{"cells": [{"cell_type": "markdown", "id": "a7cc84f0", "metadata": {"papermill": {"duration": 0.014151, "end_time": "2023-03-15T10:00:21.342777", "exception": false, "start_time": "2023-03-15T10:00:21.328626", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 3: Initialization and Optimization\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-03-15T09:58:45.256359\n", "\n", "In this tutorial, we will review techniques for optimization and initialization of neural networks.\n", "When increasing the depth of neural networks, there are various challenges we face.\n", "Most importantly, we need to have a stable gradient flow through the network, as otherwise, we might encounter vanishing or exploding gradients.\n", "This is why we will take a closer look at the following concepts: initialization and optimization.\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,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){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/03-initialization-and-optimization.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "43d1a53d", "metadata": {"papermill": {"duration": 0.010793, "end_time": "2023-03-15T10:00:21.365097", "exception": false, "start_time": "2023-03-15T10:00:21.354304", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "f28c5752", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-03-15T10:00:21.475647Z", "iopub.status.busy": "2023-03-15T10:00:21.474116Z", "iopub.status.idle": "2023-03-15T10:00:24.925119Z", "shell.execute_reply": "2023-03-15T10:00:24.923506Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.551049, "end_time": "2023-03-15T10:00:24.928991", "exception": false, "start_time": "2023-03-15T10:00:21.377942", "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. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"torchmetrics>=0.7, <0.12\" \"matplotlib\" \"ipython[notebook]>=8.0.0, <8.12.0\" \"setuptools==67.4.0\" \"seaborn\" \"lightning>=2.0.0rc0\" \"torchvision\" \"torch>=1.8.1, <1.14.0\" \"pytorch-lightning>=1.4, <2.0.0\""]}, {"cell_type": "markdown", "id": "c02ac9b3", "metadata": {"papermill": {"duration": 0.009728, "end_time": "2023-03-15T10:00:24.952282", "exception": false, "start_time": "2023-03-15T10:00:24.942554", "status": "completed"}, "tags": []}, "source": ["<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/X5m7bC4xCLY\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "In the first half of the notebook, we will review different initialization techniques, and go step by step from the simplest initialization to methods that are nowadays used in very deep networks.\n", "In the second half, we focus on optimization comparing the optimizers SGD, SGD with Momentum, and Adam.\n", "\n", "Let's start with importing our standard libraries:"]}, {"cell_type": "code", "execution_count": 2, "id": "ca698ae6", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:24.972637Z", "iopub.status.busy": "2023-03-15T10:00:24.972250Z", "iopub.status.idle": "2023-03-15T10:00:28.205361Z", "shell.execute_reply": "2023-03-15T10:00:28.204464Z"}, "papermill": {"duration": 3.246178, "end_time": "2023-03-15T10:00:28.207625", "exception": false, "start_time": "2023-03-15T10:00:24.961447", "status": "completed"}, "tags": []}, "outputs": [], "source": ["import copy\n", "import json\n", "import math\n", "import os\n", "import urllib.request\n", "from urllib.error import HTTPError\n", "\n", "import lightning as L\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "import matplotlib_inline.backend_inline\n", "import numpy as np\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.utils.data as data\n", "from matplotlib import cm\n", "from torchvision import transforms\n", "from torchvision.datasets import FashionMNIST\n", "from tqdm.notebook import tqdm\n", "\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\")  # For export\n", "sns.set()"]}, {"cell_type": "markdown", "id": "f5ac14cd", "metadata": {"papermill": {"duration": 0.008976, "end_time": "2023-03-15T10:00:28.226672", "exception": false, "start_time": "2023-03-15T10:00:28.217696", "status": "completed"}, "tags": []}, "source": ["Instead of the `set_seed` function as in Tutorial 3, we can use Lightning's build-in function `L.seed_everything`.\n", "We will reuse the path variables `DATASET_PATH` and `CHECKPOINT_PATH` as in Tutorial 3.\n", "Adjust the paths if necessary."]}, {"cell_type": "code", "execution_count": 3, "id": "3ea56127", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:28.246970Z", "iopub.status.busy": "2023-03-15T10:00:28.246074Z", "iopub.status.idle": "2023-03-15T10:00:28.362801Z", "shell.execute_reply": "2023-03-15T10:00:28.361781Z"}, "papermill": {"duration": 0.128411, "end_time": "2023-03-15T10:00:28.364062", "exception": false, "start_time": "2023-03-15T10:00:28.235651", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Using device cuda:0\n"]}], "source": ["# Path to the folder where the datasets are/should be downloaded (e.g. MNIST)\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/InitOptim/\")\n", "\n", "# Seed everything\n", "L.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", "# Fetching the device that will be used throughout this notebook\n", "device = torch.device(\"cpu\") if not torch.cuda.is_available() else torch.device(\"cuda:0\")\n", "print(\"Using device\", device)"]}, {"cell_type": "markdown", "id": "4bfee502", "metadata": {"papermill": {"duration": 0.009041, "end_time": "2023-03-15T10:00:28.382235", "exception": false, "start_time": "2023-03-15T10:00:28.373194", "status": "completed"}, "tags": []}, "source": ["In the last part of the notebook, we will train models using three different optimizers.\n", "The pretrained models for those are downloaded below."]}, {"cell_type": "code", "execution_count": 4, "id": "b55553d0", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:28.402194Z", "iopub.status.busy": "2023-03-15T10:00:28.401494Z", "iopub.status.idle": "2023-03-15T10:00:30.273880Z", "shell.execute_reply": "2023-03-15T10:00:30.272790Z"}, "papermill": {"duration": 1.884664, "end_time": "2023-03-15T10:00:30.275903", "exception": false, "start_time": "2023-03-15T10:00:28.391239", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGD.config...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGD_results.json...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGD.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGDMom.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGDMom_results.json...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGDMom.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_Adam.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_Adam_results.json...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_Adam.tar...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/\"\n", "# Files to download\n", "pretrained_files = [\n", "    \"FashionMNIST_SGD.config\",\n", "    \"FashionMNIST_SGD_results.json\",\n", "    \"FashionMNIST_SGD.tar\",\n", "    \"FashionMNIST_SGDMom.config\",\n", "    \"FashionMNIST_SGDMom_results.json\",\n", "    \"FashionMNIST_SGDMom.tar\",\n", "    \"FashionMNIST_Adam.config\",\n", "    \"FashionMNIST_Adam_results.json\",\n", "    \"FashionMNIST_Adam.tar\",\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 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 file from the GDrive folder, or contact the author with the full output including the following error:\\n\",\n", "                e,\n", "            )"]}, {"cell_type": "markdown", "id": "09786622", "metadata": {"papermill": {"duration": 0.009451, "end_time": "2023-03-15T10:00:30.297827", "exception": false, "start_time": "2023-03-15T10:00:30.288376", "status": "completed"}, "tags": []}, "source": ["## Preparation"]}, {"cell_type": "markdown", "id": "8e317094", "metadata": {"papermill": {"duration": 0.00966, "end_time": "2023-03-15T10:00:30.316891", "exception": false, "start_time": "2023-03-15T10:00:30.307231", "status": "completed"}, "tags": []}, "source": ["Throughout this notebook, we will use a deep fully connected network, similar to our previous tutorial.\n", "We will also again apply the network to FashionMNIST, so you can relate to the results of Tutorial 3.\n", "We start by loading the FashionMNIST dataset:"]}, {"cell_type": "code", "execution_count": 5, "id": "a5bca4e5", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:30.339479Z", "iopub.status.busy": "2023-03-15T10:00:30.339228Z", "iopub.status.idle": "2023-03-15T10:00:35.392565Z", "shell.execute_reply": "2023-03-15T10:00:35.391410Z"}, "papermill": {"duration": 5.06664, "end_time": "2023-03-15T10:00:35.394054", "exception": false, "start_time": "2023-03-15T10:00:30.327414", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2c93e471bc6e4c42bc77a062b6ca855a", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/26421880 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw\n"]}, {"name": "stdout", "output_type": "stream", "text": ["\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "3ca38b7d571d4dc29abf6245a6b139ef", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/29515 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw\n", "\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c7987ed8990b41b78b194e6cf2620a89", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/4422102 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw\n", "\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f0d8b2633b3c451bb94687d203c4b709", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/5148 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/15/s/.datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/15/s/.datasets/FashionMNIST/raw\n", "\n"]}], "source": ["\n", "# Transformations applied on each image => first make them a tensor, then normalize them with mean 0 and std 1\n", "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.2861,), (0.3530,))])\n", "\n", "# Loading the training dataset. We need to split it into a training and validation part\n", "train_dataset = FashionMNIST(root=DATASET_PATH, train=True, transform=transform, download=True)\n", "train_set, val_set = torch.utils.data.random_split(train_dataset, [50000, 10000])\n", "\n", "# Loading the test set\n", "test_set = FashionMNIST(root=DATASET_PATH, train=False, transform=transform, download=True)"]}, {"cell_type": "markdown", "id": "3e45cbbd", "metadata": {"papermill": {"duration": 0.01021, "end_time": "2023-03-15T10:00:35.416388", "exception": false, "start_time": "2023-03-15T10:00:35.406178", "status": "completed"}, "tags": []}, "source": ["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."]}, {"cell_type": "code", "execution_count": 6, "id": "09a4e44d", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:35.439264Z", "iopub.status.busy": "2023-03-15T10:00:35.438919Z", "iopub.status.idle": "2023-03-15T10:00:35.444890Z", "shell.execute_reply": "2023-03-15T10:00:35.444083Z"}, "papermill": {"duration": 0.019457, "end_time": "2023-03-15T10:00:35.446351", "exception": false, "start_time": "2023-03-15T10:00:35.426894", "status": "completed"}, "tags": []}, "outputs": [], "source": ["train_loader = data.DataLoader(train_set, batch_size=1024, shuffle=True, drop_last=False)\n", "val_loader = data.DataLoader(val_set, batch_size=1024, shuffle=False, drop_last=False)\n", "test_loader = data.DataLoader(test_set, batch_size=1024, shuffle=False, drop_last=False)"]}, {"cell_type": "markdown", "id": "5b1e2709", "metadata": {"papermill": {"duration": 0.010233, "end_time": "2023-03-15T10:00:35.470980", "exception": false, "start_time": "2023-03-15T10:00:35.460747", "status": "completed"}, "tags": []}, "source": ["In comparison to the previous tutorial, we have changed the parameters of the normalization transformation `transforms.Normalize`.\n", "The normalization is now designed to give us an expected mean of 0 and a standard deviation of 1 across pixels.\n", "This will be particularly relevant for the discussion about initialization we will look at below, and hence we change it here.\n", "It should be noted that in most classification tasks, both normalization techniques (between -1 and 1 or mean 0 and stddev 1) have shown to work well.\n", "We can calculate the normalization parameters by determining the mean and standard deviation on the original images:"]}, {"cell_type": "code", "execution_count": 7, "id": "85611bfe", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:35.493398Z", "iopub.status.busy": "2023-03-15T10:00:35.492479Z", "iopub.status.idle": "2023-03-15T10:00:35.634945Z", "shell.execute_reply": "2023-03-15T10:00:35.634090Z"}, "papermill": {"duration": 0.156044, "end_time": "2023-03-15T10:00:35.637160", "exception": false, "start_time": "2023-03-15T10:00:35.481116", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Mean 0.2860405743122101\n", "Std 0.3530242443084717\n"]}], "source": ["print(\"Mean\", (train_dataset.data.float() / 255.0).mean().item())\n", "print(\"Std\", (train_dataset.data.float() / 255.0).std().item())"]}, {"cell_type": "markdown", "id": "71425e83", "metadata": {"papermill": {"duration": 0.01105, "end_time": "2023-03-15T10:00:35.665361", "exception": false, "start_time": "2023-03-15T10:00:35.654311", "status": "completed"}, "tags": []}, "source": ["We can verify the transformation by looking at the statistics of a single batch:"]}, {"cell_type": "code", "execution_count": 8, "id": "b36b5d33", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:35.688173Z", "iopub.status.busy": "2023-03-15T10:00:35.687630Z", "iopub.status.idle": "2023-03-15T10:00:35.846236Z", "shell.execute_reply": "2023-03-15T10:00:35.844950Z"}, "papermill": {"duration": 0.173906, "end_time": "2023-03-15T10:00:35.849924", "exception": false, "start_time": "2023-03-15T10:00:35.676018", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Mean: 0.020\n", "Standard deviation: 1.011\n", "Maximum: 2.022\n", "Minimum: -0.810\n"]}], "source": ["imgs, _ = next(iter(train_loader))\n", "print(f\"Mean: {imgs.mean().item():5.3f}\")\n", "print(f\"Standard deviation: {imgs.std().item():5.3f}\")\n", "print(f\"Maximum: {imgs.max().item():5.3f}\")\n", "print(f\"Minimum: {imgs.min().item():5.3f}\")"]}, {"cell_type": "markdown", "id": "cd2b08ba", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.010833, "end_time": "2023-03-15T10:00:35.873603", "exception": false, "start_time": "2023-03-15T10:00:35.862770", "status": "completed"}, "tags": []}, "source": ["Note that the maximum and minimum are not 1 and -1 anymore, but shifted towards the positive values.\n", "This is because FashionMNIST contains a lot of black pixels, similar to MNIST.\n", "\n", "Next, we create a linear neural network. We use the same setup as in the previous tutorial."]}, {"cell_type": "code", "execution_count": 9, "id": "c2dbb6ca", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:35.898486Z", "iopub.status.busy": "2023-03-15T10:00:35.897904Z", "iopub.status.idle": "2023-03-15T10:00:35.909369Z", "shell.execute_reply": "2023-03-15T10:00:35.907788Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.027276, "end_time": "2023-03-15T10:00:35.912477", "exception": false, "start_time": "2023-03-15T10:00:35.885201", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class BaseNetwork(nn.Module):\n", "    def __init__(self, act_fn, input_size=784, num_classes=10, hidden_sizes=[512, 256, 256, 128]):\n", "        \"\"\"\n", "        Args:\n", "            act_fn: Object of the activation function that should be used as non-linearity in the network.\n", "            input_size: Size of the input images in pixels\n", "            num_classes: Number of classes we want to predict\n", "            hidden_sizes: A list of integers specifying the hidden layer sizes in the NN\n", "        \"\"\"\n", "        super().__init__()\n", "\n", "        # Create the network based on the specified hidden sizes\n", "        layers = []\n", "        layer_sizes = [input_size] + hidden_sizes\n", "        for layer_index in range(1, len(layer_sizes)):\n", "            layers += [nn.Linear(layer_sizes[layer_index - 1], layer_sizes[layer_index]), act_fn]\n", "        layers += [nn.Linear(layer_sizes[-1], num_classes)]\n", "        # A module list registers a list of modules as submodules (e.g. for parameters)\n", "        self.layers = nn.ModuleList(layers)\n", "\n", "        self.config = {\n", "            \"act_fn\": act_fn.__class__.__name__,\n", "            \"input_size\": input_size,\n", "            \"num_classes\": num_classes,\n", "            \"hidden_sizes\": hidden_sizes,\n", "        }\n", "\n", "    def forward(self, x):\n", "        x = x.view(x.size(0), -1)\n", "        for layer in self.layers:\n", "            x = layer(x)\n", "        return x"]}, {"cell_type": "markdown", "id": "da1aa4ff", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.010374, "end_time": "2023-03-15T10:00:35.937936", "exception": false, "start_time": "2023-03-15T10:00:35.927562", "status": "completed"}, "tags": []}, "source": ["For the activation functions, we make use of PyTorch's `torch.nn` library instead of implementing ourselves.\n", "However, we also define an `Identity` activation function.\n", "Although this activation function would significantly limit the\n", "network's modeling capabilities, we will use it in the first steps of\n", "our discussion about initialization (for simplicity)."]}, {"cell_type": "code", "execution_count": 10, "id": "859c0f5b", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:35.959925Z", "iopub.status.busy": "2023-03-15T10:00:35.959594Z", "iopub.status.idle": "2023-03-15T10:00:35.963648Z", "shell.execute_reply": "2023-03-15T10:00:35.963133Z"}, "papermill": {"duration": 0.016868, "end_time": "2023-03-15T10:00:35.965106", "exception": false, "start_time": "2023-03-15T10:00:35.948238", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Identity(nn.Module):\n", "    def forward(self, x):\n", "        return x\n", "\n", "\n", "act_fn_by_name = {\"tanh\": nn.Tanh, \"relu\": nn.ReLU, \"identity\": Identity}"]}, {"cell_type": "markdown", "id": "58f94d27", "metadata": {"papermill": {"duration": 0.011699, "end_time": "2023-03-15T10:00:35.989151", "exception": false, "start_time": "2023-03-15T10:00:35.977452", "status": "completed"}, "tags": []}, "source": ["Finally, we define a few plotting functions that we will use for our discussions.\n", "These functions help us to (1) visualize the weight/parameter distribution inside a network, (2) visualize the gradients that the parameters at different layers receive, and (3) the activations, i.e. the output of the linear layers.\n", "The detailed code is not important, but feel free to take a closer look if interested."]}, {"cell_type": "code", "execution_count": 11, "id": "9e8c93fc", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:36.013711Z", "iopub.status.busy": "2023-03-15T10:00:36.013110Z", "iopub.status.idle": "2023-03-15T10:00:36.029424Z", "shell.execute_reply": "2023-03-15T10:00:36.028756Z"}, "papermill": {"duration": 0.030934, "end_time": "2023-03-15T10:00:36.031070", "exception": false, "start_time": "2023-03-15T10:00:36.000136", "status": "completed"}, "tags": []}, "outputs": [], "source": ["##############################################################\n", "\n", "\n", "def plot_dists(val_dict, color=\"C0\", xlabel=None, stat=\"count\", use_kde=True):\n", "    columns = len(val_dict)\n", "    fig, ax = plt.subplots(1, columns, figsize=(columns * 3, 2.5))\n", "    fig_index = 0\n", "    for key in sorted(val_dict.keys()):\n", "        key_ax = ax[fig_index % columns]\n", "        sns.histplot(\n", "            val_dict[key],\n", "            ax=key_ax,\n", "            color=color,\n", "            bins=50,\n", "            stat=stat,\n", "            kde=use_kde and ((val_dict[key].max() - val_dict[key].min()) > 1e-8),\n", "        )  # Only plot kde if there is variance\n", "        hidden_dim_str = (\n", "            r\"(%i $\\to$ %i)\" % (val_dict[key].shape[1], val_dict[key].shape[0]) if len(val_dict[key].shape) > 1 else \"\"\n", "        )\n", "        key_ax.set_title(f\"{key} {hidden_dim_str}\")\n", "        if xlabel is not None:\n", "            key_ax.set_xlabel(xlabel)\n", "        fig_index += 1\n", "    fig.subplots_adjust(wspace=0.4)\n", "    return fig\n", "\n", "\n", "##############################################################\n", "\n", "\n", "def visualize_weight_distribution(model, color=\"C0\"):\n", "    weights = {}\n", "    for name, param in model.named_parameters():\n", "        if name.endswith(\".bias\"):\n", "            continue\n", "        key_name = f\"Layer {name.split('.')[1]}\"\n", "        weights[key_name] = param.detach().view(-1).cpu().numpy()\n", "\n", "    # Plotting\n", "    fig = plot_dists(weights, color=color, xlabel=\"Weight vals\")\n", "    fig.suptitle(\"Weight distribution\", fontsize=14, y=1.05)\n", "    plt.show()\n", "    plt.close()\n", "\n", "\n", "##############################################################\n", "\n", "\n", "def visualize_gradients(model, color=\"C0\", print_variance=False):\n", "    \"\"\"\n", "    Args:\n", "        net: Object of class BaseNetwork\n", "        color: Color in which we want to visualize the histogram (for easier separation of activation functions)\n", "    \"\"\"\n", "    model.eval()\n", "    small_loader = data.DataLoader(train_set, batch_size=1024, shuffle=False)\n", "    imgs, labels = next(iter(small_loader))\n", "    imgs, labels = imgs.to(device), labels.to(device)\n", "\n", "    # Pass one batch through the network, and calculate the gradients for the weights\n", "    model.zero_grad()\n", "    preds = model(imgs)\n", "    loss = F.cross_entropy(preds, labels)  # Same as nn.CrossEntropyLoss, but as a function instead of module\n", "    loss.backward()\n", "    # We limit our visualization to the weight parameters and exclude the bias to reduce the number of plots\n", "    grads = {\n", "        name: params.grad.view(-1).cpu().clone().numpy()\n", "        for name, params in model.named_parameters()\n", "        if \"weight\" in name\n", "    }\n", "    model.zero_grad()\n", "\n", "    # Plotting\n", "    fig = plot_dists(grads, color=color, xlabel=\"Grad magnitude\")\n", "    fig.suptitle(\"Gradient distribution\", fontsize=14, y=1.05)\n", "    plt.show()\n", "    plt.close()\n", "\n", "    if print_variance:\n", "        for key in sorted(grads.keys()):\n", "            print(f\"{key} - Variance: {np.var(grads[key])}\")\n", "\n", "\n", "##############################################################\n", "\n", "\n", "def visualize_activations(model, color=\"C0\", print_variance=False):\n", "    model.eval()\n", "    small_loader = data.DataLoader(train_set, batch_size=1024, shuffle=False)\n", "    imgs, labels = next(iter(small_loader))\n", "    imgs, labels = imgs.to(device), labels.to(device)\n", "\n", "    # Pass one batch through the network, and calculate the gradients for the weights\n", "    feats = imgs.view(imgs.shape[0], -1)\n", "    activations = {}\n", "    with torch.no_grad():\n", "        for layer_index, layer in enumerate(model.layers):\n", "            feats = layer(feats)\n", "            if isinstance(layer, nn.Linear):\n", "                activations[f\"Layer {layer_index}\"] = feats.view(-1).detach().cpu().numpy()\n", "\n", "    # Plotting\n", "    fig = plot_dists(activations, color=color, stat=\"density\", xlabel=\"Activation vals\")\n", "    fig.suptitle(\"Activation distribution\", fontsize=14, y=1.05)\n", "    plt.show()\n", "    plt.close()\n", "\n", "    if print_variance:\n", "        for key in sorted(activations.keys()):\n", "            print(f\"{key} - Variance: {np.var(activations[key])}\")\n", "\n", "\n", "##############################################################"]}, {"cell_type": "markdown", "id": "44e143da", "metadata": {"papermill": {"duration": 0.011086, "end_time": "2023-03-15T10:00:36.055523", "exception": false, "start_time": "2023-03-15T10:00:36.044437", "status": "completed"}, "tags": []}, "source": ["## Initialization\n", "\n", "Before starting our discussion about initialization, it should be noted that there exist many very good blog posts about the topic of neural network initialization (for example [deeplearning.ai](https://www.deeplearning.ai/ai-notes/initialization/), or a more [math-focused blog post](https://pouannes.github.io/blog/initialization)).\n", "In case something remains unclear after this tutorial, we recommend skimming through these blog posts as well.\n", "\n", "When initializing a neural network, there are a few properties we would like to have.\n", "First, the variance of the input should be propagated through the model to the last layer, so that we have a similar standard deviation for the output neurons.\n", "If the variance would vanish the deeper we go in our model, it becomes much harder to optimize the model as the input to the next layer is basically a single constant value.\n", "Similarly, if the variance increases, it is likely to explode (i.e. head to infinity) the deeper we design our model.\n", "The second property we look out for in initialization techniques is a gradient distribution with equal variance across layers.\n", "If the first layer receives much smaller gradients than the last layer, we will have difficulties in choosing an appropriate learning rate.\n", "\n", "As a starting point for finding a good method, we will analyze different initialization based on our linear neural network with no activation function (i.e. an identity).\n", "We do this because initializations depend on the specific activation\n", "function used in the network, and we can adjust the initialization\n", "schemes later on for our specific choice."]}, {"cell_type": "code", "execution_count": 12, "id": "30643406", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:36.078326Z", "iopub.status.busy": "2023-03-15T10:00:36.077820Z", "iopub.status.idle": "2023-03-15T10:00:37.370352Z", "shell.execute_reply": "2023-03-15T10:00:37.369712Z"}, "papermill": {"duration": 1.306465, "end_time": "2023-03-15T10:00:37.372591", "exception": false, "start_time": "2023-03-15T10:00:36.066126", "status": "completed"}, "tags": []}, "outputs": [], "source": ["model = BaseNetwork(act_fn=Identity()).to(device)"]}, {"cell_type": "markdown", "id": "00685161", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.010649, "end_time": "2023-03-15T10:00:37.395592", "exception": false, "start_time": "2023-03-15T10:00:37.384943", "status": "completed"}, "tags": []}, "source": ["### Constant initialization\n", "\n", "The first initialization we can consider is to initialize all weights with the same constant value.\n", "Intuitively, setting all weights to zero is not a good idea as the propagated gradient will be zero.\n", "However, what happens if we set all weights to a value slightly larger or smaller than 0?\n", "To find out, we can implement a function for setting all parameters below and visualize the gradients."]}, {"cell_type": "code", "execution_count": 13, "id": "5a074e3d", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:37.418946Z", "iopub.status.busy": "2023-03-15T10:00:37.418440Z", "iopub.status.idle": "2023-03-15T10:00:47.733147Z", "shell.execute_reply": "2023-03-15T10:00:47.732251Z"}, "papermill": {"duration": 10.328034, "end_time": "2023-03-15T10:00:47.734537", "exception": false, "start_time": "2023-03-15T10:00:37.406503", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"</svg>\n"], "text/plain": ["<Figure size 1500x250 with 5 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Layer 0 - Variance: 2.0582759380340576\n", "Layer 2 - Variance: 13.489116668701172\n", "Layer 4 - Variance: 22.100568771362305\n", "Layer 6 - Variance: 36.20956802368164\n", "Layer 8 - Variance: 14.831439018249512\n"]}], "source": ["def const_init(model, fill=0.0):\n", "    for name, param in model.named_parameters():\n", "        param.data.fill_(fill)\n", "\n", "\n", "const_init(model, fill=0.005)\n", "visualize_gradients(model)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "2114634b", "metadata": {"papermill": {"duration": 0.018114, "end_time": "2023-03-15T10:00:47.775188", "exception": false, "start_time": "2023-03-15T10:00:47.757074", "status": "completed"}, "tags": []}, "source": ["As we can see, only the first and the last layer have diverse gradient distributions while the other three layers have the same gradient for all weights (note that this value is unequal 0, but often very close to it).\n", "Having the same gradient for parameters that have been initialized with the same values means that we will always have the same value for those parameters.\n", "This would make our layer useless and reduce our effective number of parameters to 1.\n", "Thus, we cannot use a constant initialization to train our networks."]}, {"cell_type": "markdown", "id": "c27856ca", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.019247, "end_time": "2023-03-15T10:00:47.812251", "exception": false, "start_time": "2023-03-15T10:00:47.793004", "status": "completed"}, "tags": []}, "source": ["### Constant variance\n", "\n", "From the experiment above, we have seen that a constant value is not working.\n", "So instead, how about we initialize the parameters by randomly sampling from a distribution like a Gaussian?\n", "The most intuitive way would be to choose one variance that is used for all layers in the network.\n", "Let's implement it below, and visualize the activation distribution across layers."]}, {"cell_type": "code", "execution_count": 14, "id": "afd455cf", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:47.848531Z", "iopub.status.busy": "2023-03-15T10:00:47.848336Z", "iopub.status.idle": "2023-03-15T10:00:55.361820Z", "shell.execute_reply": "2023-03-15T10:00:55.360457Z"}, "papermill": {"duration": 7.533864, "end_time": "2023-03-15T10:00:55.363585", "exception": false, "start_time": "2023-03-15T10:00:47.829721", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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model.named_parameters():\n", "        param.data.normal_(mean=0.0, std=std)\n", "\n", "\n", "var_init(model, std=0.01)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "8c23e50a", "metadata": {"papermill": {"duration": 0.021311, "end_time": "2023-03-15T10:00:55.408787", "exception": false, "start_time": "2023-03-15T10:00:55.387476", "status": "completed"}, "tags": []}, "source": ["The variance of the activation becomes smaller and smaller across layers, and almost vanishes in the last layer.\n", "Alternatively, we could use a higher standard deviation:"]}, {"cell_type": "code", "execution_count": 15, "id": "e36ce9b9", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:00:55.451764Z", "iopub.status.busy": "2023-03-15T10:00:55.451425Z", "iopub.status.idle": "2023-03-15T10:01:03.213007Z", "shell.execute_reply": "2023-03-15T10:01:03.211795Z"}, "papermill": {"duration": 7.785652, "end_time": "2023-03-15T10:01:03.214890", "exception": false, "start_time": "2023-03-15T10:00:55.429238", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"markdown", "id": "b1417792", "metadata": {"papermill": {"duration": 0.024716, "end_time": "2023-03-15T10:01:03.269454", "exception": false, "start_time": "2023-03-15T10:01:03.244738", "status": "completed"}, "tags": []}, "source": ["With a higher standard deviation, the activations are likely to explode.\n", "You can play around with the specific standard deviation values, but it will be hard to find one that gives us a good activation distribution across layers and is very specific to our model.\n", "If we would change the hidden sizes or number of layers, you would have\n", "to search all over again, which is neither efficient nor recommended."]}, {"cell_type": "markdown", "id": "0ca78c8b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.02439, "end_time": "2023-03-15T10:01:03.318330", "exception": false, "start_time": "2023-03-15T10:01:03.293940", "status": "completed"}, "tags": []}, "source": ["### How to find appropriate initialization values\n", "\n", "From our experiments above, we have seen that we need to sample the weights from a distribution, but are not sure which one exactly.\n", "As a next step, we will try to find the optimal initialization from the perspective of the activation distribution.\n", "For this, we state two requirements:\n", "\n", "1. The mean of the activations should be zero\n", "2. The variance of the activations should stay the same across every layer\n", "\n", "Suppose we want to design an initialization for the following layer: $y=Wx+b$ with $y\\in\\mathbb{R}^{d_y}$, $x\\in\\mathbb{R}^{d_x}$.\n", "Our goal is that the variance of each element of $y$ is the same as the input, i.e. $\\text{Var}(y_i)=\\text{Var}(x_i)=\\sigma_x^{2}$, and that the mean is zero.\n", "We assume $x$ to also have a mean of zero, because, in deep neural networks, $y$ would be the input of another layer.\n", "This requires the bias and weight to have an expectation of 0.\n", "Actually, as $b$ is a single element per output neuron and is constant across different inputs, we set it to 0 overall.\n", "\n", "Next, we need to calculate the variance with which we need to initialize the weight parameters.\n", "Along the calculation, we will need to following variance rule: given two independent variables, the variance of their product is $\\text{Var}(X\\cdot Y) = \\mathbb{E}(Y)^2\\text{Var}(X) + \\mathbb{E}(X)^2\\text{Var}(Y) + \\text{Var}(X)\\text{Var}(Y) = \\mathbb{E}(Y^2)\\mathbb{E}(X^2)-\\mathbb{E}(Y)^2\\mathbb{E}(X)^2$ ($X$ and $Y$ are not refering to $x$ and $y$, but any random variable).\n", "\n", "The needed variance of the weights, $\\text{Var}(w_{ij})$, is calculated as follows:\n", "\n", "$$\n", "\\begin{split}\n", "    y_i & = \\sum_{j} w_{ij}x_{j}\\hspace{10mm}\\text{Calculation of a single output neuron without bias}\\\\\n", "    \\text{Var}(y_i) = \\sigma_x^{2} & = \\text{Var}\\left(\\sum_{j} w_{ij}x_{j}\\right)\\\\\n", "    & = \\sum_{j} \\text{Var}(w_{ij}x_{j}) \\hspace{10mm}\\text{Inputs and weights are independent of each other}\\\\\n", "    & = \\sum_{j} \\text{Var}(w_{ij})\\cdot\\text{Var}(x_{j}) \\hspace{10mm}\\text{Variance rule (see above) with expectations being zero}\\\\\n", "    & = d_x \\cdot \\text{Var}(w_{ij})\\cdot\\text{Var}(x_{j}) \\hspace{10mm}\\text{Variance equal for all $d_x$ elements}\\\\\n", "    & = \\sigma_x^{2} \\cdot d_x \\cdot \\text{Var}(w_{ij})\\\\\n", "    \\Rightarrow \\text{Var}(w_{ij}) = \\sigma_{W}^2 & = \\frac{1}{d_x}\\\\\n", "\\end{split}\n", "$$\n", "\n", "Thus, we should initialize the weight distribution with a variance of the inverse of the input dimension $d_x$.\n", "Let's implement it below and check whether this holds:"]}, {"cell_type": "code", "execution_count": 16, "id": "5e22b946", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:03.371207Z", "iopub.status.busy": "2023-03-15T10:01:03.370444Z", "iopub.status.idle": "2023-03-15T10:01:16.577580Z", "shell.execute_reply": "2023-03-15T10:01:16.576735Z"}, "papermill": {"duration": 13.236577, "end_time": "2023-03-15T10:01:16.579581", "exception": false, "start_time": "2023-03-15T10:01:03.343004", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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RsfAYRHTsGmI3EZcXimkCI2KSQ2ZlQsN70bDwlyzwKC41O3VeMFqApxRTm9jR6GY0LBwmD9g3ZFfMSuqB4n+SD7XHmRwNb0bDc3/JAgMySnrWKR4wQ5JyxEPDRM8Eb0bEwuWPT9Fh11CCice7UAl6SufooOhR8FY0rPwlC7yJrr1mQ8MDZjZyq4xOmunR8GZELFwmE9gr1Gj0Gi6U0VUdCwWJoNP8aHwzIp57TB4algE9J9siHjBDsHwM43BaEaThzYhYuMyYjyHRFufNloIZT+jRHXI3DGl8LypWPo/wl9YxB5jeccESogkKWrQUKXwzKhY+kwo0886fNVQ84EnDxlC0513G0mdSgb2DC6XZVvGAGZaZsmicaoIudDMaFv6SBkyFrqVqW8QDxkCA6SJ7id7V9Gh8MyoWPoOKwLD5FIzKgYIp6lJZpqkbijS+FxV350yrCNhDBOeSYeKOMqDc30+ilI0J34yI0zfbJpjCkFswo8QFM08hhzi0jpSVCd+MioXPpAL7iOiLuedSMOOlYSrKNktTpPHNqFj4DCoidhKxxm5GigvuAVuN2nuYGdLwXkSsPCYR2Eow0D4aIh4wcwFd9s3wo+HNiFh4TCKy5HOUZoh4wFhVY1zIrs/8aHgzIhYekwhsJHKqRklVwSUdHl1AcoSUEQ1vRsTCYxCRqIrqqH4zEXHBEieUhoy6MqLhvYhYeUwisIkoZU5K+KDhjEWD70kSITU/Ct6MiIXHJCIz38072yIuuDOXqUrmmzai4M2IWHhMIirTWls3s4aC6+FCijUZfhS8GRGna4aHftSGBYFZZis4M1/Rd6/pmdDNaDg9MyvLHEZatpkzFJwOplpP7BSN7kXDyl/SkEb1PDM+KDgyoDKMgVIZUfBmRCw8JhHleS2+DxPMNOcwjmYmeu7oZjQs/CUNfdTUs+3hguMB/3Ktz9h5wJsRsfAYRDDfjvX1zHyhYBVYO/OzZ7zt0mMSEUetPbPLULAmYs3PZkQsPCYReVWgUMMTEUt+fiwROiPqSYRJbp9Jy3NhEptPU4JkFQa8eWeppSFNklJMI4wYS4GYWxllB0scmh6MJYUJCSWJnCR9vEeeNtdKlMhKdI8qAhuMNMypiOwJz/br0NcIleNIjiMAsVC+s57heInReJ5CB736ILWnGZwGcnhq1o4aW+8jLofXip1yJfCrs06sPLFRXqUxrwqTV64crAVOR4jMcBSFkpDGTMYoJwzsrM6MLREzbPqAC/dH3Dujo/dQkmtnKBBeqhURiogBLtwDh7C7prBKYUZ3dSNZpTOSrmd8C5JTcxjh2Tyf4dIq3lo4fKIAyhlR4VjONdzwew4MpxFuwoyXFENjAXVZiQyYMgEUf3Z4Ys3ZjyeCn+CoocICKsFJ9vm4lMYPMxieahyBsln3u9tSJb+YgqIF61/RIYkOn97j+3i5s+wRW6Ry4qLAkuSGgrIivZ4Xe7mgQVZRBHGiLCJ4H+ImUS78YG/oSPD2C7ZCGhr/Kccg4h/EW4OHQ7jE5xzduBmh2FOm2M+QPcNM3M+rI1YQ4yUzcdEEFxwN1oko5GmnjpMALy02ih5L5KNiHBcOiZf25dRjSSxCGU68J9+7vD7e3bV+XtTk4KPosXjQGZMf9xnMoXWOl//oPAGNeryNKM5QyIS6H76dBTx5tp8rukQaEijowul+guUpsiOBR2j346ExsJFjmGNdKAe2RY2Ehxu1N/rBO6PehnZJjGgf6LBocRQPQacXPyMbS6JWdG6HY99u53YYvc41KcWW+Lnvm2f4R6EV9KTYq3S7iF5PpYrEHSP+Np8byYx3qnKc2zA+SUcjjFaIoYJHm4yW6ucJNv4QMVRTiQg/QHGd8R5oRgkjqOxJeD0qI06kckpyuYsEZmRMhbhICeBc+5CFq3B13KyKCyMRIPhU77M1ukgaaiauhHqO5AGGm9xfUkKOJTbNAA/OG17Jvwy/Qs7khcT2l7QwYHmZ8/7xRVUN/Mark+fXT//kM14jb8LhuJ+zHu/9P1/eJPwk8iaJSc+M/5130Zgh0J+f1WtR8ESSsuKX8ibXD7j5UT86PzrFcnj0YBO8o2D7Od+QHp1Ym6ryKLVQg/u1+ib5HQROviCDrLeMQbLO62IFvweDjProsIaFFabNzxM4eZZm/+VIwIoMo2up5vpawe9AQobbGdNGdSV9ohW5F9rOpHGSMEs4rvreRd3kC7auVw0wb2xcDDm73v5t6iZfhQLW83KNh60TBRf8ZvkcSpVdb1+wnse6HWtLFlN+mQW/0LL4KkRI1WPPZfNExAW/ExHYZYhYYqZK4itlXr4KEVLtrWOVF2YmFP5OVFDfxHnRgiyfVHqJPxkXWOL2iLfzhosLfy8uuJjPLKNTPXa3n696owROInfvU0t7o8DJG7l925lR4OL09WdGiRKe+NkazDLLSV2WeZmtUOwzwpCu1BYu9HFetAUJd7+KISGM4izVsPACvDCyHREL10hEliotMRuPX4CX/GxGxMI1ElFHtRb76ScYo4+cmRojd3gzIhYek4jOui3RBD1qODJHYJT61UYUvBkRC49BRAqjgMucXaRhnkW3JgeYMz8PeC8iTte6mTF4poUPa9JqNBypsTyit7QRBW9GxOma2V+nMiq6NEPEBYsod5VSX9qIgjcjYrhmruhTalLaJdkWccEJjaC31BU/I4DhAW9GxOmaaRHY0kuNFzNrKDgdjSfxQfMTJ3gvIk7XLA9RSr0YCSQNY2/kXJYbAWVDoZvRcHpmhsqcRyUc2x4uuHEFVURjXRtR8GZELDwmEU1qvzSzirhg3oiGWkWvXxnR8GZEnK4ZIoqTKjB1DuJRMHyncGdMmp86wXsRcbpWuiEijHIwpkVcMOM0sJSU9AhlRMObEbHwmETkURfGrCIuuLoj+8SLkIkfDW9GxMJjElGl+oKpJqfg2g4Xg1SN1vRc6GY0LPwlDf0oLdmqmgpurIsRnVwUKyMa3oyIhccgggVougtGBknBLOITy0jJVkY0vBcRK49JBMvNyIXvTMQDZrWgHJsfk4YiSOObUbHwmVRg7+B8MxKjCmYFI+w5g4T/aIo0vhkVC59JBXYPrjIgZqbiAXuJiJeSszNFGt+MioXPvOjGBgKNPJpWccEMFsNWq0nUjqZI43tRsfKZVGAP4TuLSs1UPGAWYepVjM4UaXwzKhY+kwrsIkJ2zraKByyxcDU3qR+nKdL4ZlQsfCYVWBtwaWBbxQOehCwmilYCF3tQsfAZVHSGDpZcTau4YEZOupBGeeaJIoXvRcXKZ1KBvUTCb5hWccG+FMYbFolp1RRpfDMqFj6TisxDep9sq3jALKIXQ6xjsako0vhmVCx8JhXYUWRYsK3iAfskUeCj5qKmSOObUbHwmVRgT8FSpOaw5oJZuLKFMvKzNUUa34yK0zkTRudYDVTSDqZw0gtm/D4j40rXFPkZ34qKh3Mm8M1JRFIw6g4KZoFadAYpbKSMaHgzIhYek4hyZIZCNkPEA24Mpw1VdHiVEQ1vRsTCYxLRDvxGM5LuCsaOAyPkiL9XRjS8GRELj0GERwtnCP58R67g0o6AJ8hgoIxoeC8iTteS4YE3vCmZo0wFswgMU626oidN6GY0nJ7Z9oCNhHPB2fbwgDO2FyHUiZ2g0c1oGI55SwOPZnO3k+cFZ3+UWpokNykjGt6MiNM10y8Cfs37agTdNcw64xgdi+YnTfBeRJyumTLPGa9efc0mhFDDzJLLsWl+WpjgzYg4XTNriJBZ4T2aU0wNM9urtzrxEyd4MyKGa0YYKQdW7O4+2hZxwYwfyhISoG1c6GY0nI7Z9tCfl2H8MMHxKK10SUjVRhS8GRHDNTNARD8KKM4RNBpmPncJEl+pbCh0LxrunlkektRPNBPGhVJZM0U5qdUmFLwZDc/9JQtlVTxRwyrIdGJnz9DTh2fV8NCkdqKp+KDhiYfLyL5EtFXJyZz8qJ4483ChEw1NF5zck4aFv2QhSu1Ec1Rp4Hs0/mzjvWL031Pu5JFgc754CmmE18Kuy6FIZLUvveZwxtFW32OWya+yPFo/o0pTrpSVTTxxyUly8VJ2R62pZpEqCFhZPUJTQUUa5daofHAGIjq8fpA4TSyzupxQ4MsdLToJYGJ8YvWiPoCHHT5RLIT7lRqGMgZD1yJ6pBchwxKYwC5wP1gOtzdGtLVQvL/HdxWepydJdsX7eLl9KTAocbMlHGA0ihhDKvHAkjh0OU5ItbGIKOECzgr1WprnDopn0oQlhg6zAcUp8HKp3eNpIgPu+q3TXHbjiRUrzNarCJIkSr50P+6MmetbfR6RBI5H3u28Vm8gsVVpGMGX0sp5x+wcpR/kur20eN4y1s4yAjn3UWUi9ezGPZPDxO35wT11XfAoEUjhrWTwtQ/d9ZBbHq/Z+MVr5A0dA18SDMpjG5aDtRTRKaFgf2rCeivUoi511DHgOknkNYgXTJl+yIgwS7qOnwdTIvohaiS9ltHiqO6BNdd5LwguR/U5XpKliK491FFK8kMHA5+ZCfKxDTmSzPr08bxJwus5vg7fPrfixsG6BwmNQjyi4hJCS+dtCx6dKHaCBoH2LO/Y+aTiz9t9tEsvwh68kEAn9yxwk2R5OA4SUm88XAmA0c0iOpGEEPJY0tWEpsq6QHjNIt0lO+qNuljSDV8sBKmiTpgnNLWCGXwO9Avn23mC40Lh0UXhB+huHHD5gP6cvWu3wt7nWrkfdMQUGdSKT43t/9Ap4b6f5dt4OgQjEV907IILZnPHtIkMHsDaaRsNCc03SGfE7xYJ5+BWMfSRrI1vGCjdIjB4y7ybkRhrPLCHcz8FBrlcTCIrNC4sckDrwtAyik5Sb0I0kHKEE5SUkQGHokfie8RboZ+Pmq4exI9zb2rYxrvcWC1BUqPN0IwRS8pZvgC/QqnkhST1l2QtYHmZv/7xRYEM/MarE+HXT//kM16jVMLR8XUKJeknUSjJvYMWH80haaUU+vOqNAqeyFFW/FKh5PED/De9aQJ/IcEZQ8KBocib024F28/4hvzm4qjblSonaQwvr1UoeQeBki9IYGHKT2imyoaC34NADKatwhqmEzTDzxMosZncX46D6sRyMdW6FPwOHFTHeC5aC7xt+TyBkhf0STAaYwqUnkitpXdRKfly9L5ukHmj+kuLIwthvP3bVEq+CgUUf8O4JDIMioILfjMFpWsKuP6OmVWhuNJ9mYX8XI/iq/DgXcC+E2v1MhOh8LczETQTWF06LPUw/GD8fqVey9ehgqdjPlEncKbiwt+JCm6QasROKuEvPkXFT9YqCgdaLKWboeLC34uKTN1A7o0C9iuvUK9RKiUZG2VFydtVSt7I7ZuVbbEbevVBDzaCqyo7xbVRLGY+8NIwFUmwU0wCX0YU/Djq2YOIhccggkKkLBYzz2canohY8rMXEcO1ZArVFeytpVhMN0RcMDxuaQjyaiMK3oyIsKqxU0TytclmfSLiBXhhZDsihmvV8FBHrRgzRCiYGruhidyssqHQzWioqwo7hSK9LBVjR4gL5jFNzGfHuIwoeDMiFh5ze+5HqZi5qWuYAujMs3/GzwPei4i7a2byDElKxXjDwwOlWHrwsivQJhS8GQ1pVV+n4A2lUozl4YKj1D32XtHj6gRvRsTpmu0YbdSKMVOngvMRMIVKpos2ouDNiGirCjsFWx8pFmNGCAWXI7Sh9a1tXOheNKz8JQ1x1IpphoYLrgdPsUsw7Ch4MyKGa3bqpDQ9i8WYqfOCMx7i3LhoUkY0vBkReVVjB217FIsphogHjD2pczFKyQxlRMObEbHwGEQkN4rFzAEnCi6OBQ9KCzM/Gt6LiNM1Q0MYpWJMe7jgAgec3HA/DFzQZgSEVW2dwptQFlOwLeEBV8wK3Vep0qKMaHgzIhYekwjeK/duskYVzETqUEaRGGVEw5sRsfCYRGDHEHNNtkU8YPiWGZASZ340vBkRp2tm2szYM/Aa1Bw1XDBW0UfDM5Pmx0/wXkTcXTMtIidGQkVv1pMXzPLnAYtIiWtRViZ8MyoWPpOKwipJrptR4oI9CzOEPOJTNEUa34yKhc+kAhsH/I4J1lSwx/oxd6bWG4o0vhkVp3OmVRTsHUoqJmNUwb6Ew588PExc4F4krLwlCdg3sH6RGSUumMFvMXcvOpgTOQrfjIqFz6SC5dxo11DxgBlPiBW1k8BPTZHGN6Ni4TOpYKW64Oxp5QVPUiwTRSuJlj2oWPgMKipstNqMAp6CKdCTXC0SUzhRpPC9qFj5TCoYNos+b1rFBTMs2LXSfTcUaXwzKhY+k4qExWKP1baKB+xZ9I/lqaKhSOObUXE61w0VDGTPvpgVxQV7XuYEPzLJlZUJ34yK0znbKrCb8K6bBDEF+8ikF/x/1RTFGd+MioXPoIJZD16qc05UXDB3Gd7l2qqhSON7UbHymVRgOxFCNip4CubWM7Xok6VI45tRsfCZVGA7ga2ls63iAfdy1HQmBCgjGt6MiIXHJIKlmJNrtk084Iax0TPNfuZHw5sRcbpm7kG7Zy5VsyeZF4y1dU2+u4mfOsF7EXF3zbSIzjycbOt3KJjiyr200TUuIxrejIjhmjnd79hLZJ+SGSIumKHdLjjJSLtsaHQzGk7PzFqiYyORrf74hWbmA5aRw6VMaHgzGp77y4QEWo3OhFpqGK65keGnTFzgViQ8/GqGBWwfMPaZiz8NM781ZEmM1EYUvBkR4lrtNj+FQispm4BBDZcjN2ZtXvyIEQVvRsTCYxJRR3K24eGB5iPUFno07Ch4MxpOz0xCjetS/NBMmhpmgHrzbqInTfBmRJyuzVFU1Xspfmi07jQcYdc5OYHRRhS8FxGnaykYIpIUPzTXfxqmsFuPki6sjSh4MyJO18wI4csofugNEResQmy1kV0jb5cek4jG4ofBJCloWAVdz/zsGYv9cM2sIoIfxSHN5KlglY2gjbxjksJ7KpSYzBqKEUSJOS+HHxH2ninMcm8VAuM96qh3iMadRnhQSJJ5N0SaWnBOVDoYZerw6aMomiXsKkRbhEGXTJ5yMpGmGrroVmCpdZRYcx0CJZ7ieALzoDcXJ5oIcKONa6MYKSkSmBPSqbR5bvFjgrOhVwnr45plxLvGfOD3WJ6QMigguI7Yt4r1TC/9lvHEUN04RIsd70etBNalcvcr3OQ42CfMdZSDAGn3aKnAan9Suil6bCD7GSGTEybJQjnY7vF746cZVOeo6dXigXdzqZ5hJCmyViBlS6i2MHiiY7XkLCLDeH0vKiQlh8PVSFUXqpm03NOA0c+i75mJYx7rdp/Hah6uwTEn5bVFaPAMCmdmdSnY88hZuuPt9Pj5fmCti14jiW7ei/AIb2QcaE6uULDDHdR1GDH2hc0aTcvL9QTet7hxg5Mo/5r485XlvdzQ5iDe+L1EPoQ3PKL8wbsvbMR9Hlr9jbJw5cQxoKY+ZEhCrkMYhmnmmfoYXfAUYxjn4tjHozdJl6ksGeSYPzXwipmaPYvXjL7X8+cb+g0+7KgFwEY3PixzYnPM6AAiuhKa6/fbmOKp0OF5ReWGREuBh7773IaMfsDfj4BzmIzYT6OFMQ+xFjA1cLRe1zq/LTMBmx+6NqWKfk5keA3raKZTpKWgFWFScYlCJyxC26qIrsB/2K+ibINejC/i0jjpYu3iDDp55NcT2sg490FD88zrvbUk0nounsdBuUq2b4XXeEoYRvCk6Chrw9tItC1RDEL3ovQPVV/4zRwaaz+PEJg5naXXYJ2cR2fqzMlJbP9y98/uLnDB8wMv9RheWuLZ2XvjOEExGomjaXG8N6OraqfmDHo1muQYWrFBq7FRcYbSRZX8C4zf7IWCM4k5q11082ASbTxTbwajC9Yq+ELnoh6GUxVxTo4twwQaBXpGlBVudmWoMFXvKOIjSpUY5tBGS7+vfijWI6NfRqsRjRe0NjQEX0ZuTvMyUt5niTpS2WLvEjr3AvwKrZQXUuVfEtiA5WUW/ccXpTqorvLadPz10z/5jNdopTA0tw8pA4oZvEIzpfwkmim1VmlM5lgCswDlPify7thEj/p9v1RLuX7Azw/58UIXGCJY3LjMx84Kth/yLUIXvOnHQCgy0Z8Q+3hBLSW/g1zKl2OwYa0RY+ym+JmC34HBRg1ZzInoXFinfJ5ayrN0/S/IQSuY5NOc3/tRw+/BAdXNEqw1jN/+M+VS8gt6KVjBsQBjOgUE30Uv5Qv20s8fYN7YQ7kgvl79bWIpX8V/VmGw/l/Ym/3PSfuPVVz/m/6Hn4YAT0E6y4AC34kC77j7+lscpJ+IA6roPePgAt+LA+p1/00OyktaIDVNVLxdC+SNnL7xUKVFrORff6hC7UtWjLELiSz1U8z9tYYTDynu8GVEwY9DlT2IGK49W1HVVdkZDZcjNcNPqRO8GRELj0EE9URZX8acNyp4ahGXkW1bxMpjEhFGCRXDwx3NXMM9Z0fDm9Hw3F+ykEZ5mWJouGDdLy4b23aLlcPkoY76KbY5PODi1I7xMqLhzYhYeEwi+qgvY26oLjiLBPQzfjS8GRHDNbOBdP55DZYPGs75CM3Q0yZ0Kxoenhka0igukw0ND1g7fNnYmIaFw+ShjOop3vDwgEumuPkzejS8GRELj0lEWxWdUXBN1Fh/xo+GNyNi4TGI8O55EZYPGq4UKV7wo+C9iFh5TCKilFAxggcKZlhseM6Phjcj4nTNjBE+Sw2V0g0RD5hVoZvlJ0/wZkQM16oZI3wdVVRsi3jA3okE7ExQ9TO+GRULn0FFcKOQihklLtjDex8WFGl8LypWPpOKMAoXmFZxwVQabm5BkcY3o2LhM6nAZrrmZKKfFOzxi35FkcY3o2LhM6morOSBnzRUPGAvSkELijS+GRULn0lFP2IrRgbiQilxEMOCII1vRsTpm2kT0TPaqJqKnwpmqExrliE/43tRsfKZVDCgqeVsRooLZjRRXFGk8c2oWPhMKli5KMVoW8UDnrL7NUXLrP89qFj4TCrwSt4x4mym4gEzefnaliuKNL4ZFQufQUVicEju5gBTwYwEK25Bkcb3omLlM6mIEhllRFsVPN3RaYo0vhkVC59JBbYUoaZqW8UDnidTRdG+k+nKZ1KBLUWModhW8YAZv+rSgiKNb0bFwmdQwSjH2Fl7b6LigiVkty0o0vheVKx8JhXYUqTU7HnmBTMgNrgFRRrfjIqFz6QCWwq8uz3RvGAGY1xnu4oijW9GxcJnUlEZxT/Xj/ig4R6OkJ4zpOHNiFh4TCKwpSjBN9smHvB8gnVZ2fgE6+6cOdUs2FIwLNScal6wd0mtKi4rE74XFadz9lyzDHV7e655wS2rMfMyouHNiFh4TCLKERhobk41L1jHBCojGt6MiIXHJAIL55ajyedU8HQteBnZ91rwdG1O52zVHa17b9RcFcwS0paeGd2LhrtnhoZ4eKqdmAHignWUxGVj29iJpcPkIVPupDSzjrhgHTajjGwbTbP0mERgC+FaMpWlFMyVQ3rOj4Y3I2LhMXMRmGCWgom/1HBTK0tlpO26slx5TCICU/Fs3QwNayLW/GxGxMJjEpFGerQZIxSsIk61kV0DUe+umTSS1qoUHrQnmApWwZbayK4xmA/XbL5Sl8KMpkzhi/DCyHZELFwDEd1L4cFqxogX4DU/exFxumZ6Ro+jLqPpGS/Bz21sR8PpmaGhSN1Be2ypYD1SXja2HShPz+zU2dsoO2gWUwqeeLiMvCMR76kQ8si3OYU8WsnxnrTT5A+iiNXTEE7DgBdDYZ0kjPyemVED7tS8Z8k9lk1JtRQKOTBloblQRd4D7KSREA+uYky+S6Cyp+qDpPwzfLkH1lfI5Yi5NBHQaM5RuC/LKrUxFCfcRpBv8y4wypeaCW4oh+BhBxfx3Nx65o9XURZnKKwPtYRGfY/qhyBBo6oC6IZpbgE7G+wZHJjd0BF1UvjkDJTrtSRWg4lHiSWIg4wPwivzZxgqhb11E+kBBssU/OOiBMsE53P3Z+RIcqnEofUQfS5l4O1IxbEilwevMRUvOgOMKii+VT8CL0JIQVQdWnQH2g5MAWcIX+o5nlfvYI4VzX1O8MkPTQriPXKa8iMoekinoGccoDbFOKQqWNEkndfWrosujOe3CKXfYdj2vgucwLToQLSY0Mrx0mHgcCSlE8cjwLPggSpawnDEs7pPfdx/JoqmhPNeNDqmuIhXLrES8nlJCNKo3+mzZwZdrvcbsxwoT8S7QzT4Ol4zUf/CM1HGJ8Yf4D36easEUzGM2yZsclIc5ovotTgKcwTKI0ZRP2kwinZXqEge8R2yi238PL9cEXlueJIxB7hx6ujRYOAkPha/Z/RuvD6+RI5woEiOZg5xSGg1coIGw/d3zBRP99NLqUqWqUOC9uwKWqg/z/JS47LrhlkGTWwo8TRK1tQeMNhifeq6OucpeLXSGPWcXZX+y1OPhAd1afM5ly7SOjwF8DVUVgHLIskjujhoeEfHu+XM7tRqiFL6h3vlkhrHE7xqYxLKfeeIB/oRec9MBCePrI2lclg1h7ruCd+unrsKNBRq6oBPfK8kGixcY0csI4IUMcRnQfs8V5wOnyJLMhz4DmH8NIVRUm8i8YFmRLnLsSzLoNzLQp0j6miPGKUC/Eki/eHQn2s952wXuhvCR2hDVep1NNFNLSmNArN4a1GAe4z0on5FgZ6aX4ZfofLxQoL3S8IQsPw89/vjWlyCiiCvSh9fP/Rl669R9kDPPPKZdM8B6fOVPdrthUz+xJE8YTgA52gCPMfSxtIqlR+7uIrujrH7ns//79//8Jc/f/9//vqX7//4hyn99un/AR7AXEwKZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iagoxMTQzNgplbmRvYmoKMTAgMCBvYmoKWyBdCmVuZG9iagoxNyAwIG9iago8PCAvTGVuZ3RoIDIzNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UUluADEIu+cV/kClsCfvmarqof3/tYZRLwMD2Ngk78FGJD7EkO4oV3zK6jTL8DtZ5MXPSuHkvYgKpCrCCmkHz3JWMwyeG5kClzPxWWY+mRY7FlBNxHF25DSDQYhpXEfL6TDTPOgJuT4YcWOnWa5iSOvdUr2+1/KfKspH1t0st07Z1ErdomfsSVx2Xk9taV8YdRQ3BZEOHzu8B/ki5iwuOpFu9psph5WkITgtgB+JoVTPDq8RJn5mJHjKnk7vozS89kHT9b17QUduJmQqt1BGKp6sNMaMofqNaCap7/+BnvW9vv4AQ01UuQplbmRzdHJlYW0KZW5kb2JqCjE4IDAgb2JqCjw8IC9MZW5ndGggNjEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzU1VzBQsLQAEqamRgrmRpYKKYZcQD6IlctlaGkOZuWAWRbGQAZIGZxhAKTBmnNgenK4MrjSAMsVEMwKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvTGVuZ3RoIDkyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2NwQ3AMAgD/0zBCBACxPtUVR/p/t8mEeoHHwbZGGBhszXgwdnAl9LaN72kRZPaCFa1Rd1QnrsUpVhdR6VMwk+ZO39SdBztcA7b39blOE3j6F/30P0BD0oeCwplbmRzdHJlYW0KZW5kb2JqCjIwIDAgb2JqCjw8IC9MZW5ndGggMzA3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2SS24DMQxD9z6FLhDA+tme86Qoupjef9snJemKHNkWRWqWukxZUx6QNJOEf+nwcLGd8jtsz2Zm4Fqil4nllOfQFWLuonzZzEZdWSfF6oRmOrfoUTkXBzZNqp+rLKXdLngO1yaeW/YRP7zQoB7UNS4JN3RXo2UpNGOq+3/Se/yMMuBqTF1sUqt7HzxeRFXo6AdHiSJjlxfn40EJ6UrCaFqIlXdFA0Hu8rTKewnu295qyLIHqZjOOylmsOt0Ui5uF4chHsjyqPDlo9hrQs/4sCsl9EjYhjNyJ+5oxubUyOKQ/t6NBEuPrmgh8+CvbtYuYLxTOkViZE5yrGmLVU73UBTTucO9DBD1bEVDKXOR1epfw84La5ZsFnhK+gUeo90mSw5W2duoTu+tPNnQ9x9a13QfCmVuZHN0cmVhbQplbmRvYmoKMjEgMCBvYmoKPDwgL0xlbmd0aCAyNDQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZFNcgUhCIT3nqIv8KrkVz3PpFJZTO6/Dc28JCtaheYD0wITR/ASQ+yJlRMfMnwv6DJ8tzI78DrZmXBPuG5cw2XDM2Fb4DsqyzteQ3e2Uj+doarvGjneLlI1dGVkn3qhmgvMkIiuEVl0K5d1QNOU7lLhGmxbghT1SqwnnaA06BHK8HeUa3x1E0+vseRUzSFaza0TGoqwbHhB1MkkEbUNiyeWcyFR+aobqzouYJMl4vSA3KCVZnx6UkkRMIN8rMlozAI20JO7ZxfGmkseRY5XNJiwO0k18ID34ra+9zZxj/MX+IV33/8rDn3XAj5/AEv+XQYKZW5kc3RyZWFtCmVuZG9iagoyMiAwIG9iago8PCAvTGVuZ3RoIDIzMSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1TzmSBCEMy3mFPjBVGNtAv6entjbY+X+6kplOkPAhydMTHZl4mSMjsGbH21pkIGbgU0zFv/a0DxOq9+AeIpSLC2GGkXDWrONuno4X/3aVz1gH7zb4illeENjCTNZXFmcu2wVjaZzEOclujF0TsY11radTWEcwoQyEdLbDlCBzVKT0yY4y5ug4kSeei+/22yx2OX4O6ws2jSEV5/gqeoI2g6Lsee8CGnJB/13d+B5Fu+glIBsJFtZRYu6c5YRfvXZ0HrUoEnNCmkEuEyHN6SqmEJpQrLOjoFJRcKk+p+isn3/lX1wtCmVuZHN0cmVhbQplbmRvYmoKMjMgMCBvYmoKPDwgL0xlbmd0aCAyNDkgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVA7jkQhDOs5hS/wJPIjcB5Gqy1m79+uA5opUEx+tjMk0BGBRwwxlK/jJa2groG/i0LxbuLrg8Igq0NSIM56D4h07KY2kRM6HZwzP2E3Y47ARTEGnOl0pj0HJjn7wgqEcxtl7FZIJ4mqIo7qM44pnip7n3gWLO3INlsnkj3kIOFSUonJpZ+Uyj9typQKOmbRBCwSueBkE004y7tJUowZlDLqHqZ2In2sPMijOuhkTc6sI5nZ00/bmfgccLdf2mROlcd0Hsz4nLTOgzkVuvfjiTYHTY3a6Oz3E2kqL1K7HVqdfnUSld0Y5xgSl2d/Gd9k//kH/odaIgplbmRzdHJlYW0KZW5kb2JqCjI0IDAgb2JqCjw8IC9MZW5ndGggMzk1IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD1SS27FQAjb5xRcoNLwm895UlXdvPtva0NSqSq8iTHGMH3KkLnlS10ScYXJt16uWzymfC5bWpl5iLuLjSU+ttyX7iG2XXQusTgdR/ILMp0qRKjNqtGh+EKWhQeQTvChC8J9Of7jL4DB17ANuOE9MkGwJOYpQsZuURmaEkERYeeRFaikUJ9Zwt9R7uv3MgVqb4ylC2Mc9Am0BUJtSMQC6kAAROyUVK2QjmckE78V3WdiHGDn0bIBrhlURJZ77MeIqc6ojLxExD5PTfoolkwtVsZuUxlf/JSM1Hx0BSqpNPKU8tBVs9ALWIl5EvY5/Ej459ZsIYY6btbyieUfM8UyEs5gSzlgoZfjR+DbWXURrh25uM50gR+V1nBMtOt+yPVP/nTbWs11vHIIokDlTUHwuw6uRrHExDI+nY0peqIssBqavEYzwWEQEdb3w8gDGv1yvBA0p2sitFgim7ViRI2KbHM9vQTWTO/FOdbDE8Js753WobIzMyohgtq6hmrrQHazvvNwtp8/M+iibQplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSYoDMAy75xX6QCFek7ynQ5lD5//Xyg6FOQQJr5KTlphYCw8xhB8sPfiRIXM3/Rt+otm7WXqSydn/mOciU1H4UqguYkJdiBvPoRHwPaFrElmxvfE5LKOZc74HH4W4BDOhAWN9STK5qOaVIRNODHUcDlqkwrhrYsPiWtE8jdxu+0ZmZSaEDY9kQtwYgIgg6wKyGCyUNjYTMlnOA+0NyQ1aYNepG1GLgiuU1gl0olbEqszgs+bWdjdDLfLgqH3x+mhWl2CF0Uv1WHhfhT6YqZl27pJCeuFNOyLMHgqkMjstK7V7xOpugfo/y1Lw/cn3+B2vD838XJwKZW5kc3RyZWFtCmVuZG9iagoyNiAwIG9iago8PCAvTGVuZ3RoIDk0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWNwRHAIAgE/1RBCQoK2k8mk4f2/40QMnxg5w7uhAULtnlGHwWVJl4VWAdKY9xQj0C94XItydwFD3Anf9rQVJyW03dpkUlVKdykEnn/DmcmkKh50WOd9wtj+yM8CmVuZHN0cmVhbQplbmRvYmoKMjcgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRVJLbkQxCNu/U3CBSOGXkPO0qrqY3n9bm0zVzeAJYGx4y1OmZMqwuSUjJNeUT30iQ6ym/DRyJCKm+EkJBXaVj8drS6yN7JGoFJ/a8eOx9Eam2RVa9e7Rpc2iUc3KyDnIEKGeFbqye9QO2fB6XEi675TNIRzL/1CBLGXdcgolQVvQd+wR3w8droIrgmGway6D7WUy1P/6hxZc7333YscugBas577BDgCopxO0BcgZ2u42KWgAVbqLScKj8npudqJso1Xp+RwAMw4wcsCIJVsdvtHeAJZ9XehFjYr9K0BRWUD8yNV2wd4xyUhwFuYGjr1wPMWZcEs4xgJAir3iGHrwJdjmL1euiJrwCXW6ZC+8wp7a5udCkwh3rQAOXmTDraujqJbt6TyC9mdFckaM1Is4OiGSWtI5guLSoB5a41w3seJtI7G5V9/uH+GcL1z26xdL7ITECmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0xlbmd0aCAxNjQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZDHcQUxDEPvqgIlMIAK9azH8w/r/q+G9NNBehhCDGJPwrBcV3FhdMOPty0zDX9HGe7G+jJjvNVYICfoAwyRiavRpPp2xRmq9OTVYq6jolwvOiISzJLjq0AjfDqyx5O2tjP9dF4f7CHvE/8qKuduYQEuqu5A+VIf8dSP2VHqmqGPKitrHmraV4RdEUrbPi6nMk7dvQNa4b2Vqz3a7z8edjryCmVuZHN0cmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0xlbmd0aCA3MiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohngJggbRDFIBZEsZmJGUQdnAGRy+BKAwAl2xbJCmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCA0NyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlyWEFYuF0wsB8wC0ZZwCiKewZUGALlnDScKZW5kc3RyZWFtCmVuZG9iagozMSAwIG9iago8PCAvVHlwZSAvWE9iamVjdCAvU3VidHlwZSAvRm9ybSAvQkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0xlbmd0aCAzOQovRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJzjMjQwUzA2NVXI5TI3NgKzcsAsI3MjIAski2BBZDO40gAV8wp8CmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAxNjMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZA7EgMhDEN7TqEj+CMDPs9mMik2929j2GxSwNNYIIO7E4LU2oKJ6IKHtiXdBe+tBGdj/Ok2bjUS5AR1gFak42iUUn25xWmVdPFoNnMrC60THWYOepSjGaAQOhXe7aLkcqbuzvlDcPVf9b9i3TmbiYHJyh0IzepT3Pk2O6K6usn+pMfcrNd+K+xVYWlZS8sJt527ZkAJ3FM52qs9Px8KOvYKZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvTGVuZ3RoIDIxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9ULmNBDEMy12FGljAeu2pZxaLS6b/9Ej59iLRFkVSKjWZkikvdZQlWVPeOnyWxA55huVuZDYlKkUvk7Al99AK8X2J5hT33dWWs0M0l2g5fgszKqobHdNLNppwKhO6oNzDM/oNbXQDVocesVsg0KRg17YgcscPGAzBmROLIgxKTQb/rnKPn16LGz7D8UMUkZIO5jX/WP3ycw2vU48nkW5vvuJenKkOAxEckpq8I11YsS4SEWk1QU3PwFotgLu3Xv4btCO6DED2icRxmlKOob9rcKXPL+UnU9gKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvTGVuZ3RoIDgzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4m9j5RlMLevw0QJW64J909XB0JmSluM8NDBp4MLIZdcYH0ljALXEdQjp3so2HVvuoEjfWmUvPvD5Se7KzihusBAkIaZgplbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8IC9MZW5ndGggNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhyuDKw0A4bQNmAplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggMTYwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQORIDMQgEc72CJ0hcgvesy7XB+v+pB9ZHoukCNBy6Fk3KehRoPumxRqG60GvoLEqSRMEWkh1Qp2OIOyhITEhjkki2HoMjmlizXZiZVCqzUuG0acXCv9la1chEjXCN/InpBlT8T+pclPBNg6+SMfoYVLw7g4xJ+F5F3Fox7f5EMLEZ9glvRSYFhImxqdm+z2CGzPcK1zjH8w1MgjfrCmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0xlbmd0aCAzMzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVJLcsUgDNtzCl2gM/gH5DzpdLp4vf+2kpNFRg5g9DHlholKfFkgt6PWxLeNzECF4a+rzIXPSNvIOojLkIu4ki2Fe0Qs5DHEPMSC76vxHh75rMzJswfGL9l3Dyv21IRlIePFGdphFcdhFeRYsHUhqnt4U6TDqSTY44v/PsVzLQQtfEbQgF/kn6+O4PmSFmn3mG3TrnqwTDuqpLAcbE9zXiZfWme5Oh7PB8n2rtgRUrsCFIW5M85z4SjTVka0FnY2SGpcbG+O/VhK0IVuXEaKI5CfqSI8oKTJzCYK4o+cHnIqA2Hqmq50chtVcaeezDWbi7czSWbrvkixmcJ5XTiz/gxTZrV5J89yotSpCO+xZ0vQ0Dmunr2WWWh0mxO8pITPxk5PTr5XM+shORUJqWJaV8FpFJliCdsSX1NRU5p6Gf778u7xO37+ASxzfHMKZW5kc3RyZWFtCmVuZG9iagozOCAwIG9iago8PCAvTGVuZ3RoIDMyMCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UktuBTEI288puECl8E/O86qqi777b2sTvRVMMGDjKS9Z0ku+1CXbpcPkWx/3JbFC3o/tmsxSxfcWsxTPLa9HzxG3LQoEURM9WJkvFSLUz/ToOqhwSp+BVwi3FBu8g0kAg2r4Bx6lMyBQ50DGu2IyUgOCJNhzaXEIiXImiX+kvJ7fJ62kofQ9WZnL35NLpdAdTU7oAcXKxUmgXUn5oJmYSkSSl+t9sUL0hsCSPD5HMcmA7DaJbaIFJucepSXMxBQ6sMcCvGaa1VXoYMIehymMVwuzqB5s8lsTlaQdreMZ2TDeyzBTYqHhsAXU5mJlgu7l4zWvwojtUZNdw3Duls13CNFo/hsWyuBjFZKAR6exEg1pOMCIwJ5eOMVe8xM5DsCIY52aLAxjaCaneo6JwNCes6VhxsceWvXzD1TpfIcKZW5kc3RyZWFtCmVuZG9iagozOSAwIG9iago8PCAvTGVuZ3RoIDE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwgMMUQ640AB3mA1IKZW5kc3RyZWFtCmVuZG9iago0MCAwIG9iago8PCAvTGVuZ3RoIDEzMyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFj0sOBCEIRPecoo7Axx/ncTLphXP/7YCdbhNjPYVUgbmCoT0uawOdFR8hGbbxt6mWjkVZPlR6UlYPyeCHrMbLIdygLPCCSSqGIVCLmBqRLWVut4DbNg2yspVTpY6wi6Mwj/a0bBUeX6JbInWSP4PEKi/c47odyKXWu96ii75/pAExCQplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9MZW5ndGggMzQwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSOW4EMQzr/Qp9IIBu2+/ZIEiR/L8NqdkUA3F0UpQ7WlR2y4eFVLXsdPm0ldoSN+R3ZYXECcmrEu1ShkiovFYh1e+ZMq+3NWcEyFKlwuSk5HHJgj/DpacLx/m2sa/lyB2PHlgVI6FEwDLFxOgals7usGZbfpZpwI94hJwr1i3HWAVSG9047Yr3oXktsgaIvZmWigodVokWfkHxoEeNffYYVFgg0e0cSXCMiVCRgHaB2kgMOXssdlEf9DMoMRPo2htF3EGBJZKYOcW6dPTf+NCxoP7YjDe/OirpW1pZY9I+G+2Uxiwy6XpY9HTz1seDCzTvovzn1QwSNGWNksYHrdo5hqKZUVZ4t0OTDc0xxyHzDp7DGQlK+jwUv48lEx2UyN8ODaF/Xx6jjJw23gLmoj9tFQcO4rPDXrmBFUoXa5L3AalM6IHp/6/xtb7X1x8d7YDGCmVuZHN0cmVhbQplbmRvYmoKNDIgMCBvYmoKPDwgL0xlbmd0aCAyNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVFJcgNBCLvPK/SEZqffY5crh+T/1wjKBwYNi0B0WuKgjJ8gLFe85ZGraMPfMzGC3wWHfivXbVjkQFQgSWNQNaF28Xr0HthxmAnMk9awDGasD/yMKdzoxeExGWe312XUEOxdrz2ZQcmsXMQlExdM1WEjZw4/mTIutHM9NyDnRliXYZBuVhozEo40hUghhaqbpM4EQRKMrkaNNnIU+6Uvj3SGVY2oMexzLW1fz004a9DsWKzy5JQeXXEuJxcvrBz09TYDF1FprPJASMD9bg/1c7KT33hL584W0+N7zcnywlRgxZvXbkA21eLfvIjj+4yv5+f5/ANfYFuICmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0x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"visualize_weight_distribution(model)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "0d58aa0a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.035169, "end_time": "2023-03-15T10:01:16.661623", "exception": false, "start_time": "2023-03-15T10:01:16.626454", "status": "completed"}, "tags": []}, "source": ["As we expected, the variance stays indeed constant across layers.\n", "Note that our initialization does not restrict us to a normal distribution, but allows any other distribution with a mean of 0 and variance of $1/d_x$.\n", "You often see that a uniform distribution is used for initialization.\n", "A small benefit of using a uniform instead of a normal distribution is that we can exclude the chance of initializing very large or small weights.\n", "\n", "Besides the variance of the activations, another variance we would like to stabilize is the one of the gradients.\n", "This ensures a stable optimization for deep networks.\n", "It turns out that we can do the same calculation as above starting from $\\Delta x=W\\Delta y$, and come to the conclusion that we should initialize our layers with $1/d_y$ where $d_y$ is the number of output neurons.\n", "You can do the calculation as a practice, or check a thorough explanation in [this blog post](https://pouannes.github.io/blog/initialization).\n", "As a compromise between both constraints, [Glorot and Bengio (2010)](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf?hc_location=ufi) proposed to use the harmonic mean of both values.\n", "This leads us to the well-known Xavier initialization:\n", "\n", "$$W\\sim \\mathcal{N}\\left(0,\\frac{2}{d_x+d_y}\\right)$$\n", "\n", "If we use a uniform distribution, we would initialize the weights with:\n", "\n", "$$W\\sim U\\left[-\\frac{\\sqrt{6}}{\\sqrt{d_x+d_y}}, \\frac{\\sqrt{6}}{\\sqrt{d_x+d_y}}\\right]$$\n", "\n", "Let's shortly implement it and validate its effectiveness:"]}, {"cell_type": "code", "execution_count": 17, "id": "2142ee53", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:16.731267Z", "iopub.status.busy": "2023-03-15T10:01:16.730482Z", "iopub.status.idle": "2023-03-15T10:01:30.121929Z", "shell.execute_reply": "2023-03-15T10:01:30.120781Z"}, "papermill": {"duration": 13.431786, "end_time": "2023-03-15T10:01:30.126513", "exception": false, "start_time": "2023-03-15T10:01:16.694727", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "753490ce", "metadata": {"papermill": {"duration": 0.039421, "end_time": "2023-03-15T10:01:30.207960", "exception": false, "start_time": "2023-03-15T10:01:30.168539", "status": "completed"}, "tags": []}, "source": ["We see that the Xavier initialization balances the variance of gradients and activations.\n", "Note that the significantly higher variance for the output layer is due to the large difference of input and output dimension ($128$ vs $10$).\n", "However, we currently assumed the activation function to be linear.\n", "So what happens if we add a non-linearity?\n", "In a tanh-based network, a common assumption is that for small values during the initial steps in training, the $\\tanh$ works as a linear function such that we don't have to adjust our calculation.\n", "We can check if that is the case for us as well:"]}, {"cell_type": "code", "execution_count": 18, "id": "5edd7693", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:30.287794Z", "iopub.status.busy": "2023-03-15T10:01:30.287261Z", "iopub.status.idle": "2023-03-15T10:01:43.327848Z", "shell.execute_reply": "2023-03-15T10:01:43.326768Z"}, "papermill": {"duration": 13.085097, "end_time": "2023-03-15T10:01:43.332063", "exception": false, "start_time": "2023-03-15T10:01:30.246966", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"id": "3edf447e", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.047557, "end_time": "2023-03-15T10:01:43.432937", "exception": false, "start_time": "2023-03-15T10:01:43.385380", "status": "completed"}, "tags": []}, "source": ["Although the variance decreases over depth, it is apparent that the activation distribution becomes more focused on the low values.\n", "Therefore, our variance will stabilize around 0.25 if we would go even deeper.\n", "Hence, we can conclude that the Xavier initialization works well for Tanh networks.\n", "But what about ReLU networks?\n", "Here, we cannot take the previous assumption of the non-linearity becoming linear for small values.\n", "The ReLU activation function sets (in expectation) half of the inputs to 0 so that also the expectation of the input is not zero.\n", "However, as long as the expectation of $W$ is zero and $b=0$, the expectation of the output is zero.\n", "The part where the calculation of the ReLU initialization differs from the identity is when determining $\\text{Var}(w_{ij}x_{j})$:\n", "\n", "$$\\text{Var}(w_{ij}x_{j})=\\underbrace{\\mathbb{E}[w_{ij}^2]}_{=\\text{Var}(w_{ij})}\\mathbb{E}[x_{j}^2]-\\underbrace{\\mathbb{E}[w_{ij}]^2}_{=0}\\mathbb{E}[x_{j}]^2=\\text{Var}(w_{ij})\\mathbb{E}[x_{j}^2]$$\n", "\n", "If we assume now that $x$ is the output of a ReLU activation (from a previous layer, $x=max(0,\\tilde{y})$), we can calculate the expectation as follows:\n", "\n", "\n", "$$\n", "\\begin{split}\n", "    \\mathbb{E}[x^2] & =\\mathbb{E}[\\max(0,\\tilde{y})^2]\\\\\n", "                    & =\\frac{1}{2}\\mathbb{E}[{\\tilde{y}}^2]\\hspace{2cm}\\tilde{y}\\text{ is zero-centered and symmetric}\\\\\n", "                    & =\\frac{1}{2}\\text{Var}(\\tilde{y})\n", "\\end{split}$$\n", "\n", "Thus, we see that we have an additional factor of 1/2 in the equation, so that our desired weight variance becomes $2/d_x$.\n", "This gives us the Kaiming initialization (see [He, K. et al.\n", "(2015)](https://arxiv.org/pdf/1502.01852.pdf)).\n", "Note that the Kaiming initialization does not use the harmonic mean between input and output size.\n", "In their paper (Section 2.2, Backward Propagation, last paragraph), they argue that using $d_x$ or $d_y$ both lead to stable gradients throughout the network, and only depend on the overall input and output size of the network.\n", "Hence, we can use here only the input $d_x$:"]}, {"cell_type": "code", "execution_count": 19, "id": "0fc03ebf", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:43.528534Z", "iopub.status.busy": "2023-03-15T10:01:43.528284Z", "iopub.status.idle": "2023-03-15T10:01:56.776318Z", "shell.execute_reply": "2023-03-15T10:01:56.775514Z"}, "papermill": {"duration": 13.3012, "end_time": "2023-03-15T10:01:56.781208", "exception": false, "start_time": "2023-03-15T10:01:43.480008", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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W/Z6Cx0HrwwO8mBA7NTsiED0E3/bDBiEuGIxlI7FqfeIALybEhLEIgegh5FjICHHBJGeKY399Dvqc8GJCTBiLEFUW/EIybw0FU0sm8/5enwNeTIhOzQyIiuCBZBN91EHBtFEopWXAKBsKXUuGg5kZD21VDjfYjAcF0xYpVK/VIY0uJsNOrBoZEDmwS8EOhwuG/yRl9rxWpw7wYkLs1Ox4qJKoJUkWoxAXHCVVIlQtD2l0MRkmfN+/kEPckIKv4/tCw3L01KV8J86BLiXDSSwYGTC8U0bcZGS4YN5ikHQ0rU4Y4MWE2KnZ8cCynpCTHQ8XnDcna1BanqrRxWSY8BUZJDuxcrTj4YTZSeFhinVUR8OLCTFhLOkfkoTJRGY8XHArlV9q8UYfBa8lxIxxz4ORZFSzUqlg4cCYHrMRaMAXk2LCWaTgDR+rzo6JE65lc07q0o8KaXgxIXZqNjdKmnmEZA4vKTjJeQ3mVrtPGdHwYkLs1OyIQNjgSsx2RJwwB9hNuflNyoiGFxNiwhhCBAQOXjyCUQgFF0kEz81z0kYUvJYQB7VshEDkEJwzxR003Nqa5BZgaiMKXkyICWMRAqEDbnywI+KCJaelBrL6KHgxITo1qwNiB/LJlDvXcITvQJRIyUMDupgMOzOjAyF2aEeBRh0UTFshzq3MuTai4LWE6NTMq5Pa0SJfopHhgkkiTMfhUmcEFxNhJ2ZHAyKHWGqyo+GCCRMCuXbYSRtR8GJC7NRM3IlLChwz2/FwwXJQMLQTgdrGhS4mw05sVCE6Gd9sz1soGHw90T4cThsKXUuGg5kZDfijpEQhGB0umJrDQEHLUwd4MSEmjEUIxAw5eLNKqWHZwCLfCuKN+pzwYkLs1MwJjNhOsZZqR8QFy5ZmSiFpfdIALybETs1OEbU1gLErlQoGY7wh21FdbUTBiwkxYSxnckI/b24cyhEuLvYIYzRywGsJMWMsQsRZt0QDH4kwxsiS+TEnNStE6g0BvRFigHMIpOVhjS4mQ5q1UCSuvVuiHQ8DfOQMGiNLphJOGUOI5HubQDMeDLwnWFsjK+ZdTxmLENRaBhonYkT3rGJrYsVk4xlfUYF770ATYhj41GGmzqfKoI9BvbR6K7ePFOW+3spxhubY2C7pSBKMkWgvt1tjbxoWKklFFOpn/SNTaQV0Q5UD6j53WHpMcd/nkmO6peUXS8UtzArHfl8lXGFqRWhLyt3tdPiMp9aeNSFAjaXVqiCXNqk0ENuiJy6vtkoeAhfyXori141zTM2Hd1kaTTP1ugz4lb6NAlgKsuRe9EQ8uv7puvlYWDbcUjtL21qDCYzHtrpWKTyBTY+TvJQNzxhLtyS1c2tuBVwELhAN+sixsihVGhrcFucrboCU2MjF52MbI4XWcnGo+AEfewu4fTDZKorklPseMXA4HPjdm5RzwW3k7ohIOQ+4o7iYImqWXpBGYLlXcjGt9o3rGnreWEK71hoI3yqxM4JFTFa9+EaRH407nIpzuEaWcim+J/jJersU2ZCaHJjsvYKljagUlJGWoin2e+/LJnpKUQ4PEXEL+4qcnKXDWKzttrla+lp+kOkCI4JbYWaE4kQ7DCfDS9NaqZnjMqV9eTP4VtVKCuz4lm1GofX58lJlA2578LmPk1C2Ar1Dr4bBNfW9VpKKOKH2hJ0UUy+MQhSlHuZeGRH/f+qyEsY3HuW+kEIupD1yrlvIkXreWwhhV1sK6xYprNqL6yGEOH1rd6xw4/9vTxRxgQMR8r6giYtuRXruppPUiuM8gF9RPePBGepHNRdgeXq8+v3D6g34xqvPac9//YO/8ZpqGkEK/mOecbKnzK8qpxF+zXIaMQTMZlxNsg1H6WV11/tEwYNYyoqfltO4PuDGn/rks7iR5EFNbLJLFWxv6xNHcSOmlMLwvD1LfZxXltPwb1BN4zMKKJP7vUuq4LcQMEsldLFGUvTs4wpJ2DPHn08DvAo2ltpu40Nwom+gAOI3aYvkovMyB32UAv5BJY0oha5i+/qnVNH4jGPqVbPKk0NKUpmuq3+uisYXkUBS17O0BB0UONGni7PkrAXAEIWjBeexSNbjK2tofBEd4M1Jq8RSjRIKfyMtWpE8H6Uuovj6r6yh8WXEkPrQrshazyjGhb+VGEk8WEy8xCGU11TRUIUjxE1SmjxfOOJJcZ+MyiFFfn1UHqWmIT5rmoFFKWlYEH4Ms5dBc26V96yJHT6j8iVk2JmxlSH09h7e6DDChxDGyJpC7NTYCMG9zUc1QjyAJ0aWE2KnZoXIs+4oD+GJkeWEmFATIWpv+1EM4xE+H43RyJqPRqNG5mx8xB+t/YeZKw18CjEaWVKIgxoZIeKsa8pDeGJkOSE6teyNEGnWPsXAObq20maMHPBiQuzU7KNResMUK8QAZ19bHTVj5IAXE2LCGEKw7w1CxveAga8RMeqz5Ig4qJmlFqbWQMWTEWKAS+BWF9oYOeDFhOjUzPpAZO5tQ4zDMMKllFZD3xg54MWEmDAWIUpvH2L8CAW3HJnSUgG0EQUvJsSEMYRIrrcRMY+GgqVQRoou3OtzwGsJMWMsQoTeTsQEFQoG4yBl5O/1OeDFhJgwFiG4txUxc4SCqR1HqBN9DngxISaMRYjcq9ybOULBtHn53XSnzwkvJsSEsQhRt1QiO+NHKJg2aR7A9/qc8GJCTBhDiOwlTS6YQ34aJtn2d92zHPQ54bWEmDEWISJs4GNmRCiYthJSbtV5R31OeDEhJoxFCEQPztso/ELjJivgLaVAm1DwYjLc8xUVEDq4nKIdDhfMW64p9sWIy4ZCF5NhQli2dL0cvjAHHRVa8YrItbtSyoSC15JhwrdvbLOX7xsZThivygSXqTV/umxodDEZJoRFB0QNNvbeMWka5clJetGgzIAvJoJlKwoUYZOLFeGEpU2TrNAWpY3GFpNgQhcqVCf9ykzJZ4W2nnFMRhoNryXDwcysT9Yg2Xpk1ycVLI2wXO8Hpo0oeDEhdmp2PEiKafF2e0/BknvoeoEtbUTBiwkxYSxCIFJgWDDTg4JJUn7TPiIuIwpeTIidmvGfKiIFrtkHI8QFE1ym2Hs/aiMKXkyIndo4Ith5+SPWcURoGJFUhNMUtD5+gJcSYspYhJBaBz6YhnUaRmwdY1+RHfU54cWEmDAWIdLGOTvTFVrDUq00SRtMq88JLybEhLEIUTZ8Q1pXjkJcsLSBJm7HW0d9TngxITo1sz7J3m9e8snNHKFgOfBc+tEBbUTBawlxUMtGCGlUG6M5Ga9hWaMvvcOnNqLgxYSYMBYhEDjAQTSNczQsuza5Ur3X54AXE6JTM29PLy1fudq35wj3g67vjI0DXUyGnZnRIeBr3mezOmngY1/XGFlyu3fKWIRA7OCzdGAehRhgaSQ802eHFxNip2amysDSFJvYTJUjfOR+GCNLpoQc1EwNTw7S77h6U8PTwDmF1lnaGDngxYTYqRl3KtR+Yta4UwaupdUTskZ2eDEh6qxZH5PvXefMo2HgPWPQGlkxkfCkZiZLOdAmXeeMDnN0YmI5GTozs5fDlHrPOfPyfARPjCwnxISaCFF6Tz576x/AU30WE6JTM5kxHH3vOTcuYFv4mCGMkSVniIOaOa4ox8lTTaZ+p4UPIYyRNxLiLctGnGdodv/HOerJcW7D1eZ90pOSWm2bKsUt5VbHWxZXHBXO++5+Iu/6AfrIuZ24k/OoqWDebGUjoqdMcd/1xK+ULKv6FfFo7B+ukjHDsgeQpSpDpLzDEK3UKlUjMNuWXNseiZPyDyEG2TmNNfc+2QJXyc3jVqyA2aW67y/CUMXdkdIG1D4gcJBCAiG3+gjSE8odMO5Z8vGW6ibnadsBf9mhCz5XlgoO0ltcShDseJaCE0MdiFiipHukCDziFjiJKXa8MldMlvgZlr2dtO974T/hX8gWqIckPWuiJClgl+QKy5YDuB1wqnLIVfiUgivpRnIrjsqtOoaUvaMDLvBepJIGbpTvCa6lSCMTt+9I4zHxXSkwrq4dq8FdSB6/3lZZvbR/ydwbH6SSk9/h6op3bYPGSXkWv6/SY8x7bn0hcJN69xx2DrZLiC0WJ2r3WmIv3GEP3Y4ujnnyQPVh+RB+Rd2EB6dmHx2uh+Xpgdr3D4/p4xuvPpk7//UP/sZr6ibIGRg8h+0s82uKJsRfs2gConFo5CmNE3CWUsC9/oMWUcGDUsqKnxZNOD8g/8SnpuAH51BTiHimfTKlTRVs7+kTx1CTLOnlEDBdVjztv0LRhM8oYMLM6oMts6/gtxCQZdaGNcIs5Z8smvD5NEDw1Srn0BjDK/gtNKiYpqO8oDGxfmLZhCQFjY4SLZ9WOuHzqfq6ueXJWhSF9vTwdvXPlU74IhLAFYBLAhdllOCCn5aAWUsA1yfDawou5EIfUMHP6gV8ESU8SdktuGd+lELhb6SFJ/jXCW98+Jxw3l5ZSeLLiCHONDHDtR3FuPC3EiNLPzV4AXhm4ocqSdAHiiewnF27bD5fPOFJcZ+MTj3Dl311dJoc934R48INHtbWH8DUhzZwjv3skjFywGd0uoYQO7VxvSJJjT/pnzCuaD6CZ0ZWE+Kglo0QofcIMDpoNEvkptXJA7yYDPd8RYU466tg4EuHmTqLyTAhLDrk3iLAGx0G+NJhKs9iQkwYixB11lnBwOdEafRZcqI8qNlo1PceAaPLa+ASJUfgnTVywGsJcVALRojYewRkI8QA5yo5I++skQNeTIhOzTwZuMDWIsA8GY/gexvLybAzMx5EKLPGCgY+H4zRyKIPRqdWzIAg3zsEmAdDwZJCJcVslT59g+CE1xLioGbWnYh6iwDjXCtYjoG70gokayMKXkyICWMRgnuPAONFK7gl1bmWZmn0OeDFhNipsRGitB4BVocTlRvP0QetDg/wYjJ0ZiZ9JEXXewSM4ZOGpetODa4qeXwe4LWEmDEWIUJrElDMO0PBLSOd6508B7qYDBO+IkOcdVbQsFRHIPzuvToHvJgQE8YiBOKGUKvJE9EwGOfsc73X54AXE2LCWISosr2do50hLljKRMS2ITzKc6CLyTDhCxkYUUOESTMeFEwt/TaUO3VOeC0hZoxFCNlwymSKVmqYNsk+4Xt9TngxISaMRYgk3VdctSPigqVghPMtGUYbUfBiQkwYixCIG/CdYkfEBcN/YoSa2eij4MWEmDCW3W3EDSmOWQPvBlgSnlJonUu0EQWvJcSMcd/mJ9t85J2GU96kA07rA6OMaHgxISaMRQjeUk7BPBkn6lvNYwrdq7xsDPhiQnRudq0ySfen4Oxa5QVLUyg5DZyVQDCi4cWEmDCGEBk2Si5mFf9C80allpajp00oeC0ZJnxFhSAdDdgUstQwbyVlbh0tlQ2FLibDzsyOhriVWskEnRcqCU6IsKoWhwd4MRnu+YoKiBoce3NIXMNwniRvoVh1TnQxGXZmZjEmI2rwrtotPgXHzeE/atHy5AFeTIid2qhDQdAAXuaIuIZpSyH3/qbKhkLXkmFGWHSQFOzA5pCbhuExcGspYeU54cWEmDAWIRAzhELmhLiGaQvM1FpKjvqc8GJCTBiLEIgZJKncLFsrWE46UM3+Xp8DXkyICWMIUb2sP0s30UEIBZPs4eQ+VQ76nPBaQswYixBSOoftq/NCha9LE23cektzByuzNFcRLbCPpmu6hvUehjKy7B7GQc28LyriBc7BdE3XsN7VUkaW3dU6qJlkcCdmyflxIcbApfTXxWhjR5eS4SSWjAxBzsAVczbcwNIXuWh10gAvJsROjY0Q0ts2sh0OJ6rSHbSJVbMgDmYlGxlyPy9KRocBPhJgjJEl82JOavbBqL0lnTdCDPCRK2iMLJlCeFBjcxbJ+1mrOgMf2aPGyJJJpQc1eyjLx95zLRkhHsATI8sJEe+78IkQqfdcM++MET4fjdHImo/GjLEIUVrPNSPDFJxKtpgId8Tk4JCfdVsz8Dk7aBNvNTe8ZbmEuzMzjmNLcJDtJ857kptsu9Ce2yKTfu+VE4JzrWSAZDhEKtzLz+GapYO6bObhM+QrNzjnzEz7ri9Vl/bFSh/79gbzxlGadQlKsoSX9p1RH0rqC3rJUWr1UHFpsvpNMCgH+GPI+/aqHAxPqbbCyzXlemwqxojvgkyScg/4yQZXqTTbWkoWyO1TqyeZkpOCBS4VKTDgI0mw1OGcKHrpyo1h1Uok9H06ws9DNRAAR9/jSsAV+kCHFDbKfs8Nwh/sEF5IsQQnG97HHhfM46aPNRdSitCvRjlZHZ0s2jCVHce4KHi2pMwCgtfusyeGrhCfb6n37+kLHoAxG0nVCmgJNr5HOkmKu6SC1xlL5QvwTfv2CgYkBJCKEz4m3zeoU8EIi+L2SN2GkOlA4Q0FaTaWt+xrv+oqe9l74QuQ9T37I8Nc9VKpopfJSN0w4CxtLlslhhR85byv69dQpWaFlMrGVfUk1MxSmqf2LhQkB3w7nOCLQdi2CyBlO2KH5VrJxZZ+UaFkaaMV2mME7zEeYeznvh4iHUZ5z+OD05/7jnRJclaCe1obhifHHj+6TfbrXU+HlaX3vp4QZWuOdkc5+vOFmcDmOIeU4mzKCIU/AL+iJMSD08CPSgfA8vSg8PuHRQikiMRrTxzPf/2Dv/GakhBwwPC+qez3veJXVIVIv2ZVCMwYmBFSMQe8MK42e4blwAaZ1Pf9tB7E9QE//sgnH60tcoqokDdxhILtDX3iZG1xZWMSa45cfrKcwefToDonm/LZVFVS8FtoIGVpgljzklX6URrwg3IGWWyRZ1n4lZbsn1DO4POp+oqH4jlFs2TXXJf+XC2DL8Ifbx8S76co/hf2NH95M1+XjhdicKlK/p3/b87u/yoi+FDhduZ2cP+cBU/sjUTwQfyP4uDMJQofUCH+WipkcbmL1JpQMlzgW+kA/83hJVUZDlP+gA7pYekCRBFajudLFzyp67NRYsDfr48SpY6a1Ocft2ezuOb3Rf41LKFSaANY27jQM05cQ4admFlDkmJtUsvfhMsKluNUtbTSbtqIghcTIt83NYAQ1c1K/D+CZ0ZWE+KgZkZEDb1WvRkRCtYjQhlZdkQc1IwOsZfytwPigmlLmI1bxoqyodDFZJgQFh3yfcH7dwM8jActz6rjYcJYhKi9Vr15YyiYtgLHPaY7fU54MSEmjBFBOt9r1Y8jQsPSNWxPYdJGFLyUEFPGIkTsteqTEeKC0wY0tqKm2oiCFxNipxaNEKnXqjc6nGiWorp9TtQmFLyYDDuzYGQo9+Xu3w0w/su7/gRoIwpeTIgJY1ljcr1WvZkhLpjDJrsDuxCnEQ2vJcSMsQhBs1r1CuYktZ9CW1XX+ih4MSEmjEUI7rXq7Yg44exkMb13+1BGNLyYEBPGffm11fK3I+KEhz0mZWXAF5NiwhlSBNer1ZsxccGyCxcjt1Y4yoiG1xJixliECL3KuBkTF4zpoKTjvXEZ0fBiQuzUzHsjtJKt0WQlaLggqDimS2VEwYsJMWEsQuTNFe+rHREXnDYpwd3ygLURBS8mxISxCFFlMb0WO0dccNqcYzmabfU54cWEmDCGEOTxyZDNeqWGMQhckAJZoz4KXkuIgxobIWirtbBZetMw3pOSpxC0PjzAiwmxUzNzBKXWRIRMtKFgFW9rI8uG4TPGIkQLn7xpSvoQnsq2mBATahAiSjKD9FMZGStYLUVpI6uuUJ3UqhFCdt1kFdYIccHXNo62sejmzknMvDMigoeQo1mx1LBarNZGVl3DPqnZ8YDYgSgkOx4ueBDiMrKuEDs1MyIkZZKqsyuWCh6EuIwsK8RBrRghEDvEWExTUg2r7QttZNVdjZOaHRHwD3Ht3o6ICx6EuIysK8SEsQiB2IFTdHZEXLB6ZwzyrPnOOIgZ91pa00mer1Fhjk5MLCdDZ2ZqSpaEuEGSyEfCc3RmYjUZDmZmNCRqLfKS8aEfwRMjywnRqbGJMVLagqRBG8JzdGJiORnumYkKZWOEjsE+Fhes5sjLxKpT5M7LbvNlt5XqvZ0NH8AzI6sJcVAzwyHjxtZU7Hz4CJ4YWU6ICTURAhGDC6mYieARPNVnMSEm1EQIRAyuRLtK+Qie6rOYEJ2a3eYrTo7OBXN8X8NqplQ2Vp0qD2JmPBRpDOyc6XujYR1dKCPLRhczxiJE7Oc4TXShYDUeBnkWHQ8TviJD7p3P7Hh4AE9FW0yITs3b+aH23mfFMH4AT4wsJ8ROzYyI6lvzMxMzaFg/GJeNVR+MGV+RgXrvMzM/PIKnoi0mxISaCNHO8+Zox8MDeKrPYkJ0anZAlN76zDjRClbPxWXi7R6LtyxjYA7Q4BvZ76dPKKS9d42c6m+nbSp+J1DrCyl5YLW0ynctozpzK1lBWwmYDHr+ddmqJ45t4zfEEFomgJzwZLnMKJVXY3I90zBISWLuddwLy+HtBvMWELX7lpJafCtQIHCWY/okDRFqq8nYTnFJuqIv5PA7LOeoauX+acxN0WffykJ7z8H5HYZe0Kmd48fUdab6sXNRUv3iFmEvdNhLNQc5mMppcyX0Pm8CS/Pekm9cpIsLtyLbxYcN4w7XJ1UOoEDf+wdafJajd0k2Mij5sOfUSTFFXJ8c9Zc85CMVseYUcX0ZQ6sGznHPO4OoUgoB+uIe5No/jYFWRQfv2wmY2ArZCoyoxuMncW83Zsch7jlbkQP7BDyCMFOrGoHhJu+zAhY+ONySHFslCMHxkgu4WYgTN/h/1E5RSspTxb3CpSVZa6uUe5YDhiXk8q2KAwdcLe15QZBK0qYw8OFUlh1M+B9GmHQ1kLNz3YSMMMYAkXoKGV4X+R0ugUNp99hx9fFIRJJt0p6yixFTiXYY19rWvaTZEO5xY4nHC95s6gnfAEvZc1RwTakNQQzF1MdrqNKVJvsG4+7J0SLZo3VStKKtsGIcJwjS97BB13OkVowjS4WS/mk8AEwx7Xv8eABo3+glJ4d05MERLj0fvdViwOPS+33UQK12WmGp6O57CRHYiv1CuK1ilL39Gj7dySRpXYp5ZJ9Wco17PIP7XfppGNyg1MpRiHufXN9mxWyV6v5GD9W7vaBfDiFGPc11C4iVWx2vB/Arai88OLr86Jg+LN+fan4/P+ovdRpedTB6/qOPrb+m3oKcR6qMx7ZvbL+m3kKZ1VtQphGX4UFJCN5vmOLw2A024/0R9e+/++Hn25++/+nnH7//t19+/v6vPwzHSF/+H1qub7cKZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iagoxMDIyOQplbmRvYmoKMTAgMCBvYmoKWyBdCmVuZG9iagoxNyAwIG9iago8PCAvTGVuZ3RoIDIzNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UUluADEIu+cV/kClsCfvmarqof3/tYZRLwMD2Ngk78FGJD7EkO4oV3zK6jTL8DtZ5MXPSuHkvYgKpCrCCmkHz3JWMwyeG5kClzPxWWY+mRY7FlBNxHF25DSDQYhpXEfL6TDTPOgJuT4YcWOnWa5iSOvdUr2+1/KfKspH1t0st07Z1ErdomfsSVx2Xk9taV8YdRQ3BZEOHzu8B/ki5iwuOpFu9psph5WkITgtgB+JoVTPDq8RJn5mJHjKnk7vozS89kHT9b17QUduJmQqt1BGKp6sNMaMofqNaCap7/+BnvW9vv4AQ01UuQplbmRzdHJlYW0KZW5kb2JqCjE4IDAgb2JqCjw8IC9MZW5ndGggMjQ3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSW7EMAy7+xX8wACWrMV5T4pBD+3/ryUdFO3BECNLXOLuxEQWXrZQ10KH48NGXgmbge+D1pz4GrHiP9pGpJU/VFsgEzFRJHRRNxr3SDe8CtF+pIJXqvdY8xF3K81bOnaxv/fBtOaRKqtCPOTYHNlIWtdE0fE9tN5zQ3TKIIE+NyEHRGmOXoWkv/bDdW00u7U2syeqg0emhPJJsxqa0ylmyGyox20qVjIKN6qMivtURloP8jbOMoCT44QyWk92rCai/NQnl5AXE3HCLjs7FmITCxuHtB+VPrH8fOvN+JtpraWQcUEiNMWl32e8x+d4/wCVT1wmCmVuZHN0cmVhbQplbmRvYmoKMTkgMCBvYmoKPDwgL0xlbmd0aCAzMDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZJLbgMxDEP3PoUuEMD62Z7zpCi6mN5/2ycl6Yoc2RZFapa6TFlTHpA0k4R/6fBwsZ3yO2zPZmbgWqKXieWU59AVYu6ifNnMRl1ZJ8XqhGY6t+hRORcHNk2qn6sspd0ueA7XJp5b9hE/vNCgHtQ1Lgk3dFejZSk0Y6r7f9J7/Iwy4GpMXWxSq3sfPF5EVejoB0eJImOXF+fjQQnpSsJoWoiVd0UDQe7ytMp7Ce7b3mrIsgepmM47KWaw63RSLm4XhyEeyPKo8OWj2GtCz/iwKyX0SNiGM3In7mjG5tTI4pD+3o0ES4+uaCHz4K9u1i5gvFM6RWJkTnKsaYtVTvdQFNO5w70MEPVsRUMpc5HV6l/DzgtrlmwWeEr6BR6j3SZLDlbZ26hO76082dD3H1rXdB8KZW5kc3RyZWFtCmVuZG9iagoyMCAwIG9iago8PCAvTGVuZ3RoIDI0NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkU1yBSEIhPeeoi/wquRXPc+kUllM7r8NzbwkK1qF5gPTAhNH8BJD7ImVEx8yfC/oMny3MjvwOtmZcE+4blzDZcMzYVvgOyrLO15Dd7ZSP52hqu8aOd4uUjV0ZWSfeqGaC8yQiK4RWXQrl3VA05TuUuEabFuCFPVKrCedoDToEcrwd5RrfHUTT6+x5FTNIVrNrRMairBseEHUySQRtQ2LJ5ZzIVH5qhurOi5gkyXi9IDcoJVmfHpSSREwg3ysyWjMAjbQk7tnF8aaSx5Fjlc0mLA7STXwgPfitr73NnGP8xf4hXff/ysOfdcCPn8AS/5dBgplbmRzdHJlYW0KZW5kb2JqCjIxIDAgb2JqCjw8IC9MZW5ndGggMjMxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVPOZIEIQzLeYU+MFUY20C/p6e2Ntj5f7qSmU6Q8CHJ0xMdmXiZIyOwZsfbWmQgZuBTTMW/9rQPE6r34B4ilIsLYYaRcNas426ejhf/dpXPWAfvNviKWV4Q2MJM1lcWZy7bBWNpnMQ5yW6MXROxjXWtp1NYRzChDIR0tsOUIHNUpPTJjjLm6DiRJ56L7/bbLHY5fg7rCzaNIRXn+Cp6gjaDoux57wIackH/Xd34HkW76CUgGwkW1lFi7pzlhF+9dnQetSgSc0KaQS4TIc3pKqYQmlCss6OgUlFwqT6n6Kyff+VfXC0KZW5kc3RyZWFtCmVuZG9iagoyMiAwIG9iago8PCAvTGVuZ3RoIDI0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UDuORCEM6zmFL/Ak8iNwHkarLWbv364DmilQTH62MyTQEYFHDDGUr+MlraCugb+LQvFu4uuDwiCrQ1IgznoPiHTspjaREzodnDM/YTdjjsBFMQac6XSmPQcmOfvCCoRzG2XsVkgniaoijuozjimeKnufeBYs7cg2WyeSPeQg4VJSicmln5TKP23KlAo6ZtEELBK54GQTTTjLu0lSjBmUMuoepnYifaw8yKM66GRNzqwjmdnTT9uZ+Bxwt1/aZE6Vx3QezPictM6DORW69+OJNgdNjdro7PcTaSovUrsdWp1+dRKV3RjnGBKXZ38Z32T/+Qf+h1oiCmVuZHN0cmVhbQplbmRvYmoKMjMgMCBvYmoKPDwgL0xlbmd0aCAzOTUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVJLbsVACNvnFFyg0vCbz3lSVd28+29rQ1KpKryJMcYwfcqQueVLXRJxhcm3Xq5bPKZ8LltamXmIu4uNJT623JfuIbZddC6xOB1H8gsynSpEqM2q0aH4QpaFB5BO8KELwn05/uMvgMHXsA244T0yQbAk5ilCxm5RGZoSQRFh55EVqKRQn1nC31Hu6/cyBWpvjKULYxz0CbQFQm1IxALqQABE7JRUrZCOZyQTvxXdZ2IcYOfRsgGuGVRElnvsx4ipzqiMvETEPk9N+iiWTC1Wxm5TGV/8lIzUfHQFKqk08pTy0FWz0AtYiXkS9jn8SPjn1mwhhjpu1vKJ5R8zxTISzmBLOWChl+NH4NtZdRGuHbm4znSBH5XWcEy0637I9U/+dNtazXW8cgiiQOVNQfC7Dq5GscTEMj6djSl6oiywGpq8RjPBYRAR1vfDyAMa/XK8EDSnayK0WCKbtWJEjYpscz29BNZM78U51sMTwmzvndahsjMzKiGC2rqGautAdrO+83C2nz8z6KJtCmVuZHN0cmVhbQplbmRvYmoKMjQgMCBvYmoKPDwgL0xlbmd0aCAyNDkgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVFJigMwDLvnFfpAIV6TvKdDmUPn/9fKDoU5BAmvkpOWmFgLDzGEHyw9+JEhczf9G36i2btZepLJ2f+Y5yJTUfhSqC5iQl2IG8+hEfA9oWsSWbG98Tkso5lzvgcfhbgEM6EBY31JMrmo5pUhE04MdRwOWqTCuGtiw+Ja0TyN3G77RmZlJoQNj2RC3BiAiCDrArIYLJQ2NhMyWc4D7Q3JDVpg16kbUYuCK5TWCXSiVsSqzOCz5tZ2N0Mt8uCoffH6aFaXYIXRS/VYeF+FPpipmXbukkJ64U07IsweCqQyOy0rtXvE6m6B+j/LUvD9yff4Ha8PzfxcnAplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8IC9MZW5ndGggOTQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRY3BEcAgCAT/VEEJCgraTyaTh/b/jRAyfGDnDu6EBQu2eUYfBZUmXhVYB0pj3FCPQL3hci3J3AUPcCd/2tBUnJbTd2mRSVUp3KQSef8OZyaQqHnRY533C2P7IzwKZW5kc3RyZWFtCmVuZG9iagoyNiAwIG9iago8PCAvTGVuZ3RoIDM0MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFUktuRDEI279TcIFI4ZeQ87Squpjef1ubTNXN4AlgbHjLU6ZkyrC5JSMk15RPfSJDrKb8NHIkIqb4SQkFdpWPx2tLrI3skagUn9rx47H0RqbZFVr17tGlzaJRzcrIOcgQoZ4VurJ71A7Z8HpcSLrvlM0hHMv/UIEsZd1yCiVBW9B37BHfDx2ugiuCYbBrLoPtZTLU//qHFlzvffdixy6AFqznvsEOAKinE7QFyBna7jYpaABVuotJwqPyem52omyjVen5HAAzDjBywIglWx2+0d4Aln1d6EWNiv0rQFFZQPzI1XbB3jHJSHAW5gaOvXA8xZlwSzjGAkCKveIYevAl2OYvV66ImvAJdbpkL7zCntrm50KTCHetAA5eZMOtq6Oolu3pPIL2Z0VyRozUizg6IZJa0jmC4tKgHlrjXDex4m0jsblX3+4f4ZwvXPbrF0vshMQKZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvTGVuZ3RoIDE2NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkMdxBTEMQ++qAiUwgAr1rMfzD+v+r4b000F6GEIMYk/CsFxXcWF0w4+3LTMNf0cZ7sb6MmO81VggJ+gDDJGJq9Gk+nbFGar05NVirqOiXC86IhLMkuOrQCN8OrLHk7a2M/10Xh/sIe8T/yoq525hAS6q7kD5Uh/x1I/ZUeqaoY8qK2seatpXhF0RSts+LqcyTt29A1rhvZWrPdrvPx52OvIKZW5kc3RyZWFtCmVuZG9iagoyOCAwIG9iago8PCAvTGVuZ3RoIDcyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXEC+qYm5Qi4XSAzEygGzDIC0JZyCiGeAmCBtEMUgFkSxmYkZRB2cAZHL4EoDACXbFskKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvTGVuZ3RoIDQ3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXJYQVi4XTCwHzALRlnAKIp7BlQYAuWcNJwplbmRzdHJlYW0KZW5kb2JqCjMwIDAgb2JqCjw8IC9MZW5ndGggMjU4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWRS3IEIAhE956CI4D85DyTSmUxuf82Dc5kNnaXqP2ESiOmEiznFHkwfcnyzWS26Xc5VjsbBRRFKJjJVeixAqs7U8SZa4lq62Nl5LjTOwbFG85dOalkcaOMdVR1KnBMz5X1Ud35dlmUfUcOZQrYrHMcbODKbcMYJ0abre4O94kgTydTR8XtINnwByeNfZWrK3CdbPbRSzAOBP1CE5jki0DrDIHGzVP05BLs4+N254Fgb3kRSNkQyJEhGB2Cdp1c/+LW+b3/cYY7z7UZrhzv4neY1nbHX2KSFXMBi9wpqOdrLlrXGTrekzPH5Kb7hs65YJe7g0zv+T/Wz/r+Ax4pZvoKZW5kc3RyZWFtCmVuZG9iagozMSAwIG9iago8PCAvVHlwZSAvWE9iamVjdCAvU3VidHlwZSAvRm9ybSAvQkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0xlbmd0aCAzOQovRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJzjMjQwUzA2NVXI5TI3NgKzcsAsI3MjIAski2BBZDO40gAV8wp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math.sqrt(2) / math.sqrt(param.shape[1]))\n", "\n", "\n", "model = BaseNetwork(act_fn=nn.ReLU()).to(device)\n", "kaiming_init(model)\n", "visualize_gradients(model, print_variance=True)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "e2ba6898", "metadata": {"papermill": {"duration": 0.053689, "end_time": "2023-03-15T10:01:56.891542", "exception": false, "start_time": "2023-03-15T10:01:56.837853", "status": "completed"}, "tags": []}, "source": ["The variance stays stable across layers.\n", "We can conclude that the Kaiming initialization indeed works well for ReLU-based networks.\n", "Note that for Leaky-ReLU etc., we have to slightly adjust the factor of $2$ in the variance as half of the values are not set to zero anymore.\n", "PyTorch provides a function to calculate this factor for many activation\n", "function, see `torch.nn.init.calculate_gain`\n", "([link](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.calculate_gain))."]}, {"cell_type": "markdown", "id": "7e02aab3", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.059212, "end_time": "2023-03-15T10:01:57.004533", "exception": false, "start_time": "2023-03-15T10:01:56.945321", "status": "completed"}, "tags": []}, "source": ["## Optimization\n", "\n", "<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/UcRBZbAP9hM\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "\n", "Besides initialization, selecting a suitable optimization algorithm can be an important choice for deep neural networks.\n", "Before taking a closer look at them, we should define code for training the models.\n", "Most of the following code is copied from the previous tutorial, and only slightly altered to fit our needs."]}, {"cell_type": "code", "execution_count": 20, "id": 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model_file = _get_model_file(model_path, model_name)\n", "    assert os.path.isfile(\n", "        config_file\n", "    ), f'Could not find the config file \"{config_file}\". Are you sure this is the correct path and you have your model config stored here?'\n", "    assert os.path.isfile(\n", "        model_file\n", "    ), f'Could not find the model file \"{model_file}\". Are you sure this is the correct path and you have your model stored here?'\n", "    with open(config_file) as f:\n", "        config_dict = json.load(f)\n", "    if net is None:\n", "        act_fn_name = config_dict[\"act_fn\"].pop(\"name\").lower()\n", "        assert (\n", "            act_fn_name in act_fn_by_name\n", "        ), f'Unknown activation function \"{act_fn_name}\". Please add it to the \"act_fn_by_name\" dict.'\n", "        act_fn = act_fn_by_name[act_fn_name]()\n", "        net = BaseNetwork(act_fn=act_fn, **config_dict)\n", "    net.load_state_dict(torch.load(model_file))\n", "    return net\n", "\n", "\n", "def save_model(model, model_path, model_name):\n", "    config_dict = model.config\n", "    os.makedirs(model_path, exist_ok=True)\n", "    config_file = _get_config_file(model_path, model_name)\n", "    model_file = _get_model_file(model_path, model_name)\n", "    with open(config_file, \"w\") as f:\n", "        json.dump(config_dict, f)\n", "    torch.save(model.state_dict(), model_file)\n", "\n", "\n", "def train_model(net, model_name, optim_func, max_epochs=50, batch_size=256, overwrite=False):\n", "    \"\"\"Train a model on the training set of FashionMNIST.\n", "\n", "    Args:\n", "        net: Object of BaseNetwork\n", "        model_name: (str) Name of the model, used for creating the checkpoint names\n", "        max_epochs: Number of epochs we want to (maximally) train for\n", "        patience: If the performance on the validation set has not improved for #patience epochs, we stop training early\n", "        batch_size: Size of batches used in training\n", "        overwrite: Determines how to handle the case when there already exists a checkpoint. If True, it will be overwritten. Otherwise, we skip training.\n", "    \"\"\"\n", "    file_exists = os.path.isfile(_get_model_file(CHECKPOINT_PATH, model_name))\n", "    if file_exists and not overwrite:\n", "        print(f'Model file of \"{model_name}\" already exists. Skipping training...')\n", "        with open(_get_result_file(CHECKPOINT_PATH, model_name)) as f:\n", "            results = json.load(f)\n", "    else:\n", "        if file_exists:\n", "            print(\"Model file exists, but will be overwritten...\")\n", "\n", "        # Defining optimizer, loss and data loader\n", "        optimizer = optim_func(net.parameters())\n", "        loss_module = nn.CrossEntropyLoss()\n", "        train_loader_local = data.DataLoader(\n", "            train_set, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True\n", "        )\n", "\n", "        results = None\n", "        val_scores = []\n", "        train_losses, train_scores = [], []\n", "        best_val_epoch = -1\n", "        for epoch in range(max_epochs):\n", "            train_acc, val_acc, epoch_losses = epoch_iteration(\n", "                net, loss_module, optimizer, train_loader_local, val_loader, epoch\n", "            )\n", "            train_scores.append(train_acc)\n", "            val_scores.append(val_acc)\n", "            train_losses += epoch_losses\n", "\n", "            if len(val_scores) == 1 or val_acc > val_scores[best_val_epoch]:\n", "                print(\"\\t   (New best performance, saving model...)\")\n", "                save_model(net, CHECKPOINT_PATH, model_name)\n", "                best_val_epoch = epoch\n", "\n", "    if results is None:\n", "        load_model(CHECKPOINT_PATH, model_name, net=net)\n", "        test_acc = test_model(net, test_loader)\n", "        results = {\n", "            \"test_acc\": test_acc,\n", "            \"val_scores\": val_scores,\n", "            \"train_losses\": train_losses,\n", "            \"train_scores\": train_scores,\n", "        }\n", "        with open(_get_result_file(CHECKPOINT_PATH, model_name), \"w\") as f:\n", "            json.dump(results, f)\n", "\n", "    # Plot a curve of the validation accuracy\n", "    sns.set()\n", "    plt.plot([i for i in range(1, len(results[\"train_scores\"]) + 1)], results[\"train_scores\"], label=\"Train\")\n", "    plt.plot([i for i in range(1, len(results[\"val_scores\"]) + 1)], results[\"val_scores\"], label=\"Val\")\n", "    plt.xlabel(\"Epochs\")\n", "    plt.ylabel(\"Validation accuracy\")\n", "    plt.ylim(min(results[\"val_scores\"]), max(results[\"train_scores\"]) * 1.01)\n", "    plt.title(f\"Validation performance of {model_name}\")\n", "    plt.legend()\n", "    plt.show()\n", "    plt.close()\n", "\n", "    print((f\" Test accuracy: {results['test_acc']*100.0:4.2f}% \").center(50, \"=\") + \"\\n\")\n", "    return results\n", "\n", "\n", "def epoch_iteration(net, loss_module, optimizer, train_loader_local, val_loader, epoch):\n", "    ############\n", "    # Training #\n", "    ############\n", "    net.train()\n", "    true_preds, count = 0.0, 0\n", "    epoch_losses = []\n", "    t = tqdm(train_loader_local, leave=False)\n", "    for imgs, labels in t:\n", "        imgs, labels = imgs.to(device), labels.to(device)\n", "        optimizer.zero_grad()\n", "        preds = net(imgs)\n", "        loss = loss_module(preds, labels)\n", "        loss.backward()\n", "        optimizer.step()\n", "        # Record statistics during training\n", "        true_preds += (preds.argmax(dim=-1) == labels).sum().item()\n", "        count += labels.shape[0]\n", "        t.set_description(f\"Epoch {epoch+1}: loss={loss.item():4.2f}\")\n", "        epoch_losses.append(loss.item())\n", "    train_acc = true_preds / count\n", "\n", "    ##############\n", "    # Validation #\n", "    ##############\n", "    val_acc = test_model(net, val_loader)\n", "    print(\n", "        f\"[Epoch {epoch+1:2i}] Training accuracy: {train_acc*100.0:05.2f}%, Validation accuracy: {val_acc*100.0:05.2f}%\"\n", "    )\n", "    return train_acc, val_acc, epoch_losses\n", "\n", "\n", "def test_model(net, data_loader):\n", "    \"\"\"Test a model on a specified dataset.\n", "\n", "    Args:\n", "        net: Trained model of type BaseNetwork\n", "        data_loader: DataLoader object of the dataset to test on (validation or test)\n", "    \"\"\"\n", "    net.eval()\n", "    true_preds, count = 0.0, 0\n", "    for imgs, labels in data_loader:\n", "        imgs, labels = imgs.to(device), labels.to(device)\n", "        with torch.no_grad():\n", "            preds = net(imgs).argmax(dim=-1)\n", "            true_preds += (preds == labels).sum().item()\n", "            count += labels.shape[0]\n", "    test_acc = true_preds / count\n", "    return test_acc"]}, {"cell_type": "markdown", "id": "90c3ac7b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.054123, "end_time": "2023-03-15T10:01:57.265753", "exception": false, "start_time": "2023-03-15T10:01:57.211630", "status": "completed"}, "tags": []}, "source": ["First, we need to understand what an optimizer actually does.\n", "The optimizer is responsible to update the network's parameters given the gradients.\n", "Hence, we effectively implement a function $w^{t} = f(w^{t-1}, g^{t}, ...)$ with $w$ being the parameters, and $g^{t} = \\nabla_{w^{(t-1)}} \\mathcal{L}^{(t)}$ the gradients at time step $t$.\n", "A common, additional parameter to this function is the learning rate, here denoted by $\\eta$.\n", "Usually, the learning rate can be seen as the \"step size\" of the update.\n", "A higher learning rate means that we change the weights more in the direction of the gradients, a smaller means we take shorter steps.\n", "\n", "As most optimizers only differ in the implementation of $f$, we can define a template for an optimizer in PyTorch below.\n", "We take as input the parameters of a model and a learning rate.\n", "The function `zero_grad` sets the gradients of all parameters to zero, which we have to do before calling `loss.backward()`.\n", "Finally, the `step()` function tells the optimizer to update all weights based on their gradients.\n", "The template is setup below:"]}, {"cell_type": "code", "execution_count": 21, "id": "3fc98bf2", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:57.377724Z", "iopub.status.busy": "2023-03-15T10:01:57.377347Z", "iopub.status.idle": "2023-03-15T10:01:57.386754Z", "shell.execute_reply": "2023-03-15T10:01:57.386006Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.067977, "end_time": "2023-03-15T10:01:57.388413", "exception": false, "start_time": "2023-03-15T10:01:57.320436", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class OptimizerTemplate:\n", "    def __init__(self, params, lr):\n", "        self.params = list(params)\n", "        self.lr = lr\n", "\n", "    def zero_grad(self):\n", "        # Set gradients of all parameters to zero\n", "        for p in self.params:\n", "            if p.grad is not None:\n", "                p.grad.detach_()  # For second-order optimizers important\n", "                p.grad.zero_()\n", "\n", "    @torch.no_grad()\n", "    def step(self):\n", "        # Apply update step to all parameters\n", "        for p in self.params:\n", "            if p.grad is None:  # We skip parameters without any gradients\n", "                continue\n", "            self.update_param(p)\n", "\n", "    def update_param(self, p):\n", "        # To be implemented in optimizer-specific classes\n", "        raise NotImplementedError"]}, {"cell_type": "markdown", "id": "99114a19", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.058249, "end_time": "2023-03-15T10:01:57.504361", "exception": false, "start_time": "2023-03-15T10:01:57.446112", "status": "completed"}, "tags": []}, "source": ["The first optimizer we are going to implement is the standard Stochastic Gradient Descent (SGD).\n", "SGD updates the parameters using the following equation:\n", "\n", "$$\n", "\\begin{split}\n", "    w^{(t)} & = w^{(t-1)} - \\eta \\cdot g^{(t)}\n", "\\end{split}\n", "$$\n", "\n", "As simple as the equation is also our implementation of SGD:"]}, {"cell_type": "code", "execution_count": 22, "id": "b54db8b8", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:57.635864Z", "iopub.status.busy": "2023-03-15T10:01:57.635242Z", "iopub.status.idle": "2023-03-15T10:01:57.640922Z", "shell.execute_reply": "2023-03-15T10:01:57.640223Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.067585, "end_time": "2023-03-15T10:01:57.642142", "exception": false, "start_time": "2023-03-15T10:01:57.574557", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SGD(OptimizerTemplate):\n", "    def __init__(self, params, lr):\n", "        super().__init__(params, lr)\n", "\n", "    def update_param(self, p):\n", "        p_update = -self.lr * p.grad\n", "        p.add_(p_update)  # In-place update => saves memory and does not create computation graph"]}, {"cell_type": "markdown", "id": "22bd85ea", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.053912, "end_time": "2023-03-15T10:01:57.753533", "exception": false, "start_time": "2023-03-15T10:01:57.699621", "status": "completed"}, "tags": []}, "source": ["In the lecture, we also have discussed the concept of momentum which replaces the gradient in the update by an exponential average of all past gradients including the current one:\n", "\n", "$$\n", "\\begin{split}\n", "    m^{(t)} & = \\beta_1 m^{(t-1)} + (1 - \\beta_1)\\cdot g^{(t)}\\\\\n", "    w^{(t)} & = w^{(t-1)} - \\eta \\cdot m^{(t)}\\\\\n", "\\end{split}\n", "$$\n", "\n", "Let's also implement it below:"]}, {"cell_type": "code", "execution_count": 23, "id": "0d9787c1", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:57.863419Z", "iopub.status.busy": "2023-03-15T10:01:57.863052Z", "iopub.status.idle": "2023-03-15T10:01:57.871487Z", "shell.execute_reply": "2023-03-15T10:01:57.870554Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.065488, "end_time": "2023-03-15T10:01:57.873355", "exception": false, "start_time": "2023-03-15T10:01:57.807867", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SGDMomentum(OptimizerTemplate):\n", "    def __init__(self, params, lr, momentum=0.0):\n", "        super().__init__(params, lr)\n", "        self.momentum = momentum  # Corresponds to beta_1 in the equation above\n", "        self.param_momentum = {p: torch.zeros_like(p.data) for p in self.params}  # Dict to store m_t\n", "\n", "    def update_param(self, p):\n", "        self.param_momentum[p] = (1 - self.momentum) * p.grad + self.momentum * self.param_momentum[p]\n", "        p_update = -self.lr * self.param_momentum[p]\n", "        p.add_(p_update)"]}, {"cell_type": "markdown", "id": "910287c4", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.054066, "end_time": "2023-03-15T10:01:57.986812", "exception": false, "start_time": "2023-03-15T10:01:57.932746", "status": "completed"}, "tags": []}, "source": ["Finally, we arrive at Adam.\n", "Adam combines the idea of momentum with an adaptive learning rate, which is based on an exponential average of the squared gradients, i.e. the gradients norm.\n", "Furthermore, we add a bias correction for the momentum and adaptive learning rate for the first iterations:\n", "\n", "$$\n", "\\begin{split}\n", "    m^{(t)} & = \\beta_1 m^{(t-1)} + (1 - \\beta_1)\\cdot g^{(t)}\\\\\n", "    v^{(t)} & = \\beta_2 v^{(t-1)} + (1 - \\beta_2)\\cdot \\left(g^{(t)}\\right)^2\\\\\n", "    \\hat{m}^{(t)} & = \\frac{m^{(t)}}{1-\\beta^{t}_1}, \\hat{v}^{(t)} = \\frac{v^{(t)}}{1-\\beta^{t}_2}\\\\\n", "    w^{(t)} & = w^{(t-1)} - \\frac{\\eta}{\\sqrt{v^{(t)}} + \\epsilon}\\circ \\hat{m}^{(t)}\\\\\n", "\\end{split}\n", "$$\n", "\n", "Epsilon is a small constant used to improve numerical stability for very small gradient norms.\n", "Remember that the adaptive learning rate does not replace the learning\n", "rate hyperparameter $\\eta$, but rather acts as an extra factor and\n", "ensures that the gradients of various parameters have a similar norm."]}, {"cell_type": "code", "execution_count": 24, "id": "61615c78", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:58.097304Z", "iopub.status.busy": "2023-03-15T10:01:58.096308Z", "iopub.status.idle": "2023-03-15T10:01:58.109347Z", "shell.execute_reply": "2023-03-15T10:01:58.108516Z"}, "papermill": {"duration": 0.070582, "end_time": "2023-03-15T10:01:58.111616", "exception": false, "start_time": "2023-03-15T10:01:58.041034", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Adam(OptimizerTemplate):\n", "    def __init__(self, params, lr, beta1=0.9, beta2=0.999, eps=1e-8):\n", "        super().__init__(params, lr)\n", "        self.beta1 = beta1\n", "        self.beta2 = beta2\n", "        self.eps = eps\n", "        self.param_step = {p: 0 for p in self.params}  # Remembers \"t\" for each parameter for bias correction\n", "        self.param_momentum = {p: torch.zeros_like(p.data) for p in self.params}\n", "        self.param_2nd_momentum = {p: torch.zeros_like(p.data) for p in self.params}\n", "\n", "    def update_param(self, p):\n", "        self.param_step[p] += 1\n", "\n", "        self.param_momentum[p] = (1 - self.beta1) * p.grad + self.beta1 * self.param_momentum[p]\n", "        self.param_2nd_momentum[p] = (1 - self.beta2) * (p.grad) ** 2 + self.beta2 * self.param_2nd_momentum[p]\n", "\n", "        bias_correction_1 = 1 - self.beta1 ** self.param_step[p]\n", "        bias_correction_2 = 1 - self.beta2 ** self.param_step[p]\n", "\n", "        p_2nd_mom = self.param_2nd_momentum[p] / bias_correction_2\n", "        p_mom = self.param_momentum[p] / bias_correction_1\n", "        p_lr = self.lr / (torch.sqrt(p_2nd_mom) + self.eps)\n", "        p_update = -p_lr * p_mom\n", "\n", "        p.add_(p_update)"]}, {"cell_type": "markdown", "id": "8c8d597a", "metadata": {"papermill": {"duration": 0.05471, "end_time": "2023-03-15T10:01:58.224457", "exception": false, "start_time": "2023-03-15T10:01:58.169747", "status": "completed"}, "tags": []}, "source": ["### Comparing optimizers on model training\n", "\n", "After we have implemented three optimizers (SGD, SGD with momentum, and Adam), we can start to analyze and compare them.\n", "First, we test them on how well they can optimize a neural network on the FashionMNIST dataset.\n", "We use again our linear network, this time with a ReLU activation and the kaiming initialization, which we have found before to work well for ReLU-based networks.\n", "Note that the model is over-parameterized for this task, and we can achieve similar performance with a much smaller network (for example `100,100,100`).\n", "However, our main interest is in how well the optimizer can train *deep*\n", "neural networks, hence the over-parameterization."]}, {"cell_type": "code", "execution_count": 25, "id": "d7d987e5", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:58.334703Z", "iopub.status.busy": "2023-03-15T10:01:58.334333Z", "iopub.status.idle": "2023-03-15T10:01:58.350587Z", "shell.execute_reply": "2023-03-15T10:01:58.349864Z"}, "papermill": {"duration": 0.074388, "end_time": "2023-03-15T10:01:58.352890", "exception": false, "start_time": "2023-03-15T10:01:58.278502", "status": "completed"}, "tags": []}, "outputs": [], "source": ["base_model = BaseNetwork(act_fn=nn.ReLU(), hidden_sizes=[512, 256, 256, 128])\n", "kaiming_init(base_model)"]}, {"cell_type": "markdown", "id": "4c2386f2", "metadata": {"papermill": {"duration": 0.054197, "end_time": "2023-03-15T10:01:58.465919", "exception": false, "start_time": "2023-03-15T10:01:58.411722", "status": "completed"}, "tags": []}, "source": ["For a fair comparison, we train the exact same model with the same seed with the three optimizers below.\n", "Feel free to change the hyperparameters if you want (however, you have to train your own model then)."]}, {"cell_type": "code", "execution_count": 26, "id": "a2f97064", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:58.575678Z", "iopub.status.busy": "2023-03-15T10:01:58.575208Z", "iopub.status.idle": "2023-03-15T10:01:59.072902Z", "shell.execute_reply": "2023-03-15T10:01:59.072164Z"}, "papermill": {"duration": 0.555248, "end_time": "2023-03-15T10:01:59.075098", "exception": false, "start_time": "2023-03-15T10:01:58.519850", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Model file of \"FashionMNIST_SGD\" already exists. Skipping training...\n"]}, {"data": {"application/pdf": 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"text": ["Model file of \"FashionMNIST_SGDMom\" already exists. Skipping training...\n"]}, {"data": {"application/pdf": 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"SGDMom_results = train_model(\n", "    SGDMom_model,\n", "    \"FashionMNIST_SGDMom\",\n", "    lambda params: SGDMomentum(params, lr=1e-1, momentum=0.9),\n", "    max_epochs=40,\n", "    batch_size=256,\n", ")"]}, {"cell_type": "code", "execution_count": 28, "id": "f8afa36b", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:01:59.744838Z", "iopub.status.busy": "2023-03-15T10:01:59.744259Z", "iopub.status.idle": "2023-03-15T10:02:00.175089Z", "shell.execute_reply": "2023-03-15T10:02:00.174069Z"}, "papermill": {"duration": 0.491568, "end_time": "2023-03-15T10:02:00.177938", "exception": false, "start_time": "2023-03-15T10:01:59.686370", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Model file of \"FashionMNIST_Adam\" already exists. Skipping training...\n"]}, {"data": {"application/pdf": 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==============\n", "\n"]}], "source": ["Adam_model = copy.deepcopy(base_model).to(device)\n", "Adam_results = train_model(\n", "    Adam_model, \"FashionMNIST_Adam\", lambda params: Adam(params, lr=1e-3), max_epochs=40, batch_size=256\n", ")"]}, {"cell_type": "markdown", "id": "a23430ed", "metadata": {"papermill": {"duration": 0.057053, "end_time": "2023-03-15T10:02:00.297792", "exception": false, "start_time": "2023-03-15T10:02:00.240739", "status": "completed"}, "tags": []}, "source": ["The result is that all optimizers perform similarly well with the given model.\n", "The differences are too small to find any significant conclusion.\n", "However, keep in mind that this can also be attributed to the initialization we chose.\n", "When changing the initialization to worse (e.g. constant initialization), Adam usually shows to be more robust because of its adaptive learning rate.\n", "To show the specific benefits of the optimizers, we will continue to\n", "look at some possible loss surfaces in which momentum and adaptive\n", "learning rate are crucial."]}, {"cell_type": "markdown", "id": "08a6781d", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.057075, "end_time": "2023-03-15T10:02:00.416032", "exception": false, "start_time": "2023-03-15T10:02:00.358957", "status": "completed"}, "tags": []}, "source": ["### Pathological curvatures\n", "\n", "A pathological curvature is a type of surface that is similar to ravines and is particularly tricky for plain SGD optimization.\n", "In words, pathological curvatures typically have a steep gradient in one direction with an optimum at the center, while in a second direction we have a slower gradient towards a (global) optimum.\n", "Let's first create an example surface of this and visualize it:"]}, {"cell_type": "code", "execution_count": 29, "id": "c3894800", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:00.531986Z", "iopub.status.busy": "2023-03-15T10:02:00.531593Z", "iopub.status.idle": "2023-03-15T10:02:00.538425Z", "shell.execute_reply": "2023-03-15T10:02:00.537314Z"}, "papermill": {"duration": 0.067604, "end_time": "2023-03-15T10:02:00.540479", "exception": false, "start_time": "2023-03-15T10:02:00.472875", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def pathological_curve_loss(w1, w2):\n", "    # Example of a pathological curvature. There are many more possible, feel free to experiment here!\n", "    x1_loss = torch.tanh(w1) ** 2 + 0.01 * torch.abs(w1)\n", "    x2_loss = torch.sigmoid(w2)\n", "    return x1_loss + x2_loss"]}, {"cell_type": "code", "execution_count": 30, "id": "5081d81c", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:00.662183Z", "iopub.status.busy": "2023-03-15T10:02:00.661817Z", "iopub.status.idle": "2023-03-15T10:02:02.000702Z", "shell.execute_reply": "2023-03-15T10:02:02.000051Z"}, "papermill": {"duration": 1.407378, "end_time": "2023-03-15T10:02:02.011311", "exception": false, "start_time": "2023-03-15T10:02:00.603933", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.9/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n", "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"]}, {"data": {"application/pdf": 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else:\n", "        ax.imshow(z.T[::-1], cmap=cmap, extent=(x_range[0], x_range[1], y_range[0], y_range[1]))\n", "    plt.title(title)\n", "    ax.set_xlabel(r\"$w_1$\")\n", "    ax.set_ylabel(r\"$w_2$\")\n", "    plt.tight_layout()\n", "    return ax\n", "\n", "\n", "sns.reset_orig()\n", "_ = plot_curve(pathological_curve_loss, plot_3d=True)\n", "plt.show()"]}, {"cell_type": "markdown", "id": "b74ae142", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.09566, "end_time": "2023-03-15T10:02:02.195531", "exception": false, "start_time": "2023-03-15T10:02:02.099871", "status": "completed"}, "tags": []}, "source": ["In terms of optimization, you can image that $w_1$ and $w_2$ are weight parameters, and the curvature represents the loss surface over the space of $w_1$ and $w_2$.\n", "Note that in typical networks, we have many, many more parameters than two, and such curvatures can occur in multi-dimensional spaces as well.\n", "\n", "Ideally, our optimization algorithm would find the center of the ravine and focuses on optimizing the parameters towards the direction of $w_2$.\n", "However, if we encounter a point along the ridges, the gradient is much greater in $w_1$ than $w_2$, and we might end up jumping from one side to the other.\n", "Due to the large gradients, we would have to reduce our learning rate slowing down learning significantly.\n", "\n", "To test our algorithms, we can implement a simple function to train two parameters on such a surface:"]}, {"cell_type": "code", "execution_count": 31, "id": "b5c10301", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:02.368383Z", "iopub.status.busy": "2023-03-15T10:02:02.367677Z", "iopub.status.idle": "2023-03-15T10:02:02.376721Z", "shell.execute_reply": "2023-03-15T10:02:02.376224Z"}, "papermill": {"duration": 0.097517, "end_time": "2023-03-15T10:02:02.377945", "exception": false, "start_time": "2023-03-15T10:02:02.280428", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_curve(optimizer_func, curve_func=pathological_curve_loss, num_updates=100, init=[5, 5]):\n", "    \"\"\"\n", "    Args:\n", "        optimizer_func: Constructor of the optimizer to use. Should only take a parameter list\n", "        curve_func: Loss function (e.g. pathological curvature)\n", "        num_updates: Number of updates/steps to take when optimizing\n", "        init: Initial values of parameters. Must be a list/tuple with two elements representing w_1 and w_2\n", "    Returns:\n", "        Numpy array of shape [num_updates, 3] with [t,:2] being the parameter values at step t, and [t,2] the loss at t.\n", "    \"\"\"\n", "    weights = nn.Parameter(torch.FloatTensor(init), requires_grad=True)\n", "    optim = optimizer_func([weights])\n", "\n", "    list_points = []\n", "    for _ in range(num_updates):\n", "        loss = curve_func(weights[0], weights[1])\n", "        list_points.append(torch.cat([weights.data.detach(), loss.unsqueeze(dim=0).detach()], dim=0))\n", "        optim.zero_grad()\n", "        loss.backward()\n", "        optim.step()\n", "    points = torch.stack(list_points, dim=0).numpy()\n", "    return points"]}, {"cell_type": "markdown", "id": "beb73b4b", "metadata": {"papermill": {"duration": 0.085118, "end_time": "2023-03-15T10:02:02.549433", "exception": false, "start_time": "2023-03-15T10:02:02.464315", "status": "completed"}, "tags": []}, "source": ["Next, let's apply the different optimizers on our curvature.\n", "Note that we set a much higher learning rate for the optimization algorithms as you would in a standard neural network.\n", "This is because we only have 2 parameters instead of tens of thousands or even millions."]}, {"cell_type": "code", "execution_count": 32, "id": "1c8db2ca", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:02.720374Z", "iopub.status.busy": "2023-03-15T10:02:02.719919Z", "iopub.status.idle": "2023-03-15T10:02:02.785421Z", "shell.execute_reply": "2023-03-15T10:02:02.784925Z"}, "papermill": {"duration": 0.153461, "end_time": "2023-03-15T10:02:02.786763", "exception": false, "start_time": "2023-03-15T10:02:02.633302", "status": "completed"}, "tags": []}, "outputs": [], "source": ["SGD_points = train_curve(lambda params: SGD(params, lr=10))\n", "SGDMom_points = train_curve(lambda params: SGDMomentum(params, lr=10, momentum=0.9))\n", "Adam_points = train_curve(lambda params: Adam(params, lr=1))"]}, {"cell_type": "markdown", "id": "62c19a0f", "metadata": {"papermill": {"duration": 0.085279, "end_time": "2023-03-15T10:02:02.955965", "exception": false, "start_time": "2023-03-15T10:02:02.870686", "status": "completed"}, "tags": []}, "source": ["To understand best how the different algorithms worked, we visualize the update step as a line plot through the loss surface.\n", "We will stick with a 2D representation for readability."]}, {"cell_type": "code", "execution_count": 33, "id": "58b5c749", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:03.132951Z", "iopub.status.busy": "2023-03-15T10:02:03.132522Z", "iopub.status.idle": "2023-03-15T10:02:03.534204Z", "shell.execute_reply": "2023-03-15T10:02:03.533619Z"}, "papermill": {"duration": 0.491858, "end_time": "2023-03-15T10:02:03.535887", "exception": false, "start_time": "2023-03-15T10:02:03.044029", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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fwHcT37MmNhpxNn75X2eld+LVyM9BTeYT8Sfginh1fIwktfWrBgX4E1/q66/Y6YOEmrdampo6JxIewHeTUK0Y59VqKC/V7ldM9cuRowcF5zGkdxCgOZtAOFP/KSs9whxFO2mrZmY1o0GoUX87Cx0eFvq3Gl9yRvg/Mb5EXujJwzWOFLeOYU/WDkxxa56UPBtcszGz7RcJXAJJp8H5xTnfgV8g+FaBS3B1Nsb4kvrfDfEb41Elq3s1x6NSbKUbGDsyBnGS48yy+u8amiKIrinNoalCJIrh02FyBLnvoy+wmEh0HDIJzjH7orKC8F0XM3vzc5gqZ6chAA1TyeiOljeyoiko1Du6qi53HuNRxAGendg0Vdm6xjv6HIpprRBe25QVJlkkC664ONQkuLrlyt7GrIyGTWqz4Su13HObwzIV4p3Q5VdLmHNTajhX1e3GVBZJUp3zANWSPELMTRXnOEdoiMnIjGxWKyjviHOERil2SzatlVW0D3NMQDNZUcsg28UqmmNMgIRJXZvQ1VkpY6DMa+NRlMHfrcQ8ZiJsesDHKGpa0/RFnENc87MRuHrTqaQ5SyUabRYsKHtwc4aL5UGEtKCAXl3UNGescHQ281U1F8J2bZaKLZJaddkVp4mL2Psc+YLippGyokJ37jYzZXM7Ea2CGpyQFhyTGyCRL2rcTh1uh9C2MbWAbuROaGcmltR3TNho2Cg1zcfdNX6BrDRvgyeFrSvLIg7USJEH0eBVbXzSy2ijZtDqTJI1L8Ye1YAiM9ukSGOKogTrG/tk01qEM9n0xGarEF+N2WQ5Wz9Ge+y7rtY2pi5QgjaWzyrsqLlgqYjLzbjsxSt5gAJcpTZExwZeClYWkVd3n3MjEfeDDcVGGWEZRi1JdsYp+gpt8FPharWGcCwTHEpTe1ATSr25weO4Fe+U4LSiymgeIzysXZBrdL9hBPI+8QOJlRSSE7QJEQ2+GNgYIzvAR3A2bnyx2OwC7vtetE+VSucnVRhEyooCWvRsgDXEBJPivUiseh3zehoVUcYBtj6e4Mtcu6o76mWRISTENMFBtaJ6z5oN4W9trl2rtS+zJpJIg9P+yVAKyFbV6zY9Ykv7pFk9zf7xlQFFKDQDcEcUq2bwwlyhO6cqQmpK/P0EowCjcKFxxlCmYLEyMFlvQnnSYu/8XBpN4Pjumrpy1fWdOiRM3Ro1g1ubc06SKvhUWGRMNoVjbY2MGDjAPj/A1iTS6ImGYFGaIWvaeUtq+tnwUxkDg4ZdrCCL7VfDp4ZJoGM3fEgjTtEmVgYlmptCifEJxF7hOEi1RbHOyVowO6vqprkEf7dBplLmERTMZfHybZCkwch96awumIFLTM73CU4Vg9YFtnpPmYtotAgF1BQT5zzsSrEEPg1s50r3++EQIQhHU5no6lykiiVor8AoShgir856xwglgbP1ciY2JrF4A3PC8SAwN4xnFxgLleq+dvJJA0NR1j+X2iYYJ4vXFNir+r3bDStDGoGcXQ9tgp16WV3BR8fd932X5B3RWXmma8KgTzA+LjgLVTgw9Z0ngdgeZ1k1chyGKQQ7S1kMG3cQD7AMFRZSYMdy7Th4lTPv5sSTDmQSyEllK1kU1fP3gw8ITxxBU1eb7SBQQxyCIpljIk17JwrN50n3OZc0ylpzLmn/4b3nkvxTbQwRtMEk/PeojWkwScMWbY0Ne1XnHEV84imNEqGQa2S15TCSebQ3VTFQa34v0r2NneF2sJmW0ZzKHV0O+5TH29jqD3YMShlfwoV7HNXe6Xkbu2rUCpMTBrZaZimWC0IwQXhtjMXoyhb19TDHcY3dNWTQ1c8wsiQNql9f8A/7luFAGx2W0hTuYSvLChsXqsFMQq1RE2UTKo/2uFxb4w4aJA599GPwazIzxS8ZqEiNeBLFmNila9O9LRmIsVMJHieejW61k/EHYc1BFYpilkNRn4JdOBfVObxaOxIaE87WNuYw2AJhX11LINiaBsma4rRdJInr0dR4Gznp3gnnMeboArEiKrEWQK95dUXrwSZNoubt1Ttbqo21EmJRzdmmWHSihIk5LzVB2QEH3cyHjTYOMYCLbilSallj86p920bw0ARsfblgSE0Wlydn7NMVDeIQWdAlNmEGdgfbYHRrzrrhiNfCrX+HFhIl7KGaiVI3+Lk3hZ6x+XchJrWuRzNRk6TZ++Wx8+8BV0QMV22OrsgTqcGyRoYFji8rNn4VN+vppmOq4m1klkJPisZ8NaUVi8xUWYtfVGdXl1l6saE74kEvjpclR3TnI2n2TMGQ5sU6nqArmVxhK3wOrOxll15lzcoq2ElhSQn+Hf0m8VIIx++SIXQLs9cS2DQkSpZHfIbANqURyGM4xiveQrfrGK0mi/k1hcfSOGh/dG+e8KU5QZMEwYZKo40m27jJanWvqqBmdr1GJV5pjt3JNse0xB6zEsQyXdOZYDeNrxZ5k4vDR6CjJjKlPUkpdcKfa7R6tVMyHpXcezfGqCBQJIREWssjlTSpwSgjBbpGGdQaKnnp1YhnizrHNgguNpLLZ93HWAkAqQGBUlF3r9oRaF6kZ1OnpXBp3B4jhT5KHESzj96vXWDXNZniNBaZdP5BekywsTwh6S5cc/LImpfiF+FR7SuqoTN7zX2YJdQ9F3X2qwKEpUsj3FC1TNPhrxQfE5LbcNwF0ep142o0NJN1zUa9uaPf+MJdVl2g6JqWtxFX8rwqA7Y8GQkRPiqzmBTVK0gkDV6RLWOvcK4GaSbiXeTNMJrLPRJVq1yDXatmfjRPNMY0134ExZGfLoYc1CjXxaXVmUv2i+bAi1qqgR0mfFlOSzOoQfBGhOst6AmqPiXNMoSlfGSMj8RjRCYaJyWjQoXXVDtdHyJG6taklyknTcAMupV1SJrib0q58hjG7ppa6L2vtugkEho7iXmMnuLqOZhYlgKiCl/gGHVdb7a+u66sLX22bBpRiW4TjllSNXtqOUalnpCxA2zQsmyLHEgzi+TJXwSvuEgbkI0jWtIcQstr0eOUo6qVOLA53a/hPBUZ1rYVV46YamBtzIbiB606stJbTb+SrbYxhMDxo8i643ARC3gN7/DtMMY3ixLhsg79g+YkvRKsEV6iO4RhiMBaYTpBM55RJWWTFslsistQIJDnJFn/NAYFCL1tXnbpajSiqruEuho0hhV1DYCwLlxEMDhQTVjWOdAXx4DYmnveqRuiSG6O6GUMn1vGRpxEt6FQ3Ym1BCTo/mVYy6mFToh8rC3OUTNkHJd6kYAQiGoGN9U5h4UGcDputbZGjb1FuHXOM2mqmcBqSYjb7M6TQrMxt4NRixrLvGQeNre7OVflNJQW1+Gzzg3NUr4ysr1m89K5rRQGq+dUWGoSTSETaxTN4l2kb10BreL8Mb+RNR3ij3nBF7kKyQGyHIdYE9pZpN5XSqC6DOK0Z6hqi9XmlqGCkkg4p1Gwsa4Kk3jgtROwjAn9cmVkTKh47eQs6/BMpR3ICCG3eUgagApLP+d1oZfDhoNjZdTQRRtiXztcAg5yA2/S4RTrJ42XXzA6akKsj1k3DKAmC2O5YEdIuiDqRynAySN4mcwlcm263qYyx0AmYSr4mCsyhOxVfjFsKNKdsCvsKL0dI8iGHXVL1y1trxrmBORepsuQEUEMb3IX0YpXbGfXjg1Z16awTBd0KFMiKS5xLk0ohmgtM1OzW1hfZZCGHJqukJcLxyXJa4616kDGLpW3EsKniSQ1zh7TSM/9ssVLCavHD26fLR9Q+O7qAQXw3/sNhjPuuWN3ufJv1EK8aRAeLjsVxF8w5rKRqFtEuFVrE6krZymr2kFxK6qaRLvrj4oVu46p7jtuUYelBrEj4B39Gt3GlNO/6yak5xdHHdt2bU1F1BRfKMu/9wFwKOi13VRXvcCK0Lp8LkcVjAMYxVTThFtpBFY4Zaa97O0Xr55Iu9slfrV99loxURgZhLVr3GhTqt2hvo0up/N9JyGLs/siN6PrwOojFXVrdvyoEqfdTrZ0v++tA7sjY3crvU9tfjXbHSpdVQ3qvtWUdip1V7DYUCxxma97Q4UIRVeS/Ugb42xYJHmQpiv99kREaH4vmhLnGplOhlIBymyHFN2X6Pee1f6pg5oalHOpb9sVD0aNKgpskaQuR7VRfT8q9GxD8WIjhMuyjHsZHYMKIzXKjXBM0slAmldrr6oWruu0O+XkyjAY1lUZi717kvS8AmCvImvvRwUcSQJH5XdyldlwECW6ZK1eUFbNZe+EqZVS72oBlj4K3VV+Tdf97jBTl2jqXnWuLssea9JFo9ZHU0XF1HTPXbVtV/3e3FE6by+VENt2v5+mXhQgacpFj0vsXSYdSfMcfta93ZIejQ8OZ7STatVI99yM7g1lFtFDDX7H1kVzjkxdpl53+WSNpgsMXWDfsIJHB6Y5Lai3CqoYvLdJkFWORo8V6FbE3phJ6hskgdUYjHvPoqkKVdSsUlN+L+ZDFJmOoErJw15xL7rpX9XCIrro84uiz9rwgGFkaPvWkdHMinquoNWwNxtQKRXXkv6me4oTjOtN3dpgkU2Vnbxe5HQFdtUuccx2DTEc7EvKPGHw0VUgfs02/5aJaWfjSI8oNLkwwLGl1PfWRNQ9xiSw73tvVe0aXe3rAmvGPuy9k6SbqVGdN/nPtgurV34ZBNb4kdt7aSiTxhHUp9MrMvsiTbcbk8BIS+uHkNSqlxgA607E0STxTf1ZgUPtO3koh6jDxCAUbZcFPUIjrsryhNT7wSfVJgR2WNZ+tHD0SEVWuxBzWI72UNflwWJgIk63nzqRgx5jSBqCcL3sUgmTchpgr4n/vVcT7eadHnqQZYjHJ1Ehw+bTKR/gLmMzGpd9zC7YJvkg3BFYU+MHgUGPbhhYQd6BXe2aHGB+qcQdrPu8uEeBH/qhPlTXawcCp6ObpB5Ts60jwOk4Rt3pDLaZ7HoMB1TXhPVmhS4WHU0q46qBqx7W2cF2+URQQuHjFKs6qUabnGV9NBaVoBo4qla1g4vTBV17JePBJL7mB82wwsdDwbAhZSztcqv7VgjFQzfexdHU3Dmtu1YG5gAep4iP83WAQ+zHZgiW/VgEh9L2TzaENhl9ymQPqLfHUgwqUg76ULABzq3lB5u0nkGTOzgdbDBHUKxDOsxTUmY2wDk+WE3+NXgdE0cfD3AShwVWS/rUKDwHR7Nd+AR676ZheE6Fsl1pyrqSc/yUdAe8X6ArqOu6SmEFXDbtznfJ3kI3QsiQR9VUT54QomclgEt0u5WgzE1dWxVwVcnDMdSwxrfWWNNTGFZ+Drqs5SyNKhfrq7Hb8NR91JOV9pAlHe8aPONjyUkLETsrJ+sZH3IRTeAseaOcrBSSp2aNpKSLN8iac6u1o4YpdPdjlOS9XVzqabVNbH4mk43F+4HtlJyhlKs9qlRIYEt+rcKiZmucXsKxku5LbF2+SDY7pPRMI27QoWsefc0/rJOCNnueomsuLRPphL5kBwGULqoSSOvKfFcQi72tRzb3jI3Ae7HXW2HA3AxbVuFtha14lBwyRaeHBDSOwn5bUk13eTCBqFEXWIsVwXH2qijHFbIekHDyOFYcU9dHU4S6hrg8RNX1myrVRVVaFZMl7mnFEM4tYOU11WAlXaf+EEZxtUFvTwzpmlUdbxko8CF4XIqSqnJeHRrsbrQXH5TLd788QsXUqkckNx+mUH+Xo3HLM9TkpMYsZFXVfsMuFA5/rS2wLumqD7o4X8hA2fRMxtLwaIhOl4JIwfMsl6O27YofLvPp3uxOsea4dA2gHtfCn5BN5qNunnVDZqvqABe/1oAu8yeZH31PVXD1obpAVl0xWORhvd+u1ivqHVe4e9mxRDeK1Jy13kYoS7W1/rZuAyJoo7jH0qr+lrVd1eX/FKw+aGqpYi/OoF5gaxCkmwezrnvWlfnjwusLK6YZG+zFmB8Gr8iE5BW2SutV7BtvNOhlEXWYVrKkkTsbJbB77HqjQdeXbaxthQyjQiClGpUgZUBKZFfHrSules9otJ81/oANyZKt9faKNbI1W2FlISIoHMjazMw6oXTPqlfqRJttXXojtbiSZp9CG+8cEAaoznRFSLCHTWyW0KrwWKisd+iWe9S1J8K7PurIWcGyarKrpc2KQ4huwo4ifNfDGO4C2crRcuDjtj5Jh/USVpKkK5x6zKKmURjTSy+y0+5ih5oO02DKuIIP79Rc8v1C7qqGBGPw+71zWbxyzNO8MHkE2IqaR406OoVZsa/9vlV4A6qoENrGl0oeurjmNQtrBHBwhENRedsvjZ5OOdhF3zHqFDT7qdbbxcKs2ZPmtK1iT+ydm08X9lGlIw1H5P1eJEloX0dx6p3oOYmShlTrBqV6wumKeTGIvXrgwEr2mlHx7epgVOwNdmPesIlXySPXTkCDZHoc0OpYVrRvulh6dAPeZoi93lHmzU+vixpp6bf0WgBSTdzdBq7TvL5GaZbImsmpLfhZ7O16KAsLebW/rmdr+IVZ3tdsSnNX2FHX8DXnWoaxxIGxP3+hAl50a9Arzgq/Bq9SWZOtqqNuOZS6V/jV0yyrI5d30Wy/ovVZ4dc7lGtVHPdkiyobg2iCPl3Jv/BbThaha7poiKHMh14QuuAejLOQYiydVLFbh0tDMMn4NAc+kDUqktoyQrD6P96EKGLQQb6mSuLSVNs8hvLeedVcIbvuHy2DTYmaztjPETgXsY0kMUvnaVTqKS4/Wmd6xQs7dtxLf7HDLItY4t46wI9C2ZUVcwoRmppAAzs6fcuv/IvJmu7xu50QtXsti1xuEXaoIDZ7OsQ1Gs0JV7bG2eN5cR6i1ytopaYLFfAa6be5nYGNvmvC6cIrOnsFIE3tkuDFcnEuSLEeJIxlEh11fyouWa1D7JrPb6NrqxiWZLAvYwRjF05fBnjS7FX6b1eei9QD39bmJKjz4Otl36uISQ2iFPJoYzsNTOlBtlUYJO1S0DNfZXJ661P3eS9OXHJqN1AMt6IpLZTL7JMvj7dlB7YnuElh7YokxuiKDeEJWdeWku8Xx6KRUr3JkSeyRjyP5vELOohjMbl5cprIXbc71mGvPESSxQuT6Mwp1guRhlmqq+8NP6df1DMna+kIUW8978oiu9RD9BeWRte6lfhOZUg2dnnlPfUQQlQlYFgPRbKk41dhnhOr9dLcZEfUzFQPZ+zlgwX+9FzB+fK2XgpW+9yuwcfgjtrOccUwPF8x/M8P71YQUoVB/90/+OLvf/3+2++/+vrNF9/e3/z84z+++PvPP35oF5bj/t/9gy/P9xJffxTHu+nznfitzVfibyXPu5pFeZfdj/zuplds27iCeIJ/extP7VoT8QS3B3sf2KVYeeYJmnbcN7cTXJfm9kbkE7ipBudavJ8+qBt5A/lE3QP45tjJA/btjQ8TRulZpQdU96QG3vGZB6g+los7Y9KDAQ/gsc/HJ04sWXD1jZ68//i2fjzexO3F7UsrthtZunCkq+v1LYi9Dv0W5J1Pph4rEIrua6rl2VTs3mG12+h3GhcDR9uz6s5L3B92dyo6v7k1xYslWfO268Ur3dlyD4K8HnbzTyCtb784IRqUqnrK6bHWuNSoC8LHR3X/UaPR+t2DOgE10cmvPPYgoO3h4Euc36yanyyKkg+cTP6reu15Jb2drMfOnj6peVDzCCfa0ostPED7Ps+QyYzHWg+eHR89M/eg7nwKxx6Os3rrPKeYOdP0F9an5wd2Cu+62P3Zp5+cDYjJ67gpbIbxUlZD00O+047Ul8CHxD4B33mf5rROPC3+kNsH+CG6wHLR5dWT6DZ10ZvFq2fZbZoe96pZ73IZ7Cl2TR2cQAe5D/kNepxdhZF0P6+nXofyvfOnrQmoB6BOEiyoXJFE4LSbfuzm2Hd/fPskAyfMQ1hOSx5S9fTxh/yd6Ewv9vMAnfb9AB78Oa938PH06RPLT0Q+Due0m/hy3/Hx7feS66Y2vHs8h3Ih2n/4/rsL6X7XEMsh45phID99EvEDdpLwE+y9+0CPFXUvbv/KQzoO6EOK1BfvSSP+D3lTh19X2J4lvVoqkR8mOdj12aDZqAdop/ok5zWoEc4xn+W86pJ0eXIOgIreInuS8qo+U/D5bKcFnPs4uJWO756k4oF3SM9pvUPOzh9+SOSDwvRiIw/QY78P2MGW82o79x6fPbH5Qd/pPB77eJzbi/N9T9lWI3zgX4r2Rx/evQYFiGA++PMXTzL+x9v/ATOn454KZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iago5NDc3CmVuZG9iagoxMCAwIG9iagpbIF0KZW5kb2JqCjIxIDAgb2JqCjw8IC9MZW5ndGggOTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNYyxEcAwCAN7T8EICEOAfXK5FMn+bbBjN+glHQoEMYWNgyRTpxOt/KC3wXXS05BO4EHKWJmxbeq2Wpdc2d6r9sjar89Df+1Oar1IJGYCkamxB+92fQtHHgEKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvVHlwZSAvRm9udCAvQmFzZUZvbnQgL0dDV1hEVitEZWphVnVTYW5zLU9ibGlxdWUgL0ZpcnN0Q2hhciAwCi9MYXN0Q2hhciAyNTUgL0ZvbnREZXNjcmlwdG9yIDE4IDAgUiAvU3VidHlwZSAvVHlwZTMKL05hbWUgL0dDV1hEVitEZWphVnVTYW5zLU9ibGlxdWUgL0ZvbnRCQm94IFsgLTEwMTYgLTM1MSAxNjYwIDEwNjggXQovRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXSAvQ2hhclByb2NzIDIwIDAgUgovRW5jb2RpbmcgPDwgL1R5cGUgL0VuY29kaW5nIC9EaWZmZXJlbmNlcyBbIDExOSAvdyBdID4+IC9XaWR0aHMgMTcgMCBSID4+CmVuZG9iagoxOCAwIG9iago8PCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL0ZvbnROYW1lIC9HQ1dYRFYrRGVqYVZ1U2Fucy1PYmxpcXVlIC9GbGFncyA5NgovRm9udEJCb3ggWyAtMTAxNiAtMzUxIDE2NjAgMTA2OCBdIC9Bc2NlbnQgOTI5IC9EZXNjZW50IC0yMzYgL0NhcEhlaWdodCAwCi9YSGVpZ2h0IDAgL0l0YWxpY0FuZ2xlIDAgL1N0ZW1WIDAgL01heFdpZHRoIDEzNTAgPj4KZW5kb2JqCjE3IDAgb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNTAgNjM1IDQwMCAxMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUyMSA0MDAgMTAyOCA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUwMCAxMDAwIDQ3MSA2MTcgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxNyA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA4CjYzMCA2MTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5OTUgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5MiA2MzUgNTkyIF0KZW5kb2JqCjIwIDAgb2JqCjw8IC93IDIxIDAgUiA+PgplbmRvYmoKMjYgMCBvYmoKPDwgL0xlbmd0aCA5MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1jLsNwDAIRHumuBH4OID3iaIU9v5tiC0X3D3pifNsYGSdhyO04xaypnBTTFJOqHcMaqU3HTvoJc39NMl6Lhr0D3H1FbabA5JRJJGHRJfLlWflX3w+DG8cYgplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9MZW5ndGggMTY0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2QwRFDIQhE71axJYCAQD3JZHL4v/9rQJNcZB1g96k7gZBRhzPDZ+LJg9OxNHBvFYxrCK8j9AhNApPAxMGaeAwLAadhkWMu31WWVaeVrpqNnte9Y0HVaZc1DW3agfKtjz/CNd6j8BrsHkIHsSh0bmVaC5lYPGucO8yjzOd+Ttt3PRitptSsN3LZ1z06y9RQXlr7hM5otP0n1y+7MV4fhRQ5CAplbmRzdHJlYW0KZW5kb2JqCjI4IDAgb2JqCjw8IC9MZW5ndGggMjQ3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSW7EMAy7+xX8wACWrMV5T4pBD+3/ryUdFO3BECNLXOLuxEQWXrZQ10KH48NGXgmbge+D1pz4GrHiP9pGpJU/VFsgEzFRJHRRNxr3SDe8CtF+pIJXqvdY8xF3K81bOnaxv/fBtOaRKqtCPOTYHNlIWtdE0fE9tN5zQ3TKIIE+NyEHRGmOXoWkv/bDdW00u7U2syeqg0emhPJJsxqa0ylmyGyox20qVjIKN6qMivtURloP8jbOMoCT44QyWk92rCai/NQnl5AXE3HCLjs7FmITCxuHtB+VPrH8fOvN+JtpraWQcUEiNMWl32e8x+d4/wCVT1wmCmVuZHN0cmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0xlbmd0aCA5MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9jssNwDAIQ+9MwQjhUwL7VFUPyf7Xhnx6wQ9byLgJFgwfo9qFlQNvgrEndWBdXgMVQhYZZOTbOxeLSmYWv5omqRPSJHHeRKE7TUqdD7TT2+CF5wP16R3sCmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCAxNzAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZBLEsMgDEP3nEJHAP+A87TT6YLcf1vLmXSDFGPLL0RXdOyVh8fGlI33aGNPhC1c5XQaTlMZj4u7Zl2gy2Ey02+8mrnAVGGR1eyi+hi8ofOsZoevVTMxhDeZEhpgKndyD/X1pzjt25KQbFdh0J0apLMwzJH8PRBTc9BziJH8I19ya2HQmeYXFy2rGa1lTNHsYapsLQzqjUF3yvXUeq7zMBHv8wPfQT5kCmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVI70ptBCOu/U+gCnlney3mcyaT4c/82AjsVLLBCAtICB5l4iSGqUa74JU8wXifwd708jZ/Hu5Ba8FSkH7g2beP9WLMmCpZGLIXZx74fJeR4avwbAj0XacKMTEYOJANxv9bnz3qTKYffgDRtTh8lSQ+iBbtbw44vCzJIelLDkp38sK4FVhehCXNjTSQjp1am5vnYM1zGE2MkqJoFJOkT96mCEWnGY+esJQ8yHE/14sWvt/Fa5jH1sqpAxjbBHGwnM+EURQTiF5QkN3EXTR3F0cxYc7vQUFLkvruHk5Ne95eTqMArIZzFWsIxQ09Z5mSnQQlUrZwAM6zXvjBO00YJd2q6vSv29fPMJIzbHHZWSqbBOQ7uZZM5gmSvOyZswuMQ8949gpGYN7+LLYIrlznXZPqxH0Ub6YPi+pyrKbMVJfxDlTyx4hr/n9/7+fP8/geMKH4jCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAzMDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZJLbgMxDEP3PoUuEMD62Z7zpCi6mN5/2ycl6Yoc2RZFapa6TFlTHpA0k4R/6fBwsZ3yO2zPZmbgWqKXieWU59AVYu6ifNnMRl1ZJ8XqhGY6t+hRORcHNk2qn6sspd0ueA7XJp5b9hE/vNCgHtQ1Lgk3dFejZSk0Y6r7f9J7/Iwy4GpMXWxSq3sfPF5EVejoB0eJImOXF+fjQQnpSsJoWoiVd0UDQe7ytMp7Ce7b3mrIsgepmM47KWaw63RSLm4XhyEeyPKo8OWj2GtCz/iwKyX0SNiGM3In7mjG5tTI4pD+3o0ES4+uaCHz4K9u1i5gvFM6RWJkTnKsaYtVTvdQFNO5w70MEPVsRUMpc5HV6l/DzgtrlmwWeEr6BR6j3SZLDlbZ26hO76082dD3H1rXdB8KZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvTGVuZ3RoIDIzMiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UUluxDAMu/sV/MAA1u68J8Wgh/b/11LKFAhAJba4JWJjIwIvMfg5iNz4kjWjJn5nclf8LE+FR8Kt4EkUgZfhXnaCyxvGZT8OMx+8l1bOpMaTDMhFNj08ETLYJRA6MLsGddhm2om+IeGzI1LNRpbT1xL00ioEylO23+mCEm2r+nP7rAtt+9oTTnZ76knlE4jnlqzAZeMVk8VYBj1RuUsxfZDqbKEnobwon4NsPmqIRJcoZ+CJwcEo0A7sue1n4lUhaF3dp21jqEZKx9O/DU1Nkgj5RAlntjTuFv5/z72+1/sPTiFUEQplbmRzdHJlYW0KZW5kb2JqCjM0IDAgb2JqCjw8IC9MZW5ndGggMjMxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVPOZIEIQzLeYU+MFUY20C/p6e2Ntj5f7qSmU6Q8CHJ0xMdmXiZIyOwZsfbWmQgZuBTTMW/9rQPE6r34B4ilIsLYYaRcNas426ejhf/dpXPWAfvNviKWV4Q2MJM1lcWZy7bBWNpnMQ5yW6MXROxjXWtp1NYRzChDIR0tsOUIHNUpPTJjjLm6DiRJ56L7/bbLHY5fg7rCzaNIRXn+Cp6gjaDoux57wIackH/Xd34HkW76CUgGwkW1lFi7pzlhF+9dnQetSgSc0KaQS4TIc3pKqYQmlCss6OgUlFwqT6n6Kyff+VfXC0KZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDI0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UDuORCEM6zmFL/Ak8iNwHkarLWbv364DmilQTH62MyTQEYFHDDGUr+MlraCugb+LQvFu4uuDwiCrQ1IgznoPiHTspjaREzodnDM/YTdjjsBFMQac6XSmPQcmOfvCCoRzG2XsVkgniaoijuozjimeKnufeBYs7cg2WyeSPeQg4VJSicmln5TKP23KlAo6ZtEELBK54GQTTTjLu0lSjBmUMuoepnYifaw8yKM66GRNzqwjmdnTT9uZ+Bxwt1/aZE6Vx3QezPictM6DORW69+OJNgdNjdro7PcTaSovUrsdWp1+dRKV3RjnGBKXZ38Z32T/+Qf+h1oiCmVuZHN0cmVhbQplbmRvYmoKMzYgMCBvYmoKPDwgL0xlbmd0aCAyNDkgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVFJigMwDLvnFfpAIV6TvKdDmUPn/9fKDoU5BAmvkpOWmFgLDzGEHyw9+JEhczf9G36i2btZepLJ2f+Y5yJTUfhSqC5iQl2IG8+hEfA9oWsSWbG98Tkso5lzvgcfhbgEM6EBY31JMrmo5pUhE04MdRwOWqTCuGtiw+Ja0TyN3G77RmZlJoQNj2RC3BiAiCDrArIYLJQ2NhMyWc4D7Q3JDVpg16kbUYuCK5TWCXSiVsSqzOCz5tZ2N0Mt8uCoffH6aFaXYIXRS/VYeF+FPpipmXbukkJ64U07IsweCqQyOy0rtXvE6m6B+j/LUvD9yff4Ha8PzfxcnAplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9MZW5ndGggMzQxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEVSS25EMQjbv1NwgUjhl5DztKq6mN5/W5tM1c3gCWBseMtTpmTKsLklIyTXlE99IkOspvw0ciQipvhJCQV2lY/Ha0usjeyRqBSf2vHjsfRGptkVWvXu0aXNolHNysg5yBChnhW6snvUDtnwelxIuu+UzSEcy/9QgSxl3XIKJUFb0HfsEd8PHa6CK4JhsGsug+1lMtT/+ocWXO9992LHLoAWrOe+wQ4AqKcTtAXIGdruNiloAFW6i0nCo/J6bnaibKNV6fkcADMOMHLAiCVbHb7R3gCWfV3oRY2K/StAUVlA/MjVdsHeMclIcBbmBo69cDzFmXBLOMYCQIq94hh68CXY5i9Xroia8Al1umQvvMKe2ubnQpMId60ADl5kw62ro6iW7ek8gvZnRXJGjNSLODohklrSOYLi0qAeWuNcN7HibSOxuVff7h/hnC9c9usXS+yExAplbmRzdHJlYW0KZW5kb2JqCjM4IDAgb2JqCjw8IC9MZW5ndGggMTY0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQx3EFMQxD76oCJTCACvWsx/MP6/6vhvTTQXoYQgxiT8KwXFdxYXTDj7ctMw1/RxnuxvoyY7zVWCAn6AMMkYmr0aT6dsUZqvTk1WKuo6JcLzoiEsyS46tAI3w6sseTtrYz/XReH+wh7xP/KirnbmEBLqruQPlSH/HUj9lR6pqhjyorax5q2leEXRFK2z4upzJO3b0DWuG9las92u8/HnY68gplbmRzdHJlYW0KZW5kb2JqCjM5IDAgb2JqCjw8IC9MZW5ndGggNzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZcQL6piblCLhdIDMTKAbMMgLQlnIKIZ4CYIG0QxSAWRLGZiRlEHZwBkcvgSgMAJdsWyQplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9MZW5ndGggNDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZclhBWLhdMLAfMAtGWcAoinsGVBgC5Zw0nCmVuZHN0cmVhbQplbmRvYmoKNDEgMCBvYmoKPDwgL0xlbmd0aCAyNTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZFLcgQgCET3noIjgPzkPJNKZTG5/zYNzmQ2dpeo/YRKI6YSLOcUeTB9yfLNZLbpdzlWOxsFFEUomMlV6LECqztTxJlriWrrY2XkuNM7BsUbzl05qWRxo4x1VHUqcEzPlfVR3fl2WZR9Rw5lCtiscxxs4MptwxgnRput7g73iSBPJ1NHxe0g2fAHJ419lasrcJ1s9tFLMA4E/UITmOSLQOsMgcbNU/TkEuzj43bngWBveRFI2RDIkSEYHYJ2nVz/4tb5vf9xhjvPtRmuHO/id5jWdsdfYpIVcwGL3Cmo52suWtcZOt6TM8fkpvuGzrlgl7uDTO/5P9bP+v4DHilm+gplbmRzdHJlYW0KZW5kb2JqCjQyIDAgb2JqCjw8IC9UeXBlIC9YT2JqZWN0IC9TdWJ0eXBlIC9Gb3JtIC9CQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvTGVuZ3RoIDM5Ci9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nOMyNDBTMDY1VcjlMjc2ArNywCwjcyMgCySLYEFkM7jSABXzCnwKZW5kc3RyZWFtCmVuZG9iago0MyAwIG9iago8PCAvTGVuZ3RoIDIxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9ULmNBDEMy12FGljAeu2pZxaLS6b/9Ej59iLRFkVSKjWZkikvdZQlWVPeOnyWxA55huVuZDYlKkUvk7Al99AK8X2J5hT33dWWs0M0l2g5fgszKqobHdNLNppwKhO6oNzDM/oNbXQDVocesVsg0KRg17YgcscPGAzBmROLIgxKTQb/rnKPn16LGz7D8UMUkZIO5jX/WP3ycw2vU48nkW5vvuJenKkOAxEckpq8I11YsS4SEWk1QU3PwFotgLu3Xv4btCO6DED2icRxmlKOob9rcKXPL+UnU9gKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvTGVuZ3RoIDgzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4m9j5RlMLevw0QJW64J909XB0JmSluM8NDBp4MLIZdcYH0ljALXEdQjp3so2HVvuoEjfWmUvPvD5Se7KzihusBAkIaZgplbmRzdHJlYW0KZW5kb2JqCjQ1IDAgb2JqCjw8IC9MZW5ndGggNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhyuDKw0A4bQNmAplbmRzdHJlYW0KZW5kb2JqCjQ2IDAgb2JqCjw8IC9MZW5ndGggMTYwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQORIDMQgEc72CJ0hcgvesy7XB+v+pB9ZHoukCNBy6Fk3KehRoPumxRqG60GvoLEqSRMEWkh1Qp2OIOyhITEhjkki2HoMjmlizXZiZVCqzUuG0acXCv9la1chEjXCN/InpBlT8T+pclPBNg6+SMfoYVLw7g4xJ+F5F3Fox7f5EMLEZ9glvRSYFhImxqdm+z2CGzPcK1zjH8w1MgjfrCmVuZHN0cmVhbQplbmRvYmoKNDcgMCBvYmoKPDwgL0xlbmd0aCA3MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMzZTMFCwMAISpqaGCuZGlgophlxAPoiVywUTywGzzCzMgSwjC5CWHC5DC2MwbWJspGBmYgZkWSAxILoyuNIAmJoTAwplbmRzdHJlYW0KZW5kb2JqCjQ4IDAgb2JqCjw8IC9MZW5ndGggMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDCAwxRDrjQAHeYDUgplbmRzdHJlYW0KZW5kb2JqCjQ5IDAgb2JqCjw8IC9MZW5ndGggMTMzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWPSw4EIQhE95yijsDHH+dxMumFc//tgJ1uE2M9hVSBuYKhPS5rA50VHyEZtvG3qZaORVk+VHpSVg/J4Iesxssh3KAs8IJJKoYhUIuYGpEtZW63gNs2DbKylVOljrCLozCP9rRsFR5folsidZI/g8QqL9zjuh3Ipda73qKLvn+kATEJCmVuZHN0cmVhbQplbmRvYmoKNTAgMCBvYmoKPDwgL0xlbmd0aCAyNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVFJcgNBCLvPK/SEZqffY5crh+T/1wjKBwYNi0B0WuKgjJ8gLFe85ZGraMPfMzGC3wWHfivXbVjkQFQgSWNQNaF28Xr0HthxmAnMk9awDGasD/yMKdzoxeExGWe312XUEOxdrz2ZQcmsXMQlExdM1WEjZw4/mTIutHM9NyDnRliXYZBuVhozEo40hUghhaqbpM4EQRKMrkaNNnIU+6Uvj3SGVY2oMexzLW1fz004a9DsWKzy5JQeXXEuJxcvrBz09TYDF1FprPJASMD9bg/1c7KT33hL584W0+N7zcnywlRgxZvXbkA21eLfvIjj+4yv5+f5/ANfYFuICmVuZHN0cmVhbQplbmRvYmoKNTEgMCBvYmoKPDwgL0xlbmd0aCAxNzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTZBJDkMhDEP3nMIXqIQzwOc8v6q6aO+/rUMHdYH85CBwPDzQcSQudGTojI4rmxzjwLMgY+LROP/JuD7EMUHdoi1Yl3bH2cwSc8IyMQK2RsnZPKLAD8dcCBJklx++wCAiXY/5VvNZk/TPtzvdj7q0Zl89osCJ7AjFsAFXgP26x4FLwvle0+SXKiVjE4fygeoiUjY7oRC1VOxyqoqz3ZsrcBX0/NFD7u0FtSM83wplbmRzdHJlYW0KZW5kb2JqCjUyIDAgb2JqCjw8IC9MZW5ndGggNzUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicM7U0UjBQMDYAEqZmRgqmJuYKKYZcQD6IlctlaGQKZuVwGVmaKVhYABkmZuZQIZiGHC5jU3OgAUBFxqZgGqo/hyuDKw0AlZAS7wplbmRzdHJlYW0KZW5kb2JqCjUzIDAgb2JqCjw8IC9MZW5ndGggMjE1IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVROQ4DIQzs9xX+QCSML3hPoijN/r/NjNFWHsFchrSUIZnyUpOoIeVTPnqZLpy63NfMajTnlrQtc4C4trwvrZLAiWaIg8FpmLgBmjwBQ9fRqFFDFx7Q1KVTKLDcBD6Kt24P3WO1gZe2IeeJIGIoGSxBzalFExZtzyekNb9eixvel+3dyFOlxpYYgQYBVjgc1+jX8JU9TybRdBUy1Ks1yxgJE0UiPPmOptUT61o00jIS1MYRrGoDvDv9ME4AABNxywJkn0qUs+TEb7H0swZX+v4Bn0dUlgplbmRzdHJlYW0KZW5kb2JqCjI0IDAgb2JqCjw8IC9UeXBlIC9Gb250IC9CYXNlRm9udCAvQk1RUURWK0RlamFWdVNhbnMgL0ZpcnN0Q2hhciAwIC9MYXN0Q2hhciAyNTUKL0ZvbnREZXNjcmlwdG9yIDIzIDAgUiAvU3VidHlwZSAvVHlwZTMgL05hbWUgL0JNUVFEVitEZWphVnVTYW5zCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0KL0NoYXJQcm9jcyAyNSAwIFIKL0VuY29kaW5nIDw8IC9UeXBlIC9FbmNvZGluZwovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDYgL3BlcmlvZCA0OCAvemVybyAvb25lIC90d28gNTMgL2ZpdmUgNTUgL3NldmVuIDY1IC9BIDY4IC9ECjcxIC9HIDc3IC9NIDgwIC9QIDgzIC9TIDk3IC9hIDk5IC9jIC9kIC9lIDEwMyAvZyAvaCAvaSAxMDggL2wgL20gMTExIC9vIDExNAovciAxMTYgL3QgL3UgL3YgXQo+PgovV2lkdGhzIDIyIDAgUiA+PgplbmRvYmoKMjMgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvQk1RUURWK0RlamFWdVNhbnMgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0FzY2VudCA5MjkgL0Rlc2NlbnQgLTIzNiAvQ2FwSGVpZ2h0IDAKL1hIZWlnaHQgMCAvSXRhbGljQW5nbGUgMCAvU3RlbVYgMCAvTWF4V2lkdGggMTM0MiA+PgplbmRvYmoKMjIgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMjUgMCBvYmoKPDwgL0EgMjYgMCBSIC9EIDI3IDAgUiAvRyAyOCAwIFIgL00gMjkgMCBSIC9QIDMwIDAgUiAvUyAzMSAwIFIgL2EgMzIgMCBSCi9jIDMzIDAgUiAvZCAzNCAwIFIgL2UgMzUgMCBSIC9maXZlIDM2IDAgUiAvZyAzNyAwIFIgL2ggMzggMCBSIC9pIDM5IDAgUgovbCA0MCAwIFIgL20gNDEgMCBSIC9vIDQzIDAgUiAvb25lIDQ0IDAgUiAvcGVyaW9kIDQ1IDAgUiAvciA0NiAwIFIKL3NldmVuIDQ3IDAgUiAvc3BhY2UgNDggMCBSIC90IDQ5IDAgUiAvdHdvIDUwIDAgUiAvdSA1MSAwIFIgL3YgNTIgMCBSCi96ZXJvIDUzIDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjIgMTkgMCBSIC9GMSAyNCAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDAgL2NhIDEgPj4KL0EyIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDEgPj4KL0EzIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDAuOCAvY2EgMC44ID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9JMSAxMyAwIFIgL00wIDE0IDAgUiAvTTEgMTUgMCBSIC9NMiAxNiAwIFIgL0YxLURlamFWdVNhbnMtbWludXMgNDIgMCBSCj4+CmVuZG9iagoxMyAwIG9iago8PCAvVHlwZSAvWE9iamVjdCAvU3VidHlwZSAvSW1hZ2UgL1dpZHRoIDMwNCAvSGVpZ2h0IDM4NAovQ29sb3JTcGFjZSBbL0luZGV4ZWQgL0RldmljZVJHQiAyNTAgKP3nJPrmIvjmIfbmH/PlHvHlHO7lG+zkGunkGefkGeTjGOHjGN/jGNziGNriGNfiGdThGtLhG8/hHM3gHcrgHsfgH8XfIcLfIr/fJL3eJrreJ7fdXCm13Suy3Syv3C6t3DCq2zKn2zOl2jWi2jef2Tid2Tqa2DyX2D6V1z+S10GQ1kON1kSL1UaI1UeG1EmD00uB00x+0k580k950VF30FJ00FRyz1VwzlZtzlhrzVlpzFtnzFxcZMtdYspfYMlgXslhW8hiWcdkV8ZlVcZmU8VnUcRoT8NpTcJrS8JsScFtR8BuRb9vRL5wQr5xQL1yPrxzPbt0O7p1Obl2OLl2Nrh3Nbd4M7Z5MrV6MLR6L7N7LrJ8LLF9K7F9KrB+XCmvf1worn8nrYAmrIElq4EkqoIjqYIjqIMip4Qhp4QhpoUgpYUgpIUfo4YfooYfoYceoIcen4genogenYgenIkem4kemokemYoemIoel4oflosflYsflIsfk4sfkowgkYwgkIwgj4whjowhjYwhjI0ii40iio0jiY0iiY0jiI0jh40kho0khY0khI0lg40lgo4mgY4mgI4mf44nfo4nfY4nfI5cKHuOXCh6jlwpeY5cKXiOKneOKnaOKnWOK3SOK3OOLHKOLHGOLHCOLW+OLW6OLm2OLmyOLmuOL2qNL2mNMGiNMGeNMWaNMWWNMWSNMmONMmKNM2GNM2CNNF+NNF6NNV2MNVxcjDZbjDZajDdZjDdYjDhXjDhWizlVizlUizpTizpSiztRijtQijxOijxNij1MiT1KiT5JiT5IiD9HiD9Fh0BEh0BDh0FChkFBhkJAhUI+hUI9hEM8hEM7g0M6g0Q5gkQ3gUU2gUU1gEU0f0Uyf0YxfkYwfUYvfEYtfEcse0crekdcKHhHJ3dHJXVII3RIInNIIXJIIHFIHnBIHW9IHG5IGmxIGWtHGGpHFmlHFWdHFGZHEmVHEWNHD2JGDmFGDF9GC15GCVxcRQhbRQZaRQVYRANXRAJVRAFUKV0KL0JpdHNQZXJDb21wb25lbnQgOCAvRmlsdGVyIC9GbGF0ZURlY29kZQovRGVjb2RlUGFybXMgPDwgL1ByZWRpY3RvciAxMCAvQ29sb3JzIDEgL0NvbHVtbnMgMzA0ID4+IC9MZW5ndGggNTQgMCBSID4+CnN0cmVhbQp4nO3dZ3hWxbbA8XPvucde7xFEUDpIs1AvvUkXpHekiIB0AgRIKCEEQgIkBELvvQkIiBQBQUWQYkUEUVEQUREpNtBP9593eMd5Z3byBgTPVtf6sJ/9zJ5ZM+v3Yc5MyIn/+IcT/+XEf1vxz9D4HyP+FYybAnHzzTffcsstt9566+23337HHXfcfffd/0tkyZIle/bsuXPnLlSoUPHixStUqFC7du3mzZt37ty5X79+UVFRsbGxcXFxPHmnhXa+0oee9GcUY8lAniykIyu5mYF5mI051eR6LeYCrbXbpbnFu0ACJmAC5l+w66xlgt15551XwLJmzWqB1alTR4NFR0dDNXr0aJ68azD6WGDkyarAyG2C/YFmAiZgAuYfsDBUGWult+MTt912mw2WI0eOPHnyKLCKFStmEoyeCoyxZLDBmEdNmDFYeDOXTcAETMB8DeZudGGo/pnOdu+542cKrEWLFhmA8TVTYBns+xlv/R5uLoqACdiNBwtP5WKlt3VpLReMyx5F3XPPPf8mNFjhwoVLlCgBQ926dSF59tlnIyIihg4dOmrUKMB48k4L7XylDz3pz6jfwEhHVnIzgydYBmYebOE3MwETMAHzLVgYp4yp0tPSPwy76667roDdf//9Dz74YN68eRVYpUqVXLD4+HgXjJ4KjLFkIM/9Cozc+kdiYc3Cs7lyAiZgAuZfMHejy5gpPJW+RepTqwK799570wVr2bJlly5d+vfvD1JcXBxgPHmnhXa+pgtGVg2m931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"plt.show()"]}, {"cell_type": "markdown", "id": "e9d4f8de", "metadata": {"papermill": {"duration": 0.084989, "end_time": "2023-03-15T10:02:03.711661", "exception": false, "start_time": "2023-03-15T10:02:03.626672", "status": "completed"}, "tags": []}, "source": ["We can clearly see that SGD is not able to find the center of the optimization curve and has a problem converging due to the steep gradients in $w_1$.\n", "In contrast, Adam and SGD with momentum nicely converge as the changing direction of $w_1$ is canceling itself out.\n", "On such surfaces, it is crucial to use momentum."]}, {"cell_type": "markdown", "id": "79def1c7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.09082, "end_time": "2023-03-15T10:02:03.888934", "exception": false, "start_time": "2023-03-15T10:02:03.798114", "status": "completed"}, "tags": []}, "source": ["### Steep optima\n", "\n", "A second type of challenging loss surfaces are steep optima.\n", "In those, we have a larger part of the surface having very small gradients while around the optimum, we have very large gradients.\n", "For instance, take the following loss surfaces:"]}, {"cell_type": "code", "execution_count": 34, "id": "2afddee2", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:04.068145Z", "iopub.status.busy": "2023-03-15T10:02:04.066884Z", "iopub.status.idle": "2023-03-15T10:02:05.467972Z", "shell.execute_reply": "2023-03-15T10:02:05.467217Z"}, "papermill": {"duration": 1.50042, "end_time": "2023-03-15T10:02:05.477960", "exception": false, "start_time": "2023-03-15T10:02:03.977540", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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plot_curve(comb_func, x_range=(-2, 2), y_range=(-2, 2), plot_3d=True, title=\"Steep optima\")"]}, {"cell_type": "markdown", "id": "8f24c037", "metadata": {"papermill": {"duration": 0.109214, "end_time": "2023-03-15T10:02:05.697538", "exception": false, "start_time": "2023-03-15T10:02:05.588324", "status": "completed"}, "tags": []}, "source": ["Most of the loss surface has very little to no gradients.\n", "However, close to the optima, we have very steep gradients.\n", "To reach the minimum when starting in a region with lower gradients, we expect an adaptive learning rate to be crucial.\n", "To verify this hypothesis, we can run our three optimizers on the surface:"]}, {"cell_type": "code", "execution_count": 35, "id": "61773f2e", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:02:05.922014Z", "iopub.status.busy": "2023-03-15T10:02:05.921312Z", "iopub.status.idle": "2023-03-15T10:02:06.526589Z", "shell.execute_reply": "2023-03-15T10:02:06.526021Z"}, "papermill": {"duration": 0.719889, "end_time": "2023-03-15T10:02:06.528806", "exception": false, "start_time": "2023-03-15T10:02:05.808917", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"metadata": {}, "output_type": "display_data"}], "source": ["SGD_points = train_curve(lambda params: SGD(params, lr=0.5), comb_func, init=[0, 0])\n", "SGDMom_points = train_curve(lambda params: SGDMomentum(params, lr=1, momentum=0.9), comb_func, init=[0, 0])\n", "Adam_points = train_curve(lambda params: Adam(params, lr=0.2), comb_func, init=[0, 0])\n", "\n", "all_points = np.concatenate([SGD_points, SGDMom_points, Adam_points], axis=0)\n", "ax = plot_curve(comb_func, x_range=(-2, 2), y_range=(-2, 2), plot_3d=False, title=\"Steep optima\")\n", "ax.plot(SGD_points[:, 0], SGD_points[:, 1], color=\"red\", marker=\"o\", zorder=3, label=\"SGD\", alpha=0.7)\n", "ax.plot(SGDMom_points[:, 0], SGDMom_points[:, 1], color=\"blue\", marker=\"o\", zorder=2, label=\"SGDMom\", alpha=0.7)\n", "ax.plot(Adam_points[:, 0], Adam_points[:, 1], color=\"grey\", marker=\"o\", zorder=1, label=\"Adam\", alpha=0.7)\n", "ax.set_xlim(-2, 2)\n", "ax.set_ylim(-2, 2)\n", "plt.legend()\n", "plt.show()"]}, {"cell_type": "markdown", "id": "9b8bf667", "metadata": {"papermill": {"duration": 0.106255, "end_time": "2023-03-15T10:02:06.746813", "exception": false, "start_time": "2023-03-15T10:02:06.640558", "status": "completed"}, "tags": []}, "source": ["SGD first takes very small steps until it touches the border of the optimum.\n", "First reaching a point around $(-0.75,-0.5)$, the gradient direction has changed and pushes the parameters to $(0.8,0.5)$ from which SGD cannot recover anymore (only with many, many steps).\n", "A similar problem has SGD with momentum, only that it continues the direction of the touch of the optimum.\n", "The gradients from this time step are so much larger than any other point that the momentum $m_t$ is overpowered by it.\n", "Finally, Adam is able to converge in the optimum showing the importance of adaptive learning rates."]}, {"cell_type": "markdown", "id": "2a27e2b7", "metadata": {"papermill": {"duration": 0.109293, "end_time": "2023-03-15T10:02:06.961662", "exception": false, "start_time": "2023-03-15T10:02:06.852369", "status": "completed"}, "tags": []}, "source": ["### What optimizer to take\n", "\n", "After seeing the results on optimization, what is our conclusion?\n", "Should we always use Adam and never look at SGD anymore?\n", "The short answer: no.\n", "There are many papers saying that in certain situations, SGD (with momentum) generalizes better where Adam often tends to overfit [5,6].\n", "This is related to the idea of finding wider optima.\n", "For instance, see the illustration of different optima below (credit: [Keskar et al., 2017](https://arxiv.org/pdf/1609.04836.pdf)):\n", "\n", "<center width=\"100%\"><img src=\"https://github.com/Lightning-AI/lightning-tutorials/raw/main/course_UvA-DL/03-initialization-and-optimization/flat_vs_sharp_minima.svg\" width=\"500px\"></center>\n", "\n", "The black line represents the training loss surface, while the dotted red line is the test loss.\n", "Finding sharp, narrow minima can be helpful for finding the minimal training loss.\n", "However, this doesn't mean that it also minimizes the test loss as especially flat minima have shown to generalize better.\n", "You can imagine that the test dataset has a slightly shifted loss surface due to the different examples than in the training set.\n", "A small change can have a significant influence for sharp minima, while flat minima are generally more robust to this change.\n", "\n", "In the next tutorial, we will see that some network types can still be better optimized with SGD and learning rate scheduling than Adam.\n", "Nevertheless, Adam is the most commonly used optimizer in Deep Learning\n", "as it usually performs better than other optimizers, especially for deep\n", "networks."]}, {"cell_type": "markdown", "id": "3f2695f0", "metadata": {"papermill": {"duration": 0.107774, "end_time": "2023-03-15T10:02:07.179262", "exception": false, "start_time": "2023-03-15T10:02:07.071488", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we have looked at initialization and optimization techniques for neural networks.\n", "We have seen that a good initialization has to balance the preservation of the gradient variance as well as the activation variance.\n", "This can be achieved with the Xavier initialization for tanh-based networks, and the Kaiming initialization for ReLU-based networks.\n", "In optimization, concepts like momentum and adaptive learning rate can help with challenging loss surfaces but don't guarantee an increase in performance for neural networks.\n", "\n", "\n", "## References\n", "\n", "[1] Glorot, Xavier, and Yoshua Bengio.\n", "\"Understanding the difficulty of training deep feedforward neural networks.\"\n", "Proceedings of the thirteenth international conference on artificial intelligence and statistics.\n", "2010.\n", "[link](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf)\n", "\n", "[2] He, Kaiming, et al.\n", "\"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.\"\n", "Proceedings of the IEEE international conference on computer vision.\n", "2015.\n", "[link](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)\n", "\n", "[3] Kingma, Diederik P. & Ba, Jimmy.\n", "\"Adam: A Method for Stochastic Optimization.\"\n", "Proceedings of the third international conference for learning representations (ICLR).\n", "2015.\n", "[link](https://arxiv.org/abs/1412.6980)\n", "\n", "[4] Keskar, Nitish Shirish, et al.\n", "\"On large-batch training for deep learning: Generalization gap and sharp minima.\"\n", "Proceedings of the fifth international conference for learning representations (ICLR).\n", "2017.\n", "[link](https://arxiv.org/abs/1609.04836)\n", "\n", "[5] Wilson, Ashia C., et al.\n", "\"The Marginal Value of Adaptive Gradient Methods in Machine Learning.\"\n", "Advances in neural information processing systems.\n", "2017.\n", "[link](https://papers.nips.cc/paper/7003-the-marginal-value-of-adaptive-gradient-methods-in-machine-learning.pdf)\n", "\n", "[6] Ruder, Sebastian.\n", "\"An overview of gradient descent optimization algorithms.\"\n", "arXiv preprint.\n", "2017.\n", "[link](https://arxiv.org/abs/1609.04747)"]}, {"cell_type": "markdown", "id": "c9f62e17", "metadata": {"papermill": {"duration": 0.10781, "end_time": "2023-03-15T10:02:07.395235", "exception": false, "start_time": "2023-03-15T10:02:07.287425", "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 [Slack](https://www.pytorchlightning.ai/community)!\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 3: Initialization and Optimization\n", "   :card_description: In this tutorial, we will review techniques for optimization and initialization of neural networks. When increasing the depth of neural networks, there are various challenges...\n", "   :tags: Image,Initialization,Optimizers,GPU/TPU,UvA-DL-Course\n", "   :image: _static/images/course_UvA-DL/03-initialization-and-optimization.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab_type,colab,id,-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": "3.9.16"}, "papermill": {"default_parameters": {}, "duration": 108.776527, "end_time": "2023-03-15T10:02:08.932684", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/03-initialization-and-optimization/Initialization_and_Optimization.ipynb", "output_path": ".notebooks/course_UvA-DL/03-initialization-and-optimization.ipynb", "parameters": {}, "start_time": 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