{ "cells": [ { "cell_type": "markdown", "id": "7e3b7339", "metadata": { "papermill": { "duration": 0.011104, "end_time": "2023-10-11T15:29:11.954277", "exception": false, "start_time": "2023-10-11T15:29:11.943173", "status": "completed" }, "tags": [] }, "source": [ "\n", "# Tutorial 2: Activation Functions\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-10-11T15:26:46.260596\n", "\n", "In this tutorial, we will take a closer look at (popular) activation functions and investigate their effect on optimization properties in neural networks.\n", "Activation functions are a crucial part of deep learning models as they add the non-linearity to neural networks.\n", "There is a great variety of activation functions in the literature, and some are more beneficial than others.\n", "The goal of this tutorial is to show the importance of choosing a good activation function (and how to do so), and what problems might occur if we don't.\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/02-activation-functions.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": "a5b63cba", "metadata": { "papermill": { "duration": 0.010013, "end_time": "2023-10-11T15:29:11.975526", "exception": false, "start_time": "2023-10-11T15:29:11.965513", "status": "completed" }, "tags": [] }, "source": [ "## Setup\n", "This notebook requires some packages besides pytorch-lightning." ] }, { "cell_type": "code", "execution_count": 1, "id": "756ff121", "metadata": { "colab": {}, "colab_type": "code", "execution": { "iopub.execute_input": "2023-10-11T15:29:11.996484Z", "iopub.status.busy": "2023-10-11T15:29:11.996249Z", "iopub.status.idle": "2023-10-11T15:34:16.729212Z", "shell.execute_reply": "2023-10-11T15:34:16.727829Z" }, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": { "duration": 304.746763, "end_time": "2023-10-11T15:34:16.732085", "exception": false, "start_time": "2023-10-11T15:29:11.985322", "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 \"matplotlib\" \"setuptools>=68.0.0, <68.3.0\" \"torch>=1.8.1, <2.1.0\" \"torchmetrics>=0.7, <1.3\" \"seaborn\" \"lightning>=2.0.0\" \"torchvision\" \"matplotlib>=3.0.0, <3.9.0\" \"pytorch-lightning>=1.4, <2.1.0\" \"urllib3\" \"ipython[notebook]>=8.0.0, <8.17.0\"" ] }, { "cell_type": "markdown", "id": "5ff9ab7e", "metadata": { "papermill": { "duration": 0.016777, "end_time": "2023-10-11T15:34:16.766464", "exception": false, "start_time": "2023-10-11T15:34:16.749687", "status": "completed" }, "tags": [] }, "source": [ "
\n", "Before we start, we import our standard libraries and set up basic functions:" ] }, { "cell_type": "code", "execution_count": 2, "id": "6551088f", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:16.796267Z", "iopub.status.busy": "2023-10-11T15:34:16.795850Z", "iopub.status.idle": "2023-10-11T15:34:19.190816Z", "shell.execute_reply": "2023-10-11T15:34:19.189744Z" }, "papermill": { "duration": 2.409701, "end_time": "2023-10-11T15:34:19.192527", "exception": false, "start_time": "2023-10-11T15:34:16.782826", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import json\n", "import math\n", "import os\n", "import urllib.request\n", "import warnings\n", "from urllib.error import HTTPError\n", "\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.optim as optim\n", "import torch.utils.data as data\n", "import torchvision\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": "42a8816c", "metadata": { "papermill": { "duration": 0.007418, "end_time": "2023-10-11T15:34:19.207666", "exception": false, "start_time": "2023-10-11T15:34:19.200248", "status": "completed" }, "tags": [] }, "source": [ "We will define a function to set a seed on all libraries we might interact with in this tutorial (here numpy and torch).\n", "This allows us to make our training reproducible.\n", "However, note that in contrast to the CPU, the same seed on different GPU architectures can give different results.\n", "All models here have been trained on an NVIDIA GTX1080Ti.\n", "\n", "Additionally, the following cell defines two paths: `DATASET_PATH` and `CHECKPOINT_PATH`.\n", "The dataset path is the directory where we will download datasets used in the notebooks.\n", "It is recommended to store all datasets from PyTorch in one joined directory to prevent duplicate downloads.\n", "The checkpoint path is the directory where we will store trained model weights and additional files.\n", "The needed files will be automatically downloaded.\n", "In case you are on Google Colab, it is recommended to change the\n", "directories to start from the current directory (i.e. remove `../` for\n", "both dataset and checkpoint path)." ] }, { "cell_type": "code", "execution_count": 3, "id": "779a3515", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:19.248001Z", "iopub.status.busy": "2023-10-11T15:34:19.247647Z", "iopub.status.idle": "2023-10-11T15:34:19.353312Z", "shell.execute_reply": "2023-10-11T15:34:19.352282Z" }, "papermill": { "duration": 0.115766, "end_time": "2023-10-11T15:34:19.354701", "exception": false, "start_time": "2023-10-11T15:34:19.238935", "status": "completed" }, "tags": [] }, "outputs": [ { "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/Activation_Functions/\")\n", "\n", "\n", "# Function for setting the seed\n", "def set_seed(seed):\n", " np.random.seed(seed)\n", " torch.manual_seed(seed)\n", " if torch.cuda.is_available(): # GPU operation have separate seed\n", " torch.cuda.manual_seed(seed)\n", " torch.cuda.manual_seed_all(seed)\n", "\n", "\n", "set_seed(42)\n", "\n", "# Additionally, some operations on a GPU are implemented stochastic for efficiency\n", "# We want to 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": "5e735538", "metadata": { "papermill": { "duration": 0.007299, "end_time": "2023-10-11T15:34:19.369510", "exception": false, "start_time": "2023-10-11T15:34:19.362211", "status": "completed" }, "tags": [] }, "source": [ "The following cell downloads all pretrained models we will use in this notebook.\n", "The files are stored on a separate [repository](https://github.com/phlippe/saved_models) to reduce the size of the notebook repository, especially for building the documentation on ReadTheDocs.\n", "In case the download below fails, you can download the models from a [Google Drive folder](https://drive.google.com/drive/folders/1sFpZUpDJVjiYEvIqISqfkFizfsTnPf4s?usp=sharing).\n", "Please let me (Phillip) know if an error occurs so it can be fixed for all students." ] }, { "cell_type": "code", "execution_count": 4, "id": "b785b259", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:19.385189Z", "iopub.status.busy": "2023-10-11T15:34:19.384992Z", "iopub.status.idle": "2023-10-11T15:34:24.552453Z", "shell.execute_reply": "2023-10-11T15:34:24.551118Z" }, "papermill": { "duration": 5.178575, "end_time": "2023-10-11T15:34:24.555337", "exception": false, "start_time": "2023-10-11T15:34:19.376762", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_elu.config...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_elu.tar...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_leakyrelu.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_leakyrelu.tar...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_relu.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_relu.tar...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_sigmoid.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_sigmoid.tar...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_swish.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_swish.tar...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_tanh.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_tanh.tar...\n" ] } ], "source": [ "# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/\"\n", "# Files to download\n", "pretrained_files = [\n", " \"FashionMNIST_elu.config\",\n", " \"FashionMNIST_elu.tar\",\n", " \"FashionMNIST_leakyrelu.config\",\n", " \"FashionMNIST_leakyrelu.tar\",\n", " \"FashionMNIST_relu.config\",\n", " \"FashionMNIST_relu.tar\",\n", " \"FashionMNIST_sigmoid.config\",\n", " \"FashionMNIST_sigmoid.tar\",\n", " \"FashionMNIST_swish.config\",\n", " \"FashionMNIST_swish.tar\",\n", " \"FashionMNIST_tanh.config\",\n", " \"FashionMNIST_tanh.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": "40ca0758", "metadata": { "papermill": { "duration": 0.017613, "end_time": "2023-10-11T15:34:24.591379", "exception": false, "start_time": "2023-10-11T15:34:24.573766", "status": "completed" }, "tags": [] }, "source": [ "## Common activation functions" ] }, { "cell_type": "markdown", "id": "764e8271", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.010868, "end_time": "2023-10-11T15:34:24.619909", "exception": false, "start_time": "2023-10-11T15:34:24.609041", "status": "completed" }, "tags": [] }, "source": [ "As a first step, we will implement some common activation functions by ourselves.\n", "Of course, most of them can also be found in the `torch.nn` package (see the [documentation](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) for an overview).\n", "However, we'll write our own functions here for a better understanding and insights.\n", "\n", "For an easier time of comparing various activation functions, we start\n", "with defining a base class from which all our future modules will\n", "inherit:" ] }, { "cell_type": "code", "execution_count": 5, "id": "28e4694c", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:24.642857Z", "iopub.status.busy": "2023-10-11T15:34:24.642470Z", "iopub.status.idle": "2023-10-11T15:34:24.648948Z", "shell.execute_reply": "2023-10-11T15:34:24.647932Z" }, "lines_to_next_cell": 2, "papermill": { "duration": 0.019857, "end_time": "2023-10-11T15:34:24.650412", "exception": false, "start_time": "2023-10-11T15:34:24.630555", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class ActivationFunction(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.name = self.__class__.__name__\n", " self.config = {\"name\": self.name}" ] }, { "cell_type": "markdown", "id": "405bcc19", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.007682, "end_time": "2023-10-11T15:34:24.665859", "exception": false, "start_time": "2023-10-11T15:34:24.658177", "status": "completed" }, "tags": [] }, "source": [ "Every activation function will be an `nn.Module` so that we can integrate them nicely in a network.\n", "We will use the `config` dictionary to store adjustable parameters for some activation functions.\n", "\n", "Next, we implement two of the \"oldest\" activation functions that are still commonly used for various tasks: sigmoid and tanh.\n", "Both the sigmoid and tanh activation can be also found as PyTorch functions (`torch.sigmoid`, `torch.tanh`) or as modules (`nn.Sigmoid`, `nn.Tanh`).\n", "Here, we implement them by hand:" ] }, { "cell_type": "code", "execution_count": 6, "id": "e381f7e7", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:24.682884Z", "iopub.status.busy": "2023-10-11T15:34:24.682509Z", "iopub.status.idle": "2023-10-11T15:34:24.689732Z", "shell.execute_reply": "2023-10-11T15:34:24.688696Z" }, "lines_to_next_cell": 2, "papermill": { "duration": 0.017429, "end_time": "2023-10-11T15:34:24.691091", "exception": false, "start_time": "2023-10-11T15:34:24.673662", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class Sigmoid(ActivationFunction):\n", " def forward(self, x):\n", " return 1 / (1 + torch.exp(-x))\n", "\n", "\n", "class Tanh(ActivationFunction):\n", " def forward(self, x):\n", " x_exp, neg_x_exp = torch.exp(x), torch.exp(-x)\n", " return (x_exp - neg_x_exp) / (x_exp + neg_x_exp)" ] }, { "cell_type": "markdown", "id": "b7952e36", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.009747, "end_time": "2023-10-11T15:34:24.708620", "exception": false, "start_time": "2023-10-11T15:34:24.698873", "status": "completed" }, "tags": [] }, "source": [ "Another popular activation function that has allowed the training of deeper networks, is the Rectified Linear Unit (ReLU).\n", "Despite its simplicity of being a piecewise linear function, ReLU has one major benefit compared to sigmoid and tanh: a strong, stable gradient for a large range of values.\n", "Based on this idea, a lot of variations of ReLU have been proposed, of which we will implement the following three: LeakyReLU, ELU, and Swish.\n", "LeakyReLU replaces the zero settings in the negative part with a smaller slope to allow gradients to flow also in this part of the input.\n", "Similarly, ELU replaces the negative part with an exponential decay.\n", "The third, most recently proposed activation function is Swish, which is actually the result of a large experiment with the purpose of finding the \"optimal\" activation function.\n", "Compared to the other activation functions, Swish is both smooth and non-monotonic (i.e. contains a change of sign in the gradient).\n", "This has been shown to prevent dead neurons as in standard ReLU activation, especially for deep networks.\n", "If interested, a more detailed discussion of the benefits of Swish can be found in [this paper](https://arxiv.org/abs/1710.05941) [1].\n", "\n", "Let's implement the four activation functions below:" ] }, { "cell_type": "code", "execution_count": 7, "id": "58ddf70f", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:24.726347Z", "iopub.status.busy": "2023-10-11T15:34:24.725965Z", "iopub.status.idle": "2023-10-11T15:34:24.736087Z", "shell.execute_reply": "2023-10-11T15:34:24.735016Z" }, "papermill": { "duration": 0.020846, "end_time": "2023-10-11T15:34:24.737573", "exception": false, "start_time": "2023-10-11T15:34:24.716727", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class ReLU(ActivationFunction):\n", " def forward(self, x):\n", " return x * (x > 0).float()\n", "\n", "\n", "class LeakyReLU(ActivationFunction):\n", " def __init__(self, alpha=0.1):\n", " super().__init__()\n", " self.config[\"alpha\"] = alpha\n", "\n", " def forward(self, x):\n", " return torch.where(x > 0, x, self.config[\"alpha\"] * x)\n", "\n", "\n", "class ELU(ActivationFunction):\n", " def forward(self, x):\n", " return torch.where(x > 0, x, torch.exp(x) - 1)\n", "\n", "\n", "class Swish(ActivationFunction):\n", " def forward(self, x):\n", " return x * torch.sigmoid(x)" ] }, { "cell_type": "markdown", "id": "273064f3", "metadata": { "papermill": { "duration": 0.007767, "end_time": "2023-10-11T15:34:24.753199", "exception": false, "start_time": "2023-10-11T15:34:24.745432", "status": "completed" }, "tags": [] }, "source": [ "For later usage, we summarize all our activation functions in a dictionary mapping the name to the class object.\n", "In case you implement a new activation function by yourself, add it here to include it in future comparisons as well:" ] }, { "cell_type": "code", "execution_count": 8, "id": "ca7b2929", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:24.770266Z", "iopub.status.busy": "2023-10-11T15:34:24.769906Z", "iopub.status.idle": "2023-10-11T15:34:24.775733Z", "shell.execute_reply": "2023-10-11T15:34:24.774644Z" }, "papermill": { "duration": 0.015986, "end_time": "2023-10-11T15:34:24.777017", "exception": false, "start_time": "2023-10-11T15:34:24.761031", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "act_fn_by_name = {\"sigmoid\": Sigmoid, \"tanh\": Tanh, \"relu\": ReLU, \"leakyrelu\": LeakyReLU, \"elu\": ELU, \"swish\": Swish}" ] }, { "cell_type": "markdown", "id": "914299a0", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.007876, "end_time": "2023-10-11T15:34:24.792947", "exception": false, "start_time": "2023-10-11T15:34:24.785071", "status": "completed" }, "tags": [] }, "source": [ "### Visualizing activation functions\n", "\n", "To get an idea of what each activation function actually does, we will visualize them in the following.\n", "Next to the actual activation value, the gradient of the function is an important aspect as it is crucial for optimizing the neural network.\n", "PyTorch allows us to compute the gradients simply by calling the `backward` function:" ] }, { "cell_type": "code", "execution_count": 9, "id": "28272b20", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:24.810160Z", "iopub.status.busy": "2023-10-11T15:34:24.809801Z", "iopub.status.idle": "2023-10-11T15:34:24.816644Z", "shell.execute_reply": "2023-10-11T15:34:24.815585Z" }, "lines_to_next_cell": 2, "papermill": { "duration": 0.017133, "end_time": "2023-10-11T15:34:24.817931", "exception": false, "start_time": "2023-10-11T15:34:24.800798", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def get_grads(act_fn, x):\n", " \"\"\"Computes the gradients of an activation function at specified positions.\n", "\n", " Args:\n", " act_fn: An object of the class \"ActivationFunction\" with an implemented forward pass.\n", " x: 1D input tensor.\n", "\n", " Returns:\n", " A tensor with the same size of x containing the gradients of act_fn at x.\n", " \"\"\"\n", " x = x.clone().requires_grad_() # Mark the input as tensor for which we want to store gradients\n", " out = act_fn(x)\n", " out.sum().backward() # Summing results in an equal gradient flow to each element in x\n", " return x.grad # Accessing the gradients of x by \"x.grad\"" ] }, { "cell_type": "markdown", "id": "bdb5d4da", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.00785, "end_time": "2023-10-11T15:34:24.833692", "exception": false, "start_time": "2023-10-11T15:34:24.825842", "status": "completed" }, "tags": [] }, "source": [ "Now we can visualize all our activation functions including their gradients:" ] }, { "cell_type": "code", "execution_count": 10, "id": "1113d1d5", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:24.850890Z", "iopub.status.busy": "2023-10-11T15:34:24.850539Z", "iopub.status.idle": "2023-10-11T15:34:26.771919Z", "shell.execute_reply": "2023-10-11T15:34:26.771039Z" }, "papermill": { "duration": 1.932691, "end_time": "2023-10-11T15:34:26.774267", "exception": false, "start_time": "2023-10-11T15:34:24.841576", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/pdf": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def vis_act_fn(act_fn, ax, x):\n", " # Run activation function\n", " y = act_fn(x)\n", " y_grads = get_grads(act_fn, x)\n", " # Push x, y and gradients back to cpu for plotting\n", " x, y, y_grads = x.cpu().numpy(), y.cpu().numpy(), y_grads.cpu().numpy()\n", " # Plotting\n", " ax.plot(x, y, linewidth=2, label=\"ActFn\")\n", " ax.plot(x, y_grads, linewidth=2, label=\"Gradient\")\n", " ax.set_title(act_fn.name)\n", " ax.legend()\n", " ax.set_ylim(-1.5, x.max())\n", "\n", "\n", "# Add activation functions if wanted\n", "act_fns = [act_fn() for act_fn in act_fn_by_name.values()]\n", "x = torch.linspace(-5, 5, 1000) # Range on which we want to visualize the activation functions\n", "# Plotting\n", "cols = 2\n", "rows = math.ceil(len(act_fns) / float(cols))\n", "fig, ax = plt.subplots(rows, cols, figsize=(cols * 4, rows * 4))\n", "for i, act_fn in enumerate(act_fns):\n", " vis_act_fn(act_fn, ax[divmod(i, cols)], x)\n", "fig.subplots_adjust(hspace=0.3)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "df94434e", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.014349, "end_time": "2023-10-11T15:34:26.803705", "exception": false, "start_time": "2023-10-11T15:34:26.789356", "status": "completed" }, "tags": [] }, "source": [ "## Analysing the effect of activation functions\n", "
" ] }, { "cell_type": "markdown", "id": "192781dd", "metadata": { "papermill": { "duration": 0.014266, "end_time": "2023-10-11T15:34:26.832192", "exception": false, "start_time": "2023-10-11T15:34:26.817926", "status": "completed" }, "tags": [] }, "source": [ "After implementing and visualizing the activation functions, we are aiming to gain insights into their effect.\n", "We do this by using a simple neural network trained on\n", "[FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) and\n", "examine various aspects of the model, including the performance and\n", "gradient flow." ] }, { "cell_type": "markdown", "id": "5915365d", "metadata": { "papermill": { "duration": 0.010381, "end_time": "2023-10-11T15:34:26.854537", "exception": false, "start_time": "2023-10-11T15:34:26.844156", "status": "completed" }, "tags": [] }, "source": [ "### Setup" ] }, { "cell_type": "markdown", "id": "25e406d8", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.010309, "end_time": "2023-10-11T15:34:26.875094", "exception": false, "start_time": "2023-10-11T15:34:26.864785", "status": "completed" }, "tags": [] }, "source": [ "Firstly, let's set up a neural network.\n", "The chosen network views the images as 1D tensors and pushes them through a sequence of linear layers and a specified activation function.\n", "Feel free to experiment with other network architectures." ] }, { "cell_type": "code", "execution_count": 11, "id": "bf6a727b", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:26.897072Z", "iopub.status.busy": "2023-10-11T15:34:26.896882Z", "iopub.status.idle": "2023-10-11T15:34:26.903792Z", "shell.execute_reply": "2023-10-11T15:34:26.902951Z" }, "lines_to_next_cell": 2, "papermill": { "duration": 0.019806, "end_time": "2023-10-11T15:34:26.905120", "exception": false, "start_time": "2023-10-11T15:34:26.885314", "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", " \"\"\"Base Network.\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", " layer_size_last = layer_sizes[0]\n", " for layer_size in layer_sizes[1:]:\n", " layers += [nn.Linear(layer_size_last, layer_size), act_fn]\n", " layer_size_last = layer_size\n", " layers += [nn.Linear(layer_sizes[-1], num_classes)]\n", " # nn.Sequential summarizes a list of modules into a single module, applying them in sequence\n", " self.layers = nn.Sequential(*layers)\n", "\n", " # We store all hyperparameters in a dictionary for saving and loading of the model\n", " self.config = {\n", " \"act_fn\": act_fn.config,\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) # Reshape images to a flat vector\n", " out = self.layers(x)\n", " return out" ] }, { "cell_type": "markdown", "id": "e4fdb8c5", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.01037, "end_time": "2023-10-11T15:34:26.925928", "exception": false, "start_time": "2023-10-11T15:34:26.915558", "status": "completed" }, "tags": [] }, "source": [ "We also add functions for loading and saving the model.\n", "The hyperparameters are stored in a configuration file (simple json file):" ] }, { "cell_type": "code", "execution_count": 12, "id": "6a761220", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:26.947509Z", "iopub.status.busy": "2023-10-11T15:34:26.947338Z", "iopub.status.idle": "2023-10-11T15:34:26.954802Z", "shell.execute_reply": "2023-10-11T15:34:26.953978Z" }, "papermill": { "duration": 0.019774, "end_time": "2023-10-11T15:34:26.956095", "exception": false, "start_time": "2023-10-11T15:34:26.936321", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def _get_config_file(model_path, model_name):\n", " # Name of the file for storing hyperparameter details\n", " return os.path.join(model_path, model_name + \".config\")\n", "\n", "\n", "def _get_model_file(model_path, model_name):\n", " # Name of the file for storing network parameters\n", " return os.path.join(model_path, model_name + \".tar\")\n", "\n", "\n", "def load_model(model_path, model_name, net=None):\n", " \"\"\"Loads a saved model from disk.\n", "\n", " Args:\n", " model_path: Path of the checkpoint directory\n", " model_name: Name of the model (str)\n", " net: (Optional) If given, the state dict is loaded into this model. Otherwise, a new model is created.\n", " \"\"\"\n", " config_file, model_file = _get_config_file(model_path, model_name), _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", " act_fn = act_fn_by_name[act_fn_name](**config_dict.pop(\"act_fn\"))\n", " net = BaseNetwork(act_fn=act_fn, **config_dict)\n", " net.load_state_dict(torch.load(model_file, map_location=device))\n", " return net\n", "\n", "\n", "def save_model(model, model_path, model_name):\n", " \"\"\"Given a model, we save the state_dict and hyperparameters.\n", "\n", " Args:\n", " model: Network object to save parameters from\n", " model_path: Path of the checkpoint directory\n", " model_name: Name of the model (str)\n", " \"\"\"\n", " config_dict = model.config\n", " os.makedirs(model_path, exist_ok=True)\n", " config_file, model_file = _get_config_file(model_path, model_name), _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)" ] }, { "cell_type": "markdown", "id": "1856e6fd", "metadata": { "papermill": { "duration": 0.010433, "end_time": "2023-10-11T15:34:26.976932", "exception": false, "start_time": "2023-10-11T15:34:26.966499", "status": "completed" }, "tags": [] }, "source": [ "We also set up the dataset we want to train it on, namely [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist).\n", "FashionMNIST is a more complex version of MNIST and contains black-and-white images of clothes instead of digits.\n", "The 10 classes include trousers, coats, shoes, bags and more.\n", "To load this dataset, we will make use of yet another PyTorch package, namely `torchvision` ([documentation](https://pytorch.org/vision/stable/index.html)).\n", "The `torchvision` package consists of popular datasets, model architectures, and common image transformations for computer vision.\n", "We will use the package for many of the notebooks in this course to simplify our dataset handling.\n", "\n", "Let's load the dataset below, and visualize a few images to get an impression of the data." ] }, { "cell_type": "code", "execution_count": 13, "id": "9eabcded", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:26.998518Z", "iopub.status.busy": "2023-10-11T15:34:26.998347Z", "iopub.status.idle": "2023-10-11T15:34:35.934786Z", "shell.execute_reply": "2023-10-11T15:34:35.933892Z" }, "papermill": { "duration": 8.948942, "end_time": "2023-10-11T15:34:35.936335", "exception": false, "start_time": "2023-10-11T15:34:26.987393", "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/13/s/.datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/26421880 [00:00 first make them a tensor, then normalize them in the range -1 to 1\n", "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n", "\n", "# Loading the training dataset. We need to split it into a training and validation part\n", "train_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": "d33f0d80", "metadata": { "papermill": { "duration": 0.014959, "end_time": "2023-10-11T15:34:35.970651", "exception": false, "start_time": "2023-10-11T15:34:35.955692", "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": 14, "id": "a87c22c3", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:35.998259Z", "iopub.status.busy": "2023-10-11T15:34:35.997866Z", "iopub.status.idle": "2023-10-11T15:34:36.003647Z", "shell.execute_reply": "2023-10-11T15:34:36.002736Z" }, "papermill": { "duration": 0.021245, "end_time": "2023-10-11T15:34:36.004956", "exception": false, "start_time": "2023-10-11T15:34:35.983711", "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": "code", "execution_count": 15, "id": "70f9ac8b", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:36.032819Z", "iopub.status.busy": "2023-10-11T15:34:36.032482Z", "iopub.status.idle": "2023-10-11T15:34:36.324611Z", "shell.execute_reply": "2023-10-11T15:34:36.323638Z" }, "papermill": { "duration": 0.307723, "end_time": "2023-10-11T15:34:36.326015", "exception": false, "start_time": "2023-10-11T15:34:36.018292", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/pdf": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exmp_imgs = [train_set[i][0] for i in range(16)]\n", "# Organize the images into a grid for nicer visualization\n", "img_grid = torchvision.utils.make_grid(torch.stack(exmp_imgs, dim=0), nrow=4, normalize=True, pad_value=0.5)\n", "img_grid = img_grid.permute(1, 2, 0)\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.title(\"FashionMNIST examples\")\n", "plt.imshow(img_grid)\n", "plt.axis(\"off\")\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "markdown", "id": "c35847c3", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.020034, "end_time": "2023-10-11T15:34:36.366799", "exception": false, "start_time": "2023-10-11T15:34:36.346765", "status": "completed" }, "tags": [] }, "source": [ "### Visualizing the gradient flow after initialization\n", "\n", "As mentioned previously, one important aspect of activation functions is how they propagate gradients through the network.\n", "Imagine we have a very deep neural network with more than 50 layers.\n", "The gradients for the input layer, i.e. the very first layer, have passed >50 times the activation function, but we still want them to be of a reasonable size.\n", "If the gradient through the activation function is (in expectation) considerably smaller than 1, our gradients will vanish until they reach the input layer.\n", "If the gradient through the activation function is larger than 1, the gradients exponentially increase and might explode.\n", "\n", "To get a feeling of how every activation function influences the\n", "gradients, we can look at a freshly initialized network and measure the\n", "gradients for each parameter for a batch of 256 images:" ] }, { "cell_type": "code", "execution_count": 16, "id": "0b37265c", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:36.403620Z", "iopub.status.busy": "2023-10-11T15:34:36.403431Z", "iopub.status.idle": "2023-10-11T15:34:36.411171Z", "shell.execute_reply": "2023-10-11T15:34:36.410350Z" }, "papermill": { "duration": 0.025324, "end_time": "2023-10-11T15:34:36.412310", "exception": false, "start_time": "2023-10-11T15:34:36.386986", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def visualize_gradients(net, color=\"C0\"):\n", " \"\"\"Visualize gradients.\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", " net.eval()\n", " small_loader = data.DataLoader(train_set, batch_size=256, 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", " net.zero_grad()\n", " preds = net(imgs)\n", " loss = F.cross_entropy(preds, labels)\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.data.view(-1).cpu().clone().numpy()\n", " for name, params in net.named_parameters()\n", " if \"weight\" in name\n", " }\n", " net.zero_grad()\n", "\n", " # Plotting\n", " columns = len(grads)\n", " fig, ax = plt.subplots(1, columns, figsize=(columns * 3.5, 2.5))\n", " fig_index = 0\n", " for key in grads:\n", " key_ax = ax[fig_index % columns]\n", " sns.histplot(data=grads[key], bins=30, ax=key_ax, color=color, kde=True)\n", " key_ax.set_title(str(key))\n", " key_ax.set_xlabel(\"Grad magnitude\")\n", " fig_index += 1\n", " fig.suptitle(\n", " f\"Gradient magnitude distribution for activation function {net.config['act_fn']['name']}\", fontsize=14, y=1.05\n", " )\n", " fig.subplots_adjust(wspace=0.45)\n", " plt.show()\n", " plt.close()" ] }, { "cell_type": "code", "execution_count": 17, "id": "a5e7dafe", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:34:36.445128Z", "iopub.status.busy": "2023-10-11T15:34:36.444848Z", "iopub.status.idle": "2023-10-11T15:35:02.687332Z", "shell.execute_reply": "2023-10-11T15:35:02.686277Z" }, "papermill": { "duration": 26.261002, "end_time": "2023-10-11T15:35:02.688805", "exception": false, "start_time": "2023-10-11T15:34:36.427803", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/pdf": 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MrL3Z8wIafQIlPPaq5tj7FyM+o/SWh2GZJYr5tqdYXcpMIsUQCZ+xJYTrpWWidQFdCs2F2iyeDAw5xcche5U+Wv3CD2sHTWLtNM+/JfPJT4nbLWSOdN5WOpn1T2NkoiRNVFfxzVJ62+qaF7pHY5tWwRTsV0fBhdBKahdsWjG1VuUKwyQudBnxdDCUYsH/vFHK+B6mxrBAuB4QnzipLVmCp/ChuzcQrdniG0LgZUMIrTeOgdcmdP/D+CVprWsUfOlCC4xBj5AtYuBsNDGIqpi2tTvE+BS7icJAiSxY13arUyuks8/zhcRjPqPz0NEypo05dxNXOm+r+fZjJqR+ExAZOIC5fKFrecswekwoxlCgEWajFXqftusELVirmPsYG/r2ozf5duUSffWh41q0tfubhibJJznHAmAU+ebtaWHBxIDVma/94Zwt9CKmT7tncst0xRtTcTTrp/d9zgjUUWwnjCjguo1FzamCpqyv3FEujIGAoibmsJ9EMBJ8WAXJkZ7vYpntMAoOa99ScwYLB7k2r04bRibY2JZWelBJQs+wjDNmGc3DjHHrq4bBAZigGCDuEE1sw8eXYHtLlmmGoTD1XsNOnKCcOOgVLKuuRmA2e2wh2FokMNq0ibXRIDNOVuXLDzOd9q2lNYcBZLCHxTLkYP7z9LsUyb4mBsgwzELSVVXTKpz6mAv0+r/CNHAbJhc3hiit9jcWtYzTlgYPm0uV1EVX9jVj+7hYdAbWvM12xXAmSbFrPzHVfi2PHRFTKontfVAE1OJMsofSiN20xx8oaPL9nIjcmDCpTXPwzRZS5urxzEnTT4+iNlGmIwg7tt3lPQI/I6TgEX/NxzzR0fLSlfPdoz7tDEZ4rk/o+tuf/I7nhBgkzwktWBrXaIX3jzGInzHGADMLGx8Tk5wjQHju1Afpbgb4xNXQyjrG4PjAFEf7wU6QBTMeO7q0s545wK/hIs5on8yDq2h9Ign6KsbAll58hUCDj8ihZenEFn+eBAP8ChxCHcQxFhgFhR3/vQIN3Mqf9iPS0MyJLJXzJjTAr0GDWJgfWssRGuP70hA/HQ1QuxlzFTRPW8INfgUasFFCXWBriUfb+9KQH4mTKNQt1Fp4adTFxyX1efvsC3cpaHXx6P3LQhg+CQWVaVILTYITBQf8YgqgUQ0UVKZbKVDMYM22v5PT/rMQwShbNAu98ETEAb8SEQwDBxOiMEfCU9n982djolCNLTCYJyoO/LW4gI4OLkqFNftkOIc8HsKQYQ0PlLw8hOGF3H7aGyylPex1ygycITUH5vzqMaAwNbRc76+GJgb4dn+1Bw0P5SULioMphzzTcMCJFjCM5ZmdG7oZDQuBqXAzsr3UKUvwCAvaTRLyRM8Ab0XEUmISUZkOIk3ZggeYmTiCFLv5GhoZ4c2IWEgMInDm4Hd0eh0eYF54YSNuYeJngPciYiUxicAUD75MnhMDLLymC0UmfkZ4MyIWEoOI6F0VTPLz5cwA135XbKlXhkZGeC8iVhKTCNg7galYzjzcoxpgKnu1y8WhiRHejIaH8pIFZTh7mvwmBrinHpJ+D340coI3I2IhMphI9IAQnfIKDzAz7URfqr3cjAyN+F5UrGQmFY35AcuUX3iA+XgR+c6YJ4pGfDMqFjLz/i06rP4weU8MsHmesuU0UTTie1GxkplUiGu1P72eqLjBzICVg9qD38jQAG9GxEJiEEEtMaY0ZR4eYKYIS/ZeOBM04HtRsZKZVBRXpvj5e4zZxqrCvJ/JGfHNSJilJQPKl2fx0wF6wMxZJ7n1ElQ3ckZwMxIWAoMH3qnXFqdsxAMcEqdAo4/GxM+A70XFSmZSYamR2pSXeIDNH0MkdyoGikZ8MyoWMoMKZu5rlujxRMUB83o4oq16ZmiE9yJiJTGJqC6lBFNyIuIGQ3PSWnva0aGREd6MiIXEfMkLThqdq85EHLDA9oY6LXHiZ4D3ImIlMYlg5kmJ0yZxQwuVpsqMqid2RngzGh7KCxZ4KY32phjAEWbuUe0OTkMbA7oXDSuByUN2NSlz0555OOBChSEEmegZ4M2IWEhMItR5DXSXOxNxwHTBil2bGto40M1oWMj77k58wq9ZMuGT888Am9EtMU7sDPBWRCwlJhFmLTDj75mIA2bi4dxfcc783ODNiFhIDCJCdCGXVs4H5wgzH3oN5jp65ucG70XESmISIS5rzTMPNzSZD/yCnRu8GQ0P5eXzvHctq5fzwTnCrHFxDVI4sxPn0IVX8C740NCF6XU6mTswn3bpwB18T76WYzL37azmR0ofNotRsNAGwvTGrqH76SZGN/Z34Mb6Z+aPmlkHsFlC9eKZ7h7roqdnqqE7YxfYYJm1suy11BJ6yfWRMMeQipVVqin3LP7FF5zE4McCD6Cz+9YbYQlaRWeosLVMS9+eU+hZze35QvtXsu+XI8HyZPGqkDnXgZsDdOEVeyu8KIGJiPkTLFFOYdJ/2AToFtN1p1ws6r2ExmiOFpvFI9C7vb/gKIQP9L9mPEINqWsQLBDalI7x1KmyVL1//vCVyQ0tOABo6i+DkY7bzfzW0SvfNFrC9hLpuu0DU+pHK7cYut7ORP41x0iP9oopFFK3dWHrC9R5HFTMZ1/V+36zTOdtMUd38uVLsCQXvFkuakERgQUOiudMIk6v7szE7pYLImjs5yBwzIrEFPyM7BR8n3WfFQEw72hiw7TCfMn9nY/lBFJmb5j5H3q1BR/YLa5ybE7O/oShhJtHDD/O1Pg2BxhxD5k0WuuesR3WexYKoLd9tOqLKcLKT9er0RCl35GhlzWoyhWvLRYGJTBZftXaWWB0TmqYN51MYbSC4dmxNkWqhjNBfx9bFiIo3oqfZnNH87V/b3HZC3Pq2+dD6h71vKBkmWxl6UTBmsg1d5weQFYmhGEJ+PfSlwIWgEebDDe3GJOYLB8/cQElrYcx5BSKZW3BSmUADbYFuwpGK7mvHcyAhAUj/a4Qk6KZuz1xFeG6C8wILLlHxRSrqhDVV5sNmGo9UqiARFCrDIfg9Voks/2qLeYuVYRS1MsdEVX0vl/GhshaGgaLK1CrmWaFmxxTPfd7qsrSB94SLjCdW+lPHvh+rAnP0AnClbvO9VorYK2Hfu0HEaS/owJvSVmXhDi2mtSXOPM2qM12/JtjDZA+dbAPYIFg0+3FIXxWrVe8MbCi2rVZgN7fpxRnO1bBdR3ixEtW65R3SxiH2MydDSTl0D/OohuMdLhotYoUt/sXbLAZbDfyq9r9I0S5E7CGBOxQHmJ9EVbs2AmTp7LIBCO0OjWAVQO3MF7i1NJqt+SDZfSBxcZqL9g++4aABZN8d0jk30LrPOIzgsGsrEnC6JLePyZYRlcZP8JIKKtOQhjzHR2MVjLFp9QLadBYxJmmzQJFYkYzNlsacyw3z/xEjeEZVfqnMUnFe7EotMIcdfeGqFQWJMncPVtI3f5ivnuP1W72V4tyhRNmdyQPDB9h1QtrmsFaEV9o50+tDMwxFY2XfTgb7BBTHH4W3MOjmvVfevwdqCkWUTOd9wpu4uPwMwJFHvGKfSycgEUiVg6z7x4NTGBoyXM9b9ff/uR3PCdQBGcjH6fMWfA5USL5M0aJCIM4lZEtJw6xFnG8PkgnNcAnooZW1lEixwds93lFR9PK9Iel1imD4gC/hk+7UtFtvDLkCnp2lMhrBIl8RAoFyku2jfZE4QG/AoU1m63AsNQangi0mcICZq/aj8dCY3Qx40HPVyYD/Bos0NGnsbVc3j9UJj4SG1EZ48wzUS02/EPiIz4er8/bYV64PqET5KP3L4uP+CQUQMOHdttCO1NwwC+mgFf4R+8ZnCvQFXykgvp0YYPPQgQr46VA1fxExAG/EhH2+JlaYmqB8FRdg1WkyKdhInFnzNFiZkYqDvy1uIBBnEKmYQDN9gku0mfjghXcKlXwiYsDfy0uGP8Po8/DfoeV//5xM0OsCLSqkZKXx4q8kNtPe2GoNE7pfHXWEjysYBg7U87uEWZBB8aam6J4NDLAR7XQDYhYSkwiKh1O8uSqMsKJVnyzeKczPzd4MyIWElP1hprUvJ8cVUaYt6vM1fGAnxu8FxEriUkELPQUZMrbPcLZ0qDYVcHYyABvRsRCYhAR0QaP4HPWtBEWl1Ls6ZjHRgZ4LyJWEpOIDGMit8lNZYRZq4ym5sTPAG9GxEJiEtFY/3R6jj9Q3tUydkzP7IzwZjQ8lBcspIT53fxkAw1w4cvSNX/iwM6A7kXDSmDyAO2uqUxuKgMszJtT+gX/0MgIb0bEQmIQkVmCOcR5XRwwr8pT6C84QyMjvBcRK4lJBHZ+hVowHZ0HTB+drNHeEYdGRngzIhYSg4hi8R55ytg9wHwOkZ5sbWhjQPeiYSUvaSjO42OTm8oAs6olJLAnzJGdAd6MiIXEJEL5azLzcI/yyTPXUvu6ONo44ZsR8VBiXk4zxeKUJOztCFuZe2yR9sI5EDTCexGxEplMNBeKpyvGmYkbzE7X1Jo9VI4MjfhmVCxkBhU1mstEnY7PAz55XowUjfheVKxkJhV8/8p+ytg9wMHeeFPtNtdA0YhvRsVCZlAB5Ygu/lPO7gGm85F4CVcqDopGfC8qVjKTCiYoheUwHR8HTP+sVmJPrzpSNOKbUbGQmVSoU8+978zEParqqocGJWd+RngzGh7KCxaYyFR8npJ2DzBTrhZY32d2RnQvGlYCk4dGR38/5ewe4Mqsk7Wao9PQyAhvRsRCYj538wdzGj05fhww4wrRlKWfHRoZ4a2IWErc3/1TKMnXiYgbTB8+nJKmSA6NjPBmRCwkBhFM+iyWBPtExAGzyAcTcseJnwHei4iVxCSCOZ1bCTIRccBMdJSzedaNjQzwZkQsJAYR0BCZw3qaEAcKXVqjt/rMYxMDvBcNC3nJQnY1hhonF7kBTvSqTea6P7QxoJvRsBCYPCgN6hR14uGAkzk7p4f03OAPJeI1Y0amN+qQWP+P77u8XKs5d4Ewlmq6gFdL5ZV60Eh35+qveZILbAfOe5iXvRgEPbaiKL7CloO0HmJSQ6Oi3eh7Ji573/2sa1BGnjS64Vpm+2oZ4vkuBHrV247TYmm2tVQm7Vd6H/K5KJXWixjw+aSKeAslaayfYd8Y6cac6WpeIv3ug1X24xtDpTiF23dMUSxTaU10VczgrEcKM7jA4ODwkYjPFHEh5Fo6HB1+L1a90KG+pFDu7+0V4x0tKxoaUcuOX1msAm1IpA6BEZLWL7fFFbDtrYxxo6N1vt55NxCSCx2+U0o19RvggM/EYoVvI72i0/VeONMTjPUuGhS21C+Rk8OgSggXNedY1Xu4glMMJOZg8tI9uCvTzGSRkuzTLUn/PnruRalWSkNLSr3TDPkpTTB67AfmiMUEme8j2MFk6KHGPd9Z5atealCieF2beJVfrzBaaKCY/uporiudrF1RfEsGs5ErHBz9y8Uc4Jl4OV9RoUd44A0oY3k6IYUOQE19ZL0QdDtLvMLKa3PAdNzHX/qnM+NnWJYgeEyigBE0KZnGrxQfexhBwNj0AQauAR+tFgWB5afSbyfpC13pBB+s/BM+09tpVtqh9ciRAoF8ueJgJTCuhhElaDDeX+KVRLEZ8IF/ZkpBXuN4F5TZzK1KJUOv+p0f619AFn6ecUmaY9YrzsTBrHSKdYcpr33u0yOoBqbwJA4lLnQrmGFSsHwZWIOVVyhKveK1JdqCVpQEfTAv+co6GpwS9Rr0Ecr1e6vLdvV4wbdDcm35/hoK0y7bHS1M7OsIAmUSbUb5YDrm1ncbwcquLGbRHZgS9AqbpRbQEDicxEsD0XrFpdVsybWsVk33+8eXoM3EhMwMevEl91IaxBXrIWbrIyaKSP98ZtEeYZAMU5Qpo60Mh+DYchgKAg74Vt+nTuUbfhFWDo0MlvR6bZ/ZvIPwyi0wWW+8rt/KSJ1qcLNQwk5lbYyfqVamGM2X2mN/KmYvmaLHGDZa7B09AgXtMfCFgZkBiqhXXyxWAsPEQ7cxwAUzuWFh9ImP5ZhTYPyZZ3xJs4ijikXAnQfLAUdBgAB9vLHSfREv9sqUQ0t9cSs3co9dn28NAq67ZYS1EC0TDyjFQEu6Nyj5Yoe5RI82TB7rX2PND2xqmDIsztI49a5WBSYYa9cII9+0b6SNInhLn8gVxEBGNZh7cLSdXihAsTgs/CvVT/TLdvpYLclDC9g9mR7HAg8b9hXt6lpmCAqDa/qK7DFhjS4ZJWqwpK6scBN705UhKMqDmvNAklWAws6Fo8PiZbI1521zpApUsBUVC6sPTdIVjjjkEs8fizL32WYWxogpKZK3eic4tNQG4axWBBxc5Qn4GQErj7gpPxbZgJaXHszvHo2RYIjLc12h19/+5Hc8J2AFO7rNE1hpHJXnxKzIZ4xZUayWGOO5HNq7OyYqoDZ0IvEGnngaWljHq9w+0DXGV3R2hQbEGkZo7NT5AX6FMAOllsT8sixGVN83zGD2Sv14JGC3paKERT0P4YG/Bg0M8/asRFblqeIuJxrKI8EW2PJYZSrw8GVi+A8ItviIs+sZS+NlnDbG0B19f1moxSchgIovlC9JIwEH+GICzKXg1vdqBcJg+ZT6ZHhB+TwkQDWA4sDacyMLA/piGqCJDjQwboMqe0iS41M8hE+5z4xEMNwdlinMp5GIA30tImCCRKi+zBiRnoorCJ9rRmD+KkwyKK8jEQf6WkTwaYS5tLS2nJ4OvHkkvgIq4sjHy+MrXkjsJ71eU0+l3VJInBQD2DdodXqGGFDWPC0wl02tOZoY4Nvl2hY0LOQlC8XlEmVKKD7AMHkSbNV8ImcANyNhIS5ZYH6CHCenjgFWGGSwx8zEGxoZ4c2IWEgMIiIMyMJfOhNxwAz7gyns7T1yaOWE70XFSmZSwQQ9sO6nDeKAW8bf+BJ3ZmiENyNiITGISNFBE/VTIvEBDiExh4i3G7ChlRO+FxUrmUkF08t4mbw6Bvjk7zZStPSD24OKhcygItOKjHFy7BhgFiMW7Y8OQyMjvBcRK4lJRHEK5XTKKD7CECExMvjMzwhvRsRC4n7TElmLcDpEH4HXtO1FxEo0EsHkprBap0P0gKHB5/5+duJnhDcjYiExiWh8iq1TOvEBZlY5lje3t5ahlRO+GRULmUGF8FnA3mhPVBywPfO00J+LRopGfC8qVjKTCqYcCDrdmg1w4Bt6DPbYeGLogDcjYiExiGD2wTil2Xo7whXqdWg51DM/I7wXESuJSYTw8TVMWcVHOB+XKGMjA7wZEQuJQUTzmOFS03RWPgKvaduLiJVoJIKJTVvK0xH6GLzkZzMiFqKRCHV8tZkSi4/wqF0OjWyrXa4kBhGaXG0wsacD9BF4TdteRKxEIxHNhcRkrRMRByxO+XQ00XOgm9GwkNdeNKEtN/OMO79LHzj0SXOVaWd+RngrKtYyGxdMfS1pyi4+4qzi5aWa18mJowHfjYyF0CSDt9Stu2ueyDjwiYwlSZuRsRLayCiuJGWa2YmMG34mY03SbmQshDYyoDjr5P/89oQPDxsnjjZ971iLTCpiZn5+hq2fqTjw7v8aU54pGvDNyFgJbWQ0VzSnNrs4DfhgaZza2dUCWQtNMhJ+ykXbfK4+hq/J24yMlXBGRkUrOCfng3XATzNjaOcVZ8ZrRmEMb9nFuxCEfhvq6SodCp92IyteZu3ufPSKToUurozUjT1tvgY6P5died2ZufmKRiclSYlMH+Sjb+ZryzfCCHkYLMBqBUEsLQI6joOGlSAsKkBDv/eK6io3Gz4qsSZBDsUaSazrzoqkwBnMUH2SK84KBqUXvsaAeKsgwZeXlCw2hHkpUmzS2+f4KLrc01jQM157O6A/C4tt2OeZ+Ne0xsTCJNFczlneA2Rb9Q7iWloqtfeHbra9/eKEmZibPX7hf7V/nNn8MVpqbtUFm8f9q0hpMTMMQFiApKXeCH2Zc7bXVpbLEOmtZHote/GW2J81gkKHmeM20iWcTlg4vS0kgXDVxmR39DGvkq4PEHy8zhyEmliUIfY26OLsa+AdtEiNXfpMx2uhl7hdTWcxf2hYVTCu6MZvUbqFZWAMbjQ++fCJlksNrVzRmnLFRyoHroSujjOSBrMOErDOSUnZ/M95GV6xo0BLh4gJU6y/M9PBOmS6mTdWU1GxagTK4hBY97wb9Bwd8Gc3RAwVlUSHcghltVr6Iy1wTEdPp33Oruxrvb+Qxpxn2Rp7soRs/aap0N/aqu9wlFVr928jjikYsvRMBky/bcQIHbFTkI435hq0z7M8hKoVV+bna+gJ54hDVJJtZVqKV3McU6HndohMp4/v9bH0UjJqRVIqoy3Yf0Yc9dkr9DVKLKgRmGMCU83ExTIMiX49Nr+wHtRipVSE+wWDPDi/qmjPSQGlDxOi4P+5JAMrm4brfVsqzNVQA8uW9YgANOUSd5nIUo8FvbKAKzTluIyyKdMN6zfV61UVzghpFv6ksfYL/0ove+hXYiFUGLDSP6xskGFCmDwZf+kbCctJYEZ7q/RTMRad9BYZpcIIAxZ3YKWO/mm6qGIhW9hkYwxQh7laYQbbxg0LSPrM4NYUWcHFShEVW02EK2cJ65patJLG6zUL5nThLm4ZJVO8Dqd6Vj+xfvB61gJ0VRkFFllnx4q11F65RzUzC3uMFoGGZdijsDCzHVakhQor1ilOg9ZtlMAM+LlYzYzAlMvaceY4ZeJhs2ph4Fvkjrn7BlZWqdy4E8tjXNvB8omemc24drCiytVQxEbOXdVUXXyT13trwPMSydyAcDIl34+5wLJUPXMg5nnuMQ6ALRK1NTsuLKrl+nFhJIf2TmLWhKtO2TAVlIk5KRT2pNZxHA2Qw6vpmqAoX0mIJBCbZyBn0iw+4qqBYN2yTDfjwbAQLFqCwQoQKrCqFDc9bNbXzydmNcNu2ocv+ijXQx1jEmq2sxvrXu/PeowJFkW9VuvTXrPjpAMkHKkW9PM4/oyQiUecjR9zrkfLCz/k0Qdx+vQznZnX3/tE+88JlQDJfOnicqH393NCJdoiVGJoOYWKScpCQpfoeeiemswP/crffPfDL4dv+eXbNz//8tOb//71lzc//nD5048/Xb7+5pc3//N1//HXH76xv/zxzffvfnzz7ckv8u7/AK82DEMKZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iagoxMDE2OQplbmRvYmoKMTAgMCBvYmoKWyBdCmVuZG9iagoxNyAwIG9iago8PCAvTGVuZ3RoIDIzNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UUluADEIu+cV/kClsCfvmarqof3/tYZRLwMD2Ngk78FGJD7EkO4oV3zK6jTL8DtZ5MXPSuHkvYgKpCrCCmkHz3JWMwyeG5kClzPxWWY+mRY7FlBNxHF25DSDQYhpXEfL6TDTPOgJuT4YcWOnWa5iSOvdUr2+1/KfKspH1t0st07Z1ErdomfsSVx2Xk9taV8YdRQ3BZEOHzu8B/ki5iwuOpFu9psph5WkITgtgB+JoVTPDq8RJn5mJHjKnk7vozS89kHT9b17QUduJmQqt1BGKp6sNMaMofqNaCap7/+BnvW9vv4AQ01UuQplbmRzdHJlYW0KZW5kb2JqCjE4IDAgb2JqCjw8IC9MZW5ndGggMjQ3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSW7EMAy7+xX8wACWrMV5T4pBD+3/ryUdFO3BECNLXOLuxEQWXrZQ10KH48NGXgmbge+D1pz4GrHiP9pGpJU/VFsgEzFRJHRRNxr3SDe8CtF+pIJXqvdY8xF3K81bOnaxv/fBtOaRKqtCPOTYHNlIWtdE0fE9tN5zQ3TKIIE+NyEHRGmOXoWkv/bDdW00u7U2syeqg0emhPJJsxqa0ylmyGyox20qVjIKN6qMivtURloP8jbOMoCT44QyWk92rCai/NQnl5AXE3HCLjs7FmITCxuHtB+VPrH8fOvN+JtpraWQcUEiNMWl32e8x+d4/wCVT1wmCmVuZHN0cmVhbQplbmRvYmoKMTkgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVI70ptBCOu/U+gCnlney3mcyaT4c/82AjsVLLBCAtICB5l4iSGqUa74JU8wXifwd708jZ/Hu5Ba8FSkH7g2beP9WLMmCpZGLIXZx74fJeR4avwbAj0XacKMTEYOJANxv9bnz3qTKYffgDRtTh8lSQ+iBbtbw44vCzJIelLDkp38sK4FVhehCXNjTSQjp1am5vnYM1zGE2MkqJoFJOkT96mCEWnGY+esJQ8yHE/14sWvt/Fa5jH1sqpAxjbBHGwnM+EURQTiF5QkN3EXTR3F0cxYc7vQUFLkvruHk5Ne95eTqMArIZzFWsIxQ09Z5mSnQQlUrZwAM6zXvjBO00YJd2q6vSv29fPMJIzbHHZWSqbBOQ7uZZM5gmSvOyZswuMQ8949gpGYN7+LLYIrlznXZPqxH0Ub6YPi+pyrKbMVJfxDlTyx4hr/n9/7+fP8/geMKH4jCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0xlbmd0aCAzMDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZJLbgMxDEP3PoUuEMD62Z7zpCi6mN5/2ycl6Yoc2RZFapa6TFlTHpA0k4R/6fBwsZ3yO2zPZmbgWqKXieWU59AVYu6ifNnMRl1ZJ8XqhGY6t+hRORcHNk2qn6sspd0ueA7XJp5b9hE/vNCgHtQ1Lgk3dFejZSk0Y6r7f9J7/Iwy4GpMXWxSq3sfPF5EVejoB0eJImOXF+fjQQnpSsJoWoiVd0UDQe7ytMp7Ce7b3mrIsgepmM47KWaw63RSLm4XhyEeyPKo8OWj2GtCz/iwKyX0SNiGM3In7mjG5tTI4pD+3o0ES4+uaCHz4K9u1i5gvFM6RWJkTnKsaYtVTvdQFNO5w70MEPVsRUMpc5HV6l/DzgtrlmwWeEr6BR6j3SZLDlbZ26hO76082dD3H1rXdB8KZW5kc3RyZWFtCmVuZG9iagoyMSAwIG9iago8PCAvTGVuZ3RoIDI0NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkU1yBSEIhPeeoi/wquRXPc+kUllM7r8NzbwkK1qF5gPTAhNH8BJD7ImVEx8yfC/oMny3MjvwOtmZcE+4blzDZcMzYVvgOyrLO15Dd7ZSP52hqu8aOd4uUjV0ZWSfeqGaC8yQiK4RWXQrl3VA05TuUuEabFuCFPVKrCedoDToEcrwd5RrfHUTT6+x5FTNIVrNrRMairBseEHUySQRtQ2LJ5ZzIVH5qhurOi5gkyXi9IDcoJVmfHpSSREwg3ysyWjMAjbQk7tnF8aaSx5Fjlc0mLA7STXwgPfitr73NnGP8xf4hXff/ysOfdcCPn8AS/5dBgplbmRzdHJlYW0KZW5kb2JqCjIyIDAgb2JqCjw8IC9MZW5ndGggMjMyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVRSW7EMAy7+xX8wADW7rwnxaCH9v/XUsoUCEAltrglYmMjAi8x+DmI3PiSNaMmfmdyV/wsT4VHwq3gSRSBl+FedoLLG8ZlPw4zH7yXVs6kxpMMyEU2PTwRMtglEDowuwZ12Gbaib4h4bMjUs1GltPXEvTSKgTKU7bf6YISbav6c/usC2372hNOdnvqSeUTiOeWrMBl4xWTxVgGPVG5SzF9kOpsoSehvCifg2w+aohElyhn4InBwSjQDuy57WfiVSFoXd2nbWOoRkrH078NTU2SCPlECWe2NO4W/n/Pvb7X+w9OIVQRCmVuZHN0cmVhbQplbmRvYmoKMjMgMCBvYmoKPDwgL0xlbmd0aCAyMzEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNU85kgQhDMt5hT4wVRjbQL+np7Y22Pl/upKZTpDwIcnTEx2ZeJkjI7Bmx9taZCBm4FNMxb/2tA8TqvfgHiKUiwthhpFw1qzjbp6OF/92lc9YB+82+IpZXhDYwkzWVxZnLtsFY2mcxDnJboxdE7GNda2nU1hHMKEMhHS2w5Qgc1Sk9MmOMuboOJEnnovv9tssdjl+DusLNo0hFef4KnqCNoOi7HnvAhpyQf9d3fgeRbvoJSAbCRbWUWLunOWEX712dB61KBJzQppBLhMhzekqphCaUKyzo6BSUXCpPqforJ9/5V9cLQplbmRzdHJlYW0KZW5kb2JqCjI0IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD1QO45EIQzrOYUv8CTyI3AeRqstZu/frgOaKVBMfrYzJNARgUcMMZSv4yWtoK6Bv4tC8W7i64PCIKtDUiDOeg+IdOymNpETOh2cMz9hN2OOwEUxBpzpdKY9ByY5+8IKhHMbZexWSCeJqiKO6jOOKZ4qe594FiztyDZbJ5I95CDhUlKJyaWflMo/bcqUCjpm0QQsErngZBNNOMu7SVKMGZQy6h6mdiJ9rDzIozroZE3OrCOZ2dNP25n4HHC3X9pkTpXHdB7M+Jy0zoM5Fbr344k2B02N2ujs9xNpKi9Sux1anX51EpXdGOcYEpdnfxnfZP/5B/6HWiIKZW5kc3RyZWFtCmVuZG9iagoyNSAwIG9iago8PCAvTGVuZ3RoIDM5NSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UktuxUAI2+cUXKDS8JvPeVJV3bz7b2tDUqkqvIkxxjB9ypC55UtdEnGFybderls8pnwuW1qZeYi7i40lPrbcl+4htl10LrE4HUfyCzKdKkSozarRofhCloUHkE7woQvCfTn+4y+AwdewDbjhPTJBsCTmKULGblEZmhJBEWHnkRWopFCfWcLfUe7r9zIFam+MpQtjHPQJtAVCbUjEAupAAETslFStkI5nJBO/Fd1nYhxg59GyAa4ZVESWe+zHiKnOqIy8RMQ+T036KJZMLVbGblMZX/yUjNR8dAUqqTTylPLQVbPQC1iJeRL2OfxI+OfWbCGGOm7W8onlHzPFMhLOYEs5YKGX40fg21l1Ea4dubjOdIEfldZwTLTrfsj1T/5021rNdbxyCKJA5U1B8LsOrkaxxMQyPp2NKXqiLLAamrxGM8FhEBHW98PIAxr9crwQNKdrIrRYIpu1YkSNimxzPb0E1kzvxTnWwxPCbO+d1qGyMzMqIYLauoZq60B2s77zcLafPzPoom0KZW5kc3RyZWFtCmVuZG9iagoyNiAwIG9iago8PCAvTGVuZ3RoIDEzNiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNj0EOAzEIA+95hZ9AIEB4z1ZVD9v/X0vYdtMLHsmAbFEGgSWHeIcb4dHbD99FNhVn45xfUiliIZhPcJ8wUxyNKXfyY4+AcZRqLKdoeF5Lzk3DFy13Ey2lrZeTGW+47pf3R5VtkQ1Fzy0LQtdskvkygQd8GJhHdeNppcfd9myv9vwAzmw0SQplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSYoDMAy75xX6QCFek7ynQ5lD5//Xyg6FOQQJr5KTlphYCw8xhB8sPfiRIXM3/Rt+otm7WXqSydn/mOciU1H4UqguYkJdiBvPoRHwPaFrElmxvfE5LKOZc74HH4W4BDOhAWN9STK5qOaVIRNODHUcDlqkwrhrYsPiWtE8jdxu+0ZmZSaEDY9kQtwYgIgg6wKyGCyUNjYTMlnOA+0NyQ1aYNepG1GLgiuU1gl0olbEqszgs+bWdjdDLfLgqH3x+mhWl2CF0Uv1WHhfhT6YqZl27pJCeuFNOyLMHgqkMjstK7V7xOpugfo/y1Lw/cn3+B2vD838XJwKZW5kc3RyZWFtCmVuZG9iagoyOCAwIG9iago8PCAvTGVuZ3RoIDk0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWNwRHAIAgE/1RBCQoK2k8mk4f2/40QMnxg5w7uhAULtnlGHwWVJl4VWAdKY9xQj0C94XItydwFD3Anf9rQVJyW03dpkUlVKdykEnn/DmcmkKh50WOd9wtj+yM8CmVuZHN0cmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRVJLbkQxCNu/U3CBSOGXkPO0qrqY3n9bm0zVzeAJYGx4y1OmZMqwuSUjJNeUT30iQ6ym/DRyJCKm+EkJBXaVj8drS6yN7JGoFJ/a8eOx9Eam2RVa9e7Rpc2iUc3KyDnIEKGeFbqye9QO2fB6XEi675TNIRzL/1CBLGXdcgolQVvQd+wR3w8droIrgmGway6D7WUy1P/6hxZc7333YscugBas577BDgCopxO0BcgZ2u42KWgAVbqLScKj8npudqJso1Xp+RwAMw4wcsCIJVsdvtHeAJZ9XehFjYr9K0BRWUD8yNV2wd4xyUhwFuYGjr1wPMWZcEs4xgJAir3iGHrwJdjmL1euiJrwCXW6ZC+8wp7a5udCkwh3rQAOXmTDraujqJbt6TyC9mdFckaM1Is4OiGSWtI5guLSoB5a41w3seJtI7G5V9/uH+GcL1z26xdL7ITECmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCAxNjQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZDHcQUxDEPvqgIlMIAK9azH8w/r/q+G9NNBehhCDGJPwrBcV3FhdMOPty0zDX9HGe7G+jJjvNVYICfoAwyRiavRpPp2xRmq9OTVYq6jolwvOiISzJLjq0AjfDqyx5O2tjP9dF4f7CHvE/8qKuduYQEuqu5A+VIf8dSP2VHqmqGPKitrHmraV4RdEUrbPi6nMk7dvQNa4b2Vqz3a7z8edjryCmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCA3MiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohngJggbRDFIBZEsZmJGUQdnAGRy+BKAwAl2xbJCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCA0NyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlyWEFYuF0wsB8wC0ZZwCiKewZUGALlnDScKZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvTGVuZ3RoIDI1OCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkUtyBCAIRPeegiOA/OQ8k0plMbn/Ng3OZDZ2l6j9hEojphIs5xR5MH3J8s1ktul3OVY7GwUURSiYyVXosQKrO1PEmWuJautjZeS40zsGxRvOXTmpZHGjjHVUdSpwTM+V9VHd+XZZlH1HDmUK2KxzHGzgym3DGCdGm63uDveJIE8nU0fF7SDZ8AcnjX2VqytwnWz20UswDgT9QhOY5ItA6wyBxs1T9OQS7OPjdueBYG95EUjZEMiRIRgdgnadXP/i1vm9/3GGO8+1Ga4c7+J3mNZ2x19ikhVzAYvcKajnay5a1xk63pMzx+Sm+4bOuWCXu4NM7/k/1s/6/gMeKWb6CmVuZHN0cmVhbQplbmRvYmoKMzQgMCBvYmoKPDwgL1R5cGUgL1hPYmplY3QgL1N1YnR5cGUgL0Zvcm0gL0JCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9MZW5ndGggMzkKL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnic4zI0MFMwNjVVyOUyNzYCs3LALCNzIyALJItgQWQzuNIAFfMKfAplbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8IC9MZW5ndGggMTYzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQOxIDIQxDe06hI/gjAz7PZjIpNvdvY9hsUsDTWCCDuxOC1NqCieiCh7Yl3QXvrQRnY/zpNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwutEx1mDnqUoxmgEDoV3u2i5HKm7s75Q3D1X/W/Yt05m4mBycodCM3qU9z5NjuiurrJ/qTH3KzXfivsVWFpWUvLCbedu2ZACdxTOdqrPT8fCjr2CmVuZHN0cmVhbQplbmRvYmoKMzYgMCBvYmoKPDwgL0xlbmd0aCAyMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVC5jQQxDMtdhRpYwHrtqWcWi0um//RI+fYi0RZFUio1mZIpL3WUJVlT3jp8lsQOeYblbmQ2JSpFL5OwJffQCvF9ieYU993VlrNDNJdoOX4LMyqqGx3TSzaacCoTuqDcwzP6DW10A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/65yj59eixs+w/FDFJGSDuY1/1j98nMNr1OPJ5Fub77iXpypDgMRHJKavCNdWLEuEhFpNUFNz8BaLYC7t17+G7QjugxA9onEcZpSjqG/a3Clzy/lJ1PYCmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0xlbmd0aCA4MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjLsNwDAIRHumYAR+JvY+UZTC3r8NECVuuCfdPVwdCZkpbjPDQwaeDCyGXXGB9JYwC1xHUI6d7KNh1b7qBI31plLz7w+Unuys4obrAQJCGmYKZW5kc3RyZWFtCmVuZG9iagozOCAwIG9iago8PCAvTGVuZ3RoIDUxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwUDA0MAeSRoZAlpGJQoohF0gAxMzlggnmgFkGQBqiOAeuJocrgysNAOG0DZgKZW5kc3RyZWFtCmVuZG9iagozOSAwIG9iago8PCAvTGVuZ3RoIDE2MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDkSAzEIBHO9gidIXIL3rMu1wfr/qQfWR6LpAjQcuhZNynoUaD7psUahutBr6CxKkkTBFpIdUKdjiDsoSExIY5JIth6DI5pYs12YmVQqs1LhtGnFwr/ZWtXIRI1wjfyJ6QZU/E/qXJTwTYOvkjH6GFS8O4OMSfheRdxaMe3+RDCxGfYJb0UmBYSJsanZvs9ghsz3Ctc4x/MNTII36wplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9MZW5ndGggMzM0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nC1SS3LFIAzbcwpdoDP4B+Q86XS6eL3/tpKTRUYOYPQx5YaJSnxZILej1sS3jcxAheGvq8yFz0jbyDqIy5CLuJIthXtELOQxxDzEgu+r8R4e+azMybMHxi/Zdw8r9tSEZSHjxRnaYRXHYRXkWLB1Iap7eFOkw6kk2OOL/z7Fcy0ELXxG0IBf5J+vjuD5khZp95ht0656sEw7qqSwHGxPc14mX1pnuToezwfJ9q7YEVK7AhSFuTPOc+Eo01ZGtBZ2NkhqXGxvjv1YStCFblxGiiOQn6kiPKCkycwmCuKPnB5yKgNh6pqudHIbVXGnnsw1m4u3M0lm675IsZnCeV04s/4MU2a1eSfPcqLUqQjvsWdL0NA5rp69lllodJsTvKSEz8ZOT06+VzPrITkVCaliWlfBaRSZYgnbEl9TUVOaehn++/Lu8Tt+/gEsc3xzCmVuZHN0cmVhbQplbmRvYmoKNDEgMCBvYmoKPDwgL0xlbmd0aCAzMjAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVJLbgUxCNvPKbhApfBPzvOqqou++29rE70VTDBg4ykvWdJLvtQl26XD5Fsf9yWxQt6P7ZrMUsX3FrMUzy2vR88Rty0KBFETPViZLxUi1M/06DqocEqfgVcItxQbvINJAINq+AcepTMgUOdAxrtiMlIDgiTYc2lxCIlyJol/pLye3yetpKH0PVmZy9+TS6XQHU1O6AHFysVJoF1J+aCZmEpEkpfrfbFC9IbAkjw+RzHJgOw2iW2iBSbnHqUlzMQUOrDHArxmmtVV6GDCHocpjFcLs6gebPJbE5WkHa3jGdkw3sswU2Kh4bAF1OZiZYLu5eM1r8KI7VGTXcNw7pbNdwjRaP4bFsrgYxWSgEensRINaTjAiMCeXjjFXvMTOQ7AiGOdmiwMY2gmp3qOicDQnrOlYcbHHlr18w9U6XyHCmVuZHN0cmVhbQplbmRvYmoKNDIgMCBvYmoKPDwgL0xlbmd0aCAxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0xlbmd0aCAxMzMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRY9LDgQhCET3nKKOwMcf53Ey6YVz/+2AnW4TYz2FVIG5gqE9LmsDnRUfIRm28beplo5FWT5UelJWD8ngh6zGyyHcoCzwgkkqhiFQi5gakS1lbreA2zYNsrKVU6WOsIujMI/2tGwVHl+iWyJ1kj+DxCov3OO6Hcil1rveoou+f6QBMQkKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvTGVuZ3RoIDM0MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UjluBDEM6/0KfSCAbtvv2SBIkfy/DanZFANxdFKUO1pUdsuHhVS17HT5tJXaEjfkd2WFxAnJqxLtUoZIqLxWIdXvmTKvtzVnBMhSpcLkpORxyYI/w6WnC8f5trGv5cgdjx5YFSOhRMAyxcToGpbO7rBmW36WacCPeIScK9Ytx1gFUhvdOO2K96F5LbIGiL2ZlooKHVaJFn5B8aBHjX32GFRYINHtHElwjIlQkYB2gdpIDDl7LHZRH/QzKDET6NobRdxBgSWSmDnFunT03/jQsaD+2Iw3vzoq6VtaWWPSPhvtlMYsMul6WPR089bHgws076L859UMEjRljZLGB63aOYaimVFWeLdDkw3NMcch8w6ewxkJSvo8FL+PJRMdlMjfDg2hf18eo4ycNt4C5qI/bRUHDuKzw165gRVKF2uS9wGpTOiB6f+v8bW+19cfHe2AxgplbmRzdHJlYW0KZW5kb2JqCjQ1IDAgb2JqCjw8IC9MZW5ndGggMjUxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nC1RSXIDQQi7zyv0hGan32OXK4fk/9cIygcGDYtAdFrioIyfICxXvOWRq2jD3zMxgt8Fh34r121Y5EBUIEljUDWhdvF69B7YcZgJzJPWsAxmrA/8jCnc6MXhMRlnt9dl1BDsXa89mUHJrFzEJRMXTNVhI2cOP5kyLrRzPTcg50ZYl2GQblYaMxKONIVIIYWqm6TOBEESjK5GjTZyFPulL490hlWNqDHscy1tX89NOGvQ7Fis8uSUHl1xLicXL6wc9PU2AxdRaazyQEjA/W4P9XOyk994S+fOFtPje83J8sJUYMWb125ANtXi37yI4/uMr+fn+fwDX2BbiAplbmRzdHJlYW0KZW5kb2J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4tKpqMgXCuVTGjuAv1izVt6J5AiEy40yO+DdvbThgEYvnz7mCshiOwEQ2OWamuCzRoOqia08Xni3g2d3KYL61bXuD7+dDchauQnjM6XDgeayjnHmWAkXY2spm6gfLBQqF8Ki9UC44Gp646cBBgBmaoxY4iMi5ulZOdjyIIDyI4CkVZ5s4cO63oVY01VjILZzAlBuJ2w78dintqAi9a491gtNMDTw/Ui5wzVhKZtKC1hVaHWc+IzbpCYISxdkGo1YC2lhlTQSq/LW1ZW6F4kXCavM2F5W705NFt8QajZWsk7M2wgzqnPNW/V78TNrqtlhNGsIYApoV6jhd/UA0FzBB9X+qUQ+VhsKcYqxnVfq7UlqAtSS0CZ5r4MEctpl4OFFBzwKwM/gaVdEfGHMKElsfyb46PTwTCqqKSyt9Gv6W6MBBD0QwmhRdfFe0aT3cEyg3LegL7bwz2nnrOliq80RBkdZbbWm7ARUdrWBi0k7sMWG3ChY02XTJoGZrsE50OkMloIKuZ+pE13jzgBejxtKfgF9wAuAJFehTunGUvBSIvntSgc6zAy9Vmq7f/uw7XnIiAC4UxsFsYzkOF7z/iQD/GU8ERCZWKjrL9DRmTBKh3KWp6eCBq66U9YmA2wPT6dgPFtAlh9FGMqWCvQEd/AghN70DDDfRu/Cckvtjngj4iBQWz3NIc+fr4AdQmHiAEKRZlhJffSLg47GAxZUpLlsZvekOfsTNA85R9Gb5mZjDPvBIAKYWk0QL+JDjAB+P05eNLq/sm1iF+NvXv+44wCehAIsoJzwTOTBwRV9NQMw9AQXOAFZ3ghVQePFhgE/CAxaHJvI07dQWOvxBXGAhyQWvHs58tkEszwN8GjKwLM4xMuo2knHDH0UGvIXIhYDDUt6/5DxAJ/qOQXpOXi/6fiW5nzJ6wDPl1sqUvjZZZ3LiHNaPXRNast4WMRdxwNfYwf9+ElbWkoPMuwJcnEkY4cPcNTdbkbAwl2tMPIl1zZTK9il4zdlONKwMIw0RHjS+PEw0jPClMUyFbNgaVvaSBp5ZqjLtwM3wlYYlO1vRsLAXNOgkjlX+uLab4JqK7rtOhVzgnWhY2UsaNNDppkS3E1xK1oQVUyEXeCsaFvaChuANPZ2pTwxokaIxwZmbA96JhIW15CBj3ZXCtCvdw0ynklr0siujQ7ciYWEuWIjWVJdlSnzbw1RtRaeZTCZyLvBONKzsJQ1w+XNNUwLcHoY/aH3Lejayc4W3omFhL2kQkzxKnQaGDqbWX1VbMztXeCsaFvaChsQEEa5Mu9I9HEyyNod4x84V3omGlb2koTJvUZgS4/ZwNLaUWt3ETgdvRcPCXtDA/bbpLPEVS9yuC7oT0/+8g3ci4M5SWp+5mZntNE12cOXxH697ttcSOmwrAhbGMuTujC/VT2lyO5hX15aAOh+p6eGdaFjZSxqYu1DqlC63g5k8MFgbdI+0K2XAtyJiYTGJEDhFLrppmrzBFCNE286Pd4X08FY0LOwFDZX6C6ZoHGno4GJiirVM7PTwTjSs7CUN+KAa85RIt4c1qBDb6NAV0sFb0bCwFzQIBZrMcjmwcEMDs7Vm1dn0RXTwTiQsrCUHxQQUN52O6uHAhJ7W+ZmbK7oVCQtz352ypaRHYhinyx6mVqlKc6YGcq7wRjQs7SUN2VgKq8JEww2mD+2daqxGdq7wVjQs7AUNzpoYNVntQEMHazew3t2zc4F3omFlL2nAaC/Mnz/RcIMZWHJNMTaxc4G3omFhL2kQ45jnOE80DPAl0joVsmEAdmkv5SvBUNg5sTCgJeSyoKahO1GwsLUJeCo+PseJggG+RJsnZh4Ug36g2HvaUqZENeh+A+Wjodgje44tqspFgdygl+MEZc16fWaK2STr9Woh3qacRW+7TxRWJ5tb/ujEVPzxiLcUV0vLQM0LSMsRhJCceTBCl5fZq3g7MSN3qdLyFdjqmFKcsKNSSbImui82tZV6joYp6vl1ha5Lu96Qfq2rwVFcLpSCH/M34BqE8pDEgwk+h/Y0FvnU2Wa9JAwG6DGFRBVyYm0w63j2zJd+wCDHSdGM9nAaVXidmJMzg3g9SVyirdr4U7HGUUoDx5vfpEfmGlyKZuVnbr7ky9VNj4Fp5CUyGUFLL04ZXYmB4ttBpI3awlpMQuTdhplJvJsGOjHjd6JcVwUxPrgo/sCLQ8OKvAyTDFd/OH3oqRE0o0Am0W+bKYUZ2FG4JnS3TDYXDjhjoGtSd8uIcvOg0agCs60zczsHP9XLp1I1vYKtpDaKE81FRAcrWG2saDEoI7ZCqmNq7ex887vEFn0lde+CWort/C3Q9jQF3qU4DWI5wA3NlIsfp1SLSzFelvFw4yrvSaAeGm6+Vk8V6sJrO8zJ1q13RCVx1IWLbfdIY7GjeSS5/rNoj1XaNdpYCLdlkqVw10kLvVef1EiuDzAUtv2HmNsNAjpN+JqPzWs0Z9Vuc+QUdOfjancMAG4xxFTvViPPBX6BvvoJWd1TKlyUvFTcvXtSz0tF9kule+u3P/uOl+irk8sMfqvU6CXi6vgZxdVZeFiDgvCBw4o/sr1L7tHBA1FdKWtx9e0BHe5eMyk9oVND7zQ5xWTHJWYHP0ITKxggMYAHxrGfUQY/Ka5OD1BXf0QOE+/nKHqCqufwBj+AQ5RjMGWhNLj57y2uvlM8fkQWMPimailJHVi4wY9gAfMVI16B02F+b3V1+nQ0VM1KHJwdG0MHP4CG6io6FEuLEbPae9KQ7BMq84KlZXWxVml5fj9Aaf7xiH3ZUPvKgapyEXT9+tcpzT8JBVgqRhH2gYGCG/xqClLsKcA6mIfxMCRz2Pk7eaQ/CxOOYZ7geaRyoKLDH8QFr2qqcAUEb/bP5Z5fCu8/DRlB101Yx09k3PBHkcEruODCYrkGV+oZMsLnIwMun4X/NQ0TN/hRVMDJBbPwTeDLPJd5/u4MQie7xxKzZ+T1svtXUvsJoziYZLDQp85lXClYHvHNdUofO8O16PnvuZADvsZxdqBhYS+X3tF4X+OkEHgKXpO2Ew0rw0gDr9qzdlIIPAkv2dmKhoVhoMHjj8DcRaO9I1ycaE7IqZALvBMNK3tJA0N5Kn8ZaRhgxrFW7BzwVjQs7AUNwZkcYp30AT0cNALWhsiukA7eiYaVvaQhMRY47YZ2KDMjeK83qk/cXOCtSLi3lhcu4sFQ7eT39DATpPiWn2Xg5oruRMLKXLIQTa6SJ31AD2tek6RX64zkXOGtaFjYSxpEM+jPPaKDE4eC0GaJrpAO3oqGhb2MuHHDhUlbRho6uPLuWVH/uC+kg3eiYWVvCzyijuOURLaDeWlyKKmqSqgrZcC3ImJhMYjIHp5etpNCoIMzt6Biu6q7K6SHd6JhZS9p4OZjyTMLV5Tbq3oEZeCmh7ci4d5acMANXZmSCX0/wNzRTe2Coq6MDt2JhJW5ZCEZh9VwnqbLDqYSwItuvvaFdPBWNCzsJQ2i6ogyTZcdHAzqvGoC5ZGdK7wVDQt7QQP37VO0UxrZHg68eD63kWFg5wrvRMPKXtJQDZMWTWlkexiOA0aBtnga2LnCW9GwsJcbdcwFSO3DSMMI15jizE4H70TDyl7SkI3YWqcR8obyyq4Q88xNB29Fwr21707VOuOzjVMO2Qm+Bp2GMjaMOS3NJQvJZOftlER2ho/A81zIfvHopb2kQfDHVXd4o+EJeEnaVjQsDAMNLprgUrCj9nyCL6HGqZANI5BLe0lD5e07TEg70jDAl0FgZmfDsWFlL2jwDKjW5MbJ8il4TdpONKwMIw2MF9i5MTyBLrnZioR7u6jscaZ4R4HwYO4T8JKbnUhY2UUWsrElBD9NlhN8mSynQh40WT5QhD9tL5dEL5j7UJzlBd/XPooLXw2vexM91sPHXaYUTB/xZnyrOkxM+q3qi8If8gIGTRptXXIqzS+afD3HmI7be6S0WC1Fyk2fmZgQOrdAdjXV+3YPkogFIxrAssZhDer0tKxNWJ62uJ8zMdJR0Tin4Nta2M+bkuDVRmq8rdSkShlGA6OLxSVNt54pyFWYiRhU2a0nj6VUOeDiM+8NyJ45wZnvm3AygZc9lzPzaduQWhlU5NQiDDSKCRSc1QPHZ5AeSims10T3LSAXMHMGGXX1+GostlF1Z+c9leexXmDqw6s7V8+TAaFtACW8ylVqxZnkXY4k3YSlRN6viNeL1No45Dl7WIFKYxIKNA7NvFCYVx1cZSWrYpnXXglYoucfiVXdknGXHFDDzkY9nox3oJEpHE3KBR+u1UPFVIML9e4utUTkYDPJEXxBLWhOvQhm0UeUE+Y4r8G3JOIVPw4tRoEGGERzJBM+mhrlYKizllwmwjZ3xDMwE0f1a2Py7VgHlZUikvzlCp3sm7tLARg6ir6PZxOyPl25w5qYdJ8NXmLSr6s+ontgDTyPhcMo0DrTk/ALZPJPaAKf0lOj5KVc8N2TymwK61+qO1y//dl3vEQmX8AxBouEKqdY8yVK+fwZlfJiCxOZjBdovDs5yxsgpknyCg48dSWsVfLXB3SMf9Ws8YSoDCs34xgKHRVLHfwIUa9gZMHXo2p9qi9XyT9CJP8RKcRA4jFeT8G3Dn4AhRifeTsJSsPs89666Dt14sdjAZMCr7qOeUo52+MP4MFZLkiExXHCfW+B+BP6cAn4r0F4VktTAn+APvwjNrAXDDCv7aBYTd6+/XXq8E9CQMGSAmW63BNwA19NABZzHQG8PaD6ynWRey4PufuUPayfJZzD+lGwAupp6NAH8cBdc0wDWFNVpp54Vhf+eYiIPMOaqXzqibihjyIiOh4EhANisW5+XhP+eYgoGLtScCMPV/BRNPDIMJZ51sOT+Tt68CfU4BWr/46N16vBX0nrJwwoiNNbo+K0VS283gVO0hhu7VAe8IUTFxW9FdHB13DCDiTcWwsO4N7CQctTXtkeHlhYcbMTCStzyUKmx+2nbeon4SVnW9GwMAw0BIeBhz8b7e3gwJu/2n50X0gH70TDyl7SgMHVljjlle3hvk8M7GzZKVb2kgYxlfk23ETDDe5bw8DOnq1hYS9oiMwvQunmSEMHZ70g0MWJnQ7eiYaVvaShwmVUaftIww3W7CdW0wn3hXTwVjQs7AUNekFokCnBbA9XY5lJRiZ2OngnGlb2kgZ8eE5M6jLScIXxOxRaNGFMV0gPb0XDwl7QkJ3Jrrhpr62DYS98tqI3P4/sXOGdaFjZSxqS7rBNGWY7OFU4xBHOysROB29Fw8Je0MCT7Lw3dZowbzB+x4tkNaVRV0gP70TDyl7SwJuQnUwBsQ4uaADi2iXGXSE9vBUNC3tJgzB51pj76Psedo77oM5rzsiulAHfioiFxYy/B574cFOa2Q4etl57gnp8JyJWFpOIojvZYZo0bzAzlMWi92Z3ZXToViQsrAUJ4o1l/txpyrzBmTdew732Izc9vBMNK3tJQzaak3CaMm9wtobJ1+NEzg3dioSFtYyPWpRR4T2PM2aPp4AVNP7Hjez08EZErC1WJpJxIbhJ1tDjMFlszeGeoAu8FxELg5UI+Is1liQzETdcjKNzORPU43tRsTCZVGAdUEMOU9LZAS8GTrbXm4yGcjp8KypWJisV1bhKRcJMxQ0fqFhTtBcVC5NJhffwFuBL+omKDs9MBBfiHUUdvhUVK5OVimyqTPrN70e8i0UO5ewZo1ybTCqCM56x+Hkm7XDeBS3pmEAGiq74VlSsTFYq4E5LDHWeSju8C9RPFO0YwF+brFSIkZikzpNphw+tYqBo01axMFnVT9F40bodqejwkYolRR9KxQP14+OWtIs5pHrs6FIW3TKQ+GJtCz4yibsEySoVT9WmFowLwcSAZ2pTkOeU6rGbE0RzUXMi9an5pKGYkLNn+I+nbmy7UJN7HphVfEvOrapkLTo6eGw1UEKeeYgx17ZjwAzjOWqCb17V69rDyXhKf51GymMpmiACS33jsaLzKiyP/Gz9PC71Cq8ZIFzhHR8BVnhHTOBduBb0FSRcws8xxYyqpIQ6piLtaTHWxSrqTVRJ7ghPkvcscDlTolLc6m3CkkGrJCZ8t9XmtvXXsvLZGuiXUYmqgmZhjvlaXFTFeuX9zK1gTMSYdKoG/DACJ80oLlnQ4J1HheMBW2K7kJFxMZeLB/GZV1GJlANF64CBBZ9TU0szLswZX1DDjqnkbQhN3C54xgVqOs7MaWtRMw0OjCfw1mgmSkcl6zEABqDQDTA9nkVJt0fZFPTD0RA0ZaqLs1NFPcM0IbnEhJAZnxKPSq9U+RVeQsyWnzLNPPDMFkocdZNKPMIcjtk/M5PWBn5kivmA0Sl52xAswHdhfd8exycElyNfy4sGSMYRLUHDYgtz3mpjOz6HvTTwJEGtFONrSnrBv6fAm0B54Bwjv15GAI7RlArvKKDUnf/anG/u7+L/opKLiU7VeM0nj8VRh57ZRaxXqb1IoHgenupZ9fwhHZ5r0pwXbFToh4HHHy4OLZpTEYbCbYhyRAEY9UDvcnpwIMTS+oiIybW6qlezZYfO0CYw68EhlUDakBOF9Q3H0FZt8G2/oaJ7Hc9Tz215RgPdBGOMvfgP6EulHb9nZwvttRR84NeR+ejRHWGq6B0DwB3Td6QjkUeoooI3XXzHjK6sfojzVbQHAcf6Ex3D6gq1oNmVelmhoruXqlupDtzkVo7HmFDg7/t2XiWEcuCovoTPOA4oVHzCZSKvcAVFh+zEuFG9zGoOPqLVoS+iLg5+MEBl9APXJn50RBfupwRfMdL4Z/AXHCZ4QkD6lOwcJS+0pb2ubHr6hQLV9XufKf8lhwgk8GW1Rp48qS86RFAXhwi6koOL7L7aEdFLeIiqLzN2ZV7Uwm/f/PDzTTF8/sPbn37+8e3/++Xnt3/54fzHv/x4/va7n9/+17ftz19++E7/5Z+/PKHBlugsE/Vggvnizbf//8szhyP+nfB/w/mL//7dl5rB5UAwin/x5p+/1FiEvTx3/uJfvjz9x+kqmjv9DwH4TCwKZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iago4NTc4CmVuZG9iagoxMCAwIG9iagpbIF0KZW5kb2JqCjE3IDAgb2JqCjw8IC9MZW5ndGggMjM1IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVRSW4AMQi75xX+QKWwJ++Zquqh/f+1hlEvAwPY2CTvwUYkPsSQ7ihXfMrqNMvwO1nkxc9K4eS9iAqkKsIKaQfPclYzDJ4bmQKXM/FZZj6ZFjsWUE3EcXbkNINBiGlcR8vpMNM86Am5PhhxY6dZrmJI691Svb7X8p8qykfW3Sy3TtnUSt2iZ+xJXHZeT21pXxh1FDcFkQ4fO7wH+SLmLC46kW72mymHlaQhOC2AH4mhVM8OrxEmfmYkeMqeTu+jNLz2QdP1vXtBR24mZCq3UEYqnqw0xoyh+o1oJqnv/4Ge9b2+/gBDTVS5CmVuZHN0cmVhbQplbmRvYmoKMTggMCBvYmoKPDwgL0xlbmd0aCAyNDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVFJbsQwDLv7FfzAAJasxXlPikEP7f+vJR0U7cEQI0tc4u7ERBZetlDXQofjw0ZeCZuB74PWnPgaseI/2kaklT9UWyATMVEkdFE3GvdIN7wK0X6kgleq91jzEXcrzVs6drG/98G05pEqq0I85Ngc2Uha10TR8T203nNDdMoggT43IQdEaY5ehaS/9sN1bTS7tTazJ6qDR6aE8kmzGprTKWbIbKjHbSpWMgo3qoyK+1RGWg/yNs4ygJPjhDJaT3asJqL81CeXkBcTccIuOzsWYhMLG4e0H5U+sfx86834m2mtpZBxQSI0xaXfZ7zH53j/AJVPXCYKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvTGVuZ3RoIDYxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM1NVcwULC0ABKmpkYK5kaWCimGXEA+iJXLZWhpDmblgFkWxkAGSBmcYQCkwZpzYHpyuDK40gDLFRDMCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0xlbmd0aCAyMzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZBLcgQhDEP3nEJHAH/hPJ1KzaLn/tvI7plskKrA8hNxHBNn84gIpBz8rGFmUBO8h4VD1WA7oOvAZ0BO4BoudClwo9qEc3ydw5sKmriHx2y1SKyd5Uwh6jAmSWzoScg2zmhy45zcqlTeTGu9xuKbcne7ymvalsK9h8r6OONUOasqa5E2EZlFaxvBRh7ssM+jq2jLWSrcN4xNXROVw5vF7lndyeKK769c49Uswcz3w7e/HB9X3egqx9jKhNlSk+bSOfWvltH6cLSLhXrhR3smSHB1qyBVpdbO2lN6/VPcJPr9A/TBVx0KZW5kc3RyZWFtCmVuZG9iagoyMSAwIG9iago8PCAvTGVuZ3RoIDE1NiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1j0sOwzAIRPc+xVygEl9DzpOq6iK9/7ZgJ5IRT/bMGEIFhAy8WDHNEXLgzaNumn6Dcy66FknVTZRVBHaGSII5cA7xiVQoCeaEVtU5h1VAgYX3q5PecldeAW47cPVsR9P+9hlqk4TdxJGYUn4KeN360TaJioZ5roV6gO41WCmahKwF7LENzLQSat8OrNbK89n/Gt/x+QNgEzb4CmVuZHN0cmVhbQplbmRvYmoKMjIgMCBvYmoKPDwgL0xlbmd0aCAzMDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZJLbgMxDEP3PoUuEMD62Z7zpCi6mN5/2ycl6Yoc2RZFapa6TFlTHpA0k4R/6fBwsZ3yO2zPZmbgWqKXieWU59AVYu6ifNnMRl1ZJ8XqhGY6t+hRORcHNk2qn6sspd0ueA7XJp5b9hE/vNCgHtQ1Lgk3dFejZSk0Y6r7f9J7/Iwy4GpMXWxSq3sfPF5EVejoB0eJImOXF+fjQQnpSsJoWoiVd0UDQe7ytMp7Ce7b3mrIsgepmM47KWaw63RSLm4XhyEeyPKo8OWj2GtCz/iwKyX0SNiGM3In7mjG5tTI4pD+3o0ES4+uaCHz4K9u1i5gvFM6RWJkTnKsaYtVTvdQFNO5w70MEPVsRUMpc5HV6l/DzgtrlmwWeEr6BR6j3SZLDlbZ26hO76082dD3H1rXdB8KZW5kc3RyZWFtCmVuZG9iagoyMyAwIG9iago8PCAvTGVuZ3RoIDI0NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkU1yBSEIhPeeoi/wquRXPc+kUllM7r8NzbwkK1qF5gPTAhNH8BJD7ImVEx8yfC/oMny3MjvwOtmZcE+4blzDZcMzYVvgOyrLO15Dd7ZSP52hqu8aOd4uUjV0ZWSfeqGaC8yQiK4RWXQrl3VA05TuUuEabFuCFPVKrCedoDToEcrwd5RrfHUTT6+x5FTNIVrNrRMairBseEHUySQRtQ2LJ5ZzIVH5qhurOi5gkyXi9IDcoJVmfHpSSREwg3ysyWjMAjbQk7tnF8aaSx5Fjlc0mLA7STXwgPfitr73NnGP8xf4hXff/ysOfdcCPn8AS/5dBgplbmRzdHJlYW0KZW5kb2JqCjI0IDAgb2JqCjw8IC9MZW5ndGggMjMyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVRSW7EMAy7+xX8wADW7rwnxaCH9v/XUsoUCEAltrglYmMjAi8x+DmI3PiSNaMmfmdyV/wsT4VHwq3gSRSBl+FedoLLG8ZlPw4zH7yXVs6kxpMMyEU2PTwRMtglEDowuwZ12Gbaib4h4bMjUs1GltPXEvTSKgTKU7bf6YISbav6c/usC2372hNOdnvqSeUTiOeWrMBl4xWTxVgGPVG5SzF9kOpsoSehvCifg2w+aohElyhn4InBwSjQDuy57WfiVSFoXd2nbWOoRkrH078NTU2SCPlECWe2NO4W/n/Pvb7X+w9OIVQRCmVuZHN0cmVhbQplbmRvYmoKMjUgMCBvYmoKPDwgL0xlbmd0aCAyMzEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNU85kgQhDMt5hT4wVRjbQL+np7Y22Pl/upKZTpDwIcnTEx2ZeJkjI7Bmx9taZCBm4FNMxb/2tA8TqvfgHiKUiwthhpFw1qzjbp6OF/92lc9YB+82+IpZXhDYwkzWVxZnLtsFY2mcxDnJboxdE7GNda2nU1hHMKEMhHS2w5Qgc1Sk9MmOMuboOJEnnovv9tssdjl+DusLNo0hFef4KnqCNoOi7HnvAhpyQf9d3fgeRbvoJSAbCRbWUWLunOWEX712dB61KBJzQppBLhMhzekqphCaUKyzo6BSUXCpPqforJ9/5V9cLQplbmRzdHJlYW0KZW5kb2JqCjI2IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD1QO45EIQzrOYUv8CTyI3AeRqstZu/frgOaKVBMfrYzJNARgUcMMZSv4yWtoK6Bv4tC8W7i64PCIKtDUiDOeg+IdOymNpETOh2cMz9hN2OOwEUxBpzpdKY9ByY5+8IKhHMbZexWSCeJqiKO6jOOKZ4qe594FiztyDZbJ5I95CDhUlKJyaWflMo/bcqUCjpm0QQsErngZBNNOMu7SVKMGZQy6h6mdiJ9rDzIozroZE3OrCOZ2dNP25n4HHC3X9pkTpXHdB7M+Jy0zoM5Fbr344k2B02N2ujs9xNpKi9Sux1anX51EpXdGOcYEpdnfxnfZP/5B/6HWiIKZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvTGVuZ3RoIDM5NSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UktuxUAI2+cUXKDS8JvPeVJV3bz7b2tDUqkqvIkxxjB9ypC55UtdEnGFybderls8pnwuW1qZeYi7i40lPrbcl+4htl10LrE4HUfyCzKdKkSozarRofhCloUHkE7woQvCfTn+4y+AwdewDbjhPTJBsCTmKULGblEZmhJBEWHnkRWopFCfWcLfUe7r9zIFam+MpQtjHPQJtAVCbUjEAupAAETslFStkI5nJBO/Fd1nYhxg59GyAa4ZVESWe+zHiKnOqIy8RMQ+T036KJZMLVbGblMZX/yUjNR8dAUqqTTylPLQVbPQC1iJeRL2OfxI+OfWbCGGOm7W8onlHzPFMhLOYEs5YKGX40fg21l1Ea4dubjOdIEfldZwTLTrfsj1T/5021rNdbxyCKJA5U1B8LsOrkaxxMQyPp2NKXqiLLAamrxGM8FhEBHW98PIAxr9crwQNKdrIrRYIpu1YkSNimxzPb0E1kzvxTnWwxPCbO+d1qGyMzMqIYLauoZq60B2s77zcLafPzPoom0KZW5kc3RyZWFtCmVuZG9iagoyOCAwIG9iago8PCAvTGVuZ3RoIDEzNiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNj0EOAzEIA+95hZ9AIEB4z1ZVD9v/X0vYdtMLHsmAbFEGgSWHeIcb4dHbD99FNhVn45xfUiliIZhPcJ8wUxyNKXfyY4+AcZRqLKdoeF5Lzk3DFy13Ey2lrZeTGW+47pf3R5VtkQ1Fzy0LQtdskvkygQd8GJhHdeNppcfd9myv9vwAzmw0SQplbmRzdHJlYW0KZW5kb2JqCjI5IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSYoDMAy75xX6QCFek7ynQ5lD5//Xyg6FOQQJr5KTlphYCw8xhB8sPfiRIXM3/Rt+otm7WXqSydn/mOciU1H4UqguYkJdiBvPoRHwPaFrElmxvfE5LKOZc74HH4W4BDOhAWN9STK5qOaVIRNODHUcDlqkwrhrYsPiWtE8jdxu+0ZmZSaEDY9kQtwYgIgg6wKyGCyUNjYTMlnOA+0NyQ1aYNepG1GLgiuU1gl0olbEqszgs+bWdjdDLfLgqH3x+mhWl2CF0Uv1WHhfhT6YqZl27pJCeuFNOyLMHgqkMjstK7V7xOpugfo/y1Lw/cn3+B2vD838XJwKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8PCAvTGVuZ3RoIDk0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWNwRHAIAgE/1RBCQoK2k8mk4f2/40QMnxg5w7uhAULtnlGHwWVJl4VWAdKY9xQj0C94XItydwFD3Anf9rQVJyW03dpkUlVKdykEnn/DmcmkKh50WOd9wtj+yM8CmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRVJLbkQxCNu/U3CBSOGXkPO0qrqY3n9bm0zVzeAJYGx4y1OmZMqwuSUjJNeUT30iQ6ym/DRyJCKm+EkJBXaVj8drS6yN7JGoFJ/a8eOx9Eam2RVa9e7Rpc2iUc3KyDnIEKGeFbqye9QO2fB6XEi675TNIRzL/1CBLGXdcgolQVvQd+wR3w8droIrgmGway6D7WUy1P/6hxZc7333YscugBas577BDgCopxO0BcgZ2u42KWgAVbqLScKj8npudqJso1Xp+RwAMw4wcsCIJVsdvtHeAJZ9XehFjYr9K0BRWUD8yNV2wd4xyUhwFuYGjr1wPMWZcEs4xgJAir3iGHrwJdjmL1euiJrwCXW6ZC+8wp7a5udCkwh3rQAOXmTDraujqJbt6TyC9mdFckaM1Is4OiGSWtI5guLSoB5a41w3seJtI7G5V9/uH+GcL1z26xdL7ITECmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAxNjQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZDHcQUxDEPvqgIlMIAK9azH8w/r/q+G9NNBehhCDGJPwrBcV3FhdMOPty0zDX9HGe7G+jJjvNVYICfoAwyRiavRpPp2xRmq9OTVYq6jolwvOiISzJLjq0AjfDqyx5O2tjP9dF4f7CHvE/8qKuduYQEuqu5A+VIf8dSP2VHqmqGPKitrHmraV4RdEUrbPi6nMk7dvQNa4b2Vqz3a7z8edjryCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0xlbmd0aCA3MiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohngJggbRDFIBZEsZmJGUQdnAGRy+BKAwAl2xbJCmVuZHN0cmVhbQplbmRvYmoKMzQgMCBvYmoKPDwgL0xlbmd0aCA4MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9zDkSgDAIBdCeU/wjhMgi93Eci3j/VjDRBh6reqAhOIO6wa3hYMq6dBPvU+PVxpwSCah4Sk2Wugt61LS+1L5o4Lvr5kvViT/NzxedD7sdGd0KZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDQ3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXJYQVi4XTCwHzALRlnAKIp7BlQYAuWcNJwplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggMjU4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWRS3IEIAhE956CI4D85DyTSmUxuf82Dc5kNnaXqP2ESiOmEiznFHkwfcnyzWS26Xc5VjsbBRRFKJjJVeixAqs7U8SZa4lq62Nl5LjTOwbFG85dOalkcaOMdVR1KnBMz5X1Ud35dlmUfUcOZQrYrHMcbODKbcMYJ0abre4O94kgTydTR8XtINnwByeNfZWrK3CdbPbRSzAOBP1CE5jki0DrDIHGzVP05BLs4+N254Fgb3kRSNkQyJEhGB2Cdp1c/+LW+b3/cYY7z7UZrhzv4neY1nbHX2KSFXMBi9wpqOdrLlrXGTrekzPH5Kb7hs65YJe7g0zv+T/Wz/r+Ax4pZvoKZW5kc3RyZWFtCmVuZG9iagozNyAwIG9iago8PCAvVHlwZSAvWE9iamVjdCAvU3VidHlwZSAvRm9ybSAvQkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0xlbmd0aCAzOQovRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJzjMjQwUzA2NVXI5TI3NgKzcsAsI3MjIAski2BBZDO40gAV8wp8CmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0xlbmd0aCAxNjMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZA7EgMhDEN7TqEj+CMDPs9mMik2929j2GxSwNNYIIO7E4LU2oKJ6IKHtiXdBe+tBGdj/Ok2bjUS5AR1gFak42iUUn25xWmVdPFoNnMrC60THWYOepSjGaAQOhXe7aLkcqbuzvlDcPVf9b9i3TmbiYHJyh0IzepT3Pk2O6K6usn+pMfcrNd+K+xVYWlZS8sJt527ZkAJ3FM52qs9Px8KOvYKZW5kc3RyZWFtCmVuZG9iagozOSAwIG9iago8PCAvTGVuZ3RoIDIxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9ULmNBDEMy12FGljAeu2pZxaLS6b/9Ej59iLRFkVSKjWZkikvdZQlWVPeOnyWxA55huVuZDYlKkUvk7Al99AK8X2J5hT33dWWs0M0l2g5fgszKqobHdNLNppwKhO6oNzDM/oNbXQDVocesVsg0KRg17YgcscPGAzBmROLIgxKTQb/rnKPn16LGz7D8UMUkZIO5jX/WP3ycw2vU48nkW5vvuJenKkOAxEckpq8I11YsS4SEWk1QU3PwFotgLu3Xv4btCO6DED2icRxmlKOob9rcKXPL+UnU9gKZW5kc3RyZWFtCmVuZG9iago0MCAwIG9iago8PCAvTGVuZ3RoIDgzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4m9j5RlMLevw0QJW64J909XB0JmSluM8NDBp4MLIZdcYH0ljALXEdQjp3so2HVvuoEjfWmUvPvD5Se7KzihusBAkIaZgplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9MZW5ndGggNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhyuDKw0A4bQNmAplbmRzdHJlYW0KZW5kb2JqCjQyIDAgb2JqCjw8IC9MZW5ndGggMTYwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQORIDMQgEc72CJ0hcgvesy7XB+v+pB9ZHoukCNBy6Fk3KehRoPumxRqG60GvoLEqSRMEWkh1Qp2OIOyhITEhjkki2HoMjmlizXZiZVCqzUuG0acXCv9la1chEjXCN/InpBlT8T+pclPBNg6+SMfoYVLw7g4xJ+F5F3Fox7f5EMLEZ9glvRSYFhImxqdm+z2CGzPcK1zjH8w1MgjfrCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0xlbmd0aCAzMzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVJLcsUgDNtzCl2gM/gH5DzpdLp4vf+2kpNFRg5g9DHlholKfFkgt6PWxLeNzECF4a+rzIXPSNvIOojLkIu4ki2Fe0Qs5DHEPMSC76vxHh75rMzJswfGL9l3Dyv21IRlIePFGdphFcdhFeRYsHUhqnt4U6TDqSTY44v/PsVzLQQtfEbQgF/kn6+O4PmSFmn3mG3TrnqwTDuqpLAcbE9zXiZfWme5Oh7PB8n2rtgRUrsCFIW5M85z4SjTVka0FnY2SGpcbG+O/VhK0IVuXEaKI5CfqSI8oKTJzCYK4o+cHnIqA2Hqmq50chtVcaeezDWbi7czSWbrvkixmcJ5XTiz/gxTZrV5J89yotSpCO+xZ0vQ0Dmunr2WWWh0mxO8pITPxk5PTr5XM+shORUJqWJaV8FpFJliCdsSX1NRU5p6Gf778u7xO37+ASxzfHMKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvTGVuZ3RoIDcwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMzNlMwULAwAhKmpoYK5kaWCimGXEA+iJXLBRPLAbPMLMyBLCMLkJYcLkMLYzBtYmykYGZiBmRZIDEgujK40gCYmhMDCmVuZHN0cmVhbQplbmRvYmoKNDUgMCBvYmoKPDwgL0xlbmd0aCAzMjAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVJLbgUxCNvPKbhApfBPzvOqqou++29rE70VTDBg4ykvWdJLvtQl26XD5Fsf9yWxQt6P7ZrMUsX3FrMUzy2vR88Rty0KBFETPViZLxUi1M/06DqocEqfgVcItxQbvINJAINq+AcepTMgUOdAxrtiMlIDgiTYc2lxCIlyJol/pLye3yetpKH0PVmZy9+TS6XQHU1O6AHFysVJoF1J+aCZmEpEkpfrfbFC9IbAkjw+RzHJgOw2iW2iBSbnHqUlzMQUOrDHArxmmtVV6GDCHocpjFcLs6gebPJbE5WkHa3jGdkw3sswU2Kh4bAF1OZiZYLu5eM1r8KI7VGTXcNw7pbNdwjRaP4bFsrgYxWSgEensRINaTjAiMCeXjjFXvMTOQ7AiGOdmiwMY2gmp3qOicDQnrOlYcbHHlr18w9U6XyHCmVuZHN0cmVhbQplbmRvYmoKNDYgMCBvYmoKPDwgL0xlbmd0aCAxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKNDcgMCBvYmoKPDwgL0xlbmd0aCAxMzMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRY9LDgQhCET3nKKOwMcf53Ey6YVz/+2AnW4TYz2FVIG5gqE9LmsDnRUfIRm28beplo5FWT5UelJWD8ngh6zGyyHcoCzwgkkqhiFQi5gakS1lbreA2zYNsrKVU6WOsIujMI/2tGwVHl+iWyJ1kj+DxCov3OO6Hcil1rveoou+f6QBMQkKZW5kc3RyZWFtCmVuZG9iago0OCAwIG9iago8PCAvTGVuZ3RoIDM0MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UjluBDEM6/0KfSCAbtvv2SBIkfy/DanZFANxdFKUO1pUdsuHhVS17HT5tJXaEjfkd2WFxAnJqxLtUoZIqLxWIdXvmTKvtzVnBMhSpcLkpORxyYI/w6WnC8f5trGv5cgdjx5YFSOhRMAyxcToGpbO7rBmW36WacCPeIScK9Ytx1gFUhvdOO2K96F5LbIGiL2ZlooKHVaJFn5B8aBHjX32GFRYINHtHElwjIlQkYB2gdpIDDl7LHZRH/QzKDET6NobRdxBgSWSmDnFunT03/jQsaD+2Iw3vzoq6VtaWWPSPhvtlMYsMul6WPR089bHgws076L859UMEjRljZLGB63aOYaimVFWeLdDkw3NMcch8w6ewxkJSvo8FL+PJRMdlMjfDg2hf18eo4ycNt4C5qI/bRUHDuKzw165gRVKF2uS9wGpTOiB6f+v8bW+19cfHe2AxgplbmRzdHJlYW0KZW5kb2JqCjQ5IDAgb2JqCjw8IC9MZW5ndGggMjUxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nC1RSXIDQQi7zyv0hGan32OXK4fk/9cIygcGDYtAdFrioIyfICxXvOWRq2jD3zMxgt8Fh34r121Y5EBUIEljUDWhdvF69B7YcZgJzJPWsAxmrA/8jCnc6MXhMRlnt9dl1BDsXa89mUHJrFzEJRMXTNVhI2cOP5kyLrRzPTcg50ZYl2GQblYaMxKONIVIIYWqm6TOBEESjK5GjTZyFPulL490hlWNqDHscy1tX89NOGvQ7Fis8uSUHl1xLicXL6wc9PU2AxdRaazyQEjA/W4P9XOyk994S+fOFtPje83J8sJUYMWb125ANtXi37yI4/uMr+fn+fwDX2BbiAplbmRzdHJlYW0KZW5kb2JqCjUwIDAgb2JqCjw8IC9MZW5ndGggMTc0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE2QSQ5DIQxD95zCF6iEM8DnPL+qumjvv61DB3WB/OQgcDw80HEkLnRk6IyOK5sc48CzIGPi0Tj/ybg+xDFB3aItWJd2x9nMEnPCMjECtkbJ2TyiwA/HXAgSZJcfvsAgIl2P+VbzWZP0z7c73Y+6tGZfPaLAiewIxbABV4D9useBS8L5XtPklyolYxOH8oHqIlI2O6EQtVTscqqKs92bK3AV9PzRQ+7tBbUjPN8KZW5kc3RyZWFtCmVuZG9iago1MSAwIG9iago8PCAvTGVuZ3RoIDc1IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDO1NFIwUDA2ABKmZkYKpibmCimGXEA+iJXLZWhkCmblcBlZmilYWAAZJmbmUCGYhhwuY1NzoAFARcamYBqqP4crgysNAJWQEu8KZW5kc3RyZWFtCmVuZG9iago1MiAwIG9iago8PCAvTGVuZ3RoIDg5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDWMuw2AMAxEe0/hEeK/2QchCti/xUlwY9/dk15S4kDlOhGOpoEnQfWZXiDZ6QFWReJRScz/Tb2pRVPPpu2rTQQnM471dRyomtN60FoobZMW3nB9AJwd7QplbmRzdHJlYW0KZW5kb2JqCjUzIDAgb2JqCjw8IC9MZW5ndGggMTQxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2PwQ7DMAhD7/kK/0Ck2CmhfE+naofu/68jS7sLegJjjIXQ0BuqmsOGYJvjxdIlVGv4FMVAJTfImWAOpaTSHUeRemI4GFwetBuO4rHo+hG7kmZ90MZCuiVogHusU2ncpnETxB01Beop6pyjvBC5n6ln2DSS3TSzknO4Db97z1PX/6ervMv5Bb13Lv4KZW5kc3RyZWFtCmVuZG9iago1NCAwIG9iago8PCAvTGVuZ3RoIDIxNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNSAwIG9iago8PCAvVHlwZSAvRm9udCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9GaXJzdENoYXIgMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxNCAwIFIgL1N1YnR5cGUgL1R5cGUzIC9OYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9DaGFyUHJvY3MgMTYgMCBSCi9FbmNvZGluZyA8PCAvVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ2IC9wZXJpb2QgNDggL3plcm8gL29uZSAvdHdvIC90aHJlZSAvZm91ciAvZml2ZSAvc2l4IC9zZXZlbgovZWlnaHQgNjcgL0MgNzEgL0cgNzYgL0wgODIgL1IgODUgL1UgOTcgL2EgL2IgL2MgL2QgL2UgL2YgL2cgL2ggL2kgMTA3IC9rCi9sIC9tIC9uIC9vIDExNCAvciAvcyAvdCAvdSAvdiAvdyAxMjEgL3kgXQo+PgovV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvQk1RUURWK0RlamFWdVNhbnMgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0FzY2VudCA5MjkgL0Rlc2NlbnQgLTIzNiAvQ2FwSGVpZ2h0IDAKL1hIZWlnaHQgMCAvSXRhbGljQW5nbGUgMCAvU3RlbVYgMCAvTWF4V2lkdGggMTM0MiA+PgplbmRvYmoKMTMgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTYgMCBvYmoKPDwgL0MgMTcgMCBSIC9HIDE4IDAgUiAvTCAxOSAwIFIgL1IgMjAgMCBSIC9VIDIxIDAgUiAvYSAyMiAwIFIgL2IgMjMgMCBSCi9jIDI0IDAgUiAvZCAyNSAwIFIgL2UgMjYgMCBSIC9laWdodCAyNyAwIFIgL2YgMjggMCBSIC9maXZlIDI5IDAgUgovZm91ciAzMCAwIFIgL2cgMzEgMCBSIC9oIDMyIDAgUiAvaSAzMyAwIFIgL2sgMzQgMCBSIC9sIDM1IDAgUiAvbSAzNiAwIFIKL24gMzggMCBSIC9vIDM5IDAgUiAvb25lIDQwIDAgUiAvcGVyaW9kIDQxIDAgUiAvciA0MiAwIFIgL3MgNDMgMCBSCi9zZXZlbiA0NCAwIFIgL3NpeCA0NSAwIFIgL3NwYWNlIDQ2IDAgUiAvdCA0NyAwIFIgL3RocmVlIDQ4IDAgUiAvdHdvIDQ5IDAgUgovdSA1MCAwIFIgL3YgNTEgMCBSIC93IDUyIDAgUiAveSA1MyAwIFIgL3plcm8gNTQgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNSAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDAgL2NhIDEgPj4KL0EyIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDEgPj4KL0EzIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDAuNSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCAvRjEtRGVqYVZ1U2Fucy1taW51cyAzNyAwIFIgPj4KZW5kb2JqCjIgMCBvYmoKPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFsgMTEgMCBSIF0gL0NvdW50IDEgPj4KZW5kb2JqCjU1IDAgb2JqCjw8IC9DcmVhdG9yIChNYXRwbG90bGliIHYzLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1Y2VyIChNYXRwbG90bGliIHBkZiBiYWNrZW5kIHYzLjguMCkgL0NyZWF0aW9uRGF0ZSAoRDoyMDIzMTAxMTE1MzQ1NFopCj4+CmVuZG9iagp4cmVmCjAgNTYKMDAwMDAwMDAwMCA2NTUzNSBmIAowMDAwMDAwMDE2IDAwMDAwIG4gCjAwMDAwMjEyMTcgMDAwMDAgbiAKMDAwMDAyMDk1NCAwMDAwMCBuIAowMDAwMDIwOTg2IDAwMDAwIG4gCjAwMDAwMjExMjYgMDAwMDAgbiAKMDAwMDAyMTE0NyAwMDAwMCBuIAowMDAwMDIxMTY4IDAwMDAwIG4gCjAwMDAwMDAwNjUgMDAwMDAgbiAKMDAwMDAwMDM0OSAwMDAwMCBuIAowMDAwMDA5MDIzIDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwOTAwMiAwMDAwMCBuIAowMDAwMDE5NDczIDAwMDAwIG4gCjAwMDAwMTkyNjYgMDAwMDAgbiAKMDAwMDAxODc3MyAwMDAwMCBuIAowMDAwMDIwNTI2IDAwMDAwIG4gCjAwMDAwMDkwNDMgMDAwMDAgbiAKMDAwMDAwOTM1MSAwMDAwMCBuIAowMDAwMDA5NjcxIDAwMDAwIG4gCjAwMDAwMDk4MDQgMDAwMDAgbiAKMDAwMDAxMDEwOSAwMDAwMCBuIAowMDAwMDEwMzM4IDAwMDAwIG4gCjAwMDAwMTA3MTggMDAwMDAgbiAKMDAwMDAxMTAzNSAwMDAwMCBuIAowMDAwMDExMzQwIDAwMDAwIG4gCjAwMDAwMTE2NDQgMDAwMDAgbiAKMDAwMDAxMTk2NiAwMDAwMCBuIAowMDAwMDEyNDM0IDAwMDAwIG4gCjAwMDAwMTI2NDMgMDAwMDAgbiAKMDAwMDAxMjk2NSAwMDAwMCBuIAowMDAwMDEzMTMxIDAwMDAwIG4gCjAwMDAwMTM1NDUgMDAwMDAgbiAKMDAwMDAxMzc4MiAwMDAwMCBuIAowMDAwMDEzOTI2IDAwMDAwIG4gCjAwMDAwMTQwODEgMDAwMDAgbiAKMDAwMDAxNDIwMCAwMDAwMCBuIAowMDAwMDE0NTMxIDAwMDAwIG4gCjAwMDAwMTQ3MDMgMDAwMDAgbiAKMDAwMDAxNDkzOSAwMDAwMCBuIAowMDAwMDE1MjMwIDAwMDAwIG4gCjAwMDAwMTUzODUgMDAwMDAgbiAKMDAwMDAxNTUwOCAwMDAwMCBuIAowMDAwMDE1NzQxIDAwMDAwIG4gCjAwMDAwMTYxNDggMDAwMDAgbiAKMDAwMDAxNjI5MCAwMDAwMCBuIAowMDAwMDE2NjgzIDAwMDAwIG4gCjAwMDAwMTY3NzMgMDAwMDAgbiAKMDAwMDAxNjk3OSAwMDAwMCBuIAowMDAwMDE3MzkyIDAwMDAwIG4gCjAwMDAwMTc3MTYgMDAwMDAgbiAKMDAwMDAxNzk2MyAwMDAwMCBuIAowMDAwMDE4MTEwIDAwMDAwIG4gCjA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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Seaborn prints warnings if histogram has small values. We can ignore them for now\n", "warnings.filterwarnings(\"ignore\")\n", "# Create a plot for every activation function\n", "for i, act_fn_name in enumerate(act_fn_by_name):\n", " # Setting the seed ensures that we have the same weight initialization for each activation function\n", " set_seed(42)\n", " act_fn = act_fn_by_name[act_fn_name]()\n", " net_actfn = BaseNetwork(act_fn=act_fn).to(device)\n", " visualize_gradients(net_actfn, color=f\"C{i}\")" ] }, { "cell_type": "markdown", "id": "7ecf3eb8", "metadata": { "papermill": { "duration": 0.034413, "end_time": "2023-10-11T15:35:02.759641", "exception": false, "start_time": "2023-10-11T15:35:02.725228", "status": "completed" }, "tags": [] }, "source": [ "The sigmoid activation function shows a clearly undesirable behavior.\n", "While the gradients for the output layer are very large with up to 0.1, the input layer has the lowest gradient norm across all activation functions with only 1e-5.\n", "This is due to its small maximum gradient of 1/4, and finding a suitable learning rate across all layers is not possible in this setup.\n", "All the other activation functions show to have similar gradient norms across all layers.\n", "Interestingly, the ReLU activation has a spike around 0 which is caused by its zero-part on the left, and dead neurons (we will take a closer look at this later on).\n", "\n", "Note that additionally to the activation, the initialization of the weight parameters can be crucial.\n", "By default, PyTorch uses the [Kaiming](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.kaiming_uniform_) initialization for linear layers optimized for Tanh activations.\n", "In Tutorial 4, we will take a closer look at initialization, but assume\n", "for now that the Kaiming initialization works for all activation\n", "functions reasonably well." ] }, { "cell_type": "markdown", "id": "d0290cd1", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.033436, "end_time": "2023-10-11T15:35:02.826727", "exception": false, "start_time": "2023-10-11T15:35:02.793291", "status": "completed" }, "tags": [] }, "source": [ "### Training a model\n", "\n", "Next, we want to train our model with different activation functions on FashionMNIST and compare the gained performance.\n", "All in all, our final goal is to achieve the best possible performance on a dataset of our choice.\n", "Therefore, we write a training loop in the next cell including a\n", "validation after every epoch and a final test on the best model:" ] }, { "cell_type": "code", "execution_count": 18, "id": "2d40d21c", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:35:02.895630Z", "iopub.status.busy": "2023-10-11T15:35:02.895432Z", "iopub.status.idle": "2023-10-11T15:35:02.908343Z", "shell.execute_reply": "2023-10-11T15:35:02.907501Z" }, "papermill": { "duration": 0.048898, "end_time": "2023-10-11T15:35:02.909477", "exception": false, "start_time": "2023-10-11T15:35:02.860579", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def train_model(net, model_name, max_epochs=50, patience=7, 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(\"Model file already exists. Skipping training...\")\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.SGD(net.parameters(), lr=1e-2, momentum=0.9) # Default parameters, feel free to change\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", " val_scores = []\n", " best_val_epoch = -1\n", " for epoch in range(max_epochs):\n", " ############\n", " # Training #\n", " ############\n", " net.train()\n", " true_preds, count = 0.0, 0\n", " for imgs, labels in tqdm(train_loader_local, desc=f\"Epoch {epoch+1}\", leave=False):\n", " imgs, labels = imgs.to(device), labels.to(device) # To GPU\n", " optimizer.zero_grad() # Zero-grad can be placed anywhere before \"loss.backward()\"\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()\n", " count += labels.shape[0]\n", " train_acc = true_preds / count\n", "\n", " ##############\n", " # Validation #\n", " ##############\n", " val_acc = test_model(net, val_loader)\n", " val_scores.append(val_acc)\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", "\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", " elif best_val_epoch <= epoch - patience:\n", " print(f\"Early stopping due to no improvement over the last {patience} epochs\")\n", " break\n", "\n", " # Plot a curve of the validation accuracy\n", " plt.plot([i for i in range(1, len(val_scores) + 1)], val_scores)\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(\"Validation accuracy\")\n", " plt.title(f\"Validation performance of {model_name}\")\n", " plt.show()\n", " plt.close()\n", "\n", " load_model(CHECKPOINT_PATH, model_name, net=net)\n", " test_acc = test_model(net, test_loader)\n", " print((f\" Test accuracy: {test_acc*100.0:4.2f}% \").center(50, \"=\") + \"\\n\")\n", " return test_acc\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": "672f1ff3", "metadata": { "papermill": { "duration": 0.033414, "end_time": "2023-10-11T15:35:02.976594", "exception": false, "start_time": "2023-10-11T15:35:02.943180", "status": "completed" }, "tags": [] }, "source": [ "We train one model for each activation function.\n", "We recommend using the pretrained models to save time if you are running this notebook on CPU." ] }, { "cell_type": "code", "execution_count": 19, "id": "d83e4769", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:35:03.044642Z", "iopub.status.busy": "2023-10-11T15:35:03.044464Z", "iopub.status.idle": "2023-10-11T15:35:11.715430Z", "shell.execute_reply": "2023-10-11T15:35:11.714326Z" }, "papermill": { "duration": 8.707139, "end_time": "2023-10-11T15:35:11.717319", "exception": false, "start_time": "2023-10-11T15:35:03.010180", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training BaseNetwork with sigmoid activation...\n", "Model file already exists. Skipping training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "============= Test accuracy: 10.00% ==============\n", "\n", "Training BaseNetwork with tanh activation...\n", "Model file already exists. Skipping training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "============= Test accuracy: 87.59% ==============\n", "\n", "Training BaseNetwork with relu activation...\n", "Model file already exists. Skipping training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "============= Test accuracy: 88.62% ==============\n", "\n", "Training BaseNetwork with leakyrelu activation...\n", "Model file already exists. Skipping training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "============= Test accuracy: 88.92% ==============\n", "\n", "Training BaseNetwork with elu activation...\n", "Model file already exists. Skipping training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "============= Test accuracy: 87.27% ==============\n", "\n", "Training BaseNetwork with swish activation...\n", "Model file already exists. Skipping training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "============= Test accuracy: 88.73% ==============\n", "\n" ] } ], "source": [ "for act_fn_name in act_fn_by_name:\n", " print(f\"Training BaseNetwork with {act_fn_name} activation...\")\n", " set_seed(42)\n", " act_fn = act_fn_by_name[act_fn_name]()\n", " net_actfn = BaseNetwork(act_fn=act_fn).to(device)\n", " train_model(net_actfn, f\"FashionMNIST_{act_fn_name}\", overwrite=False)" ] }, { "cell_type": "markdown", "id": "fd61f743", "metadata": { "papermill": { "duration": 0.034138, "end_time": "2023-10-11T15:35:11.786427", "exception": false, "start_time": "2023-10-11T15:35:11.752289", "status": "completed" }, "tags": [] }, "source": [ "Not surprisingly, the model using the sigmoid activation function shows to fail and does not improve upon random performance (10 classes => 1/10 for random chance).\n", "\n", "All the other activation functions gain similar performance.\n", "To have a more accurate conclusion, we would have to train the models for multiple seeds and look at the averages.\n", "However, the \"optimal\" activation function also depends on many other factors (hidden sizes, number of layers, type of layers, task, dataset, optimizer, learning rate, etc.)\n", "so that a thorough grid search would not be useful in our case.\n", "In the literature, activation functions that have shown to work well\n", "with deep networks are all types of ReLU functions we experiment with\n", "here, with small gains for specific activation functions in specific\n", "networks." ] }, { "cell_type": "markdown", "id": "76d968a3", "metadata": { "papermill": { "duration": 0.033823, "end_time": "2023-10-11T15:35:11.854340", "exception": false, "start_time": "2023-10-11T15:35:11.820517", "status": "completed" }, "tags": [] }, "source": [ "### Visualizing the activation distribution" ] }, { "cell_type": "markdown", "id": "f7031503", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.033772, "end_time": "2023-10-11T15:35:11.921776", "exception": false, "start_time": "2023-10-11T15:35:11.888004", "status": "completed" }, "tags": [] }, "source": [ "After we have trained the models, we can look at the actual activation values that find inside the model.\n", "For instance, how many neurons are set to zero in ReLU?\n", "Where do we find most values in Tanh?\n", "To answer these questions, we can write a simple function which takes a\n", "trained model, applies it to a batch of images, and plots the histogram\n", "of the activations inside the network:" ] }, { "cell_type": "code", "execution_count": 20, "id": "32cc500b", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:35:11.991014Z", "iopub.status.busy": "2023-10-11T15:35:11.990805Z", "iopub.status.idle": "2023-10-11T15:35:11.998540Z", "shell.execute_reply": "2023-10-11T15:35:11.997704Z" }, "papermill": { "duration": 0.043921, "end_time": "2023-10-11T15:35:11.999680", "exception": false, "start_time": "2023-10-11T15:35:11.955759", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def visualize_activations(net, color=\"C0\"):\n", " activations = {}\n", "\n", " net.eval()\n", " small_loader = data.DataLoader(train_set, batch_size=1024)\n", " imgs, labels = next(iter(small_loader))\n", " with torch.no_grad():\n", " layer_index = 0\n", " imgs = imgs.to(device)\n", " imgs = imgs.view(imgs.size(0), -1)\n", " # We need to manually loop through the layers to save all activations\n", " for layer_index, layer in enumerate(net.layers[:-1]):\n", " imgs = layer(imgs)\n", " activations[layer_index] = imgs.view(-1).cpu().numpy()\n", "\n", " # Plotting\n", " columns = 4\n", " rows = math.ceil(len(activations) / columns)\n", " fig, ax = plt.subplots(rows, columns, figsize=(columns * 2.7, rows * 2.5))\n", " fig_index = 0\n", " for key in activations:\n", " key_ax = ax[fig_index // columns][fig_index % columns]\n", " sns.histplot(data=activations[key], bins=50, ax=key_ax, color=color, kde=True, stat=\"density\")\n", " key_ax.set_title(f\"Layer {key} - {net.layers[key].__class__.__name__}\")\n", " fig_index += 1\n", " fig.suptitle(f\"Activation distribution for activation function {net.config['act_fn']['name']}\", fontsize=14)\n", " fig.subplots_adjust(hspace=0.4, wspace=0.4)\n", " plt.show()\n", " plt.close()" ] }, { "cell_type": "code", "execution_count": 21, "id": "8e40f30e", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:35:12.068604Z", "iopub.status.busy": "2023-10-11T15:35:12.068426Z", "iopub.status.idle": "2023-10-11T15:36:24.383543Z", "shell.execute_reply": "2023-10-11T15:36:24.382527Z" }, "papermill": { "duration": 72.440008, "end_time": "2023-10-11T15:36:24.473646", "exception": false, "start_time": "2023-10-11T15:35:12.033638", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/pdf": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, act_fn_name in enumerate(act_fn_by_name):\n", " net_actfn = load_model(model_path=CHECKPOINT_PATH, model_name=f\"FashionMNIST_{act_fn_name}\").to(device)\n", " visualize_activations(net_actfn, color=f\"C{i}\")" ] }, { "cell_type": "markdown", "id": "4fa39eb3", "metadata": { "papermill": { "duration": 0.066929, "end_time": "2023-10-11T15:36:24.610028", "exception": false, "start_time": "2023-10-11T15:36:24.543099", "status": "completed" }, "tags": [] }, "source": [ "As the model with sigmoid activation was not able to train properly, the activations are also less informative and all gathered around 0.5 (the activation at input 0).\n", "\n", "The tanh shows a more diverse behavior.\n", "While for the input layer we experience a larger amount of neurons to be close to -1 and 1, where the gradients are close to zero, the activations in the two consecutive layers are closer to zero.\n", "This is probably because the input layers look for specific features in the input image, and the consecutive layers combine those together.\n", "The activations for the last layer are again more biased to the extreme points because the classification layer can be seen as a weighted average of those values (the gradients push the activations to those extremes).\n", "\n", "The ReLU has a strong peak at 0, as we initially expected.\n", "The effect of having no gradients for negative values is that the network does not have a Gaussian-like distribution after the linear layers, but a longer tail towards the positive values.\n", "The LeakyReLU shows a very similar behavior while ELU follows again a more Gaussian-like distribution.\n", "The Swish activation seems to lie in between, although it is worth noting that Swish uses significantly higher values than other activation functions (up to 20).\n", "\n", "As all activation functions show slightly different behavior although\n", "obtaining similar performance for our simple network, it becomes\n", "apparent that the selection of the \"optimal\" activation function really\n", "depends on many factors, and is not the same for all possible networks." ] }, { "cell_type": "markdown", "id": "63455d14", "metadata": { "papermill": { "duration": 0.066848, "end_time": "2023-10-11T15:36:24.744111", "exception": false, "start_time": "2023-10-11T15:36:24.677263", "status": "completed" }, "tags": [] }, "source": [ "### Finding dead neurons in ReLU networks" ] }, { "cell_type": "markdown", "id": "8cd78fc5", "metadata": { "lines_to_next_cell": 2, "papermill": { "duration": 0.066954, "end_time": "2023-10-11T15:36:24.878533", "exception": false, "start_time": "2023-10-11T15:36:24.811579", "status": "completed" }, "tags": [] }, "source": [ "One known drawback of the ReLU activation is the occurrence of \"dead neurons\", i.e. neurons with no gradient for any training input.\n", "The issue of dead neurons is that as no gradient is provided for the layer, we cannot train the parameters of this neuron in the previous layer to obtain output values besides zero.\n", "For dead neurons to happen, the output value of a specific neuron of the linear layer before the ReLU has to be negative for all input images.\n", "Considering the large number of neurons we have in a neural network, it is not unlikely for this to happen.\n", "\n", "To get a better understanding of how much of a problem this is, and when we need to be careful, we will measure how many dead neurons different networks have.\n", "For this, we implement a function which runs the network on the whole\n", "training set and records whether a neuron is exactly 0 for all data\n", "points or not:" ] }, { "cell_type": "code", "execution_count": 22, "id": "56ebf8c2", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:36:25.026426Z", "iopub.status.busy": "2023-10-11T15:36:25.026183Z", "iopub.status.idle": "2023-10-11T15:36:25.034400Z", "shell.execute_reply": "2023-10-11T15:36:25.033661Z" }, "papermill": { "duration": 0.085995, "end_time": "2023-10-11T15:36:25.035878", "exception": false, "start_time": "2023-10-11T15:36:24.949883", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "@torch.no_grad()\n", "def measure_number_dead_neurons(net):\n", " \"\"\"Function to measure the number of dead neurons in a trained neural network.\n", "\n", " For each neuron, we create a boolean variable initially set to 1. If it has an activation unequals 0 at any time, we\n", " set this variable to 0. After running through the whole training set, only dead neurons will have a 1.\n", " \"\"\"\n", " neurons_dead = [\n", " torch.ones(layer.weight.shape[0], device=device, dtype=torch.bool)\n", " for layer in net.layers[:-1]\n", " if isinstance(layer, nn.Linear)\n", " ] # Same shapes as hidden size in BaseNetwork\n", "\n", " net.eval()\n", " for imgs, labels in tqdm(train_loader, leave=False): # Run through whole training set\n", " layer_index = 0\n", " imgs = imgs.to(device)\n", " imgs = imgs.view(imgs.size(0), -1)\n", " for layer in net.layers[:-1]:\n", " imgs = layer(imgs)\n", " if isinstance(layer, ActivationFunction):\n", " # Are all activations == 0 in the batch, and we did not record the opposite in the last batches?\n", " neurons_dead[layer_index] = torch.logical_and(neurons_dead[layer_index], (imgs == 0).all(dim=0))\n", " layer_index += 1\n", " number_neurons_dead = [t.sum().item() for t in neurons_dead]\n", " print(\"Number of dead neurons:\", number_neurons_dead)\n", " print(\n", " \"In percentage:\",\n", " \", \".join(\n", " [f\"{(100.0 * num_dead / tens.shape[0]):4.2f}%\" for tens, num_dead in zip(neurons_dead, number_neurons_dead)]\n", " ),\n", " )" ] }, { "cell_type": "markdown", "id": "f639cd82", "metadata": { "papermill": { "duration": 0.068395, "end_time": "2023-10-11T15:36:25.189451", "exception": false, "start_time": "2023-10-11T15:36:25.121056", "status": "completed" }, "tags": [] }, "source": [ "First, we can measure the number of dead neurons for an untrained network:" ] }, { "cell_type": "code", "execution_count": 23, "id": "a549238b", "metadata": { "execution": { "iopub.execute_input": "2023-10-11T15:36:25.326196Z", "iopub.status.busy": "2023-10-11T15:36:25.326015Z", "iopub.status.idle": "2023-10-11T15:36:32.795089Z", "shell.execute_reply": "2023-10-11T15:36:32.794081Z" }, "papermill": { "duration": 7.543721, "end_time": "2023-10-11T15:36:32.800472", "exception": false, "start_time": "2023-10-11T15:36:25.256751", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "457e000c8998464dac7bc54d08b88d2e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/49 [00:00