{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "intro",
   "metadata": {},
   "source": [
    "# Activation Functions and Normalization\n",
    "\n",
    "Activation functions and normalization layers are critical components that enable deep neural networks to learn complex patterns and train stably.\n",
    "\n",
    "In this tutorial, you will learn:\n",
    "\n",
    "- 🎯 **Activation Functions** - ReLU, GELU, Sigmoid, Tanh and variants\n",
    "- 📊 **Batch Normalization** - Stabilizing training with BatchNorm\n",
    "- 📐 **Layer Normalization** - Alternative normalization for sequences\n",
    "- 🔲 **Group Normalization** - For small batch sizes\n",
    "- ⚖️ **When to use each** - Practical guidelines\n",
    "\n",
    "## Why Are These Important?\n",
    "\n",
    "**Activation Functions** introduce non-linearity, allowing networks to learn complex patterns  \n",
    "**Normalization Layers** stabilize training, accelerate convergence, and act as regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "imports",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:39.419797Z",
     "start_time": "2025-10-10T11:34:37.939252Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainstate\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "activation_intro",
   "metadata": {},
   "source": [
    "## 1. Activation Functions\n",
    "\n",
    "Activation functions determine the output of a neuron given its input.\n",
    "\n",
    "### ReLU Family\n",
    "\n",
    "#### Standard ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "relu",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:39.824998Z",
     "start_time": "2025-10-10T11:34:39.425573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u888/z7PN4cOHCz0HOvRK/r+lfv3110KHA7F3XlLUkGE6PElZhoVx7hTlQp24AYC38ray06qywbkzsqIm5304d4LmPESWc7lsPy9FdXzqPLSV83vnfM6dy1nn4+zSpUuBv68dszoPc1aWIcPy/82iFFbOF8fVQ4Y5cx5G1X7OlXbCV9Rne+rUqUUev3Yqu2rVqgseE9wbzcuBMhgzZoyjmZA2ibY3R9K7lhs2bDB3axs3bmxqVvVuuA4TMXbs2DzDapWGNpPS/epzWno3W2t59fk4vZOvd/n1d84dnRRGnwuy3ynVTkqcm6fZaScsdnrnOT093RyDdm6jtcvaOUpYWJiEhoaajkmcOzqx16QPHDjwgk318tNaA+fnrbQTEt2PM73zqzUjGoPeGW7ZsqUZmkOHNCmMDg2itSZaO1BUzXZ+WkvxwQcfmOFd9O/onWZ9rTYx0/OsTb5Ke2wAAM8tO60qG7Tm9f/+7/9kxIgR5nlfLRe1Jlnj1fJRy2U9Lufa29dee83UEmsZra269DlwfW1l0PJfn0fWVgtaRmvttLZcK0mTe61h1vJaWyPos8z6PmmZr7X8eu1REUPG6T610zmNU1tX6LnVlgN6vvXZavuQqK6kQ+VpDbseW/7O+/R/QH+v75d+lgICAkxc2tpBa7z79Onj8nhQufw0867kvwkAAADAg/Xt21d++uknM3/w4EHz/DaAwlHTDQAAAABABSHpBgAAAACggpB0AwAAAABQQXimGwAAAACACkJNNwAAAAAAFYSkGwAAAACAChIoPiYnJ0dOnDgh1atXN+MuAgBgJX3KKzk52YwRm3/sVuRFGQ4A8MQy3OeSbi2smzRpYnUYAADkcfToUWncuLHVYbg1ynAAgCeW4T6XdOvdcfuJCQ8PL/cd9/j4eKlbt65P1k5w/L57/Hrs+/btk8TEROnWrZsEBvrcV4lPv/+K43fd8SclJZlE0l4+oWiU4X8gfmsRv7WI31rEX/oy3OeulO3N0bSwdkWBnZ6ebvbjiR+48uL4fff49djDwsLk/Pnz5vh9Nen21fdfcfyuP36aS18YZfgfiN9axG8t4rcW8Ze+DPe8swQAAAAAgIcg6QYAAAAAoIKQdAMAAAAAUEF870HMEsrOzpasrKwLPg+g2+gzAZ74PEN5Xej4g4KCJCAgwJLYUPH0OW5ffJYbAAAAKA2umAsZay02NlYSEhJKtK0mnjo2my92gFOS469Ro4ZERkb65PnxZnqTJSoqSuLi4nzyhhMAAABQUiTd+dgT7nr16knVqlWLTRY16dTem7W2zxeTyuKOX3+XlpZmkjLVoEEDi6IEAAAAAOuQdOdrUm5PuGvXrn3B7Um6iz/+KlWqmJ+aeOs5pak5AAAAAKtk59hk3YHTsu/YGWmVEiA9o+pIgH/F53GWtgt99dVXpUuXLo7xNnv16iXffPNNsa9ZvHixtGvXTkJDQ6Vz587y9ddfuywe+zPcWsMN17Cfyws9Hw/Poo8VHDlyRGJiYsw8AO8ybdo0ufjii6V69ermpunQoUNlz549F3zdhcpovVk7ceJE0/pJb8z2799f9u7dW4FHAgBArmXbY+Sy53+Qv7y5XiYuO2h+6rKu9+qku3HjxvLcc8/Jxo0bZcOGDXLllVfKkCFDZMeOHYVuv3r1arntttvknnvukc2bN5uLAJ22b9/u0rh8sda6onAuvZd2oJeRkWF1GAAqwE8//SQPPvigrF27VpYvX25unF5zzTWSmppa5GtKUkZPnz5dXnrpJZk3b56sW7dOqlWrJgMGDDDfJwAAVBRNrB94f5PEJOYtb2IT0836ik68LU26Bw8eLNdee620bt1a2rRpI88884yEhYWZQr4wL774ogwcOFDGjRsn7du3l6lTp0r37t3llVdeqfTYAQC+643/HpD9p86Jt1q2bJncdddd0rFjR4mOjpYFCxaY1i16k7woFyqjtZZ7zpw58uSTT5ob7NrS7d1335UTJ07I0qVLK/HoAAC+1qR8yhc7xVbI7+zr9Pe6ndc/063PU2uzNL2Lrs3MC7NmzRoZO3ZsnnV6h7y4wlpr4pxr45KSksxPbRKbv1msLutFgX0qCft2Jd3e21zo+O3nsrDz7ensnxdvO66ScH4/vfG9LQlffv99/fhX7omXad/skeAAP3l8UI7c1btFufbnCecwMTHR/KxVq1aR21yojD548KDprFSblNtFRERIz549zWtvvfXWcpXhvvYZJn5rEb+1iN9anhb/ugOnC9RwO9MsRn+/7sApuTTqwv16OSvpObA86d62bZtJsrVpmdZyL1myRDp06FDotlpY169fP886Xdb1xT2XNmXKlALr4+PjCzRn0+ZzeuK0czCdLkQ/bHqzwB2aUWtzvvfee8/Ma8dm2nT/xhtvlMmTJ5tn6y7k0KFDprXB+vXrpWvXrgWaGV599dWmQzQdAsz5+LWVwj/+8Q956KGHCt2vnkc9p6dPnzbjdnsTPS69ENXz4GvDZtmP3d5DvS+O1+3L778vH3/iufMybnHuI1CZ2TZJS0lxjNJQVjrsoru/12PGjJE+ffpIp06ditzuQmW0/WdpyvHSlOG+9hkmfmsRv7WI31qeFv++Y2dKuF28RIXl5nauLsMtv1Ju27atbNmyxbxxH3/8sYwcOdIkeUUl3qU1fvz4PHfe9S55kyZNpG7duqbzNmdagOuJ0wSiNEmEOyST+oHXZn3z5883Nw+0CaA2DdQew59//vkLvt5+vIUdu73X8cJ+pzcb9PdFnS9dr7Fpb/AlSf497QtHj18/S57whePqY9ee/vX4tZMlX026ffX999Xj14uLKR9ukdNpuTdlL20WLn+7qkO5R2Zw9+9GfbZbn8tetWpVpf/t0pThvvYZJn5rEb+1iN9anhZ/qxQtpw9eeLvGdaVevdoVUoZbfqUcHBwsrVq1MvM9evSQ//3vf+a5sNdee63AtpGRkXLy5Mk863RZ1xclJCTETPnpByT/h0SX9QNkn0pyAWbfzuqabqXHaR8Pu2nTpvL+++/L999/b2LTfw5Nvl9//XVTo6C12hMmTJCbb77ZbO98HPmPpajfOTcpL+r47a8p7Hx7A28+tguxH7OvHr+vv/++ePxLNh+Tb7bn1sjWqBIk/766mUm4y3v87nz+Ro8eLV9++aX8/PPPpgVVcS5URtt/6jp7WWVfzt/CqixluC9+honfWsRvLeK3lifF3zOqjjSICDWdphX2QKxmMZERoWY7/1IOH1bS43e7s6TJYVE9Imsz9BUrVuRZp72qFvUMuC/TWgntSVZvatib6GmHNdpjrPYO//DDD8sdd9xhWhUAZaHJBmOvw1ccTzgnEz/7Y2SNp4d2lLphud+v3khvqmrCrY98/fDDD9KixYWfW79QGa370MTbeRutudZezCnHAQAVRcfhnjQ4txV1/pTavqy/r8jxui2t6dZmY4MGDTK1stqs+4MPPpCVK1fKt99+a34/YsQIadSokUkYlT43fMUVV8jMmTPluuuuk48++sgMNaa1txVp8MurJD658BsBNrGJX4G3zzXqVg+RL/5xWYm319oIfS5en6PWGxd650V7jdX5Z5991tR62y9soqKiTFNBbVGg5xQoDf1stWzZ0jzL6gl3OIHyyMmxybjFWyU5PbdZ+dCuDeXazg3K/Sy3uzcp1zL5s88+M2N125+51o7PdHztspTRWiuiz4Y//fTTpj8QTcK1xVXDhg3N0GIAAFSUgZ0ayKt3dJdJn++Qk0l/5HVaw60Jt/6+IlmadOsFixbaMTExpiDX4UM04dZOu5QOT+J8Qd+7d29zEaDDjTzxxBOm0NZeUYvr2MUVNOGOTXL/MUT79esnr776qukBfvbs2eY525tuusnUbGuHV/bzapeZmSndunWzLF4A8ARvrz4kq/efNvPaPG3KkIotc9yBliWqb9++eda//fbbpr+QspbRjz32mCmj7rvvPtMvxGWXXWaGJ3P359oBAJ5vYKcG0qdVHek8+TuzPH9kD7mibf0KreF2i6T7rbfeKvb3Wuud37Bhw8xUmbTGuSgVXdNdGtWqVXM8H68dqunYqnqO7Rc8X331lamVcFbYs3L52Tur0c7unHsvV3rRpDdMAMAb7T2ZLM8v2+1YnjEsWiKqBHnMMCllVZJhMMtSRmtt91NPPWUmAAAqm3OCfUmLWpWScLtFR2qeoKgm3npRok25tUbZHTpSc6a1D1rToL2+/vbbbya51lqJsjQl19oK3Z/2iN6sWTPH+gMHDphEXDtlg2/RhOPo0aNy9uxZqVNHO52giTm8T+b5HBmzcIv5qe7u09zcIQcAACgNkm4vprUN48aNM89tP/roo6bzNE2WtDmfJsu//PKLqcXWYdrs9uzZU2A/HTt2lFGjRskjjzxibjB07tzZJFz/+te/pGfPnqZJIXzPuXPnyj1OLuDOXlqxV3acSDLzreqFyb8GtrM6JAAA4IFIur2YJsja++z06dPl4MGDZiw97fBGa6i1mXj37t1NbbizW2+9tcB+NMHWYdyee+45k2gfPnzY9EDbv39/mTJlitvV8gNAeW08fFb+s3KfmQ/095M5w7tKaBC99QMAgNIj6fYSCxYsKHT9448/biZ7z7I6FaZ58+YXfIZv8uTJZsrfvB4AvElqxnl5ZNEWyfn9K3FM/9bSqRF9VwAAgLLhQUwAAJw88/UuOXQ6zcx3b1pD7r+ipdUhAQAAD0bSDQDA737cHScfrDti5qsEBcisW7pKYABFJQAAKDuuJAAAEJEzqZky7uNfHctP/rm9NK9TzdKYAACA5yPpBlAm2oEeQ4XBW2gfFU98uk1OpWSY5X5t68pfLmlqdVgAAMAL0JEagFLTZFvHb4+LiyPxhlf4dNNxWbYj1szXrBokz9/chZEZAACAS3C1XAgdyxquwbkE4O6OnU2TyZ/vcCxPu7Gz1KseamlMAADAe1DT7SQ4ONjU2p04ccKMaa3LxdV02IfM0vGwfbFGpLjj199lZmZKfHy8Oad6LgHA3eTk2OTRxVslOSN3+MMbuzeSgZ0aWB0WAADwIiTdTjQ5bNGihcTExJjE+0I0sdSaXH2drybdFzr+qlWrStOmTWmC7IXv/fHjx+XMmTPmBhXgqeb/clDWHjhj5hvVqCKTr+9odUgAAMDLkHTnozWymiRqDW52dnax22rCefr0aaldu7ZPJpUXOv6AgACfbQXgC0l3amqqnDt3zswDnmhPbLJM/3aPmdevqRnDoiU8NMjqsAAAgJch6S6EJolBQUFmulDSqduEhob6bNLty8cPwHNlns+RMQu3mJ/qnj4tpFfL2laHBQAAvBCZEgDA58z5/jfZFZNk5tvUD5NHB7S1OiQAAOClSLoBAD5lw6EzMu+n/WY+KMBPZt3SVUKDAqwOCwAAeCmSbgCAz0jJOC9jF22VnN+7IhjTv410ahRhdVgAAMCLkXQDAHzGM1/tlCNn0sx8j2Y15W9/irI6JAAA4OVIugEAPuH7nSflw/VHzXzV4ACZdUu0BAZQDAIAgIpF7+UASk17q2/Tpo3ExcXRcz08wumUDHn8018dyxP+3EGa1a5maUwAAMA3cLUMAPBqOpb8+E+3yamUTLN8Vbt6cuvFTawOCwAA+AiSbgCAV/t44zH5budJM1+rWrBMu6mz+Pn5WR0WAADwESTdAMpUc3jixAmJj48384C7OnomTaZ8sdOx/OwNnaVe9VBLYwIAAL6FpBtAqWminZKSIqmpqSTdcFvZOTZ5ZPFWM0yYurlHYxnYKdLqsDzCzz//LIMHD5aGDRuaVgFLly4tdvu77rrLbJd/6tixo2ObyZMnF/h9u3btKuFoAACwFkk3AMArvbXqgKw/eMbMN6pRRSYN7mB1SB5Db6hFR0fL3LlzS7T9iy++KDExMY7p6NGjUqtWLRk2bFie7TQJd95u1apVFXQEAAC4D3ovBwB4nd2xSTLj29/MvD6+PfOWaKkeGmR1WB5j0KBBZiqpiIgIM9lpzfjZs2fl7rvvzrNdYGCgREbS2gAA4Fuo6QYAeJWM89ky5qMtkpmdY5bvuzxKLo2qbXVYPuWtt96S/v37S7NmzfKs37t3r2myHhUVJbfffrscOXLEshgBAKgs1HQDALzK7OV7ZXdssplvF1ldxl7TxuqQfIp2svjNN9/IBx98kGd9z549ZcGCBdK2bVvTtHzKlCly+eWXy/bt26V69eqF7isjI8NMdklJSeZnTk6OmcpDX699UpR3P1YhfmsRv7WI31qeHH+OU8yuKktKgqQbAOA19Bnu137eb+aDAvxk1i1dJSQwwOqwfMo777wjNWrUkKFDh+ZZ79xcvUuXLiYJ15rwRYsWyT333FPovqZNm2aS8/x05IT09PRyXyglJiaaC0d/f89r+Ef81iJ+axG/tTw5/nNZ2XnKkrSQ8j16lpyce5P/Qki6AQBeQXspf2TxFrF3qD/26rbSoWG41WH5FL0Amz9/vtx5550SHBxc7LaamLdp00b27dtX5Dbjx4+XsWPH5qnpbtKkidStW1fCw8PLfdGoPajrvjztolERv7WI31rEby1Pjj8tM3dEE6Xxh4UWX1ZdSGhoyYYhJekGUGr6BduqVSuJi4vzuC9beK+pX+yUo2fOmfmLm9eU+/4UZXVIPuenn34ySXRRNdfOdNjB/fv3mwS9KCEhIWbKT793XPHdoxeNrtqXFYjfWsRvLeK3lqfG7+8UryviL+nrPessAXAbnvhFC++1fOdJWbjhqJmvFhxgmpUH+PtZHZbH0oR4y5YtZlIHDx408/aOz7QGesSIEYV2oKbNxjt16lTgd48++qhJyg8dOiSrV6+WG264QQICAuS2226rhCMCAMA61HQDADzaqZQMefyTXx3LEwd3kCa1qloak6fbsGGD9OvXz7Fsb+I9cuRI0xmadoSWv+dxfb7vk08+MWN2F+bYsWMmwT59+rRp0nfZZZfJ2rVrzTwAAN6MpBtAmZ7bjI2NlTNnznDBDMs/i+M/3SanUzPNcv/29eSWi5pYHZbH69u3rzm3RdHEOz8dpzstLa3I13z00Ucuiw8AAE9C21AApaYX49qhkTZBLe7CHKhoizccM03LVe1qwTLtxi7mOTMAAAB3YWnSrUOBXHzxxWZ8znr16pnhRfbs2VPsa/Tuul5QOU8l7TUOAOA9jp5Jkylf7HAsT7uxs9StXrDTLQAAAJ9NurVDlQcffNA807V8+XLJysqSa665RlJTU4t9nQ4Tos+T2afDhw9XWswAAOtl59jkkUVbJTUzd7zNWy5qLNd0jLQ6LAAAAPd6pnvZsmUFarG1xnvjxo3ypz/9qcjXae12ZCQXVwDgq9787wFZf+iMmW9cs4pM+HMHq0MCAABw/47UtOdTVatWrWK30+dImzVrZgZm7969uzz77LPSsWPHQrfNyMgwk50+h6r0tTqVh75en2ct7348Fcfvu8fv/P/jiv8lT+TL77/Vx78rJklmfpf7KJI+vj3j5i5mmLDKjMWVx++rnyEAAHyF2yTdetExZswY6dOnT6Hje9q1bdtW5s+fL126dDFJ+owZM6R3796yY8cOady4caHPjU+ZMqXA+vj4eElPTy93zBqDXnj54njFHL/vHr/92LWn4ri4OAkMdJuvkkrjy++/lcefeT5H/vnRbsnMzu3A7/bu9aV5tfPmc+ipx5+cnOyyuAAAgPtxmytlfbZ7+/btsmrVqmK369Wrl5nsNOFu3769vPbaazJ16tQC248fP94xvqi9prtJkyZmmCN9Nry8F13a1F335asX3Ry/bx6/HntCQoI5fn0kxFeTbl99/608/ue+2S37T50z8+0iq8u/h0RLSGCAePLx0xkoAADezS2ulEePHi1ffvml/Pzzz4XWVhcnKChIunXrJvv27Sv09yEhIWbKTy+SXHGhqBddrtqXJ+L4ffP49Xhbt27tqOX2teP39fffquNfd+C0vLHqoJkPDvCXObd2lSrBQeLpx++rnx8AAHyFpSW9NsvThHvJkiXyww8/SIsWLUq9j+zsbNm2bZs0aNCgQmIEULiAgAAzAZUhOT1Lxi7aKvZh4R8d0EbaRZavtRIAAIDX13Rrk/IPPvhAPvvsMzNWd2xsrFkfEREhVapUMfMjRoyQRo0amWez1VNPPSWXXnqptGrVyjRvfeGFF8yQYaNGjbLyUAAAFeipL3bK8YTcZuWXtKgl91wWZXVIAAAA7p90v/rqq+Zn375986x/++235a677jLzR44cydP07uzZs3LvvfeaBL1mzZrSo0cPWb16tXTowHAxQGW2Ujl58qScOXPGPNMKVKRvd8TK4o3HzHxYSKDMHBYtAf5+VocFAADg/km3XrhfyMqVK/Msz54920wArP3f1Z6btdflkvwfA2UVn5wh4z/d5lieNLiDNKlV1dKYAAAASoPeWwAAbklv6Dz+ya9yJjXTLF/Tob7c3KN0nW0CAABYjaQbAOCWFv7vqKzYnTv+dp2wYJl2Y2fTYzgAAIAnIekGALidw6dT5akvdzqWn7uxi9QOKzj8IwAAgLsj6QYAuJXsHJs8smirpGVmm+XhFzWR/h3qWx0WAABAmZB0AwDcyms/75cNh8+a+Sa1qsiEwYxOAQAAPBdJNwDAbew4kSizl/9m5nVUsNm3dDXDhAEAAHgqrmQAlJp2ZtWiRQuJi4ujYyu4THpWtjy8cItkZecOQ/e3K1rKRc1rWR0WAABAuVDTDaDUNNEOCgoyE0k3XGXmd3vkt5MpZr59g3B5uH8bq0MCAAAoN5JuAIDl1uw/LW+uOmjmgwP8Zc7wrhIcSBEFAAA8H1c0AErNZrNJfHy8nD171swD5ZGUniWPLt4q9o/SuAFtpW1kdavDAgAAcAmSbgClpom2JtyJiYkk3Si3KZ/vlOMJ58z8pVG15J7LWlgdEgAAgMuQdAMALLNse4x8sumYma8eEigzhkWLv3ZbDgAA4CVIugEAlohLTpfxn25zLE++vqM0rlnV0piQ6+eff5bBgwdLw4YNTWeJS5cuLXb7lStXmu3yT7GxsXm2mzt3rjRv3lxCQ0OlZ8+esn79+go+EgAArEfSDQCodPpYwuOfbJOzaVlmeWDHSLmxeyOrw8LvUlNTJTo62iTJpbFnzx6JiYlxTPXq1XP8buHChTJ27FiZNGmSbNq0yex/wIABZuhBAAC8GeN0AwAq3Yfrj8oPu3OTrTphIfLsjZ0Zfs6NDBo0yEylpUl2jRo1Cv3drFmz5N5775W7777bLM+bN0+++uormT9/vjz++OPljhkAAHdF0g0AqFSHTqXK1C93Opan39xZalULtjQmuEbXrl0lIyNDOnXqJJMnT5Y+ffqY9ZmZmbJx40YZP368Y1t/f3/p37+/rFmzpsj96b50sktKSjI/c3JyzFQe+nptcVHe/ViF+K1F/NYifmt5cvw5TjG7qiwpCZJuAEClOZ+dI2MXbZFzWdlm+bZLmsqV7epbHRbKqUGDBqbm+qKLLjJJ8ptvvil9+/aVdevWSffu3eXUqVOSnZ0t9evnfa91effu3UXud9q0aTJlypQC63XIwvT09HJfKNlHYNAbAJ6G+K1F/NYifmt5cvznfr/+sJclaSFB5dpfcnJyibYj6QZQatoMuFmzZlK1alWaBKNU5v20XzYdSTDzzWpXlSeva291SHCBtm3bmsmud+/esn//fpk9e7a89957Zd6v1ozrc+DONd1NmjSRunXrSnh4eLkvGvX7S/flaReNivitRfzWIn5reXL8aZnnHfMaf1ho+VraacegJUHSDaDU9Is2JCREgoODSbpRYtuPJ8qc7/eaeR0VbNYtXaVaCMWQt7rkkktk1apVZr5OnToSEBAgJ0+ezLONLkdGRha5D/2e0Sk/vchzxYWefn+5al9WIH5rEb+1iN9anhq/v1O8roi/pK/3rLMEAPBI6VnZ8vDCLXI+x2aWH+jbUno0q2l1WKhAW7ZsMc3Old6g69Gjh6xYsSJPTYku9+rVy8IoAQCoeFQxACg1fYZHn9FMSEgwTXOAC5m+bI/sjUsx8x0bhstDV7WxOiQUIyUlRfbt2+dYPnjwoEmia9WqJU2bNjXNvo8fPy7vvvuu+f2cOXOkRYsW0rFjR/OstT7T/cMPP8h3333n2Ic2Ex85cqR57ltrwfU1OjSZvTdzAAC8FUk3gDIl3WfOnDFJt84DxVm975TM/+WgmQ8O9Jc5w7uan3BfGzZskH79+jmW7c9Va9K8YMECMwb3kSNHHL/X3skfeeQRk4hrXw9dunSR77//Ps8+hg8fbjqtmThxosTGxpqezpctW1agczUAALwNSTcAoMIknsuSRxdvdSz/a2A7aV2/uqUx4cK05/Hibqhp4u3sscceM9OFjB492kwAAPgSqhoAABVm8uc75ERi7tBOvVvWlrt7N7c6JAAAgEpF0g0AqBBfb4uRJZuPm/nqoYHywrBo8dduywEAAHwISTcAwOXiktLliSXbHMtPDekojWpUsTQmAAAAK5B0AwBcSp8FfuyTXyUhLcssX9s5UoZ2bWR1WAAAAJYg6QYAuNT/rTsiK/fEm/l61UPkmaGdxc+PZuUAAMA30Xs5gFLTBErH6q1SpQrJFPI4eCpVnvlql2P5+Zu7SM1qwZbGBAAAYCVqugGUmibaoaGhEhISQtINh/PZOfLwwi1yLivbLN/es6n0a1vP6rAAAAAsRdINAHCJV1fuly1HE8x889pV5d/Xtbc6JAAAAMuRdAMoU0dZZ86ckcTERDMPbDuWKC+u2GvmdVSwWcO7StVgnmACAADgighAqWmiferUKUlISCDphqRnZcuYhZvlfE7uZ+HBfq2ke9OaVocFAADgFqjpBgCUy3Pf7Jb98almvnOjCPnnVa2tDgkAAMBtkHQDAMps1b5TsmD1ITMfEugvs4dHS1AARQsAAICdpVdG06ZNk4svvliqV68u9erVk6FDh8qePXsu+LrFixdLu3btTO/JnTt3lq+//rpS4gUA/CEp/bw89vGvjuXHB7WTVvWqWxoTAACAu7E06f7pp5/kwQcflLVr18ry5cslKytLrrnmGklNzW2mWJjVq1fLbbfdJvfcc49s3rzZJOo6bd++vVJjBwBfN+PHoxKblGHm+7SqLSN7Nbc6JAAAALdjaUdqy5Yty7O8YMECU+O9ceNG+dOf/lToa1588UUZOHCgjBs3zixPnTrVJOyvvPKKzJs3r1LiBgBf9+WvMfLdnjNmvnpooLxwc7T4a7flAAAAcN/ey3X4IVWrVq0it1mzZo2MHTs2z7oBAwbI0qVLC90+IyPDTHZJSUnmZ05OjpnKQ1+vPTeXdz+eiuP33eN3/v9xxf+SJ/Ll9z82MV0mfPZH66Knru8okeEhPnUuXPn++9J5AwDAF7lN0q0XHWPGjJE+ffpIp06ditwuNjZW6tevn2edLuv6op4bnzJlSoH18fHxkp6eXu6Y7eMU+/v7XsdBHL/vHr8es/apkJmZaf6XAgICxNf46vuvxzt26T5JPHfeLF/VuoZc2iBA4uLixJe48v1PTk52WVwAAMD9uE3Src9263PZq1atcul+x48fn6dmXGu6mzRpInXr1pXw8PByX3T5+fmZffnSRbcdx8/xa8Lty8fvi+//e2sPy9rDuS2G6lQLkueHdZNaYaHia1z5/usNLAAA4L3cIukePXq0fPnll/Lzzz9L48aNi902MjJSTp48mWedLuv6woSEhJgpP71IcsWFsl50uWpfnojj5/g5ft85/gPxKTLtm92O5SevbmYSbl85/op6/331/AEA4CssLem1WZ4m3EuWLJEffvhBWrRoccHX9OrVS1asWJFnnXakpusBVN7/bkJCgmkWq/Pwfuezc+ThRVslPSv3+eM7ejaVS5tHWB0WAACA2/O3ukn5+++/Lx988IEZq1ufy9bp3Llzjm1GjBhhmojbPfTQQ6bX85kzZ8ru3btl8uTJsmHDBpO8A6gcmmjrM7ynT58m6fYRc3/cL1uPJpj5qDrV5PFBba0OCQAAwCNYmnS/+uqrpiOavn37SoMGDRzTwoULHdscOXJEYmJiHMu9e/c2Sfrrr78u0dHR8vHHH5uey4vrfA0AUHaabL/0w14zH+DvJ7OGd5WqwW7xdBIAAIDbs/SqqSQ1ZCtXriywbtiwYWYCAFSsc5nZ8vCiLZKdk/t9PbpfK+napAbDXAEAAJQQvbcAAIr0/LLdciA+1cx3aRwho69sZXVIqATasengwYOlYcOGpsM4bVFWnE8//VSuvvpqx8gg2s/Kt99+m2cbfRxM9+U8tWvXroKPBAAA65F0AwAK9d+98bJg9SEzHxrkL7OHd5WgAIoNX5Cammoe4Zo7d26Jk3RNur/++mvZuHGj9OvXzyTtmzdvzrNdx44dzSNj9snVw4QCAOCOeCgPAFBAQlqmPLp4q2N5/KD20rJumKUxofIMGjTITCU1Z86cPMvPPvusfPbZZ/LFF19It27dHOsDAwOLHOITAABvRZUFAKCACZ/tkJNJGWb+8tZ15M5Lm1kdEjyIPvOvQwrWqlUrz/q9e/eaJutRUVFy++23m85SAQDwdtR0Ayg1fRZTL5yDg4PNPLzLZ1uOyxdbT5j58NBAeeHmaPH3531Gyc2YMUNSUlLklltucazr2bOnLFiwQNq2bWualk+ZMkUuv/xy2b59uxk2tDAZGRlmsktKSnIk9eXtzE9frx26emqngMRvLeK3FvFby5Pjz3GK2VVlSUmQdAMoNU20w8LCJC0tjaTby8QknpMJS7c7lp++obNERoRaGhM8iw7rqQm1Ni+vV6+eY71zc/UuXbqYJLxZs2ayaNEiueeeewrd17Rp08y+8ouPj5f09PRyXyjpsKV64ejv73kN/4jfWsRvLeK3lifHfy4rO09ZkhYSVK79aauukiDpBgAYOTk2eezjXyUp/bxZHhzdUK6Pbmh1WPAgH330kYwaNUoWL14s/fv3L3bbGjVqSJs2bWTfvn1FbjN+/HgZO3ZsnpruJk2aOHpJL+9Fo9401H152kWjIn5rEb+1iN9anhx/WmbuNY7S+MNCg8u1v9DQklVMkHQDKDW9s6l3OLX5qH5hwTu8t/aw/HfvKTNfPzxEpg7paHVI8CAffvih/PWvfzWJ93XXXXfB7fX7Y//+/XLnnXcWuU1ISIiZ8tOLPFdc6OlFo6v2ZQXitxbxW4v4reWp8fs7xeuK+Ev6es86SwDcJuk+efKknDp1yszD8+2LS5Fnv97lWNbnuGtULd/dX3guTYi3bNliJnXw4EEzb+/4TGugR4wYkadJuS7PnDnTNBuPjY01k96cs3v00Uflp59+kkOHDsnq1avlhhtukICAALntttssOEIAACoPSTcA+Lis7BwZu2iLZJzP7QxkZK9m8qc2tGDwZRs2bDBDfdmH+9Im3jo/ceJEs6wdoTn3PP7666/L+fPn5cEHH5QGDRo4poceesixzbFjx0yCrR2paQdrtWvXlrVr19JaBgDg9WheDgA+7pUf9smvx3JrJKPqVpPHB7W3OiRYrG/fvsW2YtFeyJ2tXLnygvvUZucAAPgiaroBwIdtOZogr/yY25FVgL+fzL6lq1QJDrA6LAAAAK9B0g0APupcZraMXbhFsnNyazT/eWVriW5Sw+qwAAAAvApJNwD4qGnf7JIDp1LNvCbbD/ZraXVIAAAAXoekGwB80E+/xcu7aw6b+dAgf5l9S7QEBlAkAAAAuBodqQEo09iM2jNxUFCQmYdnOZuaKeMWb3Us//va9hJVN8zSmAAAALwVSTeAUtNEu3r16nLu3DmSbg+jPVI/+dl2iUvOMMs6NNgdlzazOiwAAACvRVtCAPAhn289IV/9GmPma1QNkhdu7sKNEwAAAHdLuqOiouT06dMF1ickJJjfAfD+2tLk5GRJTU0tdixfuJcTCefkyaXbHctPD+0k9cNDLY0JrkX5DACAlyTdhw4dkuzs7ALrMzIy5Pjx466IC4Ab00Q7JiZG4uPjSbo9RE6OTcZ9vFWS08+b5aFdG8qfuzS0Oiy4GOUzAAAe/kz3559/7pj/9ttvJSIiwrGshfyKFSukefPmro0QAFBuC1Yfkl/25daANogIlSlDOlkdElyI8hkAAC9JuocOHWp+6vN/I0eOzPM77cVYC/SZM2e6NkIAQLnsi0uW55ftdizPGBYtEVWCLI0JrkX5DACAlyTdOTk55meLFi3kf//7n9SpU6ei4gIAuEDm+RwZs3CLZJzP/f6+u09z6dOK725vQ/kMAICXDRl28OBB10cCAHC5l3/YK9uPJ5n5VvXC5F8D21kdEioQ5TMAAF6SdD/11FPF/n7ixIlljQcA4CKbjpyVuT/uM/OB/n4y+5auEhoUYHVYqECUzwAAeEnSvWTJkjzLWVlZ5u56YGCgtGzZkkIdACyWlnlexi7cIjm/dy7/0FWtpXPjPzrXgneifAYAwEuS7s2bNxdYl5SUJHfddZfccMMNrogLgBvTzprq169vLuR1Hu7nma92yaHTaWa+a5Ma8kDfllaHhEpA+QwAgJeM012Y8PBwmTJlikyYMMFVuwTgpjTR1iGJwsLCSLrd0I974uT/1h0x81WCAmT28K4SGOCyr3t4GMpnAACs5dKrsMTERDMBAKxxJjVTHvv4V8fyv69rLy3qVLM0JliP8hkAAA9rXv7SSy/lWbbZbBITEyPvvfeeDBo0yFWxAXBT+j+fkpIiaWlpZh7uQd+LJ5duk/jkDLPct21dub1nU6vDQiWifAYAwEuS7tmzZ+dZ9vf3l7p168rIkSNl/PjxrooNgJvSC/kTJ05IQkKCNG1KUuculm45Ll9vizXzNaoGyfSbutD838dQPgMA4H4YpxsAvMDxhHMycekOx/KzN3SWeuGhlsaEykf5DACAFz7TffToUTMBAKyRk2OTRxdtleSM82b5hm6N5NrODawOCxajfAYAwIOT7vPnz5teULX34ubNm5tJ55988kkzJigAoPLM/+WgrDlw2sw3jAiVKUM6Wh0SLEL5DACAlzQv/8c//iGffvqpTJ8+XXr16mXWrVmzRiZPniynT5+WV1991dVxAgAK8dvJZJn+7R7H8oxh0RIeGmRpTLAO5TMAAF5S0/3BBx/IggUL5G9/+5t06dLFTDr/1ltvmd+V1M8//yyDBw+Whg0bms5+li5dWuz2K1euNNvln2JjczsOAgBfknk+R8Z8tMX8VPdc1kJ6t6pjdViwkFXls72M7t69u4SEhEirVq1MHPnNnTvX1L6HhoZKz549Zf369aU+RgDwZNk5Nll74LR8t/uM+anL8H5lSrq1QNVCM78WLVpIcHBwifeTmpoq0dHRphAujT179pghUOxTvXr1SvV6APAGL674TXbGJJn51vXCZNyAtlaHBItZVT5rB27XXXed9OvXT7Zs2SJjxoyRUaNGybfffuvYZuHChTJ27FiZNGmSbNq0yex/wIABEhcXV+K4AMCTLdseI5c9/4P85c31MnHZQfNTl3U9vFuZku7Ro0fL1KlTJSMjdyxYpfPPPPOM+V1J6ZihTz/9tNxwww2l+vuaZEdGRjomHRIFQOXRmi/9P6xduzZDUllk4+Ez8urK/WY+0N9PZg/vKqFBAVaHBYtZVT7PmzfPJPYzZ86U9u3bm79188035xnCbNasWXLvvffK3XffLR06dDCvqVq1qsyfP7+URwkAnkcT6wfe3yQxiel51scmppv1JN7erUzPdG/evFlWrFghjRs3Nneq1datWyUzM1OuuuoqufHGGx3b6rNlrta1a1dzEdGpUyfznFqfPn1c/jcAFE0T7Ro1apj/eZLuypeacV4eXrhV7C3SxvRvLZ0aRVgdFtyAVeWzPjfev3//POu0FltrvJX+/Y0bN+YZK1xvmOtr9LUA4M20CfmUL3ZKYQ3JdZ1eSenvr+4QKQH+XFd5ozIl3XqxfdNNN+VZ16RJE6loDRo0MHfGL7roIpN0v/nmm9K3b19Zt26deY6sMLqd8x3/pKTcppg5OTlmKg99vc1mK/d+PBXHz/Fz/NYc/9Nf7pQjZ9LMfPemNeS+y1tUehy8/647fleeQ6vKZ+1bpX79+nnW6bKWuefOnZOzZ89KdnZ2odvs3r27yP1ShheN+K1F/NbytPjXHThdoIY7f+Ktv1934JRcGlVb3J2nnX9nzjG7qiypsKT77bffFiu0bdvWTHa9e/eW/fv3m+Zr7733XqGvmTZtmkyZMqXA+vj4eElPL/rDX9KTnJiYaD50vtjEneP33ePXY9YLab0A1vMQEOB7zZqtev9/OZgoH/4vd+zlKkH+Mv7KRnLm9CmpbL78+Xf18ScnJ7ssLqvK54pCGV404rcW8VvL0+Lfd+xMCbeLl6iwbHF3nnb+nZ3Lys5TlqSFBFVKGV6mpPvKK680zdL0jrozvQAfOnSo/PDDD1JZLrnkElm1alWRv9embNpxi3OMete/bt26Eh4eXu4PnDat1X152gfOFTh+3z1+PfbffvvNXPTq8QcGlumrxKNZ8f6fTsmQaSu2OZafvK699GjTVKzgy59/Vx+/9uTtKlaVz9q/ysmTJ/Os02UtZ6tUqWJuzOlU2Db62qJQhheN+K1F/NbytPhbpWjlxMELb9e4rtSr5xk13Z50/p2lZZ53zGv8YaEl72S0PGV4ma6UdVgQfT4rP70A/+9//yuVSXtJ1WbnxfXkqlN++gFxxYdEP3Cu2pcn4vh99/jtx+yrx1/Z77/eTZ7w2U45lZL73Xtlu3ryl57NLH2m3pc//648fleeP6vKZx0T/Ouvv86zbvny5Y6xwrXn9B49epjnzTX5t1+06XJxHbxRhheP+K1F/NbypPh7RtWRBhGhptO0wp7r1pI8MiLUbOfvIc90e9L5d+Ycb2WW4aVKun/99VfH/M6dO/OMj63Pai1btkwaNWpU4v2lpKTIvn378gw5okl0rVq1pGnTpuYO9/Hjx+Xdd981v58zZ47pHbVjx47mAkKf6da79t99911pDgMAPM4nm47Lsh2537k1qwbJczd1phM7uE35fP/998srr7wijz32mPz1r381ZfOiRYvkq6++cuxDa6xHjhxp+mXRVmpapuvQZNqbOQB4M+0cbdLgDqaXci25nRNve0muv6cTNe8VWNpew/UiTydtwpafNiF7+eWXS7y/DRs2mDE97exNyLRQXrBggRmD+8iRI47f6937Rx55xBT0OsxIly5d5Pvvv8+zDwDwNsfOpsnkz3c4lqfd2FnqVXddk2R4PqvLZ70hrgn2ww8/LC+++KLpPV1vjGsP5nbDhw83z89NnDjR3BTQmPVmQP7O1QDAGw3s1EBevaO7TPp8h5xM+qODSK3h1oRbfw/vVaqkW+90axPHqKgoWb9+vWkHb6dNx3Tc3tJ0qKQ9j+v+iqIFuzO9g64TAPiKnBybPLp4q6Rk5D6DdFP3xhTMcLvy2f4aHbKsONqUvDTjhQOAN9Hyu0+rOtJ5cm4r3fkje8gVbetTw+0DSpV0N2vWzPz0xO7hAcATzf/loKw9kNvraaMaVWTS9R2sDgluiPIZADyDc4J9SYtaJNw+okwdqdmf4SrKiBEjyhoPAOB3e2KTZfqyPWZeH9+eeUu0hIeWb2gLeDfKZwAAvCTpfuihh/IsZ2VlSVpammnCps9aU6gD3k2fG61Tp47jGVK4Xub5HBmzcItkZufWXI66rIVcGuX+w4jAWpTPAAC4nzL1kX727Nk8k/ZyumfPHrnsssvkww8/dH2UANyKJtrai3FERARJdwWZ8/1vsismycy3rV9dHrmmrdUhwQNQPgMA4H5cNrBa69at5bnnnitwlx0AUDobDp2ReT/tN/NBAX4ya3i0hAaVvBMswBnlMwAA1nLpaOaBgYFy4sQJV+4SgBvSXo3T09MlIyOj2B6OUXraS/nYRVsl5/fT+vDVbaRjwwirw4KHo3wGAMDDnun+/PPP8yzrRbeO2fnKK69Inz59XBUbADel//M6Rm9CQoI0atTI6nC8ytNf7pQjZ9LM/EXNasrf/tTS6pDgQSifAQDwkqR76NCheZb1mU4dE/TKK6+UmTNnuio2APAp3+88KR/976iZrxYcILNu6cpQIigVymcAALwk6baPAxofH29+aoEOACi70ykZ8vinvzqWJ/y5gzStXdXSmOB5KJ8BAPCCZ7q1OemDDz5ohguKjIw0k86PHj3a/A4AUDraBHj8p9vkVEqmWe7fvp4Mv7iJ1WHBw1A+AwDgBTXdZ86ckV69esnx48fl9ttvl/bt25v1O3fulAULFsiKFStk9erVUrNmzYqKFwC8zscbj8l3O0+a+VrVgmXajV0Yig2lQvkMAICXJN1PPfWUBAcHy/79+6V+/foFfnfNNdeYn7Nnz3Z1nADglY6eSZMpX+x0LE+7sbPUrR5iaUzwPJTPAAB4SfPypUuXyowZMwoU6EqbsU2fPl2WLFniyvgAwGtl59jkkcVbzTBhaliPxjKgY6TVYcEDUT4DAOAlNd067EjHjh2L/H2nTp0kNjbWFXEBcGPa9LlWrVqOeZTNW6sOyPqDZ8x845pVZOLgDlaHBA9F+QwAgJfUdGuHLIcOHSry9wcPHnRciAPwXppo6/dBjRo1SLrLaFdMksz49jczr6dw5rBoqR4aZHVY8FCUzwAAeEnSPWDAAPn3v/8tmZm5Pew6y8jIkAkTJsjAgQNdGR8AeJ2M89ny8MItkpmdO7zTfZdHSc+o2laHBQ9G+QwAgBd1pHbRRRdJ69atzbAk7dq1M0Pd7Nq1S/7zn/+Ygv29996ruGgBuAX9v9f/d73A13mUzqzlv8nu2GQz3y6yuoy9po3VIcHDUT4DAOAlSXfjxo1lzZo18ve//13Gjx/vuNjW5qVXX321vPLKK9KkCWPLAt5O//cPHz5sxv5t2LCh1eF4FH2G+/WfD5j54AB/mT28q4QEBlgdFjwc5TMAAF6SdKsWLVrIN998I2fPnpW9e/eada1ateJZMQC4gOT0LBm7aIvYGwdoDXf7BuFWhwUvQfkMAICXJN12NWvWlEsuucS10QCAF5v65U45dvacmb+keS259/Ioq0OCF6J8BgDAgztSAwCUzXc7YmXRhmNmvlpwgMy8JVoC/On5HQAAwNuRdANABTuVkiHjP93mWJ40uKM0qVXV0pgAAABQOUi6AaACaYdWj3+yTU6n5g7ldHWH+jLsosZWhwUAAIBKQtINABVo0Yaj8v2uk2a+drVgmXZjZ9OjNAAAAHwDSTeAUtOkUTtrioiIIIEsxpHTafLUFzsdy8/d1EXqhIVYGhNQGnPnzpXmzZtLaGio9OzZU9avX1/ktn379jXfB/mn6667zrHNXXfdVeD3AwcOrKSjAQDAw3ovB+C79EK5bt26puk0SXfhsnNs8sjiLZKamW2Wh1/UxDQtBzzFwoULZezYsTJv3jyTcM+ZM0cGDBgge/bskXr16hXY/tNPP5XMzNzHKNTp06clOjpahg0blmc7TbLffvttx3JICDeiAADejZpuAKgAb/z3gPzv0Fkz36RWFZkwuIPVIQGlMmvWLLn33nvl7rvvlg4dOpjku2rVqjJ//vxCt9fxwCMjIx3T8uXLzfb5k25Nsp2301YzAAB4M2q6AZSa1nBnZWWZSeeR184TSTLzuz1mXhsCzLqlq4SF8HULz6E11hs3bpTx48c71vn7+0v//v1lzZo1JdrHW2+9JbfeeqtUq1Ytz/qVK1eamnJNtq+88kp5+umnpXbt2oXuIyMjw0x2SUlJ5mdOTo6ZykNfr99f5d2PVYjfWsRvLU+O3zlmV3yXWYHz/4eSvp6rQAClpl+0Bw8elISEBGnQoIHV4biV9KxseXjhFsnKzr0Zcf8VLeXi5rWsDgsolVOnTkl2drbUr5/3kQhd3r179wVfr89+b9++3STe+ZuW33jjjdKiRQvZv3+/PPHEEzJo0CCTyAcEBBTYz7Rp02TKlCkF1sfHx0t6erqU90IpMTHRfJ/pDQVPQ/zWIn5reXL857JyHzuzf5elhQSJp+H8/yE5OVlKgqQbAFxo1vLfZM/J3C/g9g3C5eH+bawOCah0mmx37txZLrnkkjzrtebbTn/fpUsXadmypan9vuqqqwrsR2va9bly55ruJk2amD4lwsPDy33RaO+fwtMuGhXxW4v4reXJ8adlnnfMa/xhocHiaTj/f9CORkuCpBsAXGTtgdPmWW4VHOAvc4Z3leBAzyqMAFWnTh1T83zyZO5wd3a6rM9hFyc1NVU++ugjeeqppy74d6Kioszf2rdvX6FJtz7/XVhHa3qR54oLPb1odNW+rED81iJ+a3lq/M7xemL8dpz/XCV9vWedJQBwU0npWfLIoq1if8T90QFtpG1kdavDAsokODhYevToIStWrMhTs6HLvXr1Kva1ixcvNs9h33HHHRf8O8eOHTO9nPOYCgDAm5F0A4ALTPl8pxxPOGfme7aoJaMui7I6JKBctFn3G2+8Ie+8847s2rVLHnjgAVOLrb2ZqxEjRuTpaM25afnQoUMLdI6WkpIi48aNk7Vr18qhQ4dMAj9kyBBp1aqVGYoMAABvRfNyACinZdtj5ZNNx8y89lI+85Zo8fdn/HJ4tuHDh5tOZiZOnCixsbHStWtXWbZsmaNztSNHjhRoVqdjeK9atUq+++67AvvT5uq//vqrSeK1E8aGDRvKNddcI1OnTmWsbgCAVyPpBoByiE/OkCeWbHMsT76+ozSuWdXSmABXGT16tJkKo52f5de2bdsihxGsUqWKfPvtty6PEQAAd2dp8/Kff/5ZBg8ebO5268P4S5cuveBrtJDv3r27uSuuTdIWLFhQKbEC+IP+v0ZEREj16tXNvK/S5GL8km1yJjXTLA/oWF9u6t7I6rAAAADgRixNuvXZsOjoaJk7d26Jttdxga+77jrp16+fbNmyRcaMGSOjRo3izjlQyTTR1iam+symLyfdn20/JT/sjjfzdcJC5NkbOvv0+QAAAICbNS8fNGiQmUpq3rx50qJFC5k5c6ZZbt++vXl2bPbs2XTCAqBSHT6dKi/+nPsct5p+c2epHcZzqQAAAPDg3svXrFkj/fv3z7NOk21dD6ByZWdnm8kXZefY5NHFv8q5rByzfNslTeTKdrmdSwEAAAAe25Ga9p5q7zXVTpeTkpLk3LlzppOW/HSsUJ3sdFv7eKM6lYe+Xp/pLO9+PBXH77vHr8e8d+9e8/9Ut25dCQz0qK+Scnt15X7ZeCTBzDepWUWeGNTO5z4Hvvz5d/Xx++o5BADAV3j9lfK0adNkypQpBdbrMCjp6enlvlBKTEw0F175h03xBRy/7x6//djT0tIkLi7Op5LuPXFpMvv738y8jgr276saS2riGUkV3+LLn39XH39ycrLL4gIAAO7Ho66UIyMj5eTJk3nW6XJ4eHihtdxq/PjxMnbsWMey1sw1adLE1M7p68p70aWdJum+fPWik+P3zePXY9dxdvX469Wr5zNJd0ZWtjz9wR7J/r1i8o4e9aV/1yife/99/fPv6uMPDQ11WVwAAMD9eNSVcq9eveTrr7/Os2758uVmfVF0aDGd8tOLJFdcKOpFl6v25Yk4ft89fvsx+9Lxz1i+W/bGpZj5Dg2qy729GvrU8efny59/Vx6/r54/AAB8haUlfUpKihn6Syf7kGA6f+TIEUct9YgRIxzb33///XLgwAF57LHHZPfu3fKf//xHFi1aJA8//LBlxwDAN6zef0reWnXQzAcH+svMYdESFECyBAAAgOJZesW4YcMG6datm5mUNgPX+YkTJ5rlmJgYRwKudLiwr776ytRu6/jeOnTYm2++yXBhACpUUnqWPLpoq2P5sQFtpW1kdUtjAgAAgGewtHl53759TSc0RVmwYEGhr9m8eXMFRwYAf5j8+Q45kZjb8eKlUbXkr31aiEjR310AAACARz7TDcB9nmXVjgjPnz9v5r3ZN9ti5NNNx8189ZBAmTEsWvz9/SQnh6QbAAAAF0bSDaDUNNHW0QS0AyhvTrrjktPliSXbHMuTr+8ojWtWtTQmAAAAeBZ6AQKAQuijL//6+Fc5m5Zllgd2jJQbuzeyOiwAAAB4GJJuAGUep1gnb/Xh+qPy4554M18nLESevbGzV9fqAwAAoGKQdAMoNU229+3bZ0YX8MbE+9CpVJn65U7H8gs3d5Fa1YItjQkAAACeiaQbAJycz86RsYu2yLmsbLP8l55NpV+7elaHBQAAAA9F0g0ATub9tF82HUkw881rV5V/X9ve6pAAAADgwUi6AeB3248nypzv95p5fz+RWcO7SrUQBnkAAABA2ZF0A4CIpGdly5iFW+T87+Nv/71vK+netKbVYQEAAMDDkXQDgIhMX7ZH9sWlmPlOjcLlof6trQ4JAAAAXoCkG4DP+2XfKZn/y0EzHxzoL7Nv6SpBAXw9AgAAoPx4WBFAqel41WFhYZKVleXxY1cnnsuSRxdvdSw/PrCdtK5f3dKYAAAA4D2oygFQappoN2zYUOrWrevxSffkz3dITGK6me/Tqrbc1bu51SEBbmPu3LnSvHlzCQ0NlZ49e8r69euL3HbBggXm+8B50tc5s9lsMnHiRGnQoIFUqVJF+vfvL3v35nZeCACAtyLpBuCzvvo1RpZsPm7mq4cGygs3R4u/dlsOQBYuXChjx46VSZMmyaZNmyQ6OloGDBggcXFxRb4mPDxcYmJiHNPhw4fz/H769Ony0ksvybx582TdunVSrVo1s8/09NwbXwAAeCOSbgA+6WRSuvx76TbH8tQhnaRhjSqWxgS4k1mzZsm9994rd999t3To0MEkylWrVpX58+cX+Rqt3Y6MjHRM9evXz1PLPWfOHHnyySdlyJAh0qVLF3n33XflxIkTsnTp0ko6KgAAKh9JN4BSy8nJkd9++00OHTpk5j2NXvw/9vGvkpCWZZav69xAhnRtaHVYgNvIzMyUjRs3mubfdv7+/mZ5zZo1Rb4uJSVFmjVrJk2aNDGJ9Y4dOxy/O3jwoMTGxubZZ0REhGm2Xtw+AQDwdHSkBsDnvL/uiPz0W7yZr1s9RJ4e2snjn00HXOnUqVOSnZ2dp6Za6fLu3bsLfU3btm1NLbjWYCcmJsqMGTOkd+/eJvFu3LixSbjt+8i/T/vv8svIyDCTXVJSkvmpN/vKe8NPX6834DzxxqEifmsRv7U8OX7nmF3xXWYFzv8fSvp6km4APuVAfIo8+9Uux/L0m7tIzWrBlsYEeINevXqZyU4T7vbt28trr70mU6dOLdM+p02bJlOmTCmwPj4+vtzPgeuFkt4c0AtHrcX3NMRvLeK3lifHfy4rO893WVpIkHgazv8fkpOTpSRIugH4jPPZOfLwoq2OL9w7Lm0q/drWszoswO3UqVNHAgIC5OTJk3nW67I+q10SQUFB0q1bN9m3b59Ztr9O96G9lzvvs2vXroXuY/z48aYzN+eabm26riMnaKdt5b1o1BYuui9Pu2hUxG8t4reWJ8eflnneMa/xh4V63o1/zv8f8o/SURSSbgA+4z8r98vWowlmvkWdavLEte2tDglwS8HBwdKjRw9ZsWKFDB061HGRpcujR48u0T60efq2bdvk2muvNcstWrQwibfuw55kaxKtvZg/8MADhe4jJCTETPnpRZ4rLvT0otFV+7IC8VuL+K3lqfE7x+uJ8dtx/nOV9PUk3QB8wrZjifLSitzxgAP8/WTWLdFSNZivQKAoWsM8cuRIueiii+SSSy4xPY+npqaa3szViBEjpFGjRqYJuHrqqafk0ksvlVatWklCQoK88MILZsiwUaNGOS7QxowZI08//bS0bt3aJOETJkyQhg0bOhJ7AAC8EVecALxeela2jFm4Wc7n2Mzyg31bSremNa0OC3Brw4cPN8+7TZw40XR0prXTy5Ytc3SEduTIkTx3+M+ePWuGGNNta9asaWrKV69ebYYbs3vsscdM4n7fffeZxPyyyy4z+yxp8zwAADwRSTeAUtMaq2rVqplehT2h1+/nvtkt++NTzXznRhHyj6taWx0S4BG0KXlRzclXrlyZZ3n27NlmKo5+X2iNuE4AAPgKkm4ApaYXztqsVDtKcveke9XeU7Jg9SEzHxLoL7OHd5WgAM96/ggAAACeiytPAF4rMS1LHl281bE8flA7aVUvzNKYAAAA4FtIugF4rYmfb5fYpNyxfC9rVUdG9GpudUgAAADwMSTdAEpNhw7au3ev6UhJ593RF1tPyGdbTpj58NBAeWFYF/H3d++m8AAAAPA+JN0AysRms7ltwh2bmC5PLt3uWJ46tJM0iKhiaUwAAADwTSTdALzuZsC4j7dK4rkss/znLg1kSNdGVocFAAAAH0XSDcCrvLf2sPx37ykzXz88RJ4e2snqkAAAAODDSLoBeI398Sny7Ne7HMsv3BwtNaoGWxoTAAAAfBtJNwCvkJWdI2MXbpH0rNznzEf0aiZ/alPX6rAAAADg40i6AXiFV37YJ1uPJZr5qDrVZPyg9laHBAAAAJB0AyibKlWqSGhoqLiDLUcT5JUf95n5AH8/mTW8q1QJDrA6LAAAAEACrQ4AgOfx9/eXJk2aSEhIiJm30rnMbNOsPDvHZpZH92slXZvUsDQmAAAAwK1quufOnSvNmzc3tWY9e/aU9evXF7ntggULxM/PL8/kLrVtACrfc9/skgOnUs18dOMIGX1lK6tDAgAAANwn6V64cKGMHTtWJk2aJJs2bZLo6GgZMGCAxMXFFfma8PBwiYmJcUyHDx+u1JgBuIeff4uXd9bk/v+HBvmbZuVBAZZ/rQEAAAAOll+dzpo1S+699165++67pUOHDjJv3jypWrWqzJ8/v8jXaO12ZGSkY6pfv36lxgz4upycHNm/f78cPXrUzFshIS1Txn281bH8xLXtpWXdMEtiAQAAANwy6c7MzJSNGzdK//79/wjI398sr1mzpsjXpaSkSLNmzcwzpUOGDJEdO3ZUUsQA7LKzs81klQmf7ZCTSRlm/vLWdeTOS5tZFgsAAADglh2pnTp1yly056+p1uXdu3cX+pq2bduaWvAuXbpIYmKizJgxQ3r37m0S78aNGxfYPiMjw0x2SUlJ5qfWzpW3hk5fb7PZLKvpsxrH77vH7/z/44r/pdL6fOsJ+WLrCTMfUSVInr+xs3kvdKosvvz+K47fdcfvq+cQAABf4XG9l/fq1ctMdppwt2/fXl577TWZOnVqge2nTZsmU6ZMKbA+Pj5e0tPTy32hpIm/XnhZ3YOzFTh+3z1++7GnpaWZ/hcCAyvvqyQuOVMmLN3pWH60b2Pxz0iSuLjcG2qVxZfff8Xxu+74k5OTXRYXAABwP5Ym3XXq1JGAgAA5efJknvW6rM9ql0RQUJB069ZN9u3LHaM3v/Hjx5uO2pxrurVZet26dU2HbOW96NLny3VfvnrRyfH75vHrsSckJJjjr1evXqUl3Tk5Nnnky/9JckZus/bBXRrI7Ze3q5S/XTAW333/FcfvuuNnBA4AALybpUl3cHCw9OjRQ1asWCFDhw51XMjo8ujRo0u0D22evm3bNrn22msL/b2OI6xTfnqR5IoLRb3octW+PBHH77vHbz/myjz+d9cclF/2nTbzkeGh8vTQzpaee19+/xXH75rj99XzBwCAr7C8ebnWQo8cOVIuuugiueSSS2TOnDmSmppqejNXI0aMkEaNGplm4uqpp56SSy+9VFq1amVq2l544QUzZNioUaMsPhIAFWlfXIpM++aPvh5mDIuWiKpBlsYEAAAAuH3SPXz4cPN89cSJEyU2Nla6du0qy5Ytc3SuduTIkTy1AGfPnjVDjOm2NWvWNDXlq1evNsONAag82iS2sFYkFSErO0ceXrhFMs7ndjh1V+/mclnrOpXytwEAAACPTrqVNiUvqjn5ypUr8yzPnj3bTACsozfCmjZtahLvymga+/KKvbLteKKZb1m3mjw+yJrnuAEAAIDS4kEyAG5t05GzMnflfjMf6O8ns4d3ldCgAKvDAgAAAEqEpBuA20rLPC9jF26R7Jzc8bf/eVVr6dK4htVhAT5j7ty50rx5c9OqpWfPnrJ+/foit33jjTfk8ssvN49+6dS/f/8C2991112mAzrnaeDAgZVwJAAAWIekG0Cp6SgDBw4ckGPHjpn5ivLs17vk0Ok0M9+1SQ35e9+WFfa3AOS1cOFC09nppEmTZNOmTRIdHS0DBgyQuLi4Ih8Hu+222+THH3+UNWvWmOE5r7nmGjl+/Hie7TTJjomJcUwffvhhJR0RAADWIOkGUCbnz583U0X5cU+cvL/2iJkPDfKXWbdES2AAX1lAZZk1a5bpuFRHE9HOSufNmydVq1aV+fPnF7r9//3f/8nf//530yFqu3bt5M0333QMA+pMO2CMjIx0TForDgCAN3OLjtQAwNnZ1Ex57ONfHcv/vra9RNUNszQmwJdkZmbKxo0bZfz48Y512mmiNhnXWuySSEtLk6ysLKlVq1aBGvF69eqZZPvKK6+Up59+WmrXrl3oPjIyMsxkl5SUZH5qMl/eVjb6epvNVqGtdSoS8VuL+K3lyfE7x+yK7zIrcP7/UNLXk3QDcCv6Jf7vpdskPjn3QvuKNnXljkubWR0W4FNOnTol2dnZjuE77XR59+7dJdrHv/71L2nYsKFJ1J2blt94443SokUL2b9/vzzxxBMyaNAgk8gHBBTsIHHatGkyZcqUAut1qNH09HQp74VSYmKi+c6pjFEYXI34rUX81vLk+M9lZef5LksLCRJPw/n/Q3JyspQESTcAt7J0y3H5elusma9RNUim39zFdLYEwHM899xz8tFHH5labe2Eze7WW291zHfu3Fm6dOkiLVu2NNtdddVVBfajNe36XLlzTbc+K163bl0JDw8v90WjfrfovjztolERv7WI31qeHL92Emun8YeFBoun4fz/wbmMKw5JNwC3cTzhnEz8bIdj+ZmhnaV+eMm+zAC4Tp06dUzN88mTJ/Os12V9Drs4M2bMMEn3999/b5Lq4kRFRZm/tW/fvkKTbn3+W6f89CLPFRd6etHoqn1ZgfitRfzW8tT4neP1xPjtOP+5Svp6zzpLALxWTo5Nxi3eKsnpuXcgb+jWSK7r0sDqsACfFBwcLD169MjTCZq9U7RevXoV+brp06fL1KlTZdmyZXLRRRdd8O/oCAinT5+WBg34XwcAeC+SbgBlvijXyVXeXn1IVu8/beYbRITK5Os7umzfAEpPm3Xr2NvvvPOO7Nq1Sx544AFJTU01vZmrESNG5Olo7fnnn5cJEyaY3s11bO/Y2FgzpaSkmN/rz3HjxsnatWvl0KFDJoEfMmSItGrVygxFBgCAt6J5OYAyNaXRi2odr9cVzYr2nkyW55f90TnTjGHRElHF8zoWAbzJ8OHDTSczEydONMmzDgWmNdj2ztWOHDmS5///1VdfNb2e33zzzXn2o+N8T5482TRX//XXX00Sn5CQYDpZ03G8tWa8sCbkAAB4C5JuAJbKPJ8jYxZuMT/VX/u0kD6t6lgdFgARGT16tJkKo52fOdPa6+JUqVJFvv32W5fGBwCAJ6B5OQBLvbRir+w4kTv2bqt6YfLYwLZWhwQAAAC4DEk3gFLTDpW0VuvEiRNmvqw2Hj4r/1m5z8wH+vvJnOFdJTSo4Fi9AAAAgKci6QZQJvrspk5llZpxXsYu2iI5ttzlMf1bS6dGEa4LEAAAAHADJN0ALPHM17vk8Ok0M9+9aQ25/4qWVocEAAAAuBxJN4BK9+PuOPlg3REzXzU4QGbd0lUCA/g6AgAAgPfhKhdApTqTminjPv7Vsfzv69pL8zrVLI0JAAAAqCgk3QAqjc1mk38v2SanUjLMcr+2deUvlzS1OiwAAACgwpB0A6g0SzYfl2+2x5r5mlWD5Pmbuoifn5/VYQEAAAAVhqQbQJkEBgaaqaSOJ5yTSZ/tcCw/c0NnqRceWkHRAQAAAO6h5FfMAPA7f39/iYqKkri4ODN/ITk5Nnlk0RZJzjhvlm/o1kiu7dygEiIFAAAArEVNN4AKN/+Xg7L2wBkz3zAiVKYM6Wh1SAAAAEClIOkGUKH2xCbL9G/3OJZn3BIt4aFBlsYEAAAAVBaSbgCllpOTI0eOHJGYmBgzX5TM8zkyZuEW81Pdc1kL6d2yTiVGCgAAAFiLpBtAmaSnp0tGRu7QX0WZ8/1vsismycy3rhcm4wa0raToAAAAAPdA0g2gQmw4dEbm/bTfzAf6+8ns4V0lNCjA6rAAAACASkXSDcDlUjLOy9hFWyXHlrv88NVtpFOjCKvDAgAAACodSTcAl3vmq51y5Eyame/RrKbcf0VLq0MCAAAALEHSDcClVuw6KR+uP2rmqwYHyKxboiXA38/qsAAAAABLkHQDcJnTKRnyr0+2OZYn/LmDNKtdzdKYAAAAACuRdAMok4CAADPZ2Ww2eWLJNjmVktuj+VXt6smtFzexMEIAAADAeoFWBwDA8/j7+0vLli0lLi7OzKtPNh2Xb3ecNPO1qgXLtJs6i58fzcoBAADg26jpBlBuR8+kyeTPdziWn72hs9SrHmppTAAAAIA7IOkGUC45OTZ5dPFWM0yYuql7YxnYKdLqsAAAAAC34BZJ99y5c6V58+YSGhoqPXv2lPXr1xe7/eLFi6Vdu3Zm+86dO8vXX39dabEC0EQ7R44ePSqxsbHy1qoDsu7gGbO+UY0qMun6DlaHB8BNy2ft+2HixInSoEEDqVKlivTv31/27t0rlS07xyZrD5yW73afMT91GZWH828tzj/gg890L1y4UMaOHSvz5s0zBfqcOXNkwIABsmfPHqlXr16B7VevXi233XabTJs2Tf785z/LBx98IEOHDpVNmzZJp06dLDkGwBelpqbJdztj5c0d2WZZH9+eeUu0hIcGWR0aADctn6dPny4vvfSSvPPOO9KiRQuZMGGC2efOnTtNol4Zlm2PkSlf7JSYxPTf1xyUBhGhMmlwBxnYqUGlxODLOP/W4vwDPlrTPWvWLLn33nvl7rvvlg4dOpjCvWrVqjJ//vxCt3/xxRdl4MCBMm7cOGnfvr1MnTpVunfvLq+88kqlxw74qvjkDHn5x33yxfbTkpWde4d81GUt5NKo2laHBsBNy2et5dbE/cknn5QhQ4ZIly5d5N1335UTJ07I0qVLKy3heOD9TU4JR67YxHSzXn+PisP5txbnH/DRmu7MzEzZuHGjjB8/3rFOe0LW5mZr1qwp9DW6Xu+8O9O75KUtsLV5rE75aW/Lzj0uF7aN3dwf90pCUqpUrZZoXmOzFb1t7r7/uMfhedtqYpW3+ZGuS0tNk6rVzoq/f0Cx25Zmv2XdVuSP964ytnU+/j8+M5UbgxXbZufkyMJ1h6VKeuLva20y6vIWMu6aNo7/F+f/I91v7r6L2GsZt73Q/6c7bKvsvbt727b5f3eh9855v1Zt68rPpb0M0am8+73Qe+It5fPBgwfNIym6D7uIiAhTi66vvfXWW6UiaRNareEr7N3Rdfqu6O+v7hApAf6MvOBqnH9rcf4BH066T506JdnZ2VK/fv0863V59+7dhb5GC+zCttf1hcnIyDCTXVJSkvm5b98+CQsLK7B9tWrVpFGjRo5lfdasqAuoT/67XQ6lV3EsN/RPlIAikpZMCZCTOdUdyw38kyRQCr/QyhJ/ic0JdyxH+idJUBHbnhd/iXHatr5/sgRLbnPf/LLFT07kRDiW6/mnSIjkdn6Vnx7FsZwajuU6filSxa/wbdVRp21r+6VKVb+sIrc9lhMhNvP1LlLLL02q+WUWue3xnHDJ+b1BRk2/NAkrZtsTOdUlW3KT/wi/cxLu98f7nl9MTnU5//u24X7pEuGX966vs5M5YZL5+79Kdb90qVHMtnE5YZLx+7ZhfhlS0+9ckdvG51STdMltiq3nQM9FUU7lVJVzEmzmq0im1PEvetsztqqSasvdNlSypK5/apHbnrVVkRRbiJnXz4J+JoqSYAuVZFtu888QyZImgWlSJchf/tmnpXRuHCAH9u9zbFurVi2pU6eOmdf/v8OHDxe535o1a0rdunXNfFZWlrkwL4peoNv///W7Y//+/UVuGx4eLpGRuR26aVKj//NF0e+Chg0bOpZ/++23Ire1f0foPvW7obj96jOrTZr8MVa5xqtxF0ab1jZt2tSxfODAATl/vvD/ueDgYPOcrd2hQ4dMklSYwMBAiYqKciwfOXJE0tML/wzruOs6FJydPrd/7ty5Yt8Pe8J4/PhxSU0t+rPWpk0bx7zWbKakFP1Za9WqlSOZ1u92+/d2YTRe+3jxJ0+elMTE3JtBhdHmzEFBuf9z8fHxcvbs2SK3bdasmYSEhDjKqjNncvstsNPj1rgSEhIczzwr3U63L0rjxo1NbbHS1+qwe8WdC28qn+0/XVGGF3XjvDjrDpwuUMOXv9zT3687cMojWu3Yv4Pc8aZNYTj/1uL8uw/nmMvyXeYOOP9/KOnrLX+mu6Lps2VTpkwpsF4vzAq7oNXC3X5RZt+uqJNpo+MJ+LDGNULk4oYh0qiqzSQP+dn/bzQZLOz3ds41g5p0F7etJgH2WkKdL25b/f+2J24aS3Hb6t/V5NSuuG3t3xG6T/1+KC7J0+TWnrjZ91tU0q3bOT/TqtsWl3Rrsua8bXFJt/O2mmg6JzHONHnNv21RCbrze6fnWZPN4hL0/PstLkF3Hv9d91tcUqrb2pNu3TY5ObnYbe3f7xpDce+dJsZ6nu3nN/9nwrR0SUszPzWBt7/Pus/iPj/6HtvfZ41Vty3uXPi6ospwPefFfTYLs+/YmRJuFy9RYYX/n7oT+3eQ/X/Q3XH+rcX5dy+r/9nNxJ+ScEbSPDB+zv8firvucJukW2vC9GJJayec6bK9hio/XV+a7bVpnHNzN71LrjVP3bp1MzVhF2o6aq+tK8zUiGZyJjHJ1Pb4+/uJ7QJ3Ovycm0F62raaFBVoXmmTxKREiQiPkIDAfM3Li2teeYH9lnVb7ckrT1PpCt7W+fj1/bciBqu2rRkaKP7JMeb/SZ/ZdE5YczfNu1/nWuSCu827rfZqXJJtC6sxK27bwjp+Ksv/vX1be7NirWktrsBx/l1x+/W0bfX4T58+bVop6Gv0Z0mbgVu1rfP7XN5t9fg18dPfaTlW1v1q7XtxNfneVD7bf+o65/9zXe7atWupynA9d4WV4cVplaLl1MELb9e4rtSr5xk1ffpZsv8PujvOv7U4/+6F+L0n/pJ2Ampp0q21CD169JAVK1aYHk7tJ0GXR48eXehrevXqZX4/ZswYx7rly5eb9YXR2gfnmiY7TRLyJwqFKe6NuKxtfYmL8zMX8574gSsvfa/i4vw5fh88fj32vXtPOf6PSvK/VBFKc94rYlv9wtZjL+n2Vsfrym3tBZZu42uff/vxa1Jamve/KFb9/1R2+aw3GDTx1m3sSbYm0evWrZMHHnigVGV4WT53PaPqmF6atdOowm6L6K2QyIhQs53jRqqb86T/Qc6/tTj/7of4vSP+El8DisX0DvYbb7xhhg/ZtWuXKXi1qZ32lqpGjBiRpyOXhx56SJYtWyYzZ840z5VNnjxZNmzYUORFAADX0y+Y1q1bm2eQPfXLFkDlls96gaMJ+dNPPy2ff/65bNu2zexDW8LYE/uKpJ1D6bBIJpZ8v7Mv6+/pRKpicP6txfkHrGX57fXhw4ebJnoTJ040Hano3W8ttO3NRrXTH+eL+t69e5uxP3XIkSeeeMJc+GvPqIzRDQCAe5fPjz32mEnc77vvPvM8+2WXXWb2WVljdOs4xK/e0T3fOMW5NXyMU1zxOP/W4vwD1vGzFffwmRfSpmz6DLY+PF/a58EKb14c55PNixXHz/Fz/Bw/x1/+43dlueTtXHWudPgk7aVZO43SZ1i1Sa2n1fB58v8g599anH/rEb/vleGW13QD8Dx6r06HiNLeou3DfQGAp9AEQ4dF0l6atdMoT3mG1Vtw/q3F+Qcqn+fdmgDgFkm3NhHVIaJ8rLEMAAAAUCok3QAAAAAAVBCSbgAAAAAAKghJNwAAAAAAFYSkGwAAAACACkLSDQAAAABABfG5IcPsPS3rmGquGOMtOTlZQkNDPXKMuvLi+H33+PXYU1JSTA/m+r8UGOhzXyU+/f4rjt91x28vjxgJ4MIow/9A/NYifmsRv7WIv/RluM9dKesJVk2aNLE6FAAA8pRPERERVofh1ijDAQCeWIb72Xzs1rre2Thx4oRUr15d/Pz8yn1nQwv+o0ePSnh4uPgajp/j5/g5fo6//MevxbAW1g0bNvTIGoPKRBn+B+K3FvFbi/itRfylL8N9rqZbT0bjxo1duk99szzxA+cqHD/Hz/Fz/L7KVcdPDXfJUIYXRPzWIn5rEb+1iL/kZTi31AEAAAAAqCAk3QAAAAAAVBCS7nIICQmRSZMmmZ++iOPn+Dl+jp/j983j9wae/h4Sv7WI31rEby3iLz2f60gNAAAAAIDKQk03AAAAAAAVhKQbAAAAAIAKQtINAAAAAEAFIel2oeuvv16aNm0qoaGh0qBBA7nzzjvlxIkT4gsOHTok99xzj7Ro0UKqVKkiLVu2NB0UZGZmiq945plnpHfv3lK1alWpUaOGeLu5c+dK8+bNzee9Z8+esn79evEFP//8swwePFgaNmwofn5+snTpUvEl06ZNk4svvliqV68u9erVk6FDh8qePXvEV7z66qvSpUsXx9ievXr1km+++cbqsFCB5VR6ero8+OCDUrt2bQkLC5ObbrpJTp48KZ5Sztx1113mu8p5GjhwoHhK/Nr10MSJE811lb5v/fv3l71794oVzpw5I7fffrv539f49fOUkpJS7Gv69u1b4Pzff//9bllOL168WNq1a2e279y5s3z99ddipdLEv2DBggLnWV/nSdcKK1eulO7du5vOvVq1amWOyRNi17jzn3udYmNjxZOuUxZX8OefpNuF+vXrJ4sWLTJv7CeffCL79++Xm2++WXzB7t27JScnR1577TXZsWOHzJ49W+bNmydPPPGE+Aq9cBs2bJg88MAD4u0WLlwoY8eONResmzZtkujoaBkwYIDExcWJt0tNTTXHqxcDvuinn34yCcjatWtl+fLlkpWVJddcc405L76gcePG8txzz8nGjRtlw4YNcuWVV8qQIUPM9x68s5x6+OGH5YsvvjAXZPr515vpN954o3hSOaNJdkxMjGP68MMPxVPinz59urz00kvmvVq3bp1Uq1bNlDd6M6SyacKtnx397vvyyy9NcnLfffdd8HX33ntvnvOvx+Ru5fTq1avltttuMzcSNm/ebBIVnbZv3y6ecp2hN0Ocz/Phw4fFU64VDh48KNddd53JJbZs2SJjxoyRUaNGybfffiuecp2j+Y/z+deE11OuU1ZXxudfey9Hxfjss89sfn5+tszMTJsvmj59uq1FixY2X/P222/bIiIibN7skksusT344IOO5ezsbFvDhg1t06ZNs/kS/QpdsmSJzZfFxcWZ8/DTTz/ZfFXNmjVtb775ptVhoALKqYSEBFtQUJBt8eLFjnW7du0yn/k1a9bYPKGcGTlypG3IkCE2d1LS+HNycmyRkZG2F154Ic97EhISYvvwww9tlWnnzp3mff/f//7nWPfNN9+Y67zjx48X+borrrjC9tBDD9ncvZy+5ZZbbNddd12edT179rT97W9/s1mhtPG787VXSa4VHnvsMVvHjh3zrBs+fLhtwIABNneP/ccffzTbnT171uap1ym3VMLnn5ruCmyC9H//93+mGVVQUJD4osTERKlVq5bVYaACaiq0lk+b+Nn5+/ub5TVr1lgaG6z5P1e++L+enZ0tH330kbl7rs3M4X3llH7XaS2J8/edNj/UR8k86ftOm39qrVPbtm1NLfPp06fFE2jtnzZRdT7/ERERpqlxZZ9//XvapPyiiy5yrNO4tPzTGvji6PVgnTp1pFOnTjJ+/HhJS0tzu3Ja1ztvr7Rm2YrPeVmvM7Spf7NmzaRJkyYe1wLJnc5/WXXt2tU8BnL11VfLL7/8Ip50nbKmEs4/SbeL/etf/zJNn/TZryNHjshnn30mvmjfvn3y8ssvy9/+9jerQ4GLnTp1yiQb9evXz7Nel616fgfW0Ka62gSuT58+5mLSV2zbts0826vP3emzmUuWLJEOHTpYHRYqoJzS77Tg4OACzx970vedNi1/9913ZcWKFfL888+bppeDBg0y3+Puzn6O3aG80b+Xv7lsYGCguZAvLpa//OUv8v7778uPP/5oEu733ntP7rjjDrcrp3W9O5znssavN5Tmz59vrrv1fGv5pBVfx44dE09Q1PlPSkqSc+fOiTvTRFsf/9BHa3XSmx7al4E+FuAp1ymxlfD5J+m+gMcff7zQzgGcJ31OzG7cuHHmWYDvvvtOAgICZMSIEaYTEF85fnX8+HFTyOtzW/ockycry/EDvkKfmdLnnbS215foxZ0+c6e1W1prOHLkSNm5c6fVYfksTy+nKrqcufXWW01Hr9oxkD6jqM8i/+9//zO1354Qf0Wr6Pj1mW+tMdPzr8+E6w0QvVGn/f7AdbS1kV5za23rFVdcIZ9++qnUrVvX9OGAii8T9eZljx49zI0OvfmhP7XfDKs96EbXKYFWB+DuHnnkEdPzZ3GioqIc89p8SKc2bdpI+/btzd0efZDfU5selvb4tYMZ7QRC/9lef/118XSlPX5foJ9vvaGUv/deXY6MjLQsLlSu0aNHOzoS0s7FfInWfGrPskovMjSBefHFF7m488JySr/TtKlrQkJCntpuV37fVXY5o/vS73Gt6b/qqqvcOn77OdbzrbVpdrqsyZUrlDR+jSV/J17nz583jxOW5rOgTeOVnn/tQd9dymld7y7luiuuM/TRzm7dupnz7AmKOv/aOZz22u9pLrnkElm1apXHXKdEVsLnn6T7AvQumU5lbdKgMjIyxBeOX2sO9EJGL0Lffvtt8/yNL7//3pxw6HusTRW11sT+Wddl/YKDd9OWO//4xz9MTY3WlOnwS75OP/+e/D3v6SqynNLt9OJdv990qDB7D736+JirbqZXdjmjzW31mW7nJNZd49fvF73o1fNvT7K1ua29lUllxq/vt9580WeN9XOhfvjhB/P/b0+kS0JbyShXnX9XldN6fPp7bYprpz0/W1Fp5IrrDG2ero8CXXvtteIJ9DznH6LKqvPvCvo5r8jPuKuvUyrl8++yLtl83Nq1a20vv/yybfPmzbZDhw7ZVqxYYevdu7etZcuWtvT0dJu3O3bsmK1Vq1a2q666yszHxMQ4Jl9x+PBh8/5PmTLFFhYWZuZ1Sk5Otnmbjz76yPQeu2DBAtOj63333WerUaOGLTY21ubt9P20v7f6FTpr1iwzr++/L3jggQdMD7ErV67M83+elpZm8wWPP/646QH14MGDtl9//dUsa+/F3333ndWhwQXllK5v27atbd26dY51999/v61p06a2H374wbZhwwZbr169zOSu5YzG/+mnn5p5Xf/oo4+antb1M/v999/bunfvbmvdurUl1yaljV8999xzpnzREWH0f057Ytce58+dO1fp8Q8cONDWrVs38/lYtWqVOY+33XZbkZ+fffv22Z566inzudHzr8cQFRVl+9Of/mR5OX3nnXea7y+7X375xRYYGGibMWOG6aF/0qRJpuf+bdu2VXisrohfP1Pffvutbf/+/baNGzfabr31VltoaKhtx44dbnmtoLHrMdgdOHDAVrVqVdu4cePM+Z87d64tICDAtmzZMrePffbs2balS5fa9u7daz4v2lu/v7+/+b5x1+uUOy34/JN0u4gWBP369bPVqlXLfEk0b97cFNT6BewLdKgG/ccsbPIVOixLYcevQyl4I73JpBeiwcHBZmgPvfHkC+xDY+Sf9P33BUX9n+t3gC/461//amvWrJn53NetW9ckcCTc3lNOaWKU/3tbk7u///3vZmg4vSi+4YYbLLuhXJJyxvn/US8yr7nmGvNZ1QtI/ezee++9lt0gLW389mHDJkyYYKtfv765vtL/uT179lgS/+nTp02SrTcMwsPDbXfffXeeGwb5Pz9HjhwxCbb92lBv+mhSlZiYaHk5rUOZ5S+3Fi1aZGvTpo3ZXoev+uqrr2xWKk38Y8aMcWyrn5Vrr73WtmnTJre9VtCfegz5X9O1a1dzDHpzxqpytbSxP//886aSUW9y6Ge9b9++5ialO1+nXGHB59/v9+AAAAAAAICLef5DtwAAAAAAuCmSbgAAAAAAKghJNwAAAAAAFYSkGwAAAACACkLSDQAAAABABSHpBgAAAACggpB0AwAAAABQQUi6AQAAAACoICTdAAAAAABUEJJuwIfdddddMnTo0Er9mwsWLJAaNWpU6t8EAMDbUIYDnoOkGwAAAACACkLSDcDo27ev/POf/5THHntMatWqJZGRkTJ58uQ82/j5+cmrr74qgwYNkipVqkhUVJR8/PHHjt+vXLnSbJOQkOBYt2XLFrPu0KFD5vd33323JCYmmnU65f8bAACgdCjDAfdG0g3A4Z133pFq1arJunXrZPr06fLUU0/J8uXL82wzYcIEuemmm2Tr1q1y++23y6233iq7du0q0f579+4tc+bMkfDwcImJiTHTo48+WkFHAwCA76AMB9wXSTcAhy5dusikSZOkdevWMmLECLnoootkxYoVebYZNmyYjBo1Stq0aSNTp04127z88ssl2n9wcLBERESYu+N6F16nsLCwCjoaAAB8B2U44L5IugHkKbCdNWjQQOLi4vKs69WrV4Hlkt4lBwAAFYMyHHBfJN0AHIKCgvIs693snJycEr/e3z/3K8VmsznWZWVluTBCAABQGMpwwH2RdAMolbVr1xZYbt++vZmvW7eu+anPeTl3wpK/eVp2dnalxAoAAP5AGQ5Yg6QbQKksXrxY5s+fL7/99pt5dmz9+vUyevRo87tWrVpJkyZNTG+me/fula+++kpmzpyZ5/XNmzeXlJQU85zZqVOnJC0tzaIjAQDAt1CGA9Yg6QZQKlOmTJGPPvrIPDv27rvvyocffigdOnRwNG3T5d27d5vfP//88/L0008X6P30/vvvl+HDh5u76trDKgAAqHiU4YA1/GzOD24AQDH0+bAlS5bI0KFDrQ4FAACUAmU4YB1qugEAAAAAqCAk3QAAAAAAVBCalwMAAAAAUEGo6QYAAAAAoIKQdAMAAAAAUEFIugEAAAAAqCAk3QAAAAAAVBCSbgAAAAAAKghJNwAAAAAAFYSkGwAAAACACkLSDQAAAABABSHpBgAAAABAKsb/A6Isp50WIfnjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:  [-2 -1  0  1  2]\n",
      "Output: [0 0 0 1 2]\n",
      "\n",
      "✅ Advantages: Simple, fast, no gradient vanishing for positive values\n",
      "⚠️  Issue: 'Dying ReLU' problem when neurons always output 0\n"
     ]
    }
   ],
   "source": [
    "# ReLU: max(0, x)\n",
    "relu = brainstate.nn.ReLU()\n",
    "\n",
    "x = jnp.linspace(-3, 3, 100)\n",
    "y = relu(x)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y, linewidth=2, label='ReLU')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU Activation', fontweight='bold')\n",
    "plt.legend()\n",
    "\n",
    "# Test with data\n",
    "test_input = jnp.array([-2, -1, 0, 1, 2])\n",
    "test_output = relu(test_input)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.stem(test_input, test_output, basefmt=' ')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU on Sample Points', fontweight='bold')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Input:  {test_input}\")\n",
    "print(f\"Output: {test_output}\")\n",
    "print(\"\\n✅ Advantages: Simple, fast, no gradient vanishing for positive values\")\n",
    "print(\"⚠️  Issue: 'Dying ReLU' problem when neurons always output 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "leaky_relu",
   "metadata": {},
   "source": [
    "#### Leaky ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "leaky_relu_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:39.984362Z",
     "start_time": "2025-10-10T11:34:39.828971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Advantage: Allows small gradient for negative values\n",
      "   Helps prevent dying ReLU problem\n"
     ]
    }
   ],
   "source": [
    "# LeakyReLU: max(alpha * x, x)\n",
    "leaky_relu = brainstate.nn.LeakyReLU(negative_slope=0.1)\n",
    "\n",
    "y_leaky = leaky_relu(x)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(x, y, linewidth=2, label='ReLU', alpha=0.5)\n",
    "plt.plot(x, y_leaky, linewidth=2, label='Leaky ReLU (α=0.1)')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU vs Leaky ReLU', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Advantage: Allows small gradient for negative values\")\n",
    "print(\"   Helps prevent dying ReLU problem\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modern_activations",
   "metadata": {},
   "source": [
    "### Modern Activation Functions\n",
    "\n",
    "#### GELU (Gaussian Error Linear Unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "gelu",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:40.187218Z",
     "start_time": "2025-10-10T11:34:39.992930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Used in Transformers (BERT, GPT)\n",
      "✅ Smooth, differentiable everywhere\n",
      "✅ Stochastic regularization properties\n"
     ]
    }
   ],
   "source": [
    "# GELU: Smooth, probabilistic activation\n",
    "gelu = brainstate.nn.GELU()\n",
    "\n",
    "y_gelu = gelu(x)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(x, y, linewidth=2, label='ReLU', alpha=0.5)\n",
    "plt.plot(x, y_gelu, linewidth=2, label='GELU')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU vs GELU', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Used in Transformers (BERT, GPT)\")\n",
    "print(\"✅ Smooth, differentiable everywhere\")\n",
    "print(\"✅ Stochastic regularization properties\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sigmoid_tanh",
   "metadata": {},
   "source": [
    "### Classic Activations\n",
    "\n",
    "#### Sigmoid and Tanh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sigmoid_tanh_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:40.529361Z",
     "start_time": "2025-10-10T11:34:40.200598Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigmoid:\n",
      "  ✅ Output range: (0, 1) - good for probabilities\n",
      "  ⚠️  Vanishing gradients for large |x|\n",
      "\n",
      "Tanh:\n",
      "  ✅ Output range: (-1, 1) - zero-centered\n",
      "  ⚠️  Also suffers from vanishing gradients\n",
      "  📝 Often used in RNN/LSTM gates\n"
     ]
    }
   ],
   "source": [
    "# Sigmoid: 1 / (1 + exp(-x))\n",
    "sigmoid = brainstate.nn.Sigmoid()\n",
    "\n",
    "# Tanh: (exp(x) - exp(-x)) / (exp(x) + exp(-x))\n",
    "tanh = brainstate.nn.Tanh()\n",
    "\n",
    "y_sigmoid = sigmoid(x)\n",
    "y_tanh = tanh(x)\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y_sigmoid, linewidth=2, label='Sigmoid', color='blue')\n",
    "plt.axhline(0.5, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('Sigmoid: σ(x) = 1/(1+e⁻ˣ)', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.ylim([-0.1, 1.1])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y_tanh, linewidth=2, label='Tanh', color='green')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('Tanh: tanh(x)', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.ylim([-1.1, 1.1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Sigmoid:\")\n",
    "print(\"  ✅ Output range: (0, 1) - good for probabilities\")\n",
    "print(\"  ⚠️  Vanishing gradients for large |x|\")\n",
    "print(\"\\nTanh:\")\n",
    "print(\"  ✅ Output range: (-1, 1) - zero-centered\")\n",
    "print(\"  ⚠️  Also suffers from vanishing gradients\")\n",
    "print(\"  📝 Often used in RNN/LSTM gates\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "activation_comparison",
   "metadata": {},
   "source": [
    "### Comparing All Activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "all_activations",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:41.022564Z",
     "start_time": "2025-10-10T11:34:40.537638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Activation Function Guide:\n",
      "\n",
      "ReLU:        Default choice, fast, works well in most cases\n",
      "LeakyReLU:   When dying ReLU is a problem\n",
      "GELU:        Transformers, NLP models\n",
      "ELU:         Smooth variant of ReLU with negative values\n",
      "Sigmoid:     Output layer for binary classification\n",
      "Tanh:        RNN/LSTM gates, when zero-centered output needed\n"
     ]
    }
   ],
   "source": [
    "# Compare multiple activations\n",
    "activations = {\n",
    "    'ReLU': brainstate.nn.ReLU(),\n",
    "    'LeakyReLU': brainstate.nn.LeakyReLU(0.1),\n",
    "    'GELU': brainstate.nn.GELU(),\n",
    "    'ELU': brainstate.nn.ELU(),\n",
    "    'Sigmoid': brainstate.nn.Sigmoid(),\n",
    "    'Tanh': brainstate.nn.Tanh(),\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for idx, (name, activation) in enumerate(activations.items()):\n",
    "    y_act = activation(x)\n",
    "    axes[idx].plot(x, y_act, linewidth=2.5, color=f'C{idx}')\n",
    "    axes[idx].axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "    axes[idx].axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "    axes[idx].grid(alpha=0.3)\n",
    "    axes[idx].set_xlabel('Input', fontsize=10)\n",
    "    axes[idx].set_ylabel('Output', fontsize=10)\n",
    "    axes[idx].set_title(name, fontsize=12, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n📊 Activation Function Guide:\\n\")\n",
    "print(\"ReLU:        Default choice, fast, works well in most cases\")\n",
    "print(\"LeakyReLU:   When dying ReLU is a problem\")\n",
    "print(\"GELU:        Transformers, NLP models\")\n",
    "print(\"ELU:         Smooth variant of ReLU with negative values\")\n",
    "print(\"Sigmoid:     Output layer for binary classification\")\n",
    "print(\"Tanh:        RNN/LSTM gates, when zero-centered output needed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "softmax",
   "metadata": {},
   "source": [
    "### Softmax - For Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "softmax_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:41.424913Z",
     "start_time": "2025-10-10T11:34:41.045102Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logits:        [2.  1.  0.5 3.  0.1]\n",
      "Probabilities: [0.22427258 0.08250527 0.05004197 0.60963607 0.03354414]\n",
      "Sum of probs:  1.000000 (should be 1.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Softmax converts logits to valid probability distribution\n",
      "   Use for multi-class classification output layer\n"
     ]
    }
   ],
   "source": [
    "# Softmax: converts logits to probabilities\n",
    "softmax = brainstate.nn.Softmax()\n",
    "\n",
    "# Example logits from a classifier\n",
    "logits = jnp.array([2.0, 1.0, 0.5, 3.0, 0.1])\n",
    "probs = softmax(logits)\n",
    "\n",
    "print(\"Logits:       \", logits)\n",
    "print(\"Probabilities:\", probs)\n",
    "print(f\"Sum of probs:  {jnp.sum(probs):.6f} (should be 1.0)\")\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "classes = ['Cat', 'Dog', 'Bird', 'Fish', 'Horse']\n",
    "\n",
    "axes[0].bar(classes, logits, color='steelblue', alpha=0.7)\n",
    "axes[0].set_ylabel('Logit Value')\n",
    "axes[0].set_title('Raw Logits', fontweight='bold')\n",
    "axes[0].grid(axis='y', alpha=0.3)\n",
    "\n",
    "axes[1].bar(classes, probs, color='coral', alpha=0.7)\n",
    "axes[1].set_ylabel('Probability')\n",
    "axes[1].set_title('After Softmax', fontweight='bold')\n",
    "axes[1].set_ylim([0, 1])\n",
    "axes[1].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Add values on bars\n",
    "for i, v in enumerate(probs):\n",
    "    axes[1].text(i, v + 0.02, f'{v:.3f}', ha='center', fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✅ Softmax converts logits to valid probability distribution\")\n",
    "print(\"   Use for multi-class classification output layer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "normalization_intro",
   "metadata": {},
   "source": [
    "## 2. Normalization Layers\n",
    "\n",
    "Normalization stabilizes training by controlling the distribution of activations.\n",
    "\n",
    "### Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "batchnorm",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:42.214285Z",
     "start_time": "2025-10-10T11:34:41.445945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BatchNorm0d:\n",
      "BatchNorm0d(\n",
      "  in_size=(10,),\n",
      "  out_size=(10,),\n",
      "  affine=True,\n",
      "  bias_initializer=Constant(\n",
      "    value=0.0\n",
      "  ),\n",
      "  scale_initializer=Constant(\n",
      "    value=1.0\n",
      "  ),\n",
      "  dtype=<class 'numpy.float32'>,\n",
      "  track_running_stats=True,\n",
      "  momentum=Array(0.99, dtype=float32),\n",
      "  epsilon=Array(1.e-05, dtype=float32),\n",
      "  use_fast_variance=True,\n",
      "  feature_axes=(0,),\n",
      "  axis_name=None,\n",
      "  axis_index_groups=None,\n",
      "  running_mean=BatchState(\n",
      "    value=ShapedArray(float32[10])\n",
      "  ),\n",
      "  running_var=BatchState(\n",
      "    value=ShapedArray(float32[10])\n",
      "  ),\n",
      "  weight=NormalizationParamState(\n",
      "    value={\n",
      "      'bias': ShapedArray(float32[10]),\n",
      "      'scale': ShapedArray(float32[10])\n",
      "    }\n",
      "  )\n",
      ")\n",
      "\n",
      "Before BatchNorm:\n",
      "  Mean: [10.531552 10.173284  9.421941]...\n",
      "  Std:  [4.4984775 4.850637  4.945446 ]...\n",
      "\n",
      "After BatchNorm:\n",
      "  Mean: [ 2.0861626e-07 -5.5879354e-08  2.3096800e-07]...\n",
      "  Std:  [0.99999964 0.9999995  0.9999993 ]...\n",
      "\n",
      "✅ Normalizes to ~mean=0, ~std=1 across batch\n",
      "✅ Learns scale (γ) and shift (β) parameters\n"
     ]
    }
   ],
   "source": [
    "# BatchNorm: Normalizes across batch dimension\n",
    "brainstate.random.seed(42)\n",
    "batch_norm = brainstate.nn.BatchNorm0d((10,))\n",
    "\n",
    "print(\"BatchNorm0d:\")\n",
    "print(batch_norm)\n",
    "\n",
    "# Create batch of data with varying statistics\n",
    "batch_size = 32\n",
    "features = 10\n",
    "x = brainstate.random.randn(batch_size, features) * 5 + 10  # mean≈10, std≈5\n",
    "\n",
    "print()\n",
    "print(\"Before BatchNorm:\")\n",
    "print(f\"  Mean: {jnp.mean(x, axis=0)[:3]}...\")\n",
    "print(f\"  Std:  {jnp.std(x, axis=0)[:3]}...\")\n",
    "\n",
    "# Apply batch norm\n",
    "with brainstate.environ.context(fit=True):\n",
    "    y = batch_norm(x)\n",
    "\n",
    "print()\n",
    "print(\"After BatchNorm:\")\n",
    "print(f\"  Mean: {jnp.mean(y, axis=0)[:3]}...\")\n",
    "print(f\"  Std:  {jnp.std(y, axis=0)[:3]}...\")\n",
    "\n",
    "print()\n",
    "print(\"✅ Normalizes to ~mean=0, ~std=1 across batch\")\n",
    "print(\"✅ Learns scale (γ) and shift (β) parameters\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "batchnorm_visual",
   "metadata": {},
   "source": [
    "### Visualizing BatchNorm Effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "batchnorm_visualization",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:43.223147Z",
     "start_time": "2025-10-10T11:34:42.223583Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BatchNorm standardizes different distributions to similar scale\n"
     ]
    }
   ],
   "source": [
    "# Generate data with different distributions\n",
    "brainstate.random.seed(0)\n",
    "x1 = brainstate.random.randn(1000) * 10 + 50  # High variance, high mean\n",
    "x2 = brainstate.random.randn(1000) * 2 - 5     # Low variance, negative mean\n",
    "\n",
    "# Create batch norm (treating as batch dimension)\n",
    "bn = brainstate.nn.BatchNorm0d((1,))\n",
    "\n",
    "# Reshape to (batch, features)\n",
    "x1_batch = x1[:, None]\n",
    "x2_batch = x2[:, None]\n",
    "\n",
    "# Apply normalization\n",
    "with brainstate.environ.context(fit=True):\n",
    "    y1 = bn(x1_batch).flatten()\n",
    "    y2 = bn(x2_batch).flatten()\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "\n",
    "# Distribution 1 - before\n",
    "axes[0, 0].hist(np.array(x1), bins=50, alpha=0.7, color='blue', edgecolor='black')\n",
    "axes[0, 0].set_title('Distribution 1 - Before BN', fontweight='bold')\n",
    "axes[0, 0].set_xlabel('Value')\n",
    "axes[0, 0].set_ylabel('Frequency')\n",
    "axes[0, 0].axvline(jnp.mean(x1), color='red', linestyle='--', label=f'μ={jnp.mean(x1):.1f}')\n",
    "axes[0, 0].legend()\n",
    "\n",
    "# Distribution 1 - after\n",
    "axes[0, 1].hist(np.array(y1), bins=50, alpha=0.7, color='green', edgecolor='black')\n",
    "axes[0, 1].set_title('Distribution 1 - After BN', fontweight='bold')\n",
    "axes[0, 1].set_xlabel('Value')\n",
    "axes[0, 1].set_ylabel('Frequency')\n",
    "axes[0, 1].axvline(jnp.mean(y1), color='red', linestyle='--', label=f'μ={jnp.mean(y1):.2f}')\n",
    "axes[0, 1].legend()\n",
    "\n",
    "# Distribution 2 - before\n",
    "axes[1, 0].hist(np.array(x2), bins=50, alpha=0.7, color='blue', edgecolor='black')\n",
    "axes[1, 0].set_title('Distribution 2 - Before BN', fontweight='bold')\n",
    "axes[1, 0].set_xlabel('Value')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "axes[1, 0].axvline(jnp.mean(x2), color='red', linestyle='--', label=f'μ={jnp.mean(x2):.1f}')\n",
    "axes[1, 0].legend()\n",
    "\n",
    "# Distribution 2 - after\n",
    "axes[1, 1].hist(np.array(y2), bins=50, alpha=0.7, color='green', edgecolor='black')\n",
    "axes[1, 1].set_title('Distribution 2 - After BN', fontweight='bold')\n",
    "axes[1, 1].set_xlabel('Value')\n",
    "axes[1, 1].set_ylabel('Frequency')\n",
    "axes[1, 1].axvline(jnp.mean(y2), color='red', linestyle='--', label=f'μ={jnp.mean(y2):.2f}')\n",
    "axes[1, 1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"BatchNorm standardizes different distributions to similar scale\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "layernorm",
   "metadata": {},
   "source": [
    "### Layer Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "layernorm_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:45.380792Z",
     "start_time": "2025-10-10T11:34:45.023256Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LayerNorm:\n",
      "LayerNorm(\n",
      "  in_size=(10,),\n",
      "  out_size=(10,),\n",
      "  feature_axes=(0,),\n",
      "  reduction_axes=(-1,),\n",
      "  axis_name=None,\n",
      "  axis_index_groups=None,\n",
      "  weight=NormalizationParamState(\n",
      "    value={\n",
      "      'bias': ShapedArray(float32[10]),\n",
      "      'scale': ShapedArray(float32[10])\n",
      "    }\n",
      "  ),\n",
      "  epsilon=1e-06,\n",
      "  dtype=<class 'numpy.float32'>,\n",
      "  use_bias=True,\n",
      "  use_scale=True,\n",
      "  bias_init=ZeroInit(\n",
      "    unit=Unit(10.0^0)\n",
      "  ),\n",
      "  scale_init=Constant(\n",
      "    value=1.0\n",
      "  ),\n",
      "  use_fast_variance=True\n",
      ")\n",
      "\n",
      "Before LayerNorm (single sample):\n",
      "  Values: [ 3.0611591 13.874271  17.702465  19.20955    5.685323   5.9649186\n",
      "  8.997379  13.917359   5.0701327 11.3188305]\n",
      "  Mean: 10.480\n",
      "  Std:  5.313\n",
      "\n",
      "After LayerNorm:\n",
      "  Values: [-1.3963605   0.6388257   1.3593478   1.6430035  -0.9024544  -0.84983045\n",
      " -0.2790769   0.64693546 -1.0182422   0.15785426]\n",
      "  Mean: 0.000\n",
      "  Std:  1.000\n",
      "\n",
      "✅ LayerNorm works on single samples\n",
      "✅ Popular in Transformers and RNNs\n",
      "✅ Independent of batch size\n"
     ]
    }
   ],
   "source": [
    "# LayerNorm: Normalizes across features (not batch)\n",
    "layer_norm = brainstate.nn.LayerNorm((10,))\n",
    "\n",
    "print(\"LayerNorm:\")\n",
    "print(layer_norm)\n",
    "\n",
    "# Single sample\n",
    "x_single = brainstate.random.randn(10) * 5 + 10\n",
    "\n",
    "print()\n",
    "print(\"Before LayerNorm (single sample):\")\n",
    "print(f\"  Values: {x_single}\")\n",
    "print(f\"  Mean: {jnp.mean(x_single):.3f}\")\n",
    "print(f\"  Std:  {jnp.std(x_single):.3f}\")\n",
    "\n",
    "y_single = layer_norm(x_single)\n",
    "\n",
    "print()\n",
    "print(\"After LayerNorm:\")\n",
    "print(f\"  Values: {y_single}\")\n",
    "print(f\"  Mean: {jnp.mean(y_single):.3f}\")\n",
    "print(f\"  Std:  {jnp.std(y_single):.3f}\")\n",
    "\n",
    "print()\n",
    "print(\"✅ LayerNorm works on single samples\")\n",
    "print(\"✅ Popular in Transformers and RNNs\")\n",
    "print(\"✅ Independent of batch size\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norm_comparison",
   "metadata": {},
   "source": [
    "### Comparing Normalization Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "norm_comparison_table",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:45.862243Z",
     "start_time": "2025-10-10T11:34:45.382805Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Normalization Method Comparison:\n",
      "\n",
      "      Method     Normalizes Over            Best For Batch Dependent          Typical Use\n",
      "   BatchNorm     Batch + Spatial CNNs, large batches             Yes               Vision\n",
      "   LayerNorm            Features  RNNs, Transformers              No                  NLP\n",
      "   GroupNorm              Groups       Small batches              No Vision (small batch)\n",
      "InstanceNorm Spatial per channel      Style transfer              No          GANs, Style\n",
      "\n",
      "\n",
      "🎯 Quick Guide:\n",
      "  • Large batch + CNN → BatchNorm\n",
      "  • Small batch + CNN → GroupNorm\n",
      "  • Sequences/RNN/Transformer → LayerNorm\n",
      "  • Single image inference → LayerNorm or GroupNorm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "comparison = pd.DataFrame({\n",
    "    'Method': ['BatchNorm', 'LayerNorm', 'GroupNorm', 'InstanceNorm'],\n",
    "    'Normalizes Over': ['Batch + Spatial', 'Features', 'Groups', 'Spatial per channel'],\n",
    "    'Best For': ['CNNs, large batches', 'RNNs, Transformers', 'Small batches', 'Style transfer'],\n",
    "    'Batch Dependent': ['Yes', 'No', 'No', 'No'],\n",
    "    'Typical Use': ['Vision', 'NLP', 'Vision (small batch)', 'GANs, Style'],\n",
    "})\n",
    "\n",
    "print(\"\\n📊 Normalization Method Comparison:\\n\")\n",
    "print(comparison.to_string(index=False))\n",
    "\n",
    "print(\"\\n\\n🎯 Quick Guide:\")\n",
    "print(\"  • Large batch + CNN → BatchNorm\")\n",
    "print(\"  • Small batch + CNN → GroupNorm\")\n",
    "print(\"  • Sequences/RNN/Transformer → LayerNorm\")\n",
    "print(\"  • Single image inference → LayerNorm or GroupNorm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "practical_example",
   "metadata": {},
   "source": [
    "## 3. Putting It All Together\n",
    "\n",
    "Building a complete network with activations and normalization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "complete_network",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:38:34.930579Z",
     "start_time": "2025-10-10T11:38:34.122841Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modern CNN with GELU + BatchNorm + LayerNorm:\n",
      "ModernCNN(\n",
      "  conv1=Conv2d(\n",
      "    in_size=(32, 32, 3),\n",
      "    out_size=(32, 32, 64),\n",
      "    channel_first=False,\n",
      "    channels_last=True,\n",
      "    in_channels=3,\n",
      "    out_channels=64,\n",
      "    stride=(1, 1),\n",
      "    kernel_size=(3, 3),\n",
      "    lhs_dilation=(1, 1),\n",
      "    rhs_dilation=(1, 1),\n",
      "    groups=1,\n",
      "    dimension_numbers=ConvDimensionNumbers(lhs_spec=(0, 3, 1, 2), rhs_spec=(3, 2, 0, 1), out_spec=(0, 3, 1, 2)),\n",
      "    padding=SAME,\n",
      "    kernel_shape=(3, 3, 3, 64),\n",
      "    w_mask=None,\n",
      "    w_initializer=XavierNormal(\n",
      "      scale=1.0,\n",
      "      mode='fan_avg',\n",
      "      in_axis=-2,\n",
      "      out_axis=-1,\n",
      "      distribution='truncated_normal',\n",
      "      rng=RandomState([1846506472 1430574090]),\n",
      "      unit=Unit(10.0^0)\n",
      "    ),\n",
      "    b_initializer=None,\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'weight': ShapedArray(float32[3,3,3,64])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  bn1=BatchNorm2d(\n",
      "    in_size=(32, 32, 64),\n",
      "    out_size=(32, 32, 64),\n",
      "    affine=True,\n",
      "    bias_initializer=Constant(\n",
      "      value=0.0\n",
      "    ),\n",
      "    scale_initializer=Constant(\n",
      "      value=1.0\n",
      "    ),\n",
      "    dtype=<class 'numpy.float32'>,\n",
      "    track_running_stats=True,\n",
      "    momentum=Array(0.99, dtype=float32),\n",
      "    epsilon=Array(1.e-05, dtype=float32),\n",
      "    use_fast_variance=True,\n",
      "    feature_axes=(2,),\n",
      "    axis_name=None,\n",
      "    axis_index_groups=None,\n",
      "    running_mean=BatchState(\n",
      "      value=ShapedArray(float32[1,1,64])\n",
      "    ),\n",
      "    running_var=BatchState(\n",
      "      value=ShapedArray(float32[1,1,64])\n",
      "    ),\n",
      "    weight=NormalizationParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[1,1,64]),\n",
      "        'scale': ShapedArray(float32[1,1,64])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  act1=GELU(approximate=False),\n",
      "  pool1=MaxPool2d(\n",
      "    init_value=-inf,\n",
      "    computation=<function max at 0x000001C17F834220>,\n",
      "    pool_dim=2,\n",
      "    return_indices=False,\n",
      "    kernel_size=(2, 2),\n",
      "    stride=(2, 2),\n",
      "    padding=VALID,\n",
      "    channel_axis=-1\n",
      "  ),\n",
      "  conv2=Conv2d(\n",
      "    in_size=(16, 16, 64),\n",
      "    out_size=(16, 16, 128),\n",
      "    channel_first=False,\n",
      "    channels_last=True,\n",
      "    in_channels=64,\n",
      "    out_channels=128,\n",
      "    stride=(1, 1),\n",
      "    kernel_size=(3, 3),\n",
      "    lhs_dilation=(1, 1),\n",
      "    rhs_dilation=(1, 1),\n",
      "    groups=1,\n",
      "    dimension_numbers=ConvDimensionNumbers(lhs_spec=(0, 3, 1, 2), rhs_spec=(3, 2, 0, 1), out_spec=(0, 3, 1, 2)),\n",
      "    padding=SAME,\n",
      "    kernel_shape=(3, 3, 64, 128),\n",
      "    w_mask=None,\n",
      "    w_initializer=XavierNormal(\n",
      "      scale=1.0,\n",
      "      mode='fan_avg',\n",
      "      in_axis=-2,\n",
      "      out_axis=-1,\n",
      "      distribution='truncated_normal',\n",
      "      rng=RandomState([1846506472 1430574090]),\n",
      "      unit=Unit(10.0^0)\n",
      "    ),\n",
      "    b_initializer=None,\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'weight': ShapedArray(float32[3,3,64,128])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  bn2=BatchNorm2d(\n",
      "    in_size=(16, 16, 128),\n",
      "    out_size=(16, 16, 128),\n",
      "    affine=True,\n",
      "    bias_initializer=Constant(\n",
      "      value=0.0\n",
      "    ),\n",
      "    scale_initializer=Constant(\n",
      "      value=1.0\n",
      "    ),\n",
      "    dtype=<class 'numpy.float32'>,\n",
      "    track_running_stats=True,\n",
      "    momentum=Array(0.99, dtype=float32),\n",
      "    epsilon=Array(1.e-05, dtype=float32),\n",
      "    use_fast_variance=True,\n",
      "    feature_axes=(2,),\n",
      "    axis_name=None,\n",
      "    axis_index_groups=None,\n",
      "    running_mean=BatchState(\n",
      "      value=ShapedArray(float32[1,1,128])\n",
      "    ),\n",
      "    running_var=BatchState(\n",
      "      value=ShapedArray(float32[1,1,128])\n",
      "    ),\n",
      "    weight=NormalizationParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[1,1,128]),\n",
      "        'scale': ShapedArray(float32[1,1,128])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  act2=GELU(approximate=False),\n",
      "  pool2=MaxPool2d(\n",
      "    in_size=(16, 16, 128),\n",
      "    out_size=(8, 8, 128),\n",
      "    init_value=-inf,\n",
      "    computation=<function max at 0x000001C17F834220>,\n",
      "    pool_dim=2,\n",
      "    return_indices=False,\n",
      "    kernel_size=(2, 2),\n",
      "    stride=(2, 2),\n",
      "    padding=VALID,\n",
      "    channel_axis=-1\n",
      "  ),\n",
      "  fc1=Linear(\n",
      "    in_size=(8192,),\n",
      "    out_size=(256,),\n",
      "    w_mask=None,\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[256]),\n",
      "        'weight': ShapedArray(float32[8192,256])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  ln=LayerNorm(\n",
      "    in_size=(256,),\n",
      "    out_size=(256,),\n",
      "    feature_axes=(0,),\n",
      "    reduction_axes=(-1,),\n",
      "    axis_name=None,\n",
      "    axis_index_groups=None,\n",
      "    weight=NormalizationParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[256]),\n",
      "        'scale': ShapedArray(float32[256])\n",
      "      }\n",
      "    ),\n",
      "    epsilon=1e-06,\n",
      "    dtype=<class 'numpy.float32'>,\n",
      "    use_bias=True,\n",
      "    use_scale=True,\n",
      "    bias_init=ZeroInit(\n",
      "      unit=Unit(10.0^0)\n",
      "    ),\n",
      "    scale_init=Constant(\n",
      "      value=1.0\n",
      "    ),\n",
      "    use_fast_variance=True\n",
      "  ),\n",
      "  act3=GELU(approximate=False),\n",
      "  dropout=Dropout(\n",
      "    prob=0.5,\n",
      "    broadcast_dims=()\n",
      "  ),\n",
      "  fc2=Linear(\n",
      "    in_size=(256,),\n",
      "    out_size=(10,),\n",
      "    w_mask=None,\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[10]),\n",
      "        'weight': ShapedArray(float32[256,10])\n",
      "      }\n",
      "    )\n",
      "  )\n",
      ")\n",
      "\n",
      "Input: (4, 32, 32, 3)\n",
      "Output: (4, 10)\n",
      "\n",
      "Logits: [ 3.3256447  -1.429272    0.7167457   1.7075744   0.22491284 -1.362806\n",
      " -0.27098486 -0.27673492 -1.0504798  -1.4831724 ]\n"
     ]
    }
   ],
   "source": [
    "import brainunit as u\n",
    "\n",
    "\n",
    "class ModernCNN(brainstate.nn.Module):\n",
    "    \"\"\"CNN with modern activations and normalization.\"\"\"\n",
    "\n",
    "    def __init__(self, num_classes=10):\n",
    "        super().__init__()\n",
    "\n",
    "        # Block 1: Conv + BatchNorm + GELU\n",
    "        self.conv1 = brainstate.nn.Conv2d((32, 32, 3), out_channels=64, kernel_size=(3, 3), padding='SAME')\n",
    "        self.bn1 = brainstate.nn.BatchNorm2d((32, 32, 64))\n",
    "        self.act1 = brainstate.nn.GELU()\n",
    "        self.pool1 = brainstate.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
    "\n",
    "        # Block 2\n",
    "        self.conv2 = brainstate.nn.Conv2d((16, 16, 64), out_channels=128, kernel_size=(3, 3), padding='SAME')\n",
    "        self.bn2 = brainstate.nn.BatchNorm2d((16, 16, 128))\n",
    "        self.act2 = brainstate.nn.GELU()\n",
    "        self.pool2 = brainstate.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), in_size=self.bn2.out_size)\n",
    "\n",
    "        # Classifier\n",
    "        self.fc1 = brainstate.nn.Linear((128 * 8 * 8,), (256,))\n",
    "        self.ln = brainstate.nn.LayerNorm((256,))\n",
    "        self.act3 = brainstate.nn.GELU()\n",
    "        self.dropout = brainstate.nn.Dropout(prob=0.5)\n",
    "        self.fc2 = brainstate.nn.Linear((256,), (num_classes,))\n",
    "\n",
    "    def update(self, x):\n",
    "        # Block 1\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.act1(x)\n",
    "        x = self.pool1(x)\n",
    "\n",
    "        # Block 2\n",
    "        x = self.conv2(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.act2(x)\n",
    "        x = self.pool2(x)\n",
    "\n",
    "        # Classifier\n",
    "        x = u.math.flatten(x, start_axis=1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.ln(x)\n",
    "        x = self.act3(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "# Create and test\n",
    "brainstate.random.seed(0)\n",
    "model = ModernCNN(num_classes=10)\n",
    "\n",
    "# Forward pass\n",
    "x = brainstate.random.randn(4, 32, 32, 3)  # 4 images\n",
    "with brainstate.environ.context(fit=True) as env:\n",
    "    logits = model(x)\n",
    "\n",
    "print(\"Modern CNN with GELU + BatchNorm + LayerNorm:\")\n",
    "print(model)\n",
    "print()\n",
    "print(\"Input:\", x.shape)\n",
    "print(\"Output:\", logits.shape)\n",
    "print()\n",
    "print(\"Logits:\", logits[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "summary",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, you learned:\n",
    "\n",
    "✅ **Activation Functions**\n",
    "  - ReLU family (ReLU, Leaky ReLU, ELU)\n",
    "  - Modern activations (GELU, SiLU)\n",
    "  - Classic activations (Sigmoid, Tanh)\n",
    "  - Softmax for classification\n",
    "\n",
    "✅ **Normalization Layers**\n",
    "  - BatchNorm for large-batch training\n",
    "  - LayerNorm for sequences and transformers\n",
    "  - GroupNorm for small-batch scenarios\n",
    "\n",
    "✅ **Practical Guidelines**\n",
    "  - When to use each activation\n",
    "  - When to use each normalization\n",
    "  - How to combine them effectively\n",
    "\n",
    "### Quick Reference Card\n",
    "\n",
    "| Task | Activation | Normalization |\n",
    "|------|-----------|---------------|\n",
    "| **CNN (large batch)** | ReLU/GELU | BatchNorm |\n",
    "| **CNN (small batch)** | ReLU/GELU | GroupNorm |\n",
    "| **Transformer/NLP** | GELU | LayerNorm |\n",
    "| **RNN/LSTM** | Tanh (gates) | LayerNorm |\n",
    "| **Binary output** | Sigmoid | - |\n",
    "| **Multi-class output** | Softmax | - |\n",
    "\n",
    "### Best Practices\n",
    "\n",
    "1. 🎯 **Use ReLU/GELU** as default activations\n",
    "2. 📊 **Add normalization** after conv/linear layers\n",
    "3. ⚡ **Order**: Conv/Linear → Norm → Activation\n",
    "4. 🔍 **Experiment** with activation functions\n",
    "5. 📝 **Use appropriate normalization** for your batch size\n",
    "\n",
    "### Next Steps\n",
    "\n",
    "Continue with:\n",
    "- **Recurrent Networks** - Handle sequential data\n",
    "- **Training** - Optimize with gradient descent\n",
    "- **Advanced Architectures** - ResNets, Transformers"
   ]
  }
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