{"cells": [{"cell_type": "markdown", "id": "f178d0f5", "metadata": {"papermill": {"duration": 0.011038, "end_time": "2024-09-01T12:08:33.988206", "exception": false, "start_time": "2024-09-01T12:08:33.977168", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 7: Deep Energy-Based Generative Models\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2024-09-01T12:08:26.571132\n", "\n", "In this tutorial, we will look at energy-based deep learning models, and focus on their application as generative models.\n", "Energy models have been a popular tool before the huge deep learning hype around 2012 hit.\n", "However, in recent years, energy-based models have gained increasing attention because of improved training methods and tricks being proposed.\n", "Although they are still in a research stage, they have shown to outperform strong Generative Adversarial Networks\n", "in certain cases which have been the state of the art of generating images\n", "([blog post](https://ajolicoeur.wordpress.com/the-new-contender-to-gans-score-matching-with-langevin-sampling/)about strong energy-based models,\n", "[blog post](https://medium.com/syncedreview/nvidia-open-sources-hyper-realistic-face-generator-stylegan-f346e1a73826) about the power of GANs).\n", "Hence, it is important to be aware of energy-based models, and as the theory can be abstract sometimes,\n", "we will show the idea of energy-based models with a lot of examples.\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 [![Open In Colab](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAHUAAAAUCAYAAACzrHJDAAAIuUlEQVRoQ+1ZaVRURxb+qhdolmbTUVSURpZgmLhHbQVFZIlGQBEXcMvJhKiTEzfigjQg7oNEJ9GMGidnjnNMBs2czIzajksEFRE1xklCTKJiQLRFsUGkoUWw+82pamn79etGYoKek1B/4NW99/tu3e/dquJBAGD27NkHALxKf39WY39gyrOi+i3xqGtUoePJrFmznrmgtModorbTu8YRNZk5cybXTvCtwh7o6NR2KzuZMWNGh6jtVt7nA0ymT5/eJlF9POrh7PAQl6s8bGYa3PUum//htmebVtLRqW0q01M5keTk5FZFzU0oRle3+zxwg5Hgtb+PZiL/ZVohxCI+hL5JgjmfjPxZ26+33BG3dA+ealHPM4gQAo5rU59gsI8bRvl54t3Ca62mvHyUAhtOlLd5WSQpKcluBjumnoCLs1EARkVd9E8l3p9y2i7RbQ1B6pFwu/YDgW8KbHJHMTQrwnjz2oZm9M4pavOCfo5jWrgCaaMVcMs6/pNhDr0+AMN93XlxV7R6DNpyzi7W/OE+yIrsjU6rTrbKV5cd/pNyItOmTbMp6sbBB+EqaYJY4cWE3VUciNt1TpgfcRFv71Fi54xT5kSoyLvOBEJMOMxWXkFlBeBSX4u6Zkcs+3KszYRtiapbNRqF31UgetVuc8z9vBXIv1qD+F1f83B6uDlCUyfsZGepGPpmg01OB7EITQbhS9ribKy+DmP1DUiClLz4bnIHVOqa7BY+Z1wg5g3zgUvyehiNpnJKxSLc/ts76LKm0BzX3c0RNy1yXjDcB5lWoro4iNHQxM+f1kWeWQARAWQS++trISJTp061Kep25X/MycwtjuctSC5rxo7ppi7VNUox5+PhPHtrsS2O1qJ6yx1QujQUzm9sh6hbkBlvvGcN8hYnwjUjH6kjfZEd5c/jitz5Jc5U3ENnFynKl4eB7nyEgP2UZ+Yz3/rVEbyYr27qELrtC4FIC0J7sc7xWnmccdHfRRTs0VB+cA4lt+oFcRR/wUeH8FG5w2Mbx8FQ8TXEvv1xYf4wBP3O2WyL3/UVjpXWgIqaFeUPr+wTmDvUB7njH6/bOv+HRg4SqioAg5GDe1aB3ZeMTJkyRSBqkLsWqSEm0fZVBEN94zEZnYvrdx1JL5cxe+a+AbhSJecRRHW/ikTFRTa38dtQlNZ5CRKwFvUtZU/kvBoEF9Uxni/XqIM+dwKbTw3rhcxIf7gmr2M+H6SMwx8iBzJbw5oxeG3Lv5FX9B3AGaHPS8e8z77H7v9VMpvPG5ug1enh7eGK8h0LBTwUb+GInqzInlRUK65DmTPQu4c3+uQKjwKK77zwUxBX4Tq7yR1RuiwUsqlrABCM6esHdXoy47fk4+prYKy8ZF574x4V5BnHQBuf4g9Z9ld8U36L2aktZNNplNfw7zotwWTy5MkCUft4aLEopJj5/OPHl1BQqeAVOnHgNSQOqmBzq9V9cfEm/yx5ubMGKS9cYPZ3vx2OS/c6PVHUuUO7Y1Pci3BO/1zgq18byebfGemLtNF+6JRtOvMk926ibussZqM+1mNz4TWkH7rCbM5phwGRGDAaoF8fY5OHFnlldAA8sgoEXKnDukA1NgSeNjqkJT9brbN4pC9WRweYXyLugR73c+MYvyWfu0yC6+mjzN1Isfw3FKJS98CU/zI1IHFkFPR52cHL2FJk0sB6kMTERIGo9GzcPkLNfA0cwdwi/hfEYO86ZMd9w+y1egfM2T2Eh/vesMNwljSzuZRT420SW3eqy8N6aHMmwmnFUZ7/PGVPbIoNZvNU1BURdHs0bT2+HjL8sDSM2e6vi4Lj5NW8WOLVA6RTT2azxLV+bglaFNqLieqemS/gWkw7NyoAHo+2dEsiivengjKsPFoqWOvbSh/kxPaxyW/JRzH2Fl3EzD9/xjAefJqB3usKUFn/0Gb+S/d/jy3FN2yLOmnSJJtn6oehByEiHPSeXnDxFGPRnoFoaBJjcdQlbDwcjL1zTNuQpoxD7R0OG0uUTMi0fkVwdzBdYIwcwZunxrVJVLplNm54BZp7jfDfYLoNyqQi1K6KxIdHzmN+QQ2WjFIwUT2zTGdlRXo4NFXVUO4sgX5dFC7f0aP/ZlNeUjFBuL8Xjl6uRuP6aMjSjpjzsH62FDU7JhBuGccEXIvDfJFFBc/gHw80dklfCVYnRaDfpiJcutPA4F7qJsfJeUPQI+1fqMlNhFx1FM0GDqkjFVg7NojlQ0Vt4aM5ReSqcbpaCg8nCW5lRsBvbT4T1TLfFptsfh7gItzuKTdJSEiwKSrt1vcmnEXXrsLbYnWDA1bu+z2WKy9Arq+1KRqdfKsoBo0GcdtEpS/B1bO4v0cFiUhkjskvKcMrWwtAPHuwQq8Z+4LZ1vTQANfXt4J0DwZX9gWa9qh4XDM/voC9JXfwYEMMHJcfNtusn82ihvliVUwg5KrPGVf6GH94ZJpEZBen6EC4qYTHA1dXhW0JIex8txzv//c8lhzXIi/BFxOH9jGbQhZsRalTIBZZ8KkGyZAxeRQvXkFF1TWz/Hm46jNYUnjPbt3JxIkT7f6dSj8qfJJyVvBxgaIlblOyjtysNHWN9fjjqWi7glJfW3/S0Hlj2XnA8PhKT9w6g3Qx3XiXhvuxQsuT1proxBKI/AaZqY1Xz5muvY8G8XkRRCaHsfQsRAFDH/tZPbcYuHotOG0FRIqB4HR3wNVoIPLtz8ycTguu+jpEigE218vd1YCr5m+HpHMvEI9u4LTXwNWaLjl0iPwGAmIpeHx1VeCqTJdPs1/vweweQPO3HC24NhOhnTphwoQnfv6QSY2ICbkNmdSA4h87oaLaiYfn5diIEd4att2erOwJXbPUHp953p6orQVSUVWRAXBT8c/dJ5L9xhzaJGp71GR/wFP8P5V2z10NSC9T93QM2xUg8fHxT+zU9ijeU4naHon8CjFJXFzc8/kn+dN06q9QgF98SYSo2Xen2NjYZy5sR6f+4nLSK5Iam2PH/x87a1YN/t5sBgAAAABJRU5ErkJggg==){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/07-deep-energy-based-generative-models.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": "6958d72e", "metadata": {"papermill": {"duration": 0.010531, "end_time": "2024-09-01T12:08:34.008632", "exception": false, "start_time": "2024-09-01T12:08:33.998101", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "70adaca7", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2024-09-01T12:08:34.029131Z", "iopub.status.busy": "2024-09-01T12:08:34.028930Z", "iopub.status.idle": "2024-09-01T12:08:35.330634Z", "shell.execute_reply": "2024-09-01T12:08:35.329550Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 1.315087, "end_time": "2024-09-01T12:08:35.333305", "exception": false, "start_time": "2024-09-01T12:08:34.018218", "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"]}], "source": ["! pip install --quiet \"pytorch-lightning >=2.0,<2.5\" \"torch >=1.8.1,<2.5\" \"tensorboard\" \"matplotlib\" \"numpy <3.0\" \"torchmetrics >=1.0,<1.5\" \"torchvision\" \"seaborn\""]}, {"cell_type": "markdown", "id": "83e35bd5", "metadata": {"papermill": {"duration": 0.015833, "end_time": "2024-09-01T12:08:35.365743", "exception": false, "start_time": "2024-09-01T12:08:35.349910", "status": "completed"}, "tags": []}, "source": ["<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/E6PDwquBBQc\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "First, let's import our standard libraries below."]}, {"cell_type": "code", "execution_count": 2, "id": "23d74404", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:35.396195Z", "iopub.status.busy": "2024-09-01T12:08:35.395607Z", "iopub.status.idle": "2024-09-01T12:08:38.923691Z", "shell.execute_reply": "2024-09-01T12:08:38.922570Z"}, "papermill": {"duration": 3.54459, "end_time": "2024-09-01T12:08:38.926224", "exception": false, "start_time": "2024-09-01T12:08:35.381634", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Seed set to 42\n"]}], "source": ["# Standard libraries\n", "import os\n", "import random\n", "import urllib.request\n", "from urllib.error import HTTPError\n", "\n", "# Plotting\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "import matplotlib_inline.backend_inline\n", "import numpy as np\n", "\n", "# PyTorch Lightning\n", "import pytorch_lightning as pl\n", "\n", "# PyTorch\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import torch.utils.data as data\n", "\n", "# Torchvision\n", "import torchvision\n", "from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint\n", "from torchvision import transforms\n", "from torchvision.datasets import MNIST\n", "\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\")  # For export\n", "matplotlib.rcParams[\"lines.linewidth\"] = 2.0\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/tutorial8\")\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\")"]}, {"cell_type": "markdown", "id": "17d903f9", "metadata": {"papermill": {"duration": 0.016089, "end_time": "2024-09-01T12:08:38.958720", "exception": false, "start_time": "2024-09-01T12:08:38.942631", "status": "completed"}, "tags": []}, "source": ["We also have pre-trained models that we download below."]}, {"cell_type": "code", "execution_count": 3, "id": "8df5dce7", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:38.987532Z", "iopub.status.busy": "2024-09-01T12:08:38.986894Z", "iopub.status.idle": "2024-09-01T12:08:39.546052Z", "shell.execute_reply": "2024-09-01T12:08:39.544782Z"}, "papermill": {"duration": 0.573836, "end_time": "2024-09-01T12:08:39.548642", "exception": false, "start_time": "2024-09-01T12:08:38.974806", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial8/MNIST.ckpt...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial8/tensorboards/events.out.tfevents.MNIST...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial8/\"\n", "# Files to download\n", "pretrained_files = [\"MNIST.ckpt\", \"tensorboards/events.out.tfevents.MNIST\"]\n", "\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 \"/\" in file_name:\n", "        os.makedirs(file_path.rsplit(\"/\", 1)[0], exist_ok=True)\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": "29f7231c", "metadata": {"papermill": {"duration": 0.016627, "end_time": "2024-09-01T12:08:39.581955", "exception": false, "start_time": "2024-09-01T12:08:39.565328", "status": "completed"}, "tags": []}, "source": ["## Energy Models\n", "\n", "In the first part of this tutorial, we will review the theory of the energy-based models\n", "(the same theory has been discussed in Lecture 8).\n", "While most of the previous models had the goal of classification or regression,\n", "energy-based models are motivated from a different perspective: density estimation.\n", "Given a dataset with a lot of elements, we want to estimate the probability distribution over the whole data space.\n", "As an example, if we model images from CIFAR10, our goal would be to have a probability distribution\n", "over all possible images of size $32\\times32\\times3$ where those images have a high likelihood\n", "that look realistic and are one of the 10 CIFAR classes.\n", "Simple methods like interpolation between images don't work because images are extremely high-dimensional\n", "(especially for large HD images).\n", "Hence, we turn to deep learning methods that have performed well on complex data.\n", "\n", "However, how do we predict a probability distribution $p(\\mathbf{x})$ over so many dimensions using a simple neural network?\n", "The problem is that we cannot just predict a score between 0 and 1,\n", "because a probability distribution over data needs to fulfill two properties:\n", "\n", "1.\n", "The probability distribution needs to assign any possible value of\n", "$\\mathbf{x}$ a non-negative value: $p(\\mathbf{x}) \\geq 0$.\n", "2.\n", "The probability density must sum/integrate to 1 over **all** possible inputs:\n", "$\\int_{\\mathbf{x}} p(\\mathbf{x}) d\\mathbf{x} = 1$.\n", "\n", "Luckily, there are actually many approaches for this, and one of them are energy-based models.\n", "The fundamental idea of energy-based models is that you can turn any function\n", "that predicts values larger than zero into a probability distribution by dviding by its volume.\n", "Imagine we have a neural network, which has as output a single neuron, like in regression.\n", "We can call this network $E_{\\theta}(\\mathbf{x})$, where $\\theta$ are our parameters of the network,\n", "and $\\mathbf{x}$ the input data (e.g. an image).\n", "The output of $E_{\\theta}$ is a scalar value between $-\\infty$ and $\\infty$.\n", "Now, we can use basic probability theory to *normalize* the scores of all possible inputs:\n", "\n", "$$\n", "q_{\\theta}(\\mathbf{x}) = \\frac{\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)}{Z_{\\theta}} \\hspace{5mm}\\text{where}\\hspace{5mm}\n", "Z_{\\theta} = \\begin{cases}\n", "    \\int_{\\mathbf{x}}\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right) d\\mathbf{x} & \\text{if }x\\text{ is continuous}\\\\\n", "    \\sum_{\\mathbf{x}}\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right) & \\text{if }x\\text{ is discrete}\n", "\\end{cases}\n", "$$\n", "\n", "The $\\exp$-function ensures that we assign a probability greater than zero to any possible input.\n", "We use a negative sign in front of $E$ because we call $E_{\\theta}$ to be the energy function:\n", "data points with high likelihood have a low energy, while data points with low likelihood have a high energy.\n", "$Z_{\\theta}$ is our normalization terms that ensures that the density integrates/sums to 1.\n", "We can show this by integrating over $q_{\\theta}(\\mathbf{x})$:\n", "\n", "$$\n", "\\int_{\\mathbf{x}}q_{\\theta}(\\mathbf{x})d\\mathbf{x} =\n", "\\int_{\\mathbf{x}}\\frac{\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)}{\\int_{\\mathbf{\\tilde{x}}}\\exp\\left(-E_{\\theta}(\\mathbf{\\tilde{x}})\\right) d\\mathbf{\\tilde{x}}}d\\mathbf{x} =\n", "\\frac{\\int_{\\mathbf{x}}\\exp\\left(-E_{\\theta}(\\mathbf{x})\\right)d\\mathbf{x}}{\\int_{\\mathbf{\\tilde{x}}}\\exp\\left(-E_{\\theta}(\\mathbf{\\tilde{x}})\\right) d\\mathbf{\\tilde{x}}} = 1\n", "$$\n", "\n", "Note that we call the probability distribution $q_{\\theta}(\\mathbf{x})$ because this is the learned distribution by the model,\n", "and is trained to be as close as possible to the *true*, unknown distribution $p(\\mathbf{x})$.\n", "\n", "The main benefit of this formulation of the probability distribution is its great flexibility as we can choose\n", "$E_{\\theta}$ in whatever way we like, without any constraints.\n", "Nevertheless, when looking at the equation above, we can see a fundamental issue: How do we calculate $Z_{\\theta}$?\n", "There is no chance that we can calculate $Z_{\\theta}$ analytically for high-dimensional input\n", "and/or larger neural networks, but the task requires us to know $Z_{\\theta}$.\n", "Although we can't determine the exact likelihood of a point, there exist methods with which we can train energy-based models.\n", "Thus, we will look next at \"Contrastive Divergence\" for training the model."]}, {"cell_type": "markdown", "id": "b974d76c", "metadata": {"papermill": {"duration": 0.010649, "end_time": "2024-09-01T12:08:39.609104", "exception": false, "start_time": "2024-09-01T12:08:39.598455", "status": "completed"}, "tags": []}, "source": ["### Contrastive Divergence\n", "\n", "When we train a model on generative modeling, it is usually done by maximum likelihood estimation.\n", "In other words, we try to maximize the likelihood of the examples in the training set.\n", "As the exact likelihood of a point cannot be determined due to the unknown normalization constant $Z_{\\theta}$,\n", "we need to train energy-based models slightly different.\n", "We cannot just maximize the un-normalized probability $\\exp(-E_{\\theta}(\\mathbf{x}_{\\text{train}}))$\n", "because there is no guarantee that $Z_{\\theta}$ stays constant, or that $\\mathbf{x}_{\\text{train}}$\n", "is becoming more likely than the others.\n", "However, if we base our training on comparing the likelihood of points, we can create a stable objective.\n", "Namely, we can re-write our maximum likelihood objective where we maximize the probability\n", "of $\\mathbf{x}_{\\text{train}}$ compared to a randomly sampled data point of our model:\n", "\n", "$$\n", "\\begin{split}\n", "    \\nabla_{\\theta}\\mathcal{L}_{\\text{MLE}}(\\mathbf{\\theta};p) & = -\\mathbb{E}_{p(\\mathbf{x})}\\left[\\nabla_{\\theta}\\log q_{\\theta}(\\mathbf{x})\\right]\\\\[5pt]\n", "    & = \\mathbb{E}_{p(\\mathbf{x})}\\left[\\nabla_{\\theta}E_{\\theta}(\\mathbf{x})\\right] - \\mathbb{E}_{q_{\\theta}(\\mathbf{x})}\\left[\\nabla_{\\theta}E_{\\theta}(\\mathbf{x})\\right]\n", "\\end{split}\n", "$$\n", "\n", "Note that the loss is still an objective we want to minimize.\n", "Thus, we try to minimize the energy for data points from the dataset, while maximizing the energy for randomly\n", "sampled data points from our model (how we sample will be explained below).\n", "Although this objective sounds intuitive, how is it actually derived from our original distribution $q_{\\theta}(\\mathbf{x})$?\n", "The trick is that we approximate $Z_{\\theta}$ by a single Monte-Carlo sample.\n", "This gives us the exact same objective as written above.\n", "\n", "Visually, we can look at the objective as follows (figure credit - Stefano Ermon and Aditya Grover: lecture cs236/11):\n", "\n", "<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/contrastive_divergence.svg\" width=\"700px\"></center>\n", "\n", "$f_{\\theta}$ represents $\\exp(-E_{\\theta}(\\mathbf{x}))$ in our case.\n", "The point on the right, called \"correct answer\", represents a data point from the dataset\n", "(i.e. $x_{\\text{train}}$), and the left point, \"wrong answer\", a sample from our model (i.e. $x_{\\text{sample}}$).\n", "Thus, we try to \"pull up\" the probability of the data points in the dataset,\n", "while \"pushing down\" randomly sampled points.\n", "The two forces for pulling and pushing are in balance iff $q_{\\theta}(\\mathbf{x})=p(\\mathbf{x})$."]}, {"cell_type": "markdown", "id": "9bce20f3", "metadata": {"papermill": {"duration": 0.009874, "end_time": "2024-09-01T12:08:39.628903", "exception": false, "start_time": "2024-09-01T12:08:39.619029", "status": "completed"}, "tags": []}, "source": ["### Sampling from Energy-Based Models\n", "\n", "For sampling from an energy-based model, we can apply a Markov Chain Monte Carlo using Langevin Dynamics.\n", "The idea of the algorithm is to start from a random point, and slowly move towards the direction\n", "of higher probability using the gradients of $E_{\\theta}$.\n", "Nevertheless, this is not enough to fully capture the probability distribution.\n", "We need to add noise $\\omega$ at each gradient step to the current sample.\n", "Under certain conditions such as that we perform the gradient steps an infinite amount of times,\n", "we would be able to create an exact sample from our modeled distribution.\n", "However, as this is not practically possible, we usually limit the chain to $K$ steps\n", "($K$ a hyperparameter that needs to be finetuned).\n", "Overall, the sampling procedure can be summarized in the following algorithm:\n", "\n", "<center width=\"100%\" style=\"padding:15px\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/sampling.svg\" width=\"750px\"></center>"]}, {"cell_type": "markdown", "id": "c8a401f5", "metadata": {"papermill": {"duration": 0.007182, "end_time": "2024-09-01T12:08:39.644145", "exception": false, "start_time": "2024-09-01T12:08:39.636963", "status": "completed"}, "tags": []}, "source": ["### Applications of Energy-based models beyond generation\n", "\n", "Modeling the probability distribution for sampling new data is not the only application of energy-based models.\n", "Any application which requires us to compare two elements is much simpler to learn\n", "because we just need to go for the higher energy.\n", "A couple of examples are shown below (figure credit - Stefano Ermon and Aditya Grover: lecture cs236/11).\n", "A classification setup like object recognition or sequence labeling can be considered as an energy-based\n", "task as we just need to find the $Y$ input that minimizes the output $E(X, Y)$ (hence maximizes probability).\n", "Similarly, a popular application of energy-based models is denoising of images.\n", "Given an image $X$ with a lot of noise, we try to minimize the energy by finding the true input image $Y$.\n", "\n", "<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/energy_models_application.png\" width=\"600px\"></center>\n", "\n", "Nonetheless, we will focus on generative modeling here as in the next couple of lectures,\n", "we will discuss more generative deep learning approaches."]}, {"cell_type": "markdown", "id": "0236802a", "metadata": {"papermill": {"duration": 0.007168, "end_time": "2024-09-01T12:08:39.658522", "exception": false, "start_time": "2024-09-01T12:08:39.651354", "status": "completed"}, "tags": []}, "source": ["## Image generation\n", "\n", "<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/QJ94zuSQoP4\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "\n", "As an example for energy-based models, we will train a model on image generation.\n", "Specifically, we will look at how we can generate MNIST digits with a very simple CNN model.\n", "However, it should be noted that energy models are not easy to train and often diverge\n", "if the hyperparameters are not well tuned.\n", "We will rely on training tricks proposed in the paper\n", "[Implicit Generation and Generalization in Energy-Based Models](https://arxiv.org/abs/1903.08689)\n", "by Yilun Du and Igor Mordatch ([blog](https://openai.com/index/energy-based-models/)).\n", "The important part of this notebook is however to see how the theory above can actually be used in a model.\n", "\n", "### Dataset\n", "\n", "First, we can load the MNIST dataset below.\n", "Note that we need to normalize the images between -1 and 1 instead of mean 0 and std 1 because during sampling,\n", "we have to limit the input space.\n", "Scaling between -1 and 1 makes it easier to implement it."]}, {"cell_type": "code", "execution_count": 4, "id": "4168553c", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:39.675199Z", "iopub.status.busy": "2024-09-01T12:08:39.674491Z", "iopub.status.idle": "2024-09-01T12:08:42.194167Z", "shell.execute_reply": "2024-09-01T12:08:42.193183Z"}, "papermill": {"duration": 2.529897, "end_time": "2024-09-01T12:08:42.195751", "exception": false, "start_time": "2024-09-01T12:08:39.665854", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Failed to download (trying next):\n", "HTTP Error 403: Forbidden\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to /__w/13/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/9912422 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 23%|\u2588\u2588\u258e       | 2260992/9912422 [00:00<00:00, 22581514.79it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 70%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588   | 6946816/9912422 [00:00<00:00, 36853846.00it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9912422/9912422 [00:00<00:00, 36370986.68it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/13/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz to /__w/13/s/.datasets/MNIST/raw\n"]}, {"name": "stdout", "output_type": "stream", "text": ["\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Failed to download (trying next):\n", "HTTP Error 403: Forbidden\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to /__w/13/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/28881 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 28881/28881 [00:00<00:00, 2449561.07it/s]"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/13/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /__w/13/s/.datasets/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Failed to download (trying next):\n", "HTTP Error 403: Forbidden\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to /__w/13/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/1648877 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1648877/1648877 [00:00<00:00, 21682425.22it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/13/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /__w/13/s/.datasets/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Failed to download (trying next):\n", "HTTP Error 403: Forbidden\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to /__w/13/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/4542 [00:00<?, ?it/s]"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4542/4542 [00:00<00:00, 5223616.33it/s]"]}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/13/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/13/s/.datasets/MNIST/raw\n", "\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n"]}], "source": ["# Transformations applied on each image => make them a tensor and normalize between -1 and 1\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_set = MNIST(root=DATASET_PATH, train=True, transform=transform, download=True)\n", "\n", "# Loading the test set\n", "test_set = MNIST(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", "# Note that for actually training a model, we will use different data loaders\n", "# with a lower batch size.\n", "train_loader = data.DataLoader(train_set, batch_size=128, shuffle=True, drop_last=True, num_workers=4, pin_memory=True)\n", "test_loader = data.DataLoader(test_set, batch_size=256, shuffle=False, drop_last=False, num_workers=4)"]}, {"cell_type": "markdown", "id": "e4753db6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.010266, "end_time": "2024-09-01T12:08:42.221656", "exception": false, "start_time": "2024-09-01T12:08:42.211390", "status": "completed"}, "tags": []}, "source": ["### CNN Model\n", "\n", "First, we implement our CNN model.\n", "The MNIST images are of size 28x28, hence we only need a small model.\n", "As an example, we will apply several convolutions with stride 2 that downscale the images.\n", "If you are interested, you can also use a deeper model such as a small ResNet, but for simplicity,\n", "we will stick with the tiny network.\n", "\n", "It is a good practice to use a smooth activation function like Swish instead of ReLU in the energy model.\n", "This is because we will rely on the gradients we get back with respect to the input image, which should not be sparse."]}, {"cell_type": "code", "execution_count": 5, "id": "f5983829", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.240574Z", "iopub.status.busy": "2024-09-01T12:08:42.240299Z", "iopub.status.idle": "2024-09-01T12:08:42.248968Z", "shell.execute_reply": "2024-09-01T12:08:42.248086Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.019974, "end_time": "2024-09-01T12:08:42.250387", "exception": false, "start_time": "2024-09-01T12:08:42.230413", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class CNNModel(nn.Module):\n", "    def __init__(self, hidden_features=32, out_dim=1, **kwargs):\n", "        super().__init__()\n", "        # We increase the hidden dimension over layers. Here pre-calculated for simplicity.\n", "        c_hid1 = hidden_features // 2\n", "        c_hid2 = hidden_features\n", "        c_hid3 = hidden_features * 2\n", "\n", "        # Series of convolutions and Swish activation functions\n", "        self.cnn_layers = nn.Sequential(\n", "            nn.Conv2d(1, c_hid1, kernel_size=5, stride=2, padding=4),  # [16x16] - Larger padding to get 32x32 image\n", "            nn.SiLU(),\n", "            nn.Conv2d(c_hid1, c_hid2, kernel_size=3, stride=2, padding=1),  # [8x8]\n", "            nn.SiLU(),\n", "            nn.Conv2d(c_hid2, c_hid3, kernel_size=3, stride=2, padding=1),  # [4x4]\n", "            nn.SiLU(),\n", "            nn.Conv2d(c_hid3, c_hid3, kernel_size=3, stride=2, padding=1),  # [2x2]\n", "            nn.SiLU(),\n", "            nn.Flatten(),\n", "            nn.Linear(c_hid3 * 4, c_hid3),\n", "            nn.SiLU(),\n", "            nn.Linear(c_hid3, out_dim),\n", "        )\n", "\n", "    def forward(self, x):\n", "        x = self.cnn_layers(x).squeeze(dim=-1)\n", "        return x"]}, {"cell_type": "markdown", "id": "6e7a3324", "metadata": {"papermill": {"duration": 0.008486, "end_time": "2024-09-01T12:08:42.267344", "exception": false, "start_time": "2024-09-01T12:08:42.258858", "status": "completed"}, "tags": []}, "source": ["In the rest of the notebook, the output of the model will actually not represent\n", "$E_{\\theta}(\\mathbf{x})$, but $-E_{\\theta}(\\mathbf{x})$.\n", "This is a standard implementation practice for energy-based models, as some people also write the energy probability\n", "density as $q_{\\theta}(\\mathbf{x}) = \\frac{\\exp\\left(f_{\\theta}(\\mathbf{x})\\right)}{Z_{\\theta}}$.\n", "In that case, the model would actually represent $f_{\\theta}(\\mathbf{x})$.\n", "In the training loss etc., we need to be careful to not switch up the signs."]}, {"cell_type": "markdown", "id": "e39094f5", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008432, "end_time": "2024-09-01T12:08:42.284275", "exception": false, "start_time": "2024-09-01T12:08:42.275843", "status": "completed"}, "tags": []}, "source": ["### Sampling buffer\n", "\n", "In the next part, we look at the training with sampled elements.\n", "To use the contrastive divergence objective, we need to generate samples during training.\n", "Previous work has shown that due to the high dimensionality of images, we need a lot of iterations\n", "inside the MCMC sampling to obtain reasonable samples.\n", "However, there is a training trick that significantly reduces the sampling cost: using a sampling buffer.\n", "The idea is that we store the samples of the last couple of batches in a buffer,\n", "and reuse those as the starting point of the MCMC algorithm for the next batches.\n", "This reduces the sampling cost because the model requires a significantly\n", "lower number of steps to converge to reasonable samples.\n", "However, to not solely rely on previous samples and allow novel samples as well,\n", "we re-initialize 5% of our samples from scratch (random noise between -1 and 1).\n", "\n", "Below, we implement the sampling buffer.\n", "The function `sample_new_exmps` returns a new batch of \"fake\" images.\n", "We refer to those as fake images because they have been generated, but are not actually part of the dataset.\n", "As mentioned before, we use initialize 5% randomly, and 95% are randomly picked from our buffer.\n", "On this initial batch, we perform MCMC for 60 iterations to improve the image quality\n", "and come closer to samples from $q_{\\theta}(\\mathbf{x})$.\n", "In the function `generate_samples`, we implemented the MCMC for images.\n", "Note that the hyperparameters of `step_size`, `steps`, the noise standard deviation\n", "$\\sigma$ are specifically set for MNIST, and need to be finetuned for a different dataset if you want to use such."]}, {"cell_type": "code", "execution_count": 6, "id": "dc57b311", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.302611Z", "iopub.status.busy": "2024-09-01T12:08:42.302231Z", "iopub.status.idle": "2024-09-01T12:08:42.327222Z", "shell.execute_reply": "2024-09-01T12:08:42.326054Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.035972, "end_time": "2024-09-01T12:08:42.328725", "exception": false, "start_time": "2024-09-01T12:08:42.292753", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Sampler:\n", "    def __init__(self, model, img_shape, sample_size, max_len=8192):\n", "        \"\"\"Sampler.\n", "\n", "        Args:\n", "            model: Neural network to use for modeling E_theta\n", "            img_shape: Shape of the images to model\n", "            sample_size: Batch size of the samples\n", "            max_len: Maximum number of data points to keep in the buffer\n", "\n", "        \"\"\"\n", "        super().__init__()\n", "        self.model = model\n", "        self.img_shape = img_shape\n", "        self.sample_size = sample_size\n", "        self.max_len = max_len\n", "        self.examples = [(torch.rand((1,) + img_shape) * 2 - 1) for _ in range(self.sample_size)]\n", "\n", "    def sample_new_exmps(self, steps=60, step_size=10):\n", "        \"\"\"Function for getting a new batch of \"fake\" images.\n", "\n", "        Args:\n", "            steps: Number of iterations in the MCMC algorithm\n", "            step_size: Learning rate nu in the algorithm above\n", "\n", "        \"\"\"\n", "        # Choose 95% of the batch from the buffer, 5% generate from scratch\n", "        n_new = np.random.binomial(self.sample_size, 0.05)\n", "        rand_imgs = torch.rand((n_new,) + self.img_shape) * 2 - 1\n", "        old_imgs = torch.cat(random.choices(self.examples, k=self.sample_size - n_new), dim=0)\n", "        inp_imgs = torch.cat([rand_imgs, old_imgs], dim=0).detach().to(device)\n", "\n", "        # Perform MCMC sampling\n", "        inp_imgs = Sampler.generate_samples(self.model, inp_imgs, steps=steps, step_size=step_size)\n", "\n", "        # Add new images to the buffer and remove old ones if needed\n", "        self.examples = list(inp_imgs.to(torch.device(\"cpu\")).chunk(self.sample_size, dim=0)) + self.examples\n", "        self.examples = self.examples[: self.max_len]\n", "        return inp_imgs\n", "\n", "    @staticmethod\n", "    def generate_samples(model, inp_imgs, steps=60, step_size=10, return_img_per_step=False):\n", "        \"\"\"Function for sampling images for a given model.\n", "\n", "        Args:\n", "            model: Neural network to use for modeling E_theta\n", "            inp_imgs: Images to start from for sampling. If you want to generate new images, enter noise between -1 and 1.\n", "            steps: Number of iterations in the MCMC algorithm.\n", "            step_size: Learning rate nu in the algorithm above\n", "            return_img_per_step: If True, we return the sample at every iteration of the MCMC\n", "\n", "        \"\"\"\n", "        # Before MCMC: set model parameters to \"required_grad=False\"\n", "        # because we are only interested in the gradients of the input.\n", "        is_training = model.training\n", "        model.eval()\n", "        for p in model.parameters():\n", "            p.requires_grad = False\n", "        inp_imgs.requires_grad = True\n", "\n", "        # Enable gradient calculation if not already the case\n", "        had_gradients_enabled = torch.is_grad_enabled()\n", "        torch.set_grad_enabled(True)\n", "\n", "        # We use a buffer tensor in which we generate noise each loop iteration.\n", "        # More efficient than creating a new tensor every iteration.\n", "        noise = torch.randn(inp_imgs.shape, device=inp_imgs.device)\n", "\n", "        # List for storing generations at each step (for later analysis)\n", "        imgs_per_step = []\n", "\n", "        # Loop over K (steps)\n", "        for _ in range(steps):\n", "            # Part 1: Add noise to the input.\n", "            noise.normal_(0, 0.005)\n", "            inp_imgs.data.add_(noise.data)\n", "            inp_imgs.data.clamp_(min=-1.0, max=1.0)\n", "\n", "            # Part 2: calculate gradients for the current input.\n", "            out_imgs = -model(inp_imgs)\n", "            out_imgs.sum().backward()\n", "            inp_imgs.grad.data.clamp_(-0.03, 0.03)  # For stabilizing and preventing too high gradients\n", "\n", "            # Apply gradients to our current samples\n", "            inp_imgs.data.add_(-step_size * inp_imgs.grad.data)\n", "            inp_imgs.grad.detach_()\n", "            inp_imgs.grad.zero_()\n", "            inp_imgs.data.clamp_(min=-1.0, max=1.0)\n", "\n", "            if return_img_per_step:\n", "                imgs_per_step.append(inp_imgs.clone().detach())\n", "\n", "        # Reactivate gradients for parameters for training\n", "        for p in model.parameters():\n", "            p.requires_grad = True\n", "        model.train(is_training)\n", "\n", "        # Reset gradient calculation to setting before this function\n", "        torch.set_grad_enabled(had_gradients_enabled)\n", "\n", "        if return_img_per_step:\n", "            return torch.stack(imgs_per_step, dim=0)\n", "        else:\n", "            return inp_imgs"]}, {"cell_type": "markdown", "id": "68b22f1e", "metadata": {"papermill": {"duration": 0.008436, "end_time": "2024-09-01T12:08:42.345766", "exception": false, "start_time": "2024-09-01T12:08:42.337330", "status": "completed"}, "tags": []}, "source": ["The idea of the buffer becomes a bit clearer in the following algorithm."]}, {"cell_type": "markdown", "id": "db3fe681", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008491, "end_time": "2024-09-01T12:08:42.363036", "exception": false, "start_time": "2024-09-01T12:08:42.354545", "status": "completed"}, "tags": []}, "source": ["### Training algorithm\n", "\n", "With the sampling buffer being ready, we can complete our training algorithm.\n", "Below is shown a summary of the full training algorithm of an energy model on image modeling:\n", "\n", "<center width=\"100%\" style=\"padding: 15px\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/training_algorithm.svg\" width=\"700px\"></center>\n", "\n", "The first few statements in each training iteration concern the sampling of the real and fake data,\n", "as we have seen above with the sample buffer.\n", "Next, we calculate the contrastive divergence objective using our energy model $E_{\\theta}$.\n", "However, one additional training trick we need is to add a regularization loss on the output of $E_{\\theta}$.\n", "As the output of the network is not constrained and adding a large bias or not to the output\n", "doesn't change the contrastive divergence loss, we need to ensure somehow else that the output values are in a reasonable range.\n", "Without the regularization loss, the output values will fluctuate in a very large range.\n", "With this, we ensure that the values for the real data are around 0, and the fake data likely slightly lower\n", "(for noise or outliers the score can be still significantly lower).\n", "As the regularization loss is less important than the Contrastive Divergence, we have a weight factor\n", "$\\alpha$ which is usually quite some smaller than 1.\n", "Finally, we perform an update step with an optimizer on the combined loss and add the new samples to the buffer.\n", "\n", "Below, we put this training dynamic into a PyTorch Lightning module:"]}, {"cell_type": "code", "execution_count": 7, "id": "4d5c4169", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.413062Z", "iopub.status.busy": "2024-09-01T12:08:42.412676Z", "iopub.status.idle": "2024-09-01T12:08:42.433545Z", "shell.execute_reply": "2024-09-01T12:08:42.430816Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.033508, "end_time": "2024-09-01T12:08:42.436195", "exception": false, "start_time": "2024-09-01T12:08:42.402687", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class DeepEnergyModel(pl.LightningModule):\n", "    def __init__(self, img_shape, batch_size, alpha=0.1, lr=1e-4, beta1=0.0, **CNN_args):\n", "        super().__init__()\n", "        self.save_hyperparameters()\n", "\n", "        self.cnn = CNNModel(**CNN_args)\n", "        self.sampler = Sampler(self.cnn, img_shape=img_shape, sample_size=batch_size)\n", "        self.example_input_array = torch.zeros(1, *img_shape)\n", "\n", "    def forward(self, x):\n", "        z = self.cnn(x)\n", "        return z\n", "\n", "    def configure_optimizers(self):\n", "        # Energy models can have issues with momentum as the loss surfaces changes with its parameters.\n", "        # Hence, we set it to 0 by default.\n", "        optimizer = optim.Adam(self.parameters(), lr=self.hparams.lr, betas=(self.hparams.beta1, 0.999))\n", "        scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.97)  # Exponential decay over epochs\n", "        return [optimizer], [scheduler]\n", "\n", "    def training_step(self, batch, batch_idx):\n", "        # We add minimal noise to the original images to prevent the model from focusing on purely \"clean\" inputs\n", "        real_imgs, _ = batch\n", "        small_noise = torch.randn_like(real_imgs) * 0.005\n", "        real_imgs.add_(small_noise).clamp_(min=-1.0, max=1.0)\n", "\n", "        # Obtain samples\n", "        fake_imgs = self.sampler.sample_new_exmps(steps=60, step_size=10)\n", "\n", "        # Predict energy score for all images\n", "        inp_imgs = torch.cat([real_imgs, fake_imgs], dim=0)\n", "        real_out, fake_out = self.cnn(inp_imgs).chunk(2, dim=0)\n", "\n", "        # Calculate losses\n", "        reg_loss = self.hparams.alpha * (real_out**2 + fake_out**2).mean()\n", "        cdiv_loss = fake_out.mean() - real_out.mean()\n", "        loss = reg_loss + cdiv_loss\n", "\n", "        # Logging\n", "        self.log(\"loss\", loss)\n", "        self.log(\"loss_regularization\", reg_loss)\n", "        self.log(\"loss_contrastive_divergence\", cdiv_loss)\n", "        self.log(\"metrics_avg_real\", real_out.mean())\n", "        self.log(\"metrics_avg_fake\", fake_out.mean())\n", "        return loss\n", "\n", "    def validation_step(self, batch, batch_idx):\n", "        # For validating, we calculate the contrastive divergence between purely random images and unseen examples\n", "        # Note that the validation/test step of energy-based models depends on what we are interested in the model\n", "        real_imgs, _ = batch\n", "        fake_imgs = torch.rand_like(real_imgs) * 2 - 1\n", "\n", "        inp_imgs = torch.cat([real_imgs, fake_imgs], dim=0)\n", "        real_out, fake_out = self.cnn(inp_imgs).chunk(2, dim=0)\n", "\n", "        cdiv = fake_out.mean() - real_out.mean()\n", "        self.log(\"val_contrastive_divergence\", cdiv)\n", "        self.log(\"val_fake_out\", fake_out.mean())\n", "        self.log(\"val_real_out\", real_out.mean())"]}, {"cell_type": "markdown", "id": "6e925805", "metadata": {"papermill": {"duration": 0.008824, "end_time": "2024-09-01T12:08:42.455965", "exception": false, "start_time": "2024-09-01T12:08:42.447141", "status": "completed"}, "tags": []}, "source": ["We do not implement a test step because energy-based, generative models are usually not evaluated on a test set.\n", "The validation step however is used to get an idea of the difference between ennergy/likelihood\n", "of random images to unseen examples of the dataset."]}, {"cell_type": "markdown", "id": "9adbdf44", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008731, "end_time": "2024-09-01T12:08:42.473401", "exception": false, "start_time": "2024-09-01T12:08:42.464670", "status": "completed"}, "tags": []}, "source": ["### Callbacks\n", "\n", "To track the performance of our model during training, we will make extensive use of PyTorch Lightning's callback framework.\n", "Remember that callbacks can be used for running small functions at any point of the training,\n", "for instance after finishing an epoch.\n", "Here, we will use three different callbacks we define ourselves.\n", "\n", "The first callback, called `GenerateCallback`, is used for adding image generations to the model during training.\n", "After every $N$ epochs (usually $N=5$ to reduce output to TensorBoard), we take a small batch\n", "of random images and perform many MCMC iterations until the model's generation converges.\n", "Compared to the training that used 60 iterations, we use 256 here because\n", "(1) we only have to do it once compared to the training that has to do it every iteration, and\n", "(2) we do not start from a buffer here, but from scratch.\n", "It is implemented as follows:"]}, {"cell_type": "code", "execution_count": 8, "id": "6f5766e4", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.492667Z", "iopub.status.busy": "2024-09-01T12:08:42.492277Z", "iopub.status.idle": "2024-09-01T12:08:42.505165Z", "shell.execute_reply": "2024-09-01T12:08:42.504073Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.024478, "end_time": "2024-09-01T12:08:42.506671", "exception": false, "start_time": "2024-09-01T12:08:42.482193", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GenerateCallback(Callback):\n", "    def __init__(self, batch_size=8, vis_steps=8, num_steps=256, every_n_epochs=5):\n", "        super().__init__()\n", "        self.batch_size = batch_size  # Number of images to generate\n", "        self.vis_steps = vis_steps  # Number of steps within generation to visualize\n", "        self.num_steps = num_steps  # Number of steps to take during generation\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_epoch_end(self, trainer, pl_module):\n", "        # Skip for all other epochs\n", "        if trainer.current_epoch % self.every_n_epochs == 0:\n", "            # Generate images\n", "            imgs_per_step = self.generate_imgs(pl_module)\n", "            # Plot and add to tensorboard\n", "            for i in range(imgs_per_step.shape[1]):\n", "                step_size = self.num_steps // self.vis_steps\n", "                imgs_to_plot = imgs_per_step[step_size - 1 :: step_size, i]\n", "                grid = torchvision.utils.make_grid(\n", "                    imgs_to_plot, nrow=imgs_to_plot.shape[0], normalize=True, value_range=(-1, 1)\n", "                )\n", "                trainer.logger.experiment.add_image(\"generation_%i\" % i, grid, global_step=trainer.current_epoch)\n", "\n", "    def generate_imgs(self, pl_module):\n", "        pl_module.eval()\n", "        start_imgs = torch.rand((self.batch_size,) + pl_module.hparams[\"img_shape\"]).to(pl_module.device)\n", "        start_imgs = start_imgs * 2 - 1\n", "        imgs_per_step = Sampler.generate_samples(\n", "            pl_module.cnn, start_imgs, steps=self.num_steps, step_size=10, return_img_per_step=True\n", "        )\n", "        pl_module.train()\n", "        return imgs_per_step"]}, {"cell_type": "markdown", "id": "9c9c170e", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008785, "end_time": "2024-09-01T12:08:42.524207", "exception": false, "start_time": "2024-09-01T12:08:42.515422", "status": "completed"}, "tags": []}, "source": ["The second callback is called `SamplerCallback`, and simply adds a randomly picked subset of images\n", "in the sampling buffer to the TensorBoard.\n", "This helps to understand what images are currently shown to the model as \"fake\"."]}, {"cell_type": "code", "execution_count": 9, "id": "f3dcc891", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.543380Z", "iopub.status.busy": "2024-09-01T12:08:42.543000Z", "iopub.status.idle": "2024-09-01T12:08:42.551738Z", "shell.execute_reply": "2024-09-01T12:08:42.550681Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020163, "end_time": "2024-09-01T12:08:42.553183", "exception": false, "start_time": "2024-09-01T12:08:42.533020", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SamplerCallback(Callback):\n", "    def __init__(self, num_imgs=32, every_n_epochs=5):\n", "        super().__init__()\n", "        self.num_imgs = num_imgs  # Number of images to plot\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_epoch_end(self, trainer, pl_module):\n", "        if trainer.current_epoch % self.every_n_epochs == 0:\n", "            exmp_imgs = torch.cat(random.choices(pl_module.sampler.examples, k=self.num_imgs), dim=0)\n", "            grid = torchvision.utils.make_grid(exmp_imgs, nrow=4, normalize=True, value_range=(-1, 1))\n", "            trainer.logger.experiment.add_image(\"sampler\", grid, global_step=trainer.current_epoch)"]}, {"cell_type": "markdown", "id": "ca300df5", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008854, "end_time": "2024-09-01T12:08:42.570926", "exception": false, "start_time": "2024-09-01T12:08:42.562072", "status": "completed"}, "tags": []}, "source": ["Finally, our last callback is `OutlierCallback`.\n", "This callback evaluates the model by recording the (negative) energy assigned to random noise.\n", "While our training loss is almost constant across iterations,\n", "this score is likely showing the progress of the model to detect \"outliers\"."]}, {"cell_type": "code", "execution_count": 10, "id": "87d73505", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.590096Z", "iopub.status.busy": "2024-09-01T12:08:42.589692Z", "iopub.status.idle": "2024-09-01T12:08:42.598210Z", "shell.execute_reply": "2024-09-01T12:08:42.597039Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.019975, "end_time": "2024-09-01T12:08:42.599694", "exception": false, "start_time": "2024-09-01T12:08:42.579719", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class OutlierCallback(Callback):\n", "    def __init__(self, batch_size=1024):\n", "        super().__init__()\n", "        self.batch_size = batch_size\n", "\n", "    def on_epoch_end(self, trainer, pl_module):\n", "        with torch.no_grad():\n", "            pl_module.eval()\n", "            rand_imgs = torch.rand((self.batch_size,) + pl_module.hparams[\"img_shape\"]).to(pl_module.device)\n", "            rand_imgs = rand_imgs * 2 - 1.0\n", "            rand_out = pl_module.cnn(rand_imgs).mean()\n", "            pl_module.train()\n", "\n", "        trainer.logger.experiment.add_scalar(\"rand_out\", rand_out, global_step=trainer.current_epoch)"]}, {"cell_type": "markdown", "id": "09b1ad10", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008865, "end_time": "2024-09-01T12:08:42.617423", "exception": false, "start_time": "2024-09-01T12:08:42.608558", "status": "completed"}, "tags": []}, "source": ["### Running the model\n", "\n", "Finally, we can add everything together to create our final training function.\n", "The function is very similar to any other PyTorch Lightning training function we have seen so far.\n", "However, there is the small difference of that we do not test the model on a test set\n", "because we will analyse the model afterward by checking its prediction and ability to perform outlier detection."]}, {"cell_type": "code", "execution_count": 11, "id": "a5ceeb7b", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.636675Z", "iopub.status.busy": "2024-09-01T12:08:42.636298Z", "iopub.status.idle": "2024-09-01T12:08:42.646272Z", "shell.execute_reply": "2024-09-01T12:08:42.645129Z"}, "papermill": {"duration": 0.021406, "end_time": "2024-09-01T12:08:42.647737", "exception": false, "start_time": "2024-09-01T12:08:42.626331", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_model(**kwargs):\n", "    # Create a PyTorch Lightning trainer with the generation callback\n", "    trainer = pl.Trainer(\n", "        default_root_dir=os.path.join(CHECKPOINT_PATH, \"MNIST\"),\n", "        accelerator=\"auto\",\n", "        devices=1,\n", "        max_epochs=60,\n", "        gradient_clip_val=0.1,\n", "        callbacks=[\n", "            ModelCheckpoint(save_weights_only=True, mode=\"min\", monitor=\"val_contrastive_divergence\"),\n", "            GenerateCallback(every_n_epochs=5),\n", "            SamplerCallback(every_n_epochs=5),\n", "            OutlierCallback(),\n", "            LearningRateMonitor(\"epoch\"),\n", "        ],\n", "    )\n", "    # Check whether pretrained model exists. If yes, load it and skip training\n", "    pretrained_filename = os.path.join(CHECKPOINT_PATH, \"MNIST.ckpt\")\n", "    if os.path.isfile(pretrained_filename):\n", "        print(\"Found pretrained model, loading...\")\n", "        model = DeepEnergyModel.load_from_checkpoint(pretrained_filename)\n", "    else:\n", "        pl.seed_everything(42)\n", "        model = DeepEnergyModel(**kwargs)\n", "        trainer.fit(model, train_loader, test_loader)\n", "        model = DeepEnergyModel.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n", "    # No testing as we are more interested in other properties\n", "    return model"]}, {"cell_type": "code", "execution_count": 12, "id": "9aca4b5f", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:42.667149Z", "iopub.status.busy": "2024-09-01T12:08:42.666358Z", "iopub.status.idle": "2024-09-01T12:08:42.982432Z", "shell.execute_reply": "2024-09-01T12:08:42.981310Z"}, "papermill": {"duration": 0.328505, "end_time": "2024-09-01T12:08:42.985036", "exception": false, "start_time": "2024-09-01T12:08:42.656531", "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": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/lightning_fabric/utilities/cloud_io.py:57: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\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 v1.0.2 to v2.3.3. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint saved_models/tutorial8/MNIST.ckpt`\n"]}], "source": ["model = train_model(img_shape=(1, 28, 28), batch_size=train_loader.batch_size, lr=1e-4, beta1=0.0)"]}, {"cell_type": "markdown", "id": "4d10fe91", "metadata": {"papermill": {"duration": 0.0207, "end_time": "2024-09-01T12:08:43.026690", "exception": false, "start_time": "2024-09-01T12:08:43.005990", "status": "completed"}, "tags": []}, "source": ["## Analysis\n", "\n", "In the last part of the notebook, we will try to take the trained energy-based generative model,\n", "and analyse its properties."]}, {"cell_type": "markdown", "id": "144bbca4", "metadata": {"papermill": {"duration": 0.01295, "end_time": "2024-09-01T12:08:43.053095", "exception": false, "start_time": "2024-09-01T12:08:43.040145", "status": "completed"}, "tags": []}, "source": ["### TensorBoard\n", "\n", "The first thing we can look at is the TensorBoard generate during training.\n", "This can help us to understand the training dynamic even better, and shows potential issues.\n", "Let's load the TensorBoard below:"]}, {"cell_type": "code", "execution_count": 13, "id": "b8f66b35", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:43.074670Z", "iopub.status.busy": "2024-09-01T12:08:43.074453Z", "iopub.status.idle": "2024-09-01T12:08:44.097039Z", "shell.execute_reply": "2024-09-01T12:08:44.095729Z"}, "papermill": {"duration": 1.035315, "end_time": "2024-09-01T12:08:44.099591", "exception": false, "start_time": "2024-09-01T12:08:43.064276", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["\n", "      <iframe id=\"tensorboard-frame-1c80317fa3b1799d\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", "      </iframe>\n", "      <script>\n", "        (function() {\n", "          const frame = document.getElementById(\"tensorboard-frame-1c80317fa3b1799d\");\n", "          const url = new URL(\"/\", window.location);\n", "          const port = 6006;\n", "          if (port) {\n", "            url.port = port;\n", "          }\n", "          frame.src = url;\n", "        })();\n", "      </script>\n", "    "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Uncomment the following two lines to open a tensorboard in the notebook.\n", "# Adjust the path to your CHECKPOINT_PATH if needed.\n", "%load_ext tensorboard\n", "%tensorboard --logdir ../saved_models/tutorial8/tensorboards/"]}, {"cell_type": "markdown", "id": "2fa89cfa", "metadata": {"papermill": {"duration": 0.021726, "end_time": "2024-09-01T12:08:44.142380", "exception": false, "start_time": "2024-09-01T12:08:44.120654", "status": "completed"}, "tags": []}, "source": ["<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/07-deep-energy-based-generative-models/tensorboard_screenshot.png\" width=\"1000px\"></center>"]}, {"cell_type": "markdown", "id": "9a5200a8", "metadata": {"papermill": {"duration": 0.013514, "end_time": "2024-09-01T12:08:44.170684", "exception": false, "start_time": "2024-09-01T12:08:44.157170", "status": "completed"}, "tags": []}, "source": ["We see that the contrastive divergence as well as the regularization converge quickly to 0.\n", "However, the training continues although the loss is always close to zero.\n", "This is because our \"training\" data changes with the model by sampling.\n", "The progress of training can be best measured by looking at the samples across iterations,\n", "and the score for random images that decreases constantly over time."]}, {"cell_type": "markdown", "id": "81964fb6", "metadata": {"papermill": {"duration": 0.009142, "end_time": "2024-09-01T12:08:44.191652", "exception": false, "start_time": "2024-09-01T12:08:44.182510", "status": "completed"}, "tags": []}, "source": ["### Image Generation\n", "\n", "Another way of evaluating generative models is by sampling a few generated images.\n", "Generative models need to be good at generating realistic images as this truly shows that they have modeled the true data distribution.\n", "Thus, let's sample a few images of the model below:"]}, {"cell_type": "code", "execution_count": 14, "id": "b9d8651b", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:44.211768Z", "iopub.status.busy": "2024-09-01T12:08:44.211356Z", "iopub.status.idle": "2024-09-01T12:08:45.104235Z", "shell.execute_reply": "2024-09-01T12:08:45.100562Z"}, "papermill": {"duration": 0.905941, "end_time": "2024-09-01T12:08:45.106768", "exception": false, "start_time": "2024-09-01T12:08:44.200827", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Seed set to 43\n"]}], "source": ["model.to(device)\n", "pl.seed_everything(43)\n", "callback = GenerateCallback(batch_size=4, vis_steps=8, num_steps=256)\n", "imgs_per_step = callback.generate_imgs(model)\n", "imgs_per_step = imgs_per_step.cpu()"]}, {"cell_type": "markdown", "id": "79ca7b02", "metadata": {"papermill": {"duration": 0.013241, "end_time": "2024-09-01T12:08:45.133206", "exception": false, "start_time": "2024-09-01T12:08:45.119965", "status": "completed"}, "tags": []}, "source": ["The characteristic of sampling with energy-based models is that they require the iterative MCMC algorithm.\n", "To gain an insight in how the images change over iterations, we plot a few intermediate samples in the MCMC as well:"]}, {"cell_type": "code", "execution_count": 15, "id": "a3d7e586", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:45.164066Z", "iopub.status.busy": "2024-09-01T12:08:45.162922Z", "iopub.status.idle": "2024-09-01T12:08:45.724138Z", "shell.execute_reply": "2024-09-01T12:08:45.722896Z"}, "papermill": {"duration": 0.57978, "end_time": "2024-09-01T12:08:45.726666", "exception": false, "start_time": "2024-09-01T12:08:45.146886", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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KPDwgL0xlbmd0aCAyMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVC5jQQxDMtdhRpYwHrtqWcWi0um//RI+fYi0RZFUio1mZIpL3WUJVlT3jp8lsQOeYblbmQ2JSpFL5OwJffQCvF9ieYU993VlrNDNJdoOX4LMyqqGx3TSzaacCoTuqDcwzP6DW10A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/65yj59eixs+w/FDFJGSDuY1/1j98nMNr1OPJ5Fub77iXpypDgMRHJKavCNdWLEuEhFpNUFNz8BaLYC7t17+G7QjugxA9onEcZpSjqG/a3Clzy/lJ1PYCmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0xlbmd0aCA4MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjLsNwDAIRHumYAR+JvY+UZTC3r8NECVuuCfdPVwdCZkpbjPDQwaeDCyGXXGB9JYwC1xHUI6d7KNh1b7qBI31plLz7w+Unuys4obrAQJCGmYKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvTGVuZ3RoIDE2MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDkSAzEIBHO9gidIXIL3rMu1wfr/qQfWR6LpAjQcuhZNynoUaD7psUahutBr6CxKkkTBFpIdUKdjiDsoSExIY5JIth6DI5pYs12YmVQqs1LhtGnFwr/ZWtXIRI1wjfyJ6QZU/E/qXJTwTYOvkjH6GFS8O4OMSfheRdxaMe3+RDCxGfYJb0UmBYSJsanZvs9ghsz3Ctc4x/MNTII36wplbmRzdHJlYW0KZW5kb2JqCjMwIDAgb2JqCjw8IC9MZW5ndGggMzIwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSS24FMQjbzym4QKXwT87zqqqLvvtvaxO9FUwwYOMpL1nSS77UJdulw+RbH/clsULej+2azFLF9xazFM8tr0fPEbctCgRREz1YmS8VItTP9Og6qHBKn4FXCLcUG7yDSQCDavgHHqUzIFDnQMa7YjJSA4Ik2HNpcQiJciaJf6S8nt8nraSh9D1Zmcvfk0ul0B1NTugBxcrFSaBdSfmgmZhKRJKX632xQvSGwJI8PkcxyYDsNoltogUm5x6lJczEFDqwxwK8ZprVVehgwh6HKYxXC7OoHmzyWxOVpB2t4xnZMN7LMFNioeGwBdTmYmWC7uXjNa/CiO1Rk13DcO6WzXcI0Wj+GxbK4GMVkoBHp7ESDWk4wIjAnl44xV7zEzkOwIhjnZosDGNoJqd6jonA0J6zpWHGxx5a9fMPVOl8hwplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9MZW5ndGggMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDCAwxRDrjQAHeYDUgplbmRzdHJlYW0KZW5kb2JqCjMyIDAgb2JqCjw8IC9MZW5ndGggMTMzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWPSw4EIQhE95yijsDHH+dxMumFc//tgJ1uE2M9hVSBuYKhPS5rA50VHyEZtvG3qZaORVk+VHpSVg/J4Iesxssh3KAs8IJJKoYhUIuYGpEtZW63gNs2DbKylVOljrCLozCP9rRsFR5folsidZI/g8QqL9zjuh3Ipda73qKLvn+kATEJCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0xlbmd0aCAzNDAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVI5bgQxDOv9Cn0ggG7b79kgSJH8vw2p2RQDcXRSlDtaVHbLh4VUtex0+bSV2hI35HdlhcQJyasS7VKGSKi8ViHV75kyr7c1ZwTIUqXC5KTkccmCP8OlpwvH+baxr+XIHY8eWBUjoUTAMsXE6BqWzu6wZlt+lmnAj3iEnCvWLcdYBVIb3TjtiveheS2yBoi9mZaKCh1WiRZ+QfGgR4199hhUWCDR7RxJcIyJUJGAdoHaSAw5eyx2UR/0MygxE+jaG0XcQYElkpg5xbp09N/40LGg/tiMN786KulbWllj0j4b7ZTGLDLpelj0dPPWx4MLNO+i/OfVDBI0ZY2Sxget2jmGoplRVni3Q5MNzTHHIfMOnsMZCUr6PBS/jyUTHZTI3w4NoX9fHqOMnDbeAuaiP20VBw7is8NeuYEVShdrkvcBqUzogen/r/G1vtfXHx3tgMYKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvTGVuZ3RoIDI1MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwtUUlyA0EIu88r9IRmp99jlyuH5P/XCMoHBg2LQHRa4qCMnyAsV7zlkatow98zMYLfBYd+K9dtWORAVCBJY1A1oXbxevQe2HGYCcyT1rAMZqwP/Iwp3OjF4TEZZ7fXZdQQ7F2vPZlByaxcxCUTF0zVYSNnDj+ZMi60cz03IOdGWJdhkG5WGjMSjjSFSCGFqpukzgRBEoyuRo02chT7pS+PdIZVjagx7HMtbV/PTThr0OxYrPLklB5dcS4nFy+sHPT1NgMXUWms8kBIwP1uD/VzspPfeEvnzhbT43vNyfLCVGDFm9duQDbV4t+8iOP7jK/n5/n8A19gW4gKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDIxNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNiAwIG9iago8PCAvVHlwZSAvRm9udCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9GaXJzdENoYXIgMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxNSAwIFIgL1N1YnR5cGUgL1R5cGUzIC9OYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9DaGFyUHJvY3MgMTcgMCBSCi9FbmNvZGluZyA8PCAvVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgL2ZvdXIgL2ZpdmUgL3NpeCA1NiAvZWlnaHQgL25pbmUgNzEKL0cgOTcgL2EgMTAxIC9lIDEwNSAvaSAxMTAgL24gL28gMTE0IC9yIDExNiAvdCBdCj4+Ci9XaWR0aHMgMTQgMCBSID4+CmVuZG9iagoxNSAwIG9iago8PCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL0ZvbnROYW1lIC9CTVFRRFYrRGVqYVZ1U2FucyAvRmxhZ3MgMzIKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvQXNjZW50IDkyOSAvRGVzY2VudCAtMjM2IC9DYXBIZWlnaHQgMAovWEhlaWdodCAwIC9JdGFsaWNBbmdsZSAwIC9TdGVtViAwIC9NYXhXaWR0aCAxMzQyID4+CmVuZG9iagoxNCAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNyAwIG9iago8PCAvRyAxOCAwIFIgL2EgMTkgMCBSIC9lIDIwIDAgUiAvZWlnaHQgMjEgMCBSIC9maXZlIDIyIDAgUiAvZm91ciAyMyAwIFIKL2kgMjQgMCBSIC9uIDI1IDAgUiAvbmluZSAyNiAwIFIgL28gMjcgMCBSIC9vbmUgMjggMCBSIC9yIDI5IDAgUgovc2l4IDMwIDAgUiAvc3BhY2UgMzEgMCBSIC90IDMyIDAgUiAvdGhyZWUgMzMgMCBSIC90d28gMzQgMCBSIC96ZXJvIDM1IDAgUgo+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTYgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAwIC9jYSAxID4+Ci9BMiA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAxIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9JMSAxMyAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9UeXBlIC9YT2JqZWN0IC9TdWJ0eXBlIC9JbWFnZSAvV2lkdGggNjIwIC9IZWlnaHQgNzMKL0NvbG9yU3BhY2UgWyAvSW5kZXhlZCAvRGV2aWNlUkdCIDI1NQoo/////f39+/v7+fn59/f39fX18/Pz8fHx7+/v7e3t6+vr6enp5+fn5eXl4+Pj4eHh39/f3d3d29vb2dnZ19fX1dXV09PT0dHRz8/Pzc3Ny8vLycnJx8fHxcXFw8PDwcHBv7+/vb29u7u7ubm5t7e3tbW1s7OzsbGxr6+vra2tq6urqampp6enpaWlo6OjoaGhn5+fnZ2dm5ubmZmZl5eXlZWVk5OTkZGRj4+PjY2Ni4uLiYmJh4eHhYWFg4ODgYGBf39/fX19e3t7eXl5d3d3dXV1c3NzcXFxb29vbW1ta2traWlpZ2dnZWVlY2NjYWFhX19fXV1dW1tbWVlZV1dXVVVVU1NTUVFRT09PTU1NS0tLSUlJR0dHRUVFQ0NDQUFBPz8/PT09Ozs7OTk5Nzc3NTU1MzMzMTExLy8vLS0tKysrXClcKVwpJycnJSUlIyMjISEhHx8fHR0dGxsbGRkZFxcXFRUVExMTERERDw8PXHJcclxyCwsLCQkJBwcHBQUFAwMDAQEB/v7+/Pz8+vr6+Pj49vb29PT08vLy8PDw7u7u7Ozs6urq6Ojo5ubm5OTk4uLi4ODg3t7e3Nzc2tra2NjY1tbW1NTU0tLS0NDQzs7OzMzMysrKyMjIxsbGxMTEwsLCwMDAvr6+vLy8urq6uLi4tra2tLS0srKysLCwrq6urKysqqqqqKiopqampKSkoqKioKCgnp6enJycmpqamJiYlpaWlJSUkpKSkJCQjo6OjIyMioqKiIiIhoaGhISEgoKCgICAfn5+fHx8enp6eHh4dnZ2dHR0cnJycHBwbm5ubGxsampqaGhoZmZmZGRkYmJiYGBgXl5eXFxcXFxcWlpaWFhYVlZWVFRUUlJSUFBQTk5OTExMSkpKSEhIRkZGREREQkJCQEBAPj4+PDw8Ojo6ODg4NjY2NDQ0MjIyMDAwLi4uLCwsKioqXChcKFwoJiYmJCQkIiIiICAgHh4eHBwcGhoaGBgYFhYWFBQUEhISEBAQDg4ODAwMXG5cblxuCAgIBgYGBAQEAgICAAAAKQpdCi9CaXRzUGVyQ29tcG9uZW50IDggL0ZpbHRlciAvRmxhdGVEZWNvZGUKL0RlY29kZVBhcm1zIDw8IC9QcmVkaWN0b3IgMTAgL0NvbG9ycyAxIC9Db2x1bW5zIDYyMCAvQml0c1BlckNvbXBvbmVudCA4ID4+Ci9MZW5ndGggMzYgMCBSID4+CnN0cmVhbQp4nO29dUBU3db4/8wwdIdSgk0ooJIC0i0loQImYAIqiHRMEdIdApISEiIhKQgSgoQCSokoChioGEiDvzMgcmbmjM9V39/3vu+9z/rjPtfNPmt2fM7aa++99j5/Kf4j/8j/I/nr312Af+S/R/76dxfgH/nvkb/+3QX4R/575C/c/8hKFgplZ/JLayiVVqhraTU3ad7ZIdqo1/FIvUi8rFymouFBi2p13jZ5baViBdMjZr2td1sPdyuUScvsPXxAU7Wy7Dago9H06R+JsbKiYm3nn+kw0wQKon34z5Q8qFFUVD34Zzq6VRUVax78mY7D94HKaPb/mRLDWkXFu8a/+fCR5f+YNgIFaR34o3IcafsBm2R2WhQLXUCBvrJIsa5Rl3GngULCJr2jI9a66Rk7C8WKW8x7tapimfccVBaomliYutSvZjY3XrWzQOyx2weDJsmd0oCONofFPxKrekVFldN/pmPqIVCQg3N/pMPzuZKiotbHPyuIbZOiopL5sj5Pz9/T4XYAqIzB1J8VZFRFUVHt7e89u1JwBxwo/R6/9qgnfsLCwA/YquWKUzdcy2wYMFCs6zzYP2DW0yIm0dLVbdIsKV2nWlbUaNLTrXeD//be3ZvEjr2+OGFUqnfxlfQNeY3+C5ZNSoV5uwEdeo7f/kiscbC9+jMd0wZAQXrm/0yJOQ62T3+mww4H29Cf6XDvxsHmsvJPJPJ3lJzGwTb2Q8fi7yhxxMF2yHNFh6fnT3NDy+KRH7AZnRlQUh60snf98mb02bE3H96Onn5t0SArmqP09OW4TX1Bxd39Xycs+qqEUtZw55S8nFDmKDAzyt/e//WEwb2i/G1Zkv/RsCF/XQc+bL/Xx3N4sCE9PBZ/Qwk+bB5zC3//CBHV+LAtzLqRKMjPXgfPVdgen7doaLQ4ZzcxfuZEX6/l61MvT4wc11QoFq63PGM7piYmVdF2YdzycEV6MneiYOHguApvYVfrtoyDF4/tvVu8S2i71D+w4QsYNiQwivyGCjzYkIueJGBD/tzigWFb9JwnBRv4dfgpbEjPeRKwIZE/qyYIts5XR+qV6tXramuqK0V3ld1WaVbI3mU6Otj35JiZzr07sju2XBcvVbiznXeHSr26Zsfj/Y/01WU5w3Nlb1crNzfJV1T9h8IGDBm/N3qBYEO6z86T6gXAtyGtHjyMeky7eEBmRS4seHh4ku5lEGyL005zkBmRngtuLnOkCwKCDenmBBQEKtPCnJvrrCdJ9EHDqMGJfrU7Zbd2ieQJZ2/ZnCUkIb+Tbf0zhwvWw0+MqsrlFXO5111NuVmUziPZ1fXwgXaz2aveGvEIuoT0fBllIyN1pZr/WNgWfmaUSP8JDBvQkdBKkEicuSKpBAybu+MUtGFbnHefmwNoI6UEBJvn5IQbZB6kh7vLpCvJcuDBNjPhDF3i+RmXqWkPksYNBJt2z8H9esIb82WFopNqNOtVSrK3xm/RH7T69KR6j6x85xP5TWl8fFvSZTT2mRnISOTnFZaUV8vmbRMSllJuGzllYqj5fwy2n45r4GGUpF1b8JibdV8g6SqDYfNwX4DSglycn3VxdHKdIwUKGDZPN0hgkYvuU5P2nx2d50gVBAQb0s0VchBFLjhNfHz/aXqWVEHAsLnPQJrARU/ni7Y25yam3KH++g0PtsY2Y4u+LcwiatvJo7uH+x/t5k5IybqrO+K0PzNbuvbFJ+3M67nX4+L2njlxTF9gA1dMBFOKnKJy7W4xOc0DXxxOPWv7vwXbzw3WvzIbRc7PTTvNzJN0t8E+GwmyFz1dpybsLjpNk1KCNxuFpn7Rc/bSp3djH7+4QP0VJ2CfjcSYjfT4fN5qdOzLJKmCgGHzhH7BFuYnbM6+OHH+sysJHSDY7lTXtTXxce3p3rOGR7FZ31BFPDdxs7xmzwudvHyZO52HFNJF5HYnxSsefGRQG782YUNWvlRdQ5OGaIZCn+nBA12G9/4WNnfniyeGzM2PHRk0f/n2/aUp4lfx72FbdHUafXrkyMDhpydfO0A23N/DhnRxeNV94EC3ianl2GfoV/nvYZu9+Larrf2B4aMn1rbQNgVvggAFyqL7J5vevXubtdsNR8anIHX8LWyLC1+sBvdqqNXf29t/4RK0lQXDhoSoL3Jheuxke72yYl3Tw7efoYdZ/AkChJLF+fOnHtfVVlXXN5l/doYoKR5sElnZO25kCRh8fJzHz5tU1dZlVs8VJ6V0t06+ZPee2xXSeZskHutnJNysur1H+LJPcq7mMRPdts4HgmzKX44KZUrJKfwtbE42A+VFYoXCWbli0todFmMORDn+Hrb5i1aK1wWzMtOv31Y7CTmv+nvYFj+cqIuN4eLmWV/aNgid8WewIXGyePGpzlpfxogojqxWE+KK4GQVNhImad7piAEfhR8DIzO7UvcYVBbCdTaioizOu71oKab1uUztT5vb/QIaFPylD2JZcDuvpxTjTUHhQxmjO/gFMg/+0gdESeadD9ZnUHgDQl357B20b7kKW3GmwHXBrXwPPnZlbdmQIl7V+ECZb2uhlFSptGy5hLh4kXBmuZnRtg2SqrI3r132y5bea9J+R1GjceeWvfbPJQqqVWqJYFv0cHOwP2/z9s3rU6dOnx49Nfi4aTtf+tZkno38QjJ39A68vPh1fgEJfoIkbPMTF63PnD516pRlf9fNqzw86+J4b0g+fHnm/UUi3kjD5jH3xfrN6KlTJ3v0i0KCmcMi1myT0xkd+/zVnSjrT2CbPW/9amTkZH9zRRCaKiCIkWePuvmYvQuxdQP7bAR/WrQ/9+bUyZeDWtXcKDJfKj/GQjVTG7vZecJ8P4Vt+oPN6ZGXFm0y/F4wMqwXeUqDwdlxKNx+ApvH5w9vTp2wOHi7gBGFgsHQTPJtluNOEBl/BpvTx3NAQZ6pFsfDUICQ3dB7cuELhB0AwXZrG//GJO54jdfq4dH5hVz0nFvF2rS2xnJyCqnfTuJOzy4o0zA3E8vWfraHJSQouvmopqQAS8SGzKYnL8dePT18+FALEWxuDmPGLVJ5Apt5o6JiOaMjWcOCqal8fX0oqBkY6OlZ1uTdO/DJCa9QJGGz61AXvMoZFRXJFs5MQwGINyV9SGTsNQkNIltKErbFiQ8611Oj16yJZGHyx5JhsQgv2tDolO1aj8aJ8v4ENuvdOeui1kSGhwRi0XAMGRklY8SWXG3L80Qv8092EGZbdqWtiWRjZaT3RsHQcDiCMTJFROXtRaJS/ww2C+kbcUCDMNFRwQAlaDRt9PoCOSimfgLbJU3pLdFRbGGBNFgAExjaJ5rnZuVhCLv0E9g8B2oLYtdGRjD4U+BYQ8FC47fJ600SZwTBVnYzm3cdd0LjKzWW6ALxSARj3I0DD5NDw9m2aciv59oiWFDeZHFIQljPUo4xOHxd67O723ioaNiT7luefHXm7OuRl/pEsDm/f6FRxRcfxeSPRnuRw//6IWS+FGgUmRevtNbZj3gWARK2RU8351d1UtE05OilN2dZYAhycnKWtLLzrgQtQAK2Rbdpm+GqGCY4CiRk5JcZOWU0Xs8TtiIJ2DzmXJ3NM69cxpUDsAPopYJgfSLibveMeiAJMpOAbdHDfeZzVVLo97rAlnR5U4UniD+3miXMTAo2oE1merJ4KJdbBFcUGIzcn3mjsIUL8WtGCjbkwuw7mSxmDHqJMzjAKxxBx5SS83CaeLZJEjak+3SH6AYv+LIOOK4gl4PibyjZuhFlBsGm/3wvx5odFT3ne6VFU9cnJSVcub63/grdOv6dexS1NEvyMjdJ9j6SEN6jriAlLpKzdVMMI1NYJE+yoIii0dGJqcmJbiLYRlTKUrgZaXwpECgUGg5bhQ2GwQBFgwdEbdrTZAd+Ago2wCA9Ks3jiryMxeME0AiH+wTGVbdcwM9PAjaHx9pSwnGXvWFgHTA4mbdfTJKWxThB60LD5vnaTEcmj4maDOheoH+BpoXhjAqG0o9boNF+msAgkIDt68gRNdkEWgrUEiFk5F5eWDgaQ0ZJy1FQY0uYmQRsCxPjBxt2MNFiARVwDMIXEAScDEHJEFlc95LoF5dgsyZK9nCzbq9NCPUFgIWRIWjoaP18EQhvH/qwnHu9RNSThM11QP8GGz0caA8sOU1IMK0fOZacgm7N5nvGRM0Hgu2J66FI5vL7xz+9bK/hZku/kRbLX1cd7Z8iWnRL6dhzxZupUUJdDyWECyVrdJvkxJlQqL+oo7mSN8awCGn2uiKRHmZEsD0RSqGnwvxFSlAwDP1mMbw3Dgo2zwun7kQyYNEoQllSQnNNdBSJl58EbOM1onHBviuQwXC4fP//XjSlXacJQIGGzWPgXmEsM9nSSwwMoRjMMm6AXGYofu9A4KaQgO1jb0sWj/8S6UAXkftS+niTwXHMUnNcf0OYmQRs8x9eqQhyYnCDJ8CYD20ArT8FoASN9Y3nO4AkzE0CNnfn5yXXA7HAb6PQXj7BLEwMtN5eOO7ZhdWI5sYkYXNqKePGYnDvvjdVCPua0GBfcjI0gjK8QOEtYVYQbI/G9qfEC4jsPd7fqStfXrRzA1NKfYNkQW2rXFauTJWScm7s5ltllXJychpGhu3qnBRkKCq2q9I1yir1jRpN2gf7CXw2pLN9e2I0lRf6J7ChKVk3P58CuecQsC3M7FfIoPWFf0cDTe6NBfoYMNfLsHlHJCh14VULErZLlkZZCYy+iCUVGL9gOurLvpRUVOTAEALDUKQW7R1+5wHODwXbwldbTZEkemr0kkWkiYxkYqSnY2Ci9yGHw7x8UzSNPzrj6SAB22i1WGwo4N0Av0vNFsu+NpI1Yk1k4GUvGDn9leYndviZScA23bNXiJ0BZ068qVh5eeK4OGK44tn8qcgQTOwKQ+8ImIAeRpE2/Q0psb4YNAzhy5i8edPG9Ty8G9aFBJDDGeJFX5wjsG0kYHM+9aQoNRRoRTilPw9/pgBf8oaUFPYQWi9aXsE+u2n8zCDYdA+35/OvYSrU6+juszRXLljnE9/Q1vPE8rVGXHR4bO0jqXVXohJV2jTr20wPD5mm0XmjKJk3GlmMOx2rL46IELpViQ/bou0ppVB/+HcDBAkbrtPCHn8AFYkYNuTchHQU3Y+xD0ZGQ+vjhcBiyTDLwzJA35VrXUhwR0DBdkbtVggFfNmokXlFXI0NC2VgYAql8cKZKXRA2HatXrylSCjY5qwtCllpl1wsYMgIzxJIjI+N5k6OC6TGAiMR/YaCF7Z4PUQCtidpnFRkOGARNExZBdlbNyenZgnEMl8GykGdKUGQnwRslxSy2RAYnEnyC75WXpwjmL51x60MtmAMDOGTcbeXYHYNDdviE9nrtD4YwML7hlyVVy4puJFbuOcW71oqlNdl3tY+guWcJdj6iaaYttryXLQ41wQbxCapWVtRKipWdUeINxxORs1yz4LgzQHBpqKuXF4iIqTWq1d9R6OxSDCOjqvktnqz7oMKXp716fcPVSSkioir7VVVbN5vbNByc2tCFG+OhOGh0+8H26viYrftKseHbcHigWiAL/o7ERgfqtAoHr4U9rWcXCy0fpQUmCXLhIIx93wC9TIxbG5vngkHU+EogWPpI+P5soTzsgUFs/hS1kezBvggcP75msSHSNATELB5uB0V56dFoFEYBB13It91cdnSwoKdOwsKrm+KC6LBwKjottR04vUoFGzO/XrXmWnQcCxlzPpr18VU7laWl0qWy5WJZEUzUqL8YrJMT+MtGkDBhrQ/q5sc40uG8ArbcC23QEVLVVFBrlrlbmlBcggtmW/iNjNXvJJDwjZrO1yWyYpAYGkSNuXsVNRvqVepUbynU1uczRbs45Uo8YBgGQYKtkXnL8ZSGf6U5BTRKVm7ZA33t2ppNDQ/1JEV5wmhpYip0iaoPCRsC7NvG2Vi/SkRweu33BDX6Xukr6t1v8OoofI6cxBVkMy+1wS/uQpb3hVB3f1Do9Yf7tFT0wWxccSwx8RcSd6cyJ2UniOrPHCmlrfE4rmWkqyUUqtq3o6Wg9o1uuZH27U7Dw5YGGxPzxQkiGebU98Uifk+1qERvqyxorUm9qfU77Y+kNzEExXmg0UvGbewI9OgsYcYts9qEmuWp0sUfvzynZ8mHBwmADlvblYns3nJzsDp2XSQoCcgYHOyux8eQAbk9aFLM3hyfsLRafLrkpwdvC/ASwm8Cxy7NPBAgYLNTjo9JtgPS+7Ppj7wZsLR2dlpctJx0unr57Ny2awo7OWYBuOPePkhYPPsq9yxbq0vgjqw+Php+6/O085TU05Tzs6TDh03NlCQB0fdP/8VXBlI2Gz3NeSmhlFQUl/tPmn7dWrGxRnQMu0y5XiibHsQLDiummC/CAq2+VcDDTtTaalpQ+tO2DhMurq6AEWZnnGZtNPYcQVOm7SDwH2EHEadx59Xi0X7+9NsHxy9+NXFzXXGZdplxtXZyXTnZl/y9bndSLzsINgKNu7QP2A+cmq05jIFLT1zRMy6OHbOrQJXo3n48pQ0j4wo8kidtKgtLdolfVfmWmb7YEdjh+XRJtWHPSa9bZKFYuKyYNgWXS/JxTAsj34+dAxhUamCSg9OIj8bPzQ7olYgtIU36DLZEochXe+cV4tDCJuHy9vy6yGADkRAcDR3ic7xlVdr5v3pjqZ8XhbAlUP7M6lPza5Wixg2pN2oMr0vBk7DEMVTNHgG1BUTb3tKsmkBJ59VsAavk4lh85x7V5QcHRLKzBZ/zfgsaJBBekxol3PByLzDZZvPgZ+AhK1bMvcqFxvrlat17+zBXYc8WpVNifAPVRr6AC4HJGwXWu8KX4tfw34l76Ud3iLuuEYZM4qWTfKCEx4VULC5j/SpFQhyccYnd3yc+v6LS/9xM67aiKaKyTCfwntfIWFzOndMsWQzNze3jPVnvNzD1Tcuk8Wk6uObabwQowFt9crSYtHMmISbBRz+3EI513d19MteS9uU2/ygo6MoJueB5roQDp74K+wsXC3PHtY3aCjf3PnobEOS6NOTVlaGYNhmjhtn+Hotu/Rpteq6nS/PXrB3+TY/8cVx0tZ6ZGDf9vW0S7D5bL45uFocQtg+mWmvD/MBlLAp7H168t3FlWb55unm8sW2rznXBwtDkXmLdFmumkdi2BZM9mT6UXrTFu81GRlzmgG1mIfrl1f7OX0wKEqGnA/geEBi2JxO9xekJXDcbH9s+erSDHgmgJz/9LaAHAv3jb12HPwEBGyLHmZyEgI72w4eHxmfxXcrnT600pJhvZN2GYLHKkjYPho27xZrNn164o2zO94ses7OkheFoExQ0MPzuCAt2+mBVvnmwaGXI/bueIOjp/27cjiGKvyOoQ04GRI2R2vzFu2nJ4eHz7nib35MvesKgVEF7rawAesGwXZo5u296p3bNvMmJmVUVa+jSRQTK5B59qY+OyMlV9ugvVX0SraWIjOCiYM1KIgxtuX4g1pFeekswf3n7wRte+s4P4+39DHVrckLeM3AQAn32tn95OUbPNfZ9ZPl7m3MZHDg79gwrserfyCE7Z2u/BpqLKCDp8ucaBPF82x/Ga0PMMSitzb0rfrERLAh3fWFE/0u+7Oom38k1AHYzpMbaL2AX0izcgY1DDFsDs/3i2ZsSpQfeQ+xOeFeedkbhqVjP4QEJULBNj9QK7ND5qUtVGCEabAXGh2xXhMMMiRsFx+3VcseuugEEUnyeQscgwnPq8YrOiRsrwcN6g9OzS0QBTMsetzFwjG0YqqnwKkkYHthYGwPEfG+6GHOiibzyjc9Ba4MOJ7tYFNmVrOxEH3qXZXbFVKSSjr3G+vKJUukSiSlZBSevD766LGJgVT+rfKy0j2NOoMna7cU7lUvzNcwbqpsOPTc+n0XGLbJDmVuGG4m6sW7vf38x0v4oSsLM1+G+hR2cgITSrh/mOHqHwhhOyEpSE+OgW1QeWjzmWgDE+lo2yMl6Au4c9xiuqswE8K2cNFGUzhtTXKDwavPEEvxHrbKBazAtJjL4Bhoh4UYNhebE83K7d0nJpwhNnMWDEQSMBjq8IefQFNrCNiQnu+OPj3y4pKLxzdieZm7gQrNGKsKhhkSNtfzb0+8sHWFCBtDTpQmMKAZ+crwVochJwiOn2xGP7hDBCEvLujGhmKpc2RfgFMhYZtz+mJDuEayXA7PkY2hPpis1qfgXgPBplCVzxQ7YF+ISh8wqZTSPmA2ePiANm9orqKyWrVwwcmFxW9fLY926ht0Puo+On7x3Gup4CyjfeXFt2v1n/bpPxx80Q6GzVGvinNp1cO3UOMkRGmAV+u5QTYAG8w7QH81kRC2p9e4fOFo9K2Pk9CRQB96lAOAKS3bVtWZH2mEsM2dPa6Sx3+10A5KASBTh/SuAhaYVV4XlIMYNveJD2YHLpDQ4fm8WRgLpwxpegWKmYBc+pi2/+EKEIr1XXF6GF2E0t/CRloWJ9V3RqMCk4vwCvp3UR/4gvR8JBCPoOQrfgZO/buoD0IlZ3O4qWGb63rAE2MQbHtKtwWyaPTtYubXbKqp0T/QVNFwxOxO6d29e5vv3hA94Xbp3Kk+0yOHG6Ur5O5b2749JcXEr91cISkmqn64R2dv0z0VAssWtzTbpFF5Og5VmkX3M0duAhNSOHWIwWoqIWxHtnBSwn38bjuSCFj+/FyLHgA2VmTv6rhECJv7W4u6XGERRejQGcBkDT1OBorKrtMPcnSIYZu7ZHdm9CsJHYsnOwoRcJ8QfTvQbAcStjln6DAgQN7fr2SE0bIo/gFsyCldaW5UwNVdH8CpvwbbomfvzU3kgC99FJz6q7C9LUihhW2QNwbXFrwRn59KSS0oIZJ1fbuopp5x9w6fjNFx2wtDJo8e3rspZuE83LmvxXDsgxTb2tgdJ96dfiHFuuWO0p4yvvhys/2aSoIbCwl8Np4l2IL6F6CMEnLe7fM5aQA2DENU12oyIWwDqTEUcIYodVLxo1PnjIIB2NbLdq5WihC2+XMnVbdLqndBBc7gxM36+TWgqDzP3oMGBAjYvkzMkTyZsmhzpNwLTcHUC87wi+dGkXYHGsJgfiHVfwDbt5lu9Y0omit558GJvwibx6Dcdgrv9dufgFN/EbZv5yq2M8DWSbaDB1kQbLK3roeE5VdI785Py20xbK7np9706Onrtwfu6R3UL9vdZd6/32hfl/lQ5ZYEtu0vx573VyRtl69RVpLMv9tj1FQrkCSKPxvt2oTbcSdjegLZQx4THy1MbwCwkXOmma4mQwyjfhQR6/ZCV3Jx4V13bcBff6Ez7x8hPUHw+GjVWqLc+ZRUjzmZtiQA8931w2CvkBi2eUcnUsgDPttxLdwwGtYHTvw12JCe1g1SDLCA8Jrfhw3p6agtxYWi5bn5B5Zt3u0gzrKl5j8Fp/4abJ7urws2BcASZTtIwFanIX89p9HA9FgNa7Jxf054dNy6RH6tzhtrpF+/1GvYnlRrbv7MVEG65aBkcPqL1/vU6pTvyNYY9Y3YDD7Qu6+Ws7kIf53t8w0ANjKa6L5pqDjGqcGum1dDAKfOv7T57GoyIWzHt6ewhm7KNYKupOukTkQgBhiK5R1ckD9SCWFDuk6NmLy46EiqocauBXsDE4TN4wurOiBg+9kh8MXZKobLMLKA6EPg1F+DbcHddI0/GTqUu+FvZ6OkdUzbCPh7w0IyZfCm3b8EG3L6Uq0/JZxaWM4SnPxrsLnam7FTYtAZ9w+TmCDUaSgVSer1WFg1sqzvMhMIupKeFH6lRnsrTeEZy7a6zWvkh18865GVeDgkS5d2/JS2YsNeld3VPc8/zZ42bG+uy+OTwN9BcC2Eo1FktNGd5+2dp13dCOzb10f3Uhl8AdjolB6DTD4hbBa7+NeyXrvRMTMHccYCOXFOEYHb8iKrAysngs3DbezZWacZ6CmGh7NFNKADQ873GZwMHfVBQhanPokCVUGExA2Ak38NNpfP7dQoFDwyRev3YXMbH7oKTM5Zbty5CE7+JdgWP1nthqPQ/mL1o+DkX4Pt65l9ITAYfLvhENjS4J1BkOzqOzb0YfrAZoG7msoKLb3KUWvFb6cw8t+rTrkqUvJgtE9Zdb+ZyUEx33WGvY0K9Vqy13YOWDl5Tp4/WllSunSLEQi22QpGahQacXlj7i151faez/ixdOdEk4PJMUAPhRif+8kOwvt9ygnhCaky+5+eJ176mO+UWA/HzWf9GpGgZOJ1toV3xy1GrIljv3Fy5s5NYCBGBXBKTICTfwm2Sc1idqAqfulieBPvX4IN2VOQgkChsAJKT8AN9WuwWZdmBQKwXdU1wwsR+iXYXO9tZ4cB7o/OS7wZ1a/Bdjg7kQLm5Stn+xX8ioNgE7my86X16Mgl5FMhofJqve6XHzvZI0VubWBOkcqloy3S6Bl9VFQxNHbYcAc5x/0OVfk6Tal4wWMfXJAL7iMSN28THlJ2q15LjwtLuBzAvnFbmZrNlDt4SefsxiDyf2Fv9MuzfUnhHOtE7radHJshvMFgrpYzGAZ4bFT0TeB04h2ExXPHBw+/xD2/iCQ85nR0M4c34LGFpsnhzTShYUP+ELzkzzvX0gA6AgsU8TYUScIGpWRRg5kWjUJ5FXacBPcPadiWj93gm+sX8YEIFAyWNvAKb/GLNGwQlZkSo/MBjASriT3ewvNPYINQ8oCOAg3zoVXHTwXBdnN9kcXw3ppHL3Wub03akCO6u3pnRPSt6s1MsWnJ0TF5u1V0VYtK9hoce7XHl1W8vFx617Zt10uGbIYOD587bWqkJENw/cKcekoEDBeaSBEQuoZnc0mVdter1d9+xU2Ji6qEwSOeg3dcCGGbsX2aHBLMwMmXU1rZ0Lz3fo/1x1U03WQCcQMxWQTPPnClIGAb3d8iX6v3sL3tgWGnkenFWZCnbxpFh4sySpDbhxd8BR1idNLi2aH9j4yND5i8A7kFi7aZtOQoNNla/Wd41hESNpdLtlajh00PPu7usQTZWg+3WmpvGNo7QOX1Z3BlSMDmYf/hxIDZgQMH+0HmZ8Ht6RofDNDHO6y/4C0ak4Bt1vGDxbE+k95eq9UeWHS1y6XAosiDeY/jbcmRgm1xzvnsi8MHe3qfry50It2cmy9jYZhQTl0kfuZV2MS2lJgfFUm4pSXLf9UHTsPAEBTMFCunwccYwhS5cUvm9R3i4mJiRaUW8000QVdT88UT6XhK606c1ZZpOfZ6bkKtqAIfNnfdvLiV6FpccLoXt+C+1bKeilpa8kVjo/CimImjPt6mBJKjyGlo/OlYo6Jjdh0cWn3fZsWWwnW91gubgJ8ghs3z+d7yjclpaet5UvgyMsWHv4JWGo0vLxVQ4MALvLgcKNimulvUakpzt1/fdkPUbPWyC+TC+0RcoB3F+jF8O0MixGjosV55UX7ejl2aq3gjZ50qgcbC+LN04eeHhg05e3qwueLWjRs7pc6sps459TICTU0XWUGQnwRsX98dbVCUEC0U27/68nlcssoAKnOZU/AMfmYSsHk4fTTSkMnfVXjX/kfa4uTHOnIYDBGXgRdnSHBIufXta/Vyza7GQiGeWLawID/mjYJ3m4vSEjliYq8kpuTckqm9e7fhhYMWG3uagIRMkYi0tn53d5203snRV+Y1xbL4sHn0qwgsHYNAwbG4CD0MS3xpr/nFSdz74j55NGJpG546Lgdv5CGG7X0upx+KzMeHkiqQgTEkpbK+b2h57dXz6/m8pYg4X1GNYfATEJZtpEtu3ZUrnGsioq9wcW9WM+g3P7/cDFO2eylxMSU+u17Y/G2krkNrtcSOTF6ehISkTVWP+46P4sYq5KLLuadcuNiW4Iz3+G0LCdsbEz2FMoHNyYkbUnZ1mh4dwk1LkAtz504WAsOAFxtvL35+SNhcbIbbG8QztyQlJadr9Dx5ZrWAu4Nh1taymQ6YVkek1hHsIEHB5ml/7ui++p051zalbpbp7jv6ArcMuTA78eJgEgBsUKYUQX5I2FzsrPq7ZG9mJm9MzdM3GTiGC1hBzjqPmkkiAG89Q/IJfnYQbD0XHT3mHb+8PzvQ3KTVIHglmIyjtqlNv1V7Tyavr1dQeEaZ+pHj5kPDp5ozBXcW3WkYsLW1PSEnViJt/OnFnfJSqT34sCEn3t+lWTpe4k3jg/sPBuvHxNdzAjcdcBjWD12CLebeY7yRhxi2i7W7wlEr50vgCEq6eH7zpT+4WhxIww3EGIbuGbzFFYgQo48j9REMvt5YDBkZGdaLyp9rq/YSWsjThpLeS5G6tbP4i2jQ8Wx8MQx+CCwWi0BQ0kSk3RoH3BWAE10FFqB69EkSBBtikCFGPaXX2UIoyRGADgq/oKsZpjgdrhNdyhuBNqLi/5cidc81ViStCSD3QiAQXpf916bUTH/z9PhqZyaX6wtY2E1V+6EidQnOIMyaNe/iYvX19vLyAioTsu76COB2zdgOKxaGAx5BTF2HPX5+SNhsHqlt4aajIAd0ePsFcCTpAs6FxyfrpvwEOMaPucqIICYOBJvZ1OTnTx9txy8c02yoq7lxLQLOrnjvgWFHh2ohHyUZW8K2EiUjk+EzwxZtEgVCeRVKTx1szwyU3xQv0T1pIiNZVk7gsyGdLqr542CDMcZzRkaEMlKgyCi4NDqHbWxsLIyUgpZgi67bd/bi19UdHAjYlG4CXQnzCY9kY2Nh9od5hcY9sMHJ6YfquD0KGDa484sTOFwHwrK9P36Hyd/Li5GNjS2ChRGBDomtfIPTMfb4bo4XABt9hNIkKCLuGxRsHlNnbl5lvkwfzsrKGsZMhaKOEThiM2Zt9fqQvCgDAFvw5pILHng6iGFDzk625awPpAtjDQ8PC6WDY1m4msbGxqxGh1TFuXAx3tt24y1uQcI2P3NCWjgqiImFhSUsNJgcFRAlOjL21spysDk/lQKF8dpaa/K3sC26TejJbg1lXtIRQo0iZ+Yxth4bG31qVMgfjCLzjru3/xK+DgjYPOZeqEnFsoaFMTOHhgSSwekjqqzGbN4cM5VJi4JjaSNqDhJu/q/CZmL/UqGipt7SbbA4e21UuQ4/miFLzOCJaU9Pjwqdr5RuZU56nKClvWXvkRc6a4KTr98fMbolLi29K1coZ2f5nto6ghPxSIcPyr5YGAqNFT82NGxu3JKAOwPLsuZKfHw855pQ7BJsfgmbxcp1TF+vODvEsF0o2hgI6Eg8dHL4xJNH4gi0F2VkPE64Ill8AQ1wSvr8mq6T51ebASKerV8xywcLp1EfHh4279NmBYZNxjicDu61zAG4ECVG9lt9w3/js9ntb2Cn8sJkHx4cfNr7KBOF8fGPiefm5o5jDwnEAqaXKUNq5CLevicxbHMnTERoqbBsugMDAyZGspfR5JTh3Nzx3FyxzEGUKDg5vVA5wUE8CNhsnzVGBnj7lJj2mZkYNseisBT0uDa9whFOR41G+9JuUzMjWEYnHkZnXh/ZFkbrldxp0tPdoZeDQXv5RnEDbcoRRU+DQFEGpzQfmMDXQQzb4sW3dWuZfIJrHhkZPmxXCIYjvJlwleGIZqSmgJGHcCj/BLYDF57kZeZLPPU8tusaDY3cwTwM/eYbnYNmBwcsWxmolfrvZMRTx1k6mRs9u2BE73Vli9rz5q0ZpbI7t6Vyb628U1evTARbrQ8GBUP4Kju6IufGzLfCluLbfhzCwwk5I2vS1krt4yuhd8Swnd8R74/C+gp+wQ3MNioUMBS+Brgv3aa8erOTLj8Cbohh8+gqTiZDIxhxm0lun49z4h0dBVShGWPyW80c59zdfzQmMWw2mmUMgPsp6TDt7GRnI4bGVwJDM/FJDL51dJ9fnTkQw+Z6SCcVBcPEDn11dPxopROAXi0Dzs+gCMouPT4HqFgNMoOA7a1xBRUKTVH/6aL9x7ODG9BLhwlXziWiL9Nvq+uZdvfwAF1XRAyb07NOHiwZnP+17YUPZ06UYfErczks9Z6RnTtQjNW1JmLYPG3MpbzJ0MEGY1avT580iCBD/6gM4EmTh8Yqdp1xn18ABTGBYNPQr8vm4xPo+HSyc2/xzd17rkdnNLb1P9Us0xzQCqUWkNwUEsGdc/qrTmGlRgl9oKhKx1NtESGRHWISsjVNJl27thbjw/ZtbloTsGzkqbtM3Re+eU7ZFgb5EsEGp6CiZ4xdX231yYMEbO+FgQkCh5gWztVzc9ZnpcXga4BhyYNZeQVuVNxb2QGAgE0vmwtOm1VyGtdEs2fTGbzxYYP5+EWmbNtT06T7Y+eMGLYRCX4aDKeAzoz7vLvLV/mgy/innWGUTBwiEir3DUyGV36aGLbJepE1KLr1BVYzs3MukybRtGRg2NAIyjD2fMUmo4PHT64syhLDhuzbtckLEXn1ocPU7IyjTW6QL0AbbBk4YBzxpmLhzlbR6j504uyPVRFi2N5X5obA6dlLzk84OjpMNDLR4J3/9qYNS+Sraek+NjJ+acWVJYZtrq1gHYYi9KqJ7WeHiUvPNuA2/ZaYXzrhhKANTtiyu8Xo+Yi980qDgGCTKxcX2JqUpPXm7GtLg70S1/m3lB61PD5QuaXCpJGFmp2HARXMX2o1UZ2QJrSZPuxuT98THfEd/BnFctpmx8eeXwvZrkh4SFnnMhZGJdW2PIl2rVxLt8oISIAiChx9PUcCtnM5MdQovn2DywNUTzwLluBpnFVB+NCEblk5PEoM2/zeLWvRzEoGyxGF43nR1EQ6MOQ0azjThX/sbRLDZp4ZT4nNUjqEWxZcnFOLYsA/NQ20s1dQWLKguILRylyQGLaJW1x0qAjRu+O4oMXF47xh5HiwYTAwNGNsulRV66OV7SYI2PSuMGN8Nub14jzdxa8SUQErtg13wh6N9cIAOPPfVu08ZIMkCdvpNFYfWMTWGrup2TnPbw85QslW6wFMzikpyHw3ZN++/3jk3ZL7BxgnYthmpFloUFRcmUc+TwM28E3WWiqgJ1beQDQZJRU5IiZbSvextf1Kg4BvMRK4qdZYvefew/0DA/2PxZJ5ubPutgydbpao62nNubYlLdIn5pac7sOaErEdgvHJ9wcGDjemZZRXV1c3GRnqawrw7CKAbWHuPjBB8BFWxLU3ct6xJJQaGjYULN10eJYEbO9F4mhQKY09rksaO8L9MYRPA+2DQVDQ8Fh+34qAsGw6/BzwENn7uE3YBdfX1+i9iXSgsRSBjOwJRivjBgRsWesosXy3TT2Q3xanv9wOpCKwbDAYGTVdVFyKQN3K8hkxbJeWYVOxW1j85jbZE0UDtmwwNAYOQ/szc24RuFlhRRK2xbbYUIxP2s0nwKRoYeqcMAMV/HsX42hDk2HhKKo1cVl5t2TMVjxhYthGN4Z6w6KzGxxm5z1mnJpC/TGrJ3PhGIS3F5w8kltAdHdN+0r0MgRspUzUKP9NOy2n5jznpiySgihWrgcAdCC8KCiwGMa4LRIyatorcxMQbNuYc99+Pm3ekC/bajw6fHNNeEhkYu7LicPa+7qNWzVvCSUEpzWpZW6q670ntuuG+H7LZ0cUGLk7jrbUqDUrZgsIiZQRwOY6qRVEhSZPFsG5Sp7TdjupyUnAhtrc+cyVBGwfChIDUPEy7bgenJ1s9oGhiJ5eqh6arW98hhRs7Tlx8KAiZVyl3S7ifDZiHUBPwRG+WiseFzFsQ9d5KMlSpbqBcWXB7k0BvpeDe6cBq4KgpKL2v7HSBkSwIb9I4GArrMON+I5jrbQ/lCxdD4DGXcfg5UMdQBcY+4wUbEjP1pgQuC+/xDPgvXD78CIVQYZZGQNhAChwXPiDrx9jaAijysoQSAzbCG+QFyyuQGcGeHO+vJOnIIPjdCw3JpYcgQV8bXLKoPCwEIELJGFzkaT3RQUJVbwFnEOXC71sGLLvl7EstSQlAosG5i4BUWtZ2Q8QwyYQlPFs1LSz+kalXoeJcW5MHG9y+o3Oo5332x89VL1TvEMgRbRdK3ODnP49BfmqO9r72nVlE/lb96tIy8hKCPAL3SCAbfHDSzlaCjSWI1XVfMji+eED13wQQKOShUaF+AexsHFyMgf6LHU6Cg1P6xicgYZt0UYolhoVmVN5eGjI/KlZqdfSE36szPQMkexcCVGBft9bGsZ68N00CdjmmzdHoWn4RTuGhoaeHmwKX3qAPDSciTGSKz4xPjTQe/nuD7h3o6s7KdgGN631xnBtUz4+NHT8wL705cGCLpyZMZw7gZc3OpSWDGhtcm8Kyhz7BSQ0bIsXb7JRo0Iyi00shobMOmSogGKjUJTMzEzMV3gTk+LZQn0R5N4+VFSXI498N7FEsC16NLPQwSkSBfcClTlqfJ8LgwHwwjIwh4ZGJ67fsJ4jMtCbwsfXP4CGRtEDSQI25AlufywsSqDyqIWFuYnhDcTSnSU0YWHMEevWb9zEw85C5ePjQxMYQJP2njRsRTQUsIAt+caWFkMDj1QYAS8AjfIODQ9njduQci2FK5qOkpKShokpkNkISQRbum/MnmrhjNxddx7r7uDftEFMvU6udEviLbW9+xoy0/IlG7oOHu7M3ZS9s/7psyGzfJ6klALt5tul29OuRq/L3CZE+IUX9wcFV4EJytLeaBhreCijz9JpK5rK9t18N2ubzJ7ICbEv3aCA8vJNbz/sAgmb59ypdZQYYDbqH8rMHMoU4rf0BCy1vuqG6F6jY6fbhVN8vr/WYR2jjiRgc68MoETBKS8zMAP9yhiIXepj5tt15be0B0+cPyGbG062jCy25pMTkgRsPQwINFDUAOYw5lBGhuVf9cpRkStRtThz7v0+6XQ/CiwZ4DTB+ca+R1kSwoZcGE8DXn6EH10YKwtzCAMNMGrCYSjO2/Llckfen7/wXHV3TCCVFxYOxwT3znpAwzbvWkuOQaEp/ULYWMNCg+m9loxyQIH87cqO9xfGrTvUtoUz+JADpgojO+cJDduix/Fw4Icpg5jY2ADrxUC19KphtigqyGuMjo+PD7bsiV8TSO0NcMyzsgVHBBvS+QZAqBc9U3TsGpbQ4ACypSGUbY+ysuqRcVvbs4+0BWPD6f29vbDU7d/HCxBs1yjCbxYJbs0TV2hvSOfdsHH3g5a7FfHhOVXKTXf40sRvPwAMVE/+lmyReyMjFr3Z7HFXC7seVpVsv8azJkEkP4fgoxvIWU2+tSuXysAAS790CQPCN7zpiLq4wr4eK+uWii101AgyGIqCJqvzGLRlW3Ad5kQsHZ1f9gaWlFDS3OhqlasxffFh6mnlDtZASgQc8BJYjV5NkoJN5vKKJ75sBTFYSloe7U5N1f7zE4uXmis2hAT4eMFQGO9au0lSsHXTon940EtuCZbcP7S8o0XN6KLzgueze0Vrw2gvY1FwBL/1JAnYPC5swk0YcReVLN/shvbypqHPaGnV1DnnufjNtrNxKycznS8cjmAydQXDBooRmHdVxM3HASuK/e5nAY1Kz6HYqr3XHOhSt+fGu5M4gwOwQFtXupGCbf4ZM/AgwtfHCwGYxb9wtxhRBDCK6uu1duN2rd733xdMiWa5jMZgea0XkKRgE8btCFNRU1N7oZcmKFivy/Qb7xsYdOKOaju/7CtJT2QPJcci/Nq/n0wFwbbFyy86Wd1Yu64onnv9hkRemW5DrZpN7FFssYmZihoHnr5x+OpoJXujRmfA9llx3s6C3VV1rQ/2P9ZWU5RRefzw+rqbiniRuhNKiSwrV7KhlpfYfEPXSSq9/GTzyvqjvYvLhTeDhorc4eQwOjaZC189IWFzd3ge70u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8))\n", "    plt.imshow(grid)\n", "    plt.xlabel(\"Generation iteration\")\n", "    plt.xticks(\n", "        [(imgs_per_step.shape[-1] + 2) * (0.5 + j) for j in range(callback.vis_steps + 1)],\n", "        labels=[1] + list(range(step_size, imgs_per_step.shape[0] + 1, step_size)),\n", "    )\n", "    plt.yticks([])\n", "    plt.show()"]}, {"cell_type": "markdown", "id": "c7d4dfaf", "metadata": {"papermill": {"duration": 0.026625, "end_time": "2024-09-01T12:08:45.790439", "exception": false, "start_time": "2024-09-01T12:08:45.763814", "status": "completed"}, "tags": []}, "source": ["We see that although starting from noise in the very first step, the sampling algorithm obtains reasonable shapes after only 32 steps.\n", "Over the next 200 steps, the shapes become clearer and changed towards realistic digits.\n", "The specific samples can differ when you run the code on Colab, hence the following description is specific to the plots shown on the website.\n", "The first row shows an 8, where we remove unnecessary white parts over iterations.\n", "The transformation across iterations can be seen at best for the second sample, which creates a digit of 2.\n", "While the first sample after 32 iterations looks a bit like a digit, but not really,\n", "the sample is transformed more and more to a typical image of the digit 2."]}, {"cell_type": "markdown", "id": "1bb74792", "metadata": {"papermill": {"duration": 0.015629, "end_time": "2024-09-01T12:08:45.826530", "exception": false, "start_time": "2024-09-01T12:08:45.810901", "status": "completed"}, "tags": []}, "source": ["### Out-of-distribution detection\n", "\n", "A very common and strong application of energy-based models is out-of-distribution detection\n", "(sometimes referred to as \"anomaly\" detection).\n", "As more and more deep learning models are applied in production and applications,\n", "a crucial aspect of these models is to know what the models don't know.\n", "Deep learning models are usually overconfident, meaning that they classify even random images sometimes with 100% probability.\n", "Clearly, this is not something that we want to see in applications.\n", "Energy-based models can help with this problem because they are trained to detect images that do not fit the training dataset distribution.\n", "Thus, in those applications, you could train an energy-based model along with the classifier,\n", "and only output predictions if the energy-based models assign a (unnormalized) probability higher than $\\delta$ to the image.\n", "You can actually combine classifiers and energy-based objectives in a single model,\n", "as proposed in this [paper](https://arxiv.org/abs/1912.03263).\n", "\n", "In this part of the analysis, we want to test the out-of-distribution capability of our energy-based model.\n", "Remember that a lower output of the model denotes a low probability.\n", "Thus, we hope to see low scores if we enter random noise to the model:"]}, {"cell_type": "code", "execution_count": 16, "id": "765a3541", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:45.859983Z", "iopub.status.busy": "2024-09-01T12:08:45.859759Z", "iopub.status.idle": "2024-09-01T12:08:45.937804Z", "shell.execute_reply": "2024-09-01T12:08:45.936560Z"}, "papermill": {"duration": 0.096809, "end_time": "2024-09-01T12:08:45.939524", "exception": false, "start_time": "2024-09-01T12:08:45.842715", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Average score for random images: -17.878559112548828\n"]}], "source": ["with torch.no_grad():\n", "    rand_imgs = torch.rand((128,) + model.hparams.img_shape).to(model.device)\n", "    rand_imgs = rand_imgs * 2 - 1.0\n", "    rand_out = model.cnn(rand_imgs).mean()\n", "    print(f\"Average score for random images: {rand_out.item()}\")"]}, {"cell_type": "markdown", "id": "c73ac216", "metadata": {"papermill": {"duration": 0.021668, "end_time": "2024-09-01T12:08:45.982804", "exception": false, "start_time": "2024-09-01T12:08:45.961136", "status": "completed"}, "tags": []}, "source": ["As we hoped, the model assigns very low probability to those noisy images.\n", "As another reference, let's look at predictions for a batch of images from the training set:"]}, {"cell_type": "code", "execution_count": 17, "id": "17108f06", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:46.019372Z", "iopub.status.busy": "2024-09-01T12:08:46.019085Z", "iopub.status.idle": "2024-09-01T12:08:46.325572Z", "shell.execute_reply": "2024-09-01T12:08:46.324386Z"}, "papermill": {"duration": 0.324731, "end_time": "2024-09-01T12:08:46.326934", "exception": false, "start_time": "2024-09-01T12:08:46.002203", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Average score for training images: -0.01\n"]}], "source": ["with torch.no_grad():\n", "    train_imgs, _ = next(iter(train_loader))\n", "    train_imgs = train_imgs.to(model.device)\n", "    train_out = model.cnn(train_imgs).mean()\n", "    print(f\"Average score for training images: {train_out.item():4.2f}\")"]}, {"cell_type": "markdown", "id": "452bd14c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.023309, "end_time": "2024-09-01T12:08:46.366824", "exception": false, "start_time": "2024-09-01T12:08:46.343515", "status": "completed"}, "tags": []}, "source": ["The scores are close to 0 because of the regularization objective that was added to the training.\n", "So clearly, the model can distinguish between noise and real digits.\n", "However, what happens if we change the training images a little, and see which ones gets a very low score?"]}, {"cell_type": "code", "execution_count": 18, "id": "9cd49673", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:46.408127Z", "iopub.status.busy": "2024-09-01T12:08:46.407903Z", "iopub.status.idle": "2024-09-01T12:08:46.415155Z", "shell.execute_reply": "2024-09-01T12:08:46.414267Z"}, "papermill": {"duration": 0.025714, "end_time": "2024-09-01T12:08:46.416445", "exception": false, "start_time": "2024-09-01T12:08:46.390731", "status": "completed"}, "tags": []}, "outputs": [], "source": ["@torch.no_grad()\n", "def compare_images(img1, img2):\n", "    imgs = torch.stack([img1, img2], dim=0).to(model.device)\n", "    score1, score2 = model.cnn(imgs).cpu().chunk(2, dim=0)\n", "    grid = torchvision.utils.make_grid(\n", "        [img1.cpu(), img2.cpu()], nrow=2, normalize=True, value_range=(-1, 1), pad_value=0.5, padding=2\n", "    )\n", "    grid = grid.permute(1, 2, 0)\n", "    plt.figure(figsize=(4, 4))\n", "    plt.imshow(grid)\n", "    plt.xticks([(img1.shape[2] + 2) * (0.5 + j) for j in range(2)], labels=[\"Original image\", \"Transformed image\"])\n", "    plt.yticks([])\n", "    plt.show()\n", "    print(f\"Score original image: {score1}\")\n", "    print(f\"Score transformed image: {score2}\")"]}, {"cell_type": "markdown", "id": "e23645e6", "metadata": {"papermill": {"duration": 0.015714, "end_time": "2024-09-01T12:08:46.447949", "exception": false, "start_time": "2024-09-01T12:08:46.432235", "status": "completed"}, "tags": []}, "source": ["We use a random test image for this. Feel free to change it to experiment with the model yourself."]}, {"cell_type": "code", "execution_count": 19, "id": "18169100", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:46.481158Z", "iopub.status.busy": "2024-09-01T12:08:46.480758Z", "iopub.status.idle": "2024-09-01T12:08:46.795595Z", "shell.execute_reply": "2024-09-01T12:08:46.793951Z"}, "papermill": {"duration": 0.333752, "end_time": "2024-09-01T12:08:46.797530", "exception": false, "start_time": "2024-09-01T12:08:46.463778", "status": "completed"}, "tags": []}, "outputs": [], "source": ["test_imgs, _ = next(iter(test_loader))\n", "exmp_img = test_imgs[0].to(model.device)"]}, {"cell_type": "markdown", "id": "6c4482ed", "metadata": {"papermill": {"duration": 0.016076, "end_time": "2024-09-01T12:08:46.829711", "exception": false, "start_time": "2024-09-01T12:08:46.813635", "status": "completed"}, "tags": []}, "source": ["The first transformation is to add some random noise to the image:"]}, {"cell_type": "code", "execution_count": 20, "id": "cb05d970", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:46.863900Z", "iopub.status.busy": "2024-09-01T12:08:46.863582Z", "iopub.status.idle": "2024-09-01T12:08:46.982338Z", "shell.execute_reply": "2024-09-01T12:08:46.981177Z"}, "papermill": {"duration": 0.138234, "end_time": "2024-09-01T12:08:46.984043", "exception": false, "start_time": "2024-09-01T12:08:46.845809", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"text/plain": ["<Figure size 400x400 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Score original image: tensor([0.0304])\n", "Score transformed image: tensor([-0.0746])\n"]}], "source": ["img_noisy = exmp_img + torch.randn_like(exmp_img) * 0.3\n", "img_noisy.clamp_(min=-1.0, max=1.0)\n", "compare_images(exmp_img, img_noisy)"]}, {"cell_type": "markdown", "id": "a6a5e1ee", "metadata": {"papermill": {"duration": 0.023296, "end_time": "2024-09-01T12:08:47.030623", "exception": false, "start_time": "2024-09-01T12:08:47.007327", "status": "completed"}, "tags": []}, "source": ["We can see that the score considerably drops.\n", "Hence, the model can detect random Gaussian noise on the image.\n", "This is also to expect as initially, the \"fake\" samples are pure noise images.\n", "\n", "Next, we flip an image and check how this influences the score:"]}, {"cell_type": "code", "execution_count": 21, "id": "6d581f50", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:47.069757Z", "iopub.status.busy": "2024-09-01T12:08:47.069018Z", "iopub.status.idle": "2024-09-01T12:08:47.171004Z", "shell.execute_reply": "2024-09-01T12:08:47.169796Z"}, "papermill": {"duration": 0.121626, "end_time": "2024-09-01T12:08:47.172757", "exception": false, "start_time": "2024-09-01T12:08:47.051131", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"text/plain": ["<Figure size 400x400 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Score original image: tensor([0.0304])\n", "Score transformed image: tensor([-0.0004])\n"]}], "source": ["img_flipped = exmp_img.flip(dims=(1, 2))\n", "compare_images(exmp_img, img_flipped)"]}, {"cell_type": "markdown", "id": "f732b962", "metadata": {"papermill": {"duration": 0.023882, "end_time": "2024-09-01T12:08:47.221677", "exception": false, "start_time": "2024-09-01T12:08:47.197795", "status": "completed"}, "tags": []}, "source": ["If the digit can only be read in this way, for example, the 7, then we can see that the score drops.\n", "However, the score only drops slightly.\n", "This is likely because of the small size of our model.\n", "Keep in mind that generative modeling is a much harder task than classification,\n", "as we do not only need to distinguish between classes but learn **all** details/characteristics of the digits.\n", "With a deeper model, this could eventually be captured better (but at the cost of greater training instability).\n", "\n", "Finally, we check what happens if we reduce the digit significantly in size:"]}, {"cell_type": "code", "execution_count": 22, "id": "6280963c", "metadata": {"execution": {"iopub.execute_input": "2024-09-01T12:08:47.259988Z", "iopub.status.busy": "2024-09-01T12:08:47.259757Z", "iopub.status.idle": "2024-09-01T12:08:47.341829Z", "shell.execute_reply": "2024-09-01T12:08:47.340702Z"}, "papermill": {"duration": 0.102783, "end_time": "2024-09-01T12:08:47.343591", "exception": false, "start_time": "2024-09-01T12:08:47.240808", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"text/plain": ["<Figure size 400x400 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Score original image: tensor([0.0304])\n", "Score transformed image: tensor([-0.0154])\n"]}], "source": ["img_tiny = torch.zeros_like(exmp_img) - 1\n", "img_tiny[:, exmp_img.shape[1] // 2 :, exmp_img.shape[2] // 2 :] = exmp_img[:, ::2, ::2]\n", "compare_images(exmp_img, img_tiny)"]}, {"cell_type": "markdown", "id": "e2ba15df", "metadata": {"papermill": {"duration": 0.024943, "end_time": "2024-09-01T12:08:47.393571", "exception": false, "start_time": "2024-09-01T12:08:47.368628", "status": "completed"}, "tags": []}, "source": ["The score again drops but not by a large margin, although digits in the MNIST dataset usually are much larger.\n", "\n", "Overall, we can conclude that our model is good for detecting Gaussian noise and smaller transformations to existing digits.\n", "Nonetheless, to obtain a very good out-of-distribution model, we would need to train deeper models and for more iterations."]}, {"cell_type": "markdown", "id": "438518ac", "metadata": {"papermill": {"duration": 0.018065, "end_time": "2024-09-01T12:08:47.432596", "exception": false, "start_time": "2024-09-01T12:08:47.414531", "status": "completed"}, "tags": []}, "source": ["### Instability\n", "\n", "Finally, we should discuss the possible instabilities of energy-based models,\n", "in particular for the example of image generation that we have implemented in this notebook.\n", "In the process of hyperparameter search for this notebook, there have been several models that diverged.\n", "Divergence in energy-based models means that the models assign a high probability to examples of the training set which is a good thing.\n", "However, at the same time, the sampling algorithm fails and only generates noise images that obtain minimal probability scores.\n", "This happens because the model has created many local maxima in which the generated noise images fall.\n", "The energy surface over which we calculate the gradients to reach data points with high probability has \"diverged\" and is not useful for our MCMC sampling.\n", "\n", "Besides finding the optimal hyperparameters, a common trick in energy-based models is to reload stable checkpoints.\n", "If we detect that the model is diverging, we stop the training, load the model from one epoch ago where it did not diverge yet.\n", "Afterward, we continue training and hope that with a different seed the model is not diverging again.\n", "Nevertheless, this should be considered as the \"last hope\" for stabilizing the models,\n", "and careful hyperparameter tuning is the better way to do so.\n", "Sensitive hyperparameters include `step_size`, `steps` and the noise standard deviation in the sampler,\n", "and the learning rate and feature dimensionality in the CNN model."]}, {"cell_type": "markdown", "id": "aa2ee982", "metadata": {"papermill": {"duration": 0.017987, "end_time": "2024-09-01T12:08:47.468963", "exception": false, "start_time": "2024-09-01T12:08:47.450976", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we have discussed energy-based models for generative modeling.\n", "The concept relies on the idea that any strictly positive function can be turned into a probability\n", "distribution by normalizing over the whole dataset.\n", "As this is not reasonable to calculate for high dimensional data like images,\n", "we train the model using contrastive divergence and sampling via MCMC.\n", "While the idea allows us to turn any neural network into an energy-based model,\n", "we have seen that there are multiple training tricks needed to stabilize the training.\n", "Furthermore, the training time of these models is relatively long as, during every training iteration,\n", "we need to sample new \"fake\" images, even with a sampling buffer.\n", "In the next lectures and assignment, we will see different generative models (e.g. VAE, GAN, NF)\n", "that allow us to do generative modeling more stably, but with the cost of more parameters."]}, {"cell_type": "markdown", "id": "2f7eae94", "metadata": {"papermill": {"duration": 0.018062, "end_time": "2024-09-01T12:08:47.505409", "exception": false, "start_time": "2024-09-01T12:08:47.487347", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Discord](https://discord.com/invite/tfXFetEZxv)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[![Pytorch Lightning](data:image/png;base64,NDA0OiBOb3QgRm91bmQ=){height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", "   :header: Tutorial 7: Deep Energy-Based Generative Models\n", "   :card_description: In this tutorial, we will look at energy-based deep learning models, and focus on their application as generative models. Energy models have been a popular tool before the...\n", "   :tags: Image,GPU/TPU,UvA-DL-Course\n", "   :image: _static/images/course_UvA-DL/07-deep-energy-based-generative-models.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "id,colab,colab_type,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12"}, "papermill": {"default_parameters": {}, "duration": 17.219358, "end_time": "2024-09-01T12:08:50.343815", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/07-deep-energy-based-generative-models/notebook.ipynb", "output_path": ".notebooks/course_UvA-DL/07-deep-energy-based-generative-models.ipynb", "parameters": {}, "start_time": "2024-09-01T12:08:33.124457", "version": "2.6.0"}}, "nbformat": 4, "nbformat_minor": 5}