds[1][:NUM_IMGS], # Encodings per image\n", " metadata=[test_set[i][1] for i in range(NUM_IMGS)], # Adding the labels per image to the plot\n", " label_img=(test_img_embeds[0][:NUM_IMGS] + 1) / 2.0,\n", ") # Adding the original images to the plot"]}, {"cell_type": "markdown", "id": "358394b2", "metadata": {"papermill": {"duration": 0.045399, "end_time": "2025-04-03T19:25:05.235948", "exception": false, "start_time": "2025-04-03T19:25:05.190549", "status": "completed"}, "tags": []}, "source": ["Finally, we can run tensorboard to explore similarities among images:"]}, {"cell_type": "code", "execution_count": 26, "id": "db145e38", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T19:25:05.305095Z", "iopub.status.busy": "2025-04-03T19:25:05.304854Z", "iopub.status.idle": "2025-04-03T19:25:06.319902Z", "shell.execute_reply": "2025-04-03T19:25:06.318963Z"}, "papermill": {"duration": 1.051653, "end_time": "2025-04-03T19:25:06.321723", "exception": false, "start_time": "2025-04-03T19:25:05.270070", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["\n", " \n", " \n", " "], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Uncomment the next line to start the tensorboard\n", "%tensorboard --logdir tensorboard/"]}, {"cell_type": "markdown", "id": "731c876a", "metadata": {"papermill": {"duration": 0.037784, "end_time": "2025-04-03T19:25:06.405999", "exception": false, "start_time": "2025-04-03T19:25:06.368215", "status": "completed"}, "tags": []}, "source": ["You should be able to see something similar as in the following image.\n", "In case the projector stays empty, try to start the TensorBoard outside of the Jupyter notebook.\n", "\n", "
![](\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/08-deep-autoencoders/autoencoder_visualization.svg\")
![](\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/08-deep-autoencoders/deconvolution.gif\")
![](\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/08-deep-autoencoders/tensorboard_projector_screenshot.jpeg\")