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Explain Neural Net as if I am a School Kid - Part 1
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/alexcpn/1437ab868e01b9681a12b87eba78b2c1/explain_neuralnet_asifiama__schoolkid.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "WUMcoquq5pRe" | |
| }, | |
| "source": [ | |
| "### Learning Neural Network by implementation\n", | |
| "\n", | |
| "The best way to learn something in a way that sticks is to really do it.\n", | |
| "\n", | |
| "- Author - Alex Punnen\n", | |
| "- alexcpn@gmail.com\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "28Dq1uzTeAg5" | |
| }, | |
| "source": [ | |
| "**Objectives**\n", | |
| "\n", | |
| "### Section 1\n", | |
| "\n", | |
| "1. Build out a simple two layered network using plain python and numpy.\n", | |
| "2. Derive out the backpropagation throratically and learn it via implement the same equations in the neural net\n", | |
| " - Note: The only leap of faith is to translate these equations from Scalar Calculus to Matrix Calculus. The derivation of Matrix Calculs parts are also linked in\n", | |
| "3. Understand the importance of Non linear activation function.\n", | |
| "\n", | |
| "#### Section 2\n", | |
| "\n", | |
| "4. Expand this to a three layer Network.\n", | |
| "5. Derive and Implement backpropagation for inner layers\n", | |
| "\n", | |
| "\n", | |
| "#### Section 3\n", | |
| "\n", | |
| "5. Neural Network as Universal function approximaters, modify network and train it to Approximate the Sine function.\n", | |
| "6. Debug why the network is not learning effectively\n", | |
| "7. Fine Tune learning rates, batch sizes, Input Standerdization for better learning.\n", | |
| "8. Add L2 Regularisation\n", | |
| "9. Understand about weight initalization -Xavier Glorot Initialization\n", | |
| "\n", | |
| "\n", | |
| "We will simulate other problems like Exploding/Vanishing gradients and Layer Normalization\n", | |
| "\n", | |
| "**References**\n", | |
| "\n", | |
| "All the derivations are from my previous blogs https://alexcpn.github.io/html/NN/ml/\n", | |
| "\n", | |
| "I have used ChatGPT 4 also to generate answers and also to debug and check code snippets.\n", | |
| "\n", | |
| "The intial Neural Network is taken from here http://iamtrask.github.io/2015/07/12/basic-python-network/\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "sC9m5k2349vT" | |
| }, | |
| "source": [ | |
| "### Below is the sort of first simple Neural Network that we will build\n", | |
| "\n", | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "wAVdeDemeAg7" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# These are the only two imports tou need\n", | |
| "import numpy as np # for arrays and array=matrix multiplicaion and other utilites\n", | |
| "import matplotlib.pyplot as plt # for plotting graphs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dihdwBm62yKb", | |
| "outputId": "e211326d-303c-4693-e32f-6a07486ea32f" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(4, 3)\n", | |
| "(4, 1)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Lets define the Training data set and the Target for the toy example\n", | |
| "# This x defines our Input; or in Neural Network terms, the training set\n", | |
| "\n", | |
| "x = np.array(\n", | |
| " [\n", | |
| " [0,0,1],\n", | |
| " [0,1,1],\n", | |
| " [1,0,1],\n", | |
| " [1,1,1]\n", | |
| " ])\n", | |
| "# and y is the desired ouput\n", | |
| "# Basically the first row in y represents the exected ouput for the first row of the training set.\n", | |
| "# The second row represents the expected output for the second row of the training set etc. We have only four row's of training data for out toy demonstartion\n", | |
| "\n", | |
| "# Let us define the expected output\n", | |
| "# The output becomes 1 only when two of the inputs are one; Or that is the model that we need the Neural Network to Learn\n", | |
| "# [0,0,1] -> [0], [0,1,1]-> [1] etc\n", | |
| "\n", | |
| "y = np.array(\n", | |
| " [\n", | |
| " [0],\n", | |
| " [1],\n", | |
| " [1],\n", | |
| " [0]\n", | |
| " ])\n", | |
| "print(x.shape)\n", | |
| "print(y.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "69cIvPwAg-Fz" | |
| }, | |
| "source": [ | |
| "Now we need to define the weights of the layers of our neural network.\n", | |
| "Note that the input is of shape $4*3$. So the first layer should be of shape that we can take dot product of.\n", | |
| "Let us use $3*4$. The shape of the dot product woud be $4*4$ and the next layer weight should be comptaible with it. Let's take the next layer weights as size $4*1$." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "17fD9mvZCHjb" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#---------------------------------------------------------------\n", | |
| "# Boiler plate code for calculating Sigmoid, derivative etc\n", | |
| "#---------------------------------------------------------------\n", | |
| "\n", | |
| "# seed random numbers to make calculation deterministic\n", | |
| "np.random.seed(1)\n", | |
| "\n", | |
| "# pretty print numpy array\n", | |
| "np.set_printoptions(formatter={'float': '{: 0.3f}'.format})\n", | |
| "\n", | |
| "# let us code our sigmoid funciton This is the non linearity\n", | |
| "def sigmoid(x):\n", | |
| " return 1/(1+np.exp(-x))\n", | |
| "\n", | |
| "# let us add a method that takes the derivative of x as well\n", | |
| "def derv_sigmoid(x):\n", | |
| " return sigmoid(x)*(1-sigmoid(x))\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Gq_X-qEfYG_x" | |
| }, | |
| "source": [ | |
| "#### The need for Non- Linearity\n", | |
| "Why is this called a non-linearity; This is because the Output is squeezed between 0 and 1 unlike a Linear Function say of the form $f(x)=ax+b $ where the output increases propotionally.\n", | |
| "\n", | |
| "Here is a short check" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "lnxMcje6XBzj", | |
| "outputId": "f189c99a-6bbf-47c1-8abc-109aa88590f7" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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Px44dmTZtGmPGjOHEiROXfJ45KYz3QX77/PPPPWuGffTRR8yZM4e5c+fStWvXHKfdLsrnAhfOL/Oc7rzzzmy9Rplf7du3By6+bfL6O5cXuX325DYxTFH/uUDecszvzyMRyZ2KKxG5LNdeey07d+5k+fLlF9y3evXquN1utm/fniV++PBhTp48SfXq1fM1N7fbne2vs9u2bQPwDDP77rvvcDqd/PrrrwwePJhrrrmG7t2758vrh4WF8eCDDzJz5kxiYmKoUKECL7zwAoDnXLdu3ZrteVu3bs23tsjtddLS0oiJicn3Nm/ZsiVgDmPLSaVKlXA6nezYsSPbYznFLsfltHHme3Xnzp3ZnpcX3377LZGRkXz//ffcdddd9OzZk+7du1/yQraZuZ77u3MxORWkzBkVXS5Xtl6jzK/MWSTzu20uRmaP4NkzZUL23qCLUatWrQv+gelihgdWr16dAwcOZBsR8M8//3gevxTn+zwSkfyj4kpELsvIkSMpU6YM9957L4cPH872+M6dO5k6dSoAvXr1AuCNN97Iss+UKVMA816Y/DZt2jTP94ZhMG3aNLy9venWrRtg/tXXZrNl+cv17t27mTlz5iW/psvlyja8qVKlSlSpUsUz5XzLli2pVKkS7777bpZp6H/55Re2bNmSb23RvXt3fHx8ePPNN7P8Jfujjz4iPj7+kl9n/vz5OcYz71XKbaiaw+Gge/fuzJw5M8v9Hzt27Mj3v6JfThtn3v/15ptvZomf+97NTWZvwtltvmLFijz9ESInYWFhNG3alE8//TTLe2vu3LmXNTV5fnE4HPTr14/vvvsux0Lj6NGjWfaF/Gubi5E5zHPx4sWeWGJiIp9++uklH7Nfv36sX78+x2nxM8+xTJkyQPaiLie9evXC5XJl+ewCeP3117HZbLnem5ibvHweiUj+KborBYpIsVCrVi2+/PJLbr31Vho0aMDdd99No0aNSEtLY9myZXzzzTcMHDgQgOjoaAYMGMD777/PyZMn6dSpEytXruTTTz/l+uuvp0uXLvmam9PpZM6cOQwYMIA2bdrwyy+/MGvWLJ555hnP/Uu9e/dmypQpXH311fTv358jR47w9ttvU7t2bTZs2HBJr3vq1CmqVq3KTTfdRHR0NGXLlmXevHn89ddfvPbaa4C5cOgrr7zCoEGD6NSpE7fffjuHDx9m6tSp1KhRg8cffzxf2iAkJIRRo0YxYcIErr76aq677jq2bt3KO++8Q6tWrS55sdW+fftSs2ZN+vTpQ61atUhMTGTevHn89NNPtGrVij59+uT63PHjx/Pbb7/Rvn17hg4d6rmQbNSoEevWrbvEM83uctq4adOm3H777bzzzjvEx8fTrl075s+fn+fetWuvvZbvv/+eG264gd69exMTE8O7775LVFQUp0+fvqTzeemll+jduzcdOnRg8ODBxMXF8dZbb9GwYcM8H/Po0aM8//zz2eI1a9bkjjvuuKS8Mr388sssWLCANm3aMGTIEKKiooiLi2PNmjXMmzePuLg4oGDaJq969OhBtWrVuOeeexgxYgQOh4MZM2YQEhLC3r17L+mYI0aM4Ntvv+Xmm29m8ODBtGjRgri4OP7v//6Pd999l+joaGrVqkVwcDDvvvsuAQEBlClThjZt2uR4P1mfPn3o0qULo0ePZvfu3URHR/Pbb7/x448/8thjj2W7D/BC8vJ5JCL5yIIZCkWkBNq2bZsxZMgQo0aNGoaPj48REBBgtG/f3njrrbeMlJQUz37p6enGhAkTjJo1axre3t5GRESEMWrUqCz7GIY5DXLv3r2zvQ6QbUrhzCmbJ0+e7IkNGDDAKFOmjLFz506jR48ehr+/v1G5cmVj3Lhx2aYL/+ijj4w6deoYvr6+Rv369Y2PP/441ympc5vOmLOme05NTTVGjBhhREdHGwEBAUaZMmWM6OjoHNek+vrrr41mzZoZvr6+Rvny5Y077rjD2L9/f5Z9Ms/lXLlNK52TadOmGfXr1ze8vb2NypUrG0OHDs2yztPZx8vLVOz//e9/jdtuu82oVauW4efnZzidTiMqKsoYPXp0lunRDSP7VOyGYRjz5883mjVrZvj4+Bi1atUyPvzwQ+OJJ54wnE5ntufm5edtGIaxYMECAzC++eabLPG8tHFObZmcnGw88sgjRoUKFYwyZcoYffr0Mfbt25enqdjdbrfx4osvGtWrVzd8fX2NZs2aGT///HO2abdzO5fMcz/3db777jujQYMGhq+vrxEVFWV8//332Y6Zm06dOuU4TTlgdOvWLdd2yO19X716dWPAgAFZYocPHzYeeughIyIiwvD29jZCQ0ONbt26Ge+//36+tk1ucvvcONvq1auNNm3aGD4+Pka1atWMKVOm5DoVe07H6tSpk9GpU6cssePHjxvDhg0zwsPDDR8fH6Nq1arGgAEDskxL/+OPPxpRUVGeJQcyp2XP6ed36tQp4/HHHzeqVKlieHt7G3Xq1DEmT57smdo9U15+NhfzeSQil89mGEXorkwRkXwycOBAvv322wL/S7jkn+uvv55NmzbleF+RiIhIcaB7rkREpNAlJydn2d6+fTuzZ8+mc+fO1iQkIiKSD3TPlYiIFLrIyEgGDhzoWWtr+vTp+Pj4MHLkSKtTExERuWQqrkREpNBdffXV/Pe//+XQoUP4+vrStm1bXnzxxRwXyRURESkudM+ViIiIiIhIPtA9VyIiIiIiIvlAxZWIiIiIiEg+0D1XOXC73Rw4cICAgABsNpvV6YiIiIiIiEUMw+DUqVNUqVIFu/38fVMqrnJw4MABIiIirE5DRERERESKiH379lG1atXz7qPiKgcBAQGA2YCBgYGW5pKens5vv/1Gjx498Pb2tjSXkkjtW7DUvgVL7Vvw1MYFS+1bsNS+BUvtW7CKUvsmJCQQERHhqRHOR8VVDjKHAgYGBhaJ4srf35/AwEDL31glkdq3YKl9C5bat+CpjQuW2rdgqX0Lltq3YBXF9s3L7UKa0EJERERERCQfqLgSERERERHJByquRERERERE8oHuubpEhmGQkZGBy+Uq0NdJT0/Hy8uLlJSUAn+t0kjtW7CKe/s6HA68vLy0JIOIiIjkiYqrS5CWlsbBgwdJSkoq8NcyDIPQ0FD27dunC7wCoPYtWCWhff39/QkLC8PHx8fqVERERKSIU3F1kdxuNzExMTgcDqpUqYKPj0+BXjS63W5Onz5N2bJlL7homVw8tW/BKs7taxgGaWlpHD16lJiYGOrUqVPszkFEREQKl4qri5SWlobb7SYiIgJ/f/8Cfz23201aWhpOp1MXdgVA7Vuwinv7+vn54e3tzZ49ezznISIiIpKb4ne1U0QUxwtFEbl4+l0XERGRvNJVg4iIiIiISD5QcSUiIiIiIpIPVFxZxOU2WL7zOD+ui2X5zuO43IbVKWGz2Zg5c6bVabBw4UJsNhsnT57MdZ9PPvmE4ODgQsspv+Tl3ERERESkeFJxZYE5Gw/S4ZXfuf2DP3n0q3Xc/sGfdHjld+ZsPFhgr3n06FGGDh1KtWrV8PX1JTQ0lJ49e/LHH3949jl48CDXXHNNgeWQV+3atePgwYMEBQVd1nFsNlu2rw4dOuRTlhfWuXNnHnvssSyx/Do3ERERESl6NFtgIZuz8SBDP1/Duf1Uh+JTGPr5Gqbf2ZyrG4Xl++v269ePtLQ0Pv30UyIjIzl8+DDz58/n+PHjnn1CQ0Pz/XUvhY+PT77l8vHHH3P11VdnObaV8vPcRERERKRoUc9VPjAMg6S0jAt+nUpJZ9z/bcpWWAGe2Pj/28yplPQsz0tOc+V4PMPI21DCkydPsmTJEl555RW6dOlC9erVad26NaNGjeK6667z7HfusMBly5bRtGlTnE4nLVu2ZObMmdhsNtatWwecGeL266+/0qxZM/z8/OjatStHjhzhl19+oUGDBgQGBtK/f/8sCy6npqbyyCOPUKlSJZxOJx06dOCvv/7yPJ7T0LlPPvmEatWq4e/vzw033JClKDyf4OBgQkNDPV/ly5fP8Vwz9/3kk08A2L17Nzabje+//54uXbrg7+9PdHQ0y5cvz/KcP/74g86dO+Pv70+5cuXo2bMnJ06cYODAgSxatIipU6d6es12796d47l99913NGzYEF9fX2rUqMFrr72W5TVq1KjBiy++yODBgwkICKBatWq8//77eTp/ERERyX8ut8GKmDhWH7OxIiauSNzeUZIU5/a1tOdq8eLFTJ48mdWrV3Pw4EF++OEHrr/++vM+Z+HChQwfPpxNmzYRERHBmDFjGDhwYJZ93n77bSZPnsyhQ4eIjo7mrbfeonXr1gV2HsnpLqKe/fWyj2MAhxJSaDz+tzztv/m5nvj7XPhHWLZsWcqWLcvMmTO54oor8PX1veBzEhIS6NOnD7169eLLL79kz5492Ya4ZRo/fjzTpk3D39+fW265hVtuuQVfX1++/PJLTp8+zQ033MBbb73FU089BcDIkSP57rvv+PTTT6levTqTJk2iZ8+e7Nixw1P8nG3FihXcc889vPTSS1x//fXMmTOHcePGXfAc8sPo0aN59dVXqVOnDqNHj+b2229nx44deHl5sW7dOrp168bgwYOZOnUqXl5eLFiwAJfLxdSpU9m2bRuNGjXiueeeAyAkJITdu3dnOf7q1au55ZZbGD9+PLfeeivLli3jwQcfpEKFClne16+99hoTJ07kmWee4dtvv2Xo0KF06tSJevXqFUo7iIhI8XL2xWmFmDja1q6Ew26zOq0SYc7Gg0z4aTMH41MAB//ZvoqwICfj+kQVyOij0qa4t6+lPVeJiYlER0fz9ttv52n/mJgYevfuTZcuXVi3bh2PPfYY9957L7/+eqaw+frrrxk+fDjjxo1jzZo1REdH07NnT44cOVJQp1HkeXl58cknn/Dpp58SHBxM+/bteeaZZ9iwYUOuz/nyyy+x2Wx88MEHREVFcc011zBixIgc933++edp3749zZo145577mHRokVMnz6dZs2a0bFjR2666SYWLFgAmD/z6dOnM3nyZK655hqioqL44IMP8PPz46OPPsrx+FOnTuXqq69m5MiR1K1bl0ceeYSePXvm6dxvv/12T3GZWWBejCeffJLevXtTt25dJkyYwJ49e9ixYwcAkyZNomXLlrzzzjtER0fTsGFDhg0bRsWKFQkKCsLHxwd/f39Pr5nD4ch2/ClTptCtWzfGjh1L3bp1GThwIMOGDWPy5MlZ9uvVqxcPPvggtWvX5qmnnqJixYqeNhURETlb5r3dd85YxX+2O7hzxqoCv7e7tMi8vcO88D8j8/YOtfHlKQnta2nP1TXXXHNREyi8++671KxZ0zNsqkGDBixdupTXX3/dc7E9ZcoUhgwZwqBBgzzPmTVrFjNmzODpp5/O/5MA/LwdbH7uwhf7K2PiGPjxXxfc75NBrWhd0+zBcbvdnEo4RUBgQLbFTP28s1+s56Zfv3707t2bJUuW8Oeff/LLL78wadIkPvzww2w9fwBbt26lSZMmOJ1OTyy33r8mTZp4vq9cuTL+/v5ERkZmia1cuRKAnTt3kp6eTvv27T2Pe3t707p1a7Zs2ZLj8bds2cINN9yQJda2bVvmzJlzwfN+/fXX6d69u2c7LOzi/uJx9rllPvfIkSPUr1+fdevWcfPNN1/U8c61ZcsW+vbtmyXWvn173njjDVwul6cgOzsPm81GaGhoqf6DgYgUf+pZKRhW3dtdGrjcBhN+2pzr7R02YMJPm+lSrxL2f9/LhgEGBpl3cpy9bWDeWmL8G+c8jxnmg1m2s+x3VlLnfY0s8bP2u5g8z4p79r2YXDnnXP99zOVyM+qHvy/YvldFhRbpz4piNaHF8uXLs1woA/Ts2dMzXC0tLY3Vq1czatQoz+N2u53u3btnu1fmbKmpqaSmpnq2ExISAEhPTyc9PT3Lvunp6RiGgdvtxu12e+JOrwt3AravVYHQQCeHE1JyfOPYgNAgJ+1rVfC8aQzDRoaPAz9vBzZb1jeS+cuU9zGoPj4+dOvWjW7dujF69GiGDBnCuHHjuPvuuz37ZJ5X5nHPPsfM7zP3ydx2OBye7w3DwNvbO8vzcnrOue2XeS657ZP52Nn7n5tfTipVqpSl0Mt8js1mw+VyeY5jGAbp6ekXPDeAjIwM3G43fn5+2fI617mP5+Xczt4n82fu5eWVZZ/M/C90/lY7u32Leq65yfx9SE9Pz7H30UqZn0/nfk5J/lEbF4xfNx3m+dn/cCghlcxhP6GBvozpVZ+eDStbnV6x5XIbjL/Avd1jZ24kItiJDXAbBq5/L8xd7qzfuw3zy+U2P8NdhoHbALfbOOtx8xhud9bH3Qbm9jn7udxGlseNf49/5rX+fX0j5/1choFxzmvlOed/H3O7LyHnf7fTM9ykZJzn33zgYHwK9cZe+I+/cvEy23f5jiO0qZn9NpKCdDH/BhSr4urQoUNUrpz1Q7dy5cokJCSQnJzMiRMncLlcOe7zzz//5Hrcl156iQkTJmSL//bbb/j7+2eJeXl5ERoayunTp0lLS7vocxjRrQZP/vAPNsjy4ZdZNj3ZtQaJp09le96pU9ljlysyMpLTp097ikmA5ORkEhISqFatGp9//jlHjx713KO1ZMkSwBzal5CQ4Jmk4tSpU55etZSUFAzDyHLM1NRUXC4XCQkJhISE4OPjw7x58zy9Punp6fz111888MADOR63Vq1a/PHHH1mOuWTJkmyvk5PM8zlXxYoViYmJ8bTrunXrSEpKIiUlhYSEBE6fPp3lXDPzAUhKSiIhIYH69evz22+/MXz48Bxf2263Z3v9nM5t8eLFWfZZsGABtWrVIjExETAv7jPzyuRyuUhNTb3g+RcVBfH+LSxpaWkkJyezePFiMjIyrE4nR3PnzrU6hRJPbZx/1h+3MWNb5h8kz/zR8FBCCsO+Wsfgum6iKxSPm9cNA9wGZBiQ4c7h/57vbTnuk+4G19n75HQM45zjnOd10t1gcP6/6B89nUbvacsKqYUkv9jOumq0/fsf21nbOcUyt3OKnb2d5Ri5Hf+s/XI9/jn75HW/zOMnpsOx1Av3SP22ZAXHtxTuZ8TZE7NdSLEqrgrKqFGjslwgJyQkEBERQY8ePQgMDMyyb0pKCvv27aNs2bJZhszl1Q2tAvHz8+O5n7dwKOHMeNLQICdjezfg6kZZp+k2DINTp04REBCQrecqr44fP86tt97KwIEDadKkCQEBAaxatYq33nqLvn37ZjlHPz8/AgMDGTx4MC+88AIjRozgqaeeYu/evbzzzjuAOUFGYGCgp/AMCAjwHMPpdGKz2bIc09fXF4fDQWBgIIGBgTzwwAOMHz+e8PBwqlWrxuTJk0lOTubBBx/M8bjDhw+nY8eOfPDBB1x33XX89ttv/P7779leJyeZ53Ourl27MmPGDDp37sypU6eYOHEi3t7eOJ1OAgMDKVu2LABlypTxPD+z58Xf35/AwEDGjh1LdHQ0o0aN4v7778fHx4cFCxZw8803U7FiRWrVqsW6deuIi4ujbNmylC9fPtu5PfXUU7Rp04Y333yTW265heXLl/Phhx8ybdo0z+va7XZPXpkcDge+vr4XPH+r5cf712opKSn4+flx5ZVXXtLvfEFKT09n7ty5XHXVVXh7e1udTomkNs5fLrfBS68tBlJzeNSGDfjlsD8j77gyx2E/hmGQ7jJIzXCT5nKTdvb/c40Zue93oednPjfHx9ykZrizDMcqLvy87Ti9HTjsNuw2G3Yb5v/tNhyZ23bz/45/4+bj5n5nYubjNpsNx7+P2W02HHYbtizP/Xe/f4/vsJsjMM5+LfM4nJVTLvtdbM7/7uew5z3nLPlnvoYd1u+P58lvN16wfd/t35QW1cths51dmNiybNvO2sZmOyv+7zqdmdvF9N/OS7EiJo47Z6y64H49OrYp9J6ri/ljdrEqrkJDQzl8+HCW2OHDhwkMNAsWh8OBw+HIcZ/zrS3k6+ub4wx63t7e2f4xdblc2Gw27HZ7tnug8qpXkyr0bBTGypg4jpxKoVKAk9Y1y+f4D0nmBX3ma16KwMBA2rRpw9SpUz33PEVERDBkyBCeeeaZLMfNPK/g4GB++uknhg4dSvPmzWncuDHPPvss/fv3x9/fP8v5n/v92f/PzP3s2CuvvIJhGAwYMIBTp07RsmVLfv31VypUqJDtGHa7nXbt2vHBBx8wbtw4xo0bR/fu3RkzZgwTJ068YJvk9nOaMmUKgwYNonPnzoSGhjJ16lTWrFnj2f9C52a32z09V8888wxXXHEFfn5+tGnThjvuuAO73c6IESMYMGAAjRo1Ijk5mZiYmGzHaNmyJf/73/949tlnef755wkLC+O5555j8ODBWfLN6ed/Oe+JwpIf71+r2e12bDZbjp8HRUVRzq2kUBtfPLfb4FRKBnFJaZxISuNkUhp/xZz4dyhgzsxhP6l0e30JXg77mcImw03qv0VNUWa3gY+XHV8vBz5ednwcdny97Ob3/27n9L3vWdue555nn5wfc/B3bDwPfbnmgnnOGNiatrUqFEKLlCyRlYJ4be4ODsWf//aOqxpVKdL3BBVVbWtXIizIecH2teL+zIv5/LcZF3PTTgGy2WwXnIr9qaeeYvbs2fz999+eWP/+/YmLi/NMbtCmTRtat27NW2+9BZgXd9WqVWPYsGF5ntAiISGBoKAg4uPjc+y5iomJoWbNmoXyV2y3201CQgKBgYGWX5x+8cUXDBo0iPj4ePz8/CzNJb8UpfYtiUpC+xb27/zFSE9PZ/bs2fTq1UsX/gVEbWxKy3BzMimNE0npnkIpLvHM9yeS0s953Nwu6KVpvOy2XAsXX28HvrkUI9kKn5we83ZcsDg69zEvh7Wfcy63QYdXfr/gxenSp7rq4v8SZU4YAjnf3qEJQy5PUW3f89UG57K05+r06dOeaa3BnGp93bp1lC9fnmrVqjFq1ChiY2P5z3/+A8ADDzzAtGnTGDlyJIMHD+b333/nf//7H7NmzfIcY/jw4QwYMICWLVvSunVr3njjDRITEz2zB0re/ec//yEyMpLw8HDWr1/PU089xS233FJiCisRkdLGMAxOp2Zw8t8iyFMUJZ4pjM4USmmcSDS/T0xzXfJrlvFxEOzvQ7ky3tiAv2MvPLxmTK8GNK0WnLUXJ0vvjvm9XQVCFg67jXF9ohj6+Zpc7+0e1ydKhdVluLpRGNPvbH7WOkym0GK0DlNRVhLa19LiatWqVXTp0sWznXnf04ABA/jkk084ePAge/fu9Txes2ZNZs2axeOPP87UqVOpWrUqH374YZY1j2699VaOHj3Ks88+y6FDh2jatClz5szJNsmFXNihQ4c87RgWFsbNN9/MCy+8YHVaIiKFoqhPFZ7hcnMy+aweo8S07EXTvwVSZiw+OY1016V1J9ltEOTnTTl/H8qV8aGcv7dZNHn+f9b3Zcz9gv298fU6M8tmXntWBnWoWaTaujgpCRenRd3VjcK4KiqU5TuO8NuSFfTo2KbIfT4UZ8W9fYvMsMCiRMMCSw+1b8EqCe2rYYGl05yNB7NdnIYV4MVpcpqLE0lpxJ1VIJ07zO7cnqaElEufvdLXy+4pfsr9WwwF+/tQPodYZtEU6PTOl56iojrsp6RxuY1ie3FaXOgzuGAVpfYtNsMCRUREiprLWYTV7TZISEnPdm+SpxfJUxxlLZpSL2OihkCnF+XK+Hh6kbIUTf7e//YynR3zwc/HujXb1LNSOBx2G21qluf4FoM2uUyaJSL5T8WViIjIv1xugwk/bT7vIqwjvt3Aypg44pMzPEVTZpEUn5x+yZM4eNlt2YbbmUXROUVTmTPbQX7elk+icCmK+7AfEZHcqLgSEZFSzzAMDsSn8O2q/Vl6U3JyKiWDGX/sPu8+Z0/iUO7c+5H+LZDOLZrK+nqVqjVt1LMiIiWRiisRESlVMgupv/fHszE2ng2x8WyKjed4Ylqej9GlXgitapY/p2gyvw86ZxIHEREpPVRciYhIiWUYBrEnk9kYG8/fsfH8HZvAxth44nIopLzsNqoE+7E3LumCx73vylpahFVERLJRcSUiIiXCuYXUhv3xbDqQkGshVbdyAI3Dg2hUNYjG4UHUDw3A22HP01ThrWuWL/DzERGR4kfFlQBgs9n44YcfuP76661OpdhYuHAhXbp04cSJEwQHBxfoax0/fpwGDRqwcuVKatSoUaCvVVocO3aMqKgo1qxZQ9WqVa1ORy6SYRjsP3F2j5Q5xO9EUnq2fc8tpJqEB1EvNACnd85D97QIq4iIXKriN8VQcbfgJVg0KefHFk0yHy8AAwcOPG/hdPDgQa655poCee38YLPZsn116NCh0F6/c+fOPPbYY1li7dq14+DBgwQFBRX467/wwgv07ds3S2G1d+9eevfujb+/P5UqVWLEiBFkZJx/3Zu4uDjuuOMOAgMDCQ4O5p577uH06dOex7du3UqXLl2oXLkyTqeTyMhIxowZQ3p69gvWwpSSksJDDz1EhQoVKFu2LP369ePw4cPnfU5O7xmbzcbkyZMBqFixInfffTfjxo0rjFOQy2AYBvvikpj990FemfMPd320guYT59Jx0gKGfrGGdxbuZMn2Y5xISsfLbqNhlUBuaxXB89c34seH2rNxQk9mP9qRV25qwl1XVCc6IjjXwgrOTBUeGpR1XbPQIKfWYBIRkfNSz1VhsztgwQvm951GnokvmmTGu4y2JK3Q0FBLXvdshmHgcrnw8sr5bfnxxx9z9dVXe7Z9fHwKK7Uc+fj4FEq7JSUl8dFHH/Hrr796Yi6Xi969exMaGsqyZcs4ePAgd999N97e3rz44ou5HuuOO+7g4MGDzJ07l/T0dAYNGsR9993Hl19+CYC3tzd33303zZs3Jzg4mPXr1zNkyBDcbvd5j3uxbDYbMTExee6Fe/zxx5k1axbffPMNQUFBDBs2jBtvvJE//vgj1+ccPHgwy/Yvv/zCPffcQ79+/TyxQYMG0aJFCyZPnkz58hrmVRRk9kid3Rv1d2w8J3PokfJ2nNUjFR5Ek6pmj1R+TCahqcJFROSSGJJNfHy8ARjx8fHZHktOTjY2b95sJCcnnwm63YaRejrvX/MnGsa4QPP/OW2f9eVKTjBOHIk1XMkJ2Y/jduf5nAYMGGD07ds318cB44cffjAMwzBiYmIMwPjuu++Mzp07G35+fkaTJk2MZcuWZXnOkiVLjA4dOhhOp9OoWrWq8fDDDxunT5/2PP6f//zHaNGihVG2bFmjcuXKxu23324cPnzY8/iCBQsMwJg9e7bRvHlzw9vb21iwYMEF88vLY0FBQcbHH398wfNxuVzGiRMnDJfLZSxdutTo1KmT4efnZwQHBxs9evQw4uLijAEDBhiYo4M8XzExMZ78T5w44Xndb7/91oiKijJ8fHyM6tWrG6+++mqWvKpXr2688MILxqBBg4yyZcsaERERxnvvvZfrz8UwDOObb74xQkJCssRmz55t2O1249ChQ57Y9OnTjcDAQCM1NTXH42zevNkAjL/++ssT++WXXwybzWbExsbm+vqPP/640aFDB8MwzPd/VFSUMWTIEM/jO3bsMMqWLWt89NFH2Z57dvueLbMN8+LkyZOGt7e38c0333hiW7ZsMQBj+fLleTqGYRhG3759ja5du2aL16xZ0/jwww9zfV6Ov/NFRFpamjFz5kwjLS3N6lQuidvtNvYcSzR+Xn/AeGn2FuOOD/40oif8alR/6udsX7WfmWX0fnOx8fR3643P/9xtrN93wkhJzyjwHIt7Gxd1at+CpfYtWGrfglWU2vd8tcG51HOVH9KT4MUqF/+8xZPNr9y2McdtBuf2/GcOgE+Zi3/dPBo9ejSvvvoqderUYfTo0dx+++3s2LEDLy8vdu7cydVXX83zzz/PjBkzOHr0KMOGDWPYsGF8/PHHAKSnpzNx4kTq1avHkSNHGD58OAMHDmT27NlZXufpp5/m1VdfJTIyknLlyhXq+Wzbtg2AdevW0a1bNwYPHszUqVPx8vJiwYIFuFwupk6dyrZt22jUqBHPPfccACEhIezevTvL8VevXs0tt9zC+PHjufXWW1m2bBkPPvggFSpUYODAgZ79XnvtNSZOnMgzzzzDt99+y9ChQ+nUqRP16tXLMe8lS5bQokWLLLHly5fTuHFjKleu7In17NmToUOHsmnTJpo1a5btOMuXLyc4OJiWLVt6Yt27d8dut7NixQpuuOGGbM/ZsWMHc+bM4cYbbwTA6XTyxRdf0KZNG3r37s21117LnXfeyVVXXcXgwYPP0/qXbvXq1aSnp9O9e3dPrH79+lSrVo3ly5dzxRVXXPAYhw8fZtasWXz66afZHmvdujVLlizhnnvuyde8JSvDMNgXd6ZH6u/Yk2yMTSA+OeceqXqhZo9U4/BgGocHUTe0rKY3FxGRIk/FleTqySefpHfv3gBMmDCBhg0bsmPHDurXr89LL73EHXfc4bkPqU6dOrz55pt06tSJ6dOn43Q6s1xsR0ZG8uabb9KqVStOnz5N2bJlPY8999xzXHXVVRfM5/bbb8fhOHNx9fnnn1/UBBy5nU+VKlWYPHkyLVu25J133vHs37BhQ8/3Pj4++Pv7n3cY4JQpU+jWrRtjx44FoG7dumzevJnJkydnKa569erFgw8+CMBTTz3F66+/zoIFC3Itrvbs2UOVKlmL90OHDmUprADP9qFDh3I8zqFDh6hUqVKWmJeXF+XLl8/2nHbt2rFmzRpSU1O57777PEUlQNOmTXn++ee59957ue2229izZw8///xzbs1y2Q4dOoSPj0+2SUMqV66c67me69NPPyUgIMBTJJ6tSpUqrF27Nj9SlX8ZhsHeuKQsQ/vOV0jVDw2kUXjQv8WUCikRESm+VFzlB29/sxfpYix93eylcviAKw2uHAEdHs+2m9vtJuHUKQIDArDbz5l/xNv/MpK+sCZNmni+Dwszb+A+cuQI9evXZ/369WzYsIEvvvjCs49hGLjdbmJiYmjQoAGrV69m/PjxrF+/nhMnTuB2uwFzIoaoqCjP887uSTmf119/PUvvRWZOl3s+VapUYf369dx8880Xdbxzbdmyhb59+2aJtW/fnjfeeAOXy+UpDM/Ow2azERoaypEjR3I9bnJyMk6nM9fHC8LXX3/NqVOnWL9+PSNGjODVV19l5Mgz9wg+8cQTzJw5k2nTpvHLL79QocL51/vp1asXS5cuzRJr2LAhNpt5/0r16tXZtGlT/p/Iv2bMmMEdd9yRYzv6+fmRlHThdY0kZ5mF1Ib9Z+6P2hgbT0JK9slVfBx26ocFZC2kKgfg46W5lUREpGRQcZUfbLaLG563aJJZWHUZbU5qkTmZhcMn6yQXAG43eLvM459bXBUwb29vz/eZF8GZBdLp06e5//77eeSRR7I9r1q1aiQmJtKzZ0969uzJF198QUhICHv37qVnz56kpWVdc6ZMmby1XWhoKLVr184Wt9lsGEbWFWlymt3ufOfj5+eXpxzyw9l5ZOaSmUdOKlasyIkTJ7LEQkNDWblyZZZY5ux5ufWu5VTEZWRkEBcXl+05ERERAERFReFyubjvvvt44oknPAXikSNH2LZtGw6Hg+3bt2eZaCQnH3zwAampqZ7tOnXqMHv2bMLDw4HsbXJu3mlpaZw8eTJL79Xhw4fzNKHIkiVL2Lp1K19//XWOj8fFxRESEnLB44hZSO05npRlogkVUiIiImeouCpsZ88KmFlIZf4/p1kEi6jmzZuzefPmHIsdgL///pvjx4/z8ssvey7UV61aVSC5hISEZJkZbvv27RfdE9G4cWPmz5/PhAkTcnzcx8cHl8t13mM0aNAg2+x1f/zxB3Xr1s0ynPFiNWvWjM8//zxLrG3btrzwwgscOXLEM9Rv7ty5BAYGZukVPPc5J0+eZPXq1Z57uH7//Xfcbjdt2rTJ9fXdbjfp6em43W7PeQwePJjGjRtzzz33MGTIELp3706DBg1yPUZ4eHi2ntfq1avnabbAFi1a4O3tzfz58z0z/W3dupW9e/fStm3bCz7/o48+okWLFkRHR+f4+MaNG+ncufMFj1PaZBZSGzILqf3xbDwQz6lcCqkGZxVSjVRIiYhIKaXiqrC5XVkLq0yZ2+7zX8Bfjvj4eNatW5clVqFCBU/xczGeeuoprrjiCoYNG8a9995LmTJl2Lx5M3PnzmXatGlUq1YNHx8f3nrrLR544AE2btzIxIkT8+lMsuratSvTpk2jbdu2uFwunnrqqfP2hOTk6aefJjo6mgcffJAHHngAHx8fFixYwM0330zFihWpUaMGK1asYPfu3ZQtWzbHabufeOIJWrVqxcSJE7n11ltZvnw506ZNy3If16Xo2bMno0aN4sSJE54JP3r06EFUVBR33XUXkyZN4tChQ4wZM4aHHnoIX19fAFauXMndd9/N/PnzCQ8Pp0GDBlx99dUMGTKEd999l/T0dIYNG8Ztt93muafriy++wNvbm8aNG+Pr68uqVasYNWoUt956q6dN3377bZYvX86GDRuIiIhg1qxZ3HHHHfz5558FMj1+UFAQ99xzD8OHD6d8+fIEBgby8MMP07Zt2yyTWWTeC3j2xBwJCQl88803vPbaazkeOykpidWrV+frNPPFkdttsCfurB6p8xVSXnYahKqQEhERyYmKq8LWZVTujxVwj9XChQuzzSJ3zz338OGHH170sZo0acKiRYsYPXo0HTt2xDAMatWqxa233gqYvUmffPIJzzzzDG+++SbNmzfn1Vdf5brrrsuXcznba6+9xqBBg+jYsSNVqlRh6tSprF69+qKOUbduXX777TeeeeYZWrdujZ+fH23atOH2228HzMkwBgwYQFRUFMnJycTExGQ7RvPmzfnf//7Hs88+y8SJEwkLC+O5557LMpnFpWjcuLHn2Pfffz8ADoeDn3/+maFDh9K2bVvKlCnDgAEDskw8kZSUxNatW7MMkfziiy8YNmwY3bp1w263069fP958803P415eXrzyyits27YNwzCoXr06w4YN4/HHzfsB//nnH0aMGMFHH33kKcrfeecdmjRpwtixY3nllVcu61xz8/rrr3vyTU1NpWfPntmK1q1btxIfH58l9tVXX2EYhufneK4ff/yRatWq0bFjxwLJuyC53AYrYuJYfcxGhZi4PK/B5HYb7D6emGVo36bYBE6l5lJIhQXSODwwSyHl7VAhJSIikhObce7NKkJCQgJBQUHEx8cTGBiY5bGUlBRiYmKoWbNmoUwy4Ha7SUhIIDAwMPuEFnLZikv7zpo1ixEjRrBx48Yinee5inr7XnHFFTzyyCP0798/130K+3c+L+ZsPMiEnzZzMD7FEwsLcjKuTxRXNzoz0cu5hdSG/fFsPqBC6mKkp6cze/ZsevXqddE94nJhat+CpfYtWGrfglWU2vd8tcG51HMlUgz07t2b7du3Exsbe0nDOCW7Y8eOceONN+baq1VUzdl4kKGfr+Hcv4odik/hgc/XMLh9Dew2G3/H5l5I+XoKqTND++pULlvqCykREZHLpeJKpJjIXFNM8kfFihWzTC9fHLjcBhN+2pytsAI8sRl/7M4SP7eQalw1iNqVVEiJiIgUBBVXIiLFxMqYuCxDAXPTI6oy3aMq0zhchZSIiEhhUnElIlJMbDuckKf9ejcJo2/T8ALORkRERM6l4uoSaR4QkdKhKPyuHzudyvSFO/l02e487V8poGhMvCEiIlLaqLi6SJmzlSQlJeHn52dxNiJS0DIXpLZipqL45HQ+WLyLGX/EkJRmroHn47CR5sq54LMBoUFOWtfMvg6biIiIFDwVVxfJ4XAQHBzMkSNHAPD398dmu/DaMpfK7XaTlpZGSkpKkZzKurhT+xas4ty+hmGQlJTEkSNHCA4OxuFwFNprJ6Zm8Mmy3by3aCcJ/y7k2zg8iCd71iMpNYMHv1hj5njWczI/hcb1icrTelciIiKS/1RcXYLQ0FAAT4FVkAzDIDk5GT8/vwIt4kortW/BKgntGxwc7PmdL2gp6S6+XLGXdxbu4NjpNADqVi7L8Kvq0bNhZU8bTr+zebZ1rkJzWOdKRERECpeKq0tgs9kICwujUqVKpKenF+hrpaens3jxYq688krLF1AridS+Bau4t6+3t3eh9Filu9x8u3o/b87f7imYqpX3Z/hVdekTXSVbT9TVjcK4KiqU5TuO8NuSFfTo2Ia2tSupx0pERMRiKq4ug8PhKPALL4fDQUZGBk6ns1henBZ1at+CpfY9P7fb4KcNB3h97jZ2Hzfv7QoNdPJItzrc3LLqeadQd9httKlZnuNbDNrULK/CSkREpAhQcSUiUsgMw+C3zYeZ8ts2th4+BUCFMj482KU2d7SphtO78O7vEhERkfyj4kpEpJAYhsGS7cd47betrN8fD0CA04v7r4xkUPualPHVR7KIiEhxpn/JRUQKwV+745j861ZWxsQB4O/jYFD7GtzXsRZB/hoyKSIiUhKouBIRKUAbY+N59betLNx6FAAfLzt3tqnOg11qUbGsr8XZiYiISH5ScSUiUgC2Hz7FlLnb+GXjIcCcgOKWlhE83LU2VYK1ALmIiEhJpOJKRCQf7T2exBvztjFzXSxuA2w26Btdhce616VGxTJWpyciIiIFSMWViEg+OBSfwlu/b+frv/aR4TYA6NmwMsOvqke90ACLsxMREZHCoOJKROQyHD+dyvSFO/nszz2kZrgB6FinIk/2qEd0RLC1yYmIiBQnC14CuwM6jcz+2KJJ4HZBl1GFn9dFUHElInIJElLS+XDxLj5aGkNimguAVjXK8WSPerSJrGBxdiIiIsWQ3QELXjC/b/f4mfiiSWa8y2hr8roIKq5ERC5CUloGnyzbzXuLdhGfnA5Ao/BAnuxRj051Q7DZbBZnKCIiUkxl9lgteAG7KwOHqzb2Ja/C4pfNwiqnHq0iRsWViEgepGa4+HLFXt5esJNjp1MBqF2pLE9cVZerG4WqqBIREckPV46A4ztxLH6F3tiwYRSbwgpUXImInFeGy813a/bz5vwdxJ5MBqBaeX8e616Hvk3DcdhVVImIiOSLfX/B/AmwewkANgwMhw+2YlJYgYorEZEcud0GP204wBvzthNzLBGAyoG+PNKtDre0jMDbYbc4QxERkRLiyBaYPxG2zjK3bQ4wXLhtXthdaeY9V8WkwFJxJSJyFsMwmLv5MFPmbuOfQ6cAKF/Ghwc71+LOK6rj9HZYnKGIiEgJcWIPLHwZ1v8XMMBmh8qN4NAGXFc+zc+norg2YDOOzEkuikGBpeJKRASzqFq64xiv/raN9ftOAhDg9OK+jpEM6lCTsr76uBQREckXp4/Cktdg1UfgSjNjDfpAQBVY+R50GY273eMwezbujk/icJw1i2ARL7B0tSAipd7qPXFM/nUrf+6KA8DP28Gg9jW478pIgv19LM5ORESkhEhJgOXTYPnbkHbajNW8ErqNh6otzHWuMievSE8/87zMgsrtKvSUL5aKKxEptTbGxvPab1tZsPUoAD4OO/3bVOOhLrUJCfC1ODsREZESIj0F/vrQ7K1KNv+QSVhT6D4eanU5s9/5Fggu4j1WmVRciUips+PIKabM3cbsvw8B4LDbuLlFVR7uVofwYD+LsxMRESkhXBnm/VQLX4aE/WasQh3oNhYaXAclcBkTFVciUmrsi0vijXnb+WHtftyG+Zl+XXQVHutel5oVy1idnoiISMlgGLDl/+D35+HYNjMWGA6dn4bo/uAouSVIyT0zEZF/HU5I4a3ft/P1X/tIdxkAXBVVmSd61KV+aKDF2YmIiJQguxbCvAlwYI257VceOj4Bre4Fb6elqRUGFVciUmLFJabx7qKdfLpsN6kZbgA61qnIEz3q0TQi2NrkRERESpLY1WZRFbPI3PYuA20fgnbDwBlkbW6FSMWViJQ4CSnpfLgkhhlLYzidmgFAi+rleLJHPdrWqmBxdiIiIiXI0W3w+0RzGCCA3Rta3WP2VpWtZG1uFlBxJSIlRnKai0+X7+bdRTs5mWRO4dqwSiBP9qhH53oh2ErgjbMiIiKWOLkPFr0M674Eww3YIPo26DwKylW3OjvLqLgSkWIvNcPFVyv3MW3BDo6eSgWgVkgZnuhRj6sbhmK3q6gSERHJF4nHYekUWPkBuMx/c6nXG7qOgcpR1uZWBKi4EpFiK8Pl5vs1sUydv53Yk8kAVC3nx2Pd63JDs3AcKqpERETyR+opWP4OLHsL0k6ZseodoPs4iGhtbW5FiIorESl23G6Dn/8+yBtzt7HrWCIAlQN9Gda1Dre2jMDHy25xhiIiIiVERiqs+hgWT4akY2YstIlZVNXqViLXqrocKq5EpNgwDIP5W47w6m9b+eeQ+Vezcv7ePNi5Nne1rY7T22FxhiIiIiWE2wUbvoYFL0H8XjNWvhZ0HQ1RN4Bdf8jMiYorESkW/thxjMm/bmXdvpMABPh6MeTKSAZ3qElZX32UiYiI5AvDgH9mmTMAHv3HjAWEQaenoNmd4PC2Nr8iTlckIlKkrd5zgld/3cryXccBcHrbGdiuJg90iiTY38fi7EREREqQmCUwbzzErjK3ncHQcTi0vg+8/azMrNhQcSUiRdKmA/FM+W0b8/85AoCPw07/NtV4sEstKgWU/BXeRURECs2BtTD/Odj5u7nt7Q9XDIV2j4BfsKWpFTcqrkSkSNl59DRT5m5j1oaDADjsNvo1D+eRbnWoWs7f4uxERERKkGM7YMHzsOkHc9vuBS0GwZUjIKCytbkVUyquRKRI2BeXxNT52/l+zX7chhnrE12Fx7vXITKkrLXJiYiIlCTxsbDoFVj7ORguwAaNb4Yuo6B8pNXZFWsqrkTEUkcSUpi2YAf/XbmXdJdZVXVvUJknetSlQVigxdmJiIiUIElxsPR1WPk+ZKSYsbpXQ9exENrI2txKCBVXIlIgXG6DFTFxrD5mo0JMHG1rV8qyqO+JxDTeXbSTT5fvJiXdDUD72hV4okc9mlcrZ1XaIiIiJU9aIvz5DvzxJqQmmLFqbaHbOKje1trcShgVVyKS7+ZsPMiEnzZzMD4FcPCf7asIC3Iyrk8U7WtX5MMlMXy0NIbTqRkANK8WzJM969GuVkVrExcRESlJMtJgzaewaBIkmhNEUbmRWVTVuUoLABcAFVcikq/mbDzI0M/XYJwTPxSfwgOfr8Hfx0FSmguABmGBjOhZly71KmHTB7yIiEj+cLvg729hwQtwco8ZK1cDuoyBRv20AHABUnElIvnG5TaY8NPmbIUV4IklpbmoWdGfJ3rUo1ejMOx2FVUiIiL5wjBg26/mtOpHNpmxspWh00hodjd4aX3IgqbiSkTyzcqYuH+HAp7fxL6N6FAnpBAyEhERKSX2LDMXAN63wtz2DYIOj0KbB8CnjKWplSaW9wm+/fbb1KhRA6fTSZs2bVi5cmWu+6anp/Pcc89Rq1YtnE4n0dHRzJkzJ8s+48ePx2azZfmqX79+QZ+GiABHTl24sAI4nphWwJmIiIiUEgc3wBc3w8fXmIWVlxPaPwaProOOT6iwKmSW9lx9/fXXDB8+nHfffZc2bdrwxhtv0LNnT7Zu3UqlSpWy7T9mzBg+//xzPvjgA+rXr8+vv/7KDTfcwLJly2jWrJlnv4YNGzJv3jzPtpeXOuhECkOlAGe+7iciIiK5OL4TFrwIG781t20OaDEArhwJgWHW5laKWdpzNWXKFIYMGcKgQYOIiori3Xffxd/fnxkzZuS4/2effcYzzzxDr169iIyMZOjQofTq1YvXXnsty35eXl6EhoZ6vipW1AxkIoWhdc3yhAblXjjZgLAgJ61rli+8pEREREqShIPw8+PwduszhVWjfjDsL7j2dRVWFrOsSyctLY3Vq1czatQoT8xut9O9e3eWL1+e43NSU1NxOrNeuPn5+bF06dIsse3bt1OlShWcTidt27blpZdeolq1arnmkpqaSmpqqmc7IcGc/z89PZ309PSLPrf8lPn6VudRUql981+HWuX5ds2BbPHMaStGX1MPtysDt6tw8yqJ9P4teGrjgqX2LVhq34JV6O2bfBL7n29hX/k+toxkANyR3XB1GQ2hTTKTKpxcCkFRev9eTA42wzBymtirwB04cIDw8HCWLVtG27ZnFi8bOXIkixYtYsWKFdme079/f9avX8/MmTOpVasW8+fPp2/fvrhcLk9x9Msvv3D69Gnq1avHwYMHmTBhArGxsWzcuJGAgIAccxk/fjwTJkzIFv/yyy/x9/fPpzMWKfl2JMDbmx24DRtOh0GK68xMgME+BjfWcBNdwZKPHBERkWLJ4U4l8shv1D4yCx9XEgBxZWqzOewWjgdoXoHCkJSURP/+/YmPjycwMPC8+xar4uro0aMMGTKEn376CZvNRq1atejevTszZswgOTk5x9c5efIk1atXZ8qUKdxzzz057pNTz1VERATHjh27YAMWtPT0dObOnctVV12Ft7e3pbmURGrf/HMoIYUbpv/JsdNp9GkSyqQbG7Fi1zF+X76arm1bcEWtEByadj1f6f1b8NTGBUvtW7DUvgWrwNvXlY593efYl76K7fRhAIyQBrg6P4NR5+oSvwBwUXr/JiQkULFixTwVV5YNC6xYsSIOh4PDhw9niR8+fJjQ0NAcnxMSEsLMmTNJSUnh+PHjVKlShaeffprIyMhcXyc4OJi6deuyY8eOXPfx9fXF19c3W9zb29vyH2amopRLSaT2vTypGS4e+XoDx06nUT80gFduisbPx4v2dSoRv92gfZ1Kat8CpPdvwVMbFyy1b8FS+xasfG9ftxs2fQ+/Pw8nYsxYcDXoMhpb45vxsjvy77WKgaLw/r2Y17dsQgsfHx9atGjB/PnzPTG32838+fOz9GTlxOl0Eh4eTkZGBt999x19+/bNdd/Tp0+zc+dOwsJ0c59IQZn482bW7j1JoNOL9+5qgb+PZugUERG5KIYB2+fCe1fCd/eYhVWZELhmMgxbBdG3QSkrrIojS6+Ahg8fzoABA2jZsiWtW7fmjTfeIDExkUGDBgFw9913Ex4ezksvvQTAihUriI2NpWnTpsTGxjJ+/HjcbjcjR470HPPJJ5+kT58+VK9enQMHDjBu3DgcDge33367JecoUtJ9s2ofn/+5F5sNpt7WjOoVtJ6GiIjIRdn7J8ybAHuXmdu+gdDuEbhiKPiWtTY3uSiWFle33norR48e5dlnn+XQoUM0bdqUOXPmULlyZQD27t2L3X6mcy0lJYUxY8awa9cuypYtS69evfjss88IDg727LN//35uv/12jh8/TkhICB06dODPP/8kJCSksE9PpMTbGBvP6JkbAXisW1261M++Pp2IiIjk4vAmmD8Rtv1ibjt8oc190GE4+GvZkuLI8rE7w4YNY9iwYTk+tnDhwizbnTp1YvPmzec93ldffZVfqYnIecQlpnH/Z6tJy3DTrX4lHu5a2+qUREREioe4GFj4Emz4H2CYCwA3uxM6PQVB4VZnJ5fB8uJKRIofl9vgkf+uJfZkMjUq+DPl1qbYNROgiIjI+Z0+Aosnw6qPwf3v2klR10PXMVCxjqWpSf5QcSUiF+3V37aydMcx/LwdvHtXC4L8NAuViIhIrlLi4Y834c93IN1cq4rILtDtWQhvbm1ukq9UXInIRZmz8SDTF+4E4JWbmlA/1Nq14ERERIqs9GRY+T4sfR2ST5ix8BbQbRxEdrI2NykQKq5EJM92HDnNE/9bD8C9HWpyXXQVizMSEREpglwZsO5zWPgKnDpgxirWg25jof61JX4B4NJMxZWI5MmplHTu/2wViWkurogsz9PX1Lc6JRERkaLF7YYtP5oLAB/fYcaCIqDzKK1TVUqouBKRCzIMgye/Wc/Oo4mEBjqZ1r85Xg7L1iAXERGxxoKXzAKp08isccOAHx6AXQvg9GEz5l8BrhwBLQeDl2/h5yqWUHElIhc0fdFOft10GB+Hnel3NqdiWf0jISIipZDdAQteML9v9zgAttjVMPM+OLnHjPuUhXYPQ9uHwDfAokTFKiquROS8Fm87yqu/bgVg/HUNaVatnMUZiYiIWCSzx2rBC9hPHab1rrV4rV1txmwOaPMAdBwOZSpal6NYSsWViORqX1wSj3y1FrcBt7aM4PbWEVanJCIiYq3o22DL/+FY9SFhmbHQJnDblxCsfydLOxVXIpKjlHQXQ79YzcmkdJpUDWJC34bYNLuRiIiUVqePwpLXYNVH4ErzhA27N7YHlliYmBQluiNdRLIxDIPRP2xkY2wC5cv4MP3OFji9NcORiIiUQikJsOBFeLMprJhuFlbB1QFw2bywudNh0SRrc5QiQz1XIpLN5yv28t2a/dhtMO32ZoQH+1mdkoiISOFKT4G/PjR7q5LjzFhYU6jUANb/F9eVT/PzqSiuDdiMI3OSi3NnEZRSR8WViGSxek8cz/20CYCnrq5Pu9q6KVdEREoRVwas/y8sfBkS9puxCrWh61g4uhUWvghdRuNu9zjMno2745M4HGfNIqgCq1RTcSUiHkdOpTD08zWkuwx6NQ7lvisjrU5JRESkcBgGbPkJfp8Ix7aZscBw6Pw0RPcHh5e5zlWX0WYBlZ5+5rmZBZXbVfh5S5Gi4kpEAEh3uRn2xVqOnEqlTqWyTLopWhNYiIhI6bBrIcybAAfWmNt+5aDjE9DqXvA+a2h8l1G5H0M9VoKKKxH514uzt7BydxwBvl68e1cLyvrq40FEREq42NUw/zmzuALwLmMu/ttuGDiDLE1NiiddPYkIM9fG8vEfuwF47ZZoaoWUtTYhERGRgnR0mzn8b8v/mdt2b2h1j9lbVbaStblJsabiSqSU23wggae/3wDAw11r06NhqMUZiYiIFJD4/eZEFeu+AMMN2MxFgTuPgnLVrc5OSgAVVyKl2MmkNO7/fBUp6W6urBvCY93rWp2SiIhI/ks8DkunwMoPwJVqxur1hq5joHKUtblJiaLiSqSUcrkNHv1qHfvikoko78ebtzXFYdcEFiIiUoKknoLl78CytyDtlBmr3h66j4eI1pamJiWTiiuRUmrqvG0s2nYUXy87797ZgmB/H6tTEhERyR8ZqbDqY1g8GZKOmbHQJtBtHNTuBpoNVwqIiiuRUmje5sO8+fsOAF7u15iGVTQjkoiIlABuF2z4Hyx4EeL3mrHykebwv6gbwG63Nj8p8VRciZQyMccSefzrdQAMbFeDG5pVtTYhERGRy2UYsHU2zJ8IR7eYsYAw6PQUNLsTHN7W5ielhoorkVIkMTWD+z9bxanUDFrVKMczvRpYnZKIiMjliVkC8yfA/r/MbWcwdHgcWt8HPv6Wpialj4orkVLCMAxGfreBbYdPExLgy9v9m+PjpeERIiJSTB1YZy4AvHO+ue3tD1cMhXaPgF+wlZlJKabiSqSU+HBJDLM2HMTLbmP6Hc2pFOi0OiUREZGLd2wHLHgeNv1gbtu9oMVAuHIEBGitRrGWiiuRUmDZzmO89Is5Bv3ZPlG0rFHe4oxEREQuUsIBWPQKrPkMDBdgg8Y3Q5dR5qQVIkWAiiuREu7AyWQe/nItbgNubB7OXVdoBXoRESlGkuJg6euw8n3ISDFjda+GrmMhtJG1uYmcQ8WVSAmWku5i6OerOZ6YRlRYIC/e0Bib1vYQEZHiIC0R/pwOf7wJqfFmLOIKcwHg6m0tTU0kNyquREqwCT9tYv3+eIL9vXnvrhY4vR1WpyQiInJ+GWmw5lNYNAkSj5ixyo2g27NQp4cWAJYiTcWVSAn135V7+e/Kfdhs8OZtzYgor+loRUSkCHO7YeO38PvzcHKPGStXA7qMgUb9tACwFAsqrkRKoHX7TjLux00APNmjHlfWDbE4IxERkVwYBmz71ZxW/Yj5bxdlK5uz/zUfAF4+1uYnchFUXImUMMdOpzL089Wkudz0iKrM0E61rE5JREQkZ3uWwbwJsO9Pc9s3CDo8Cm0eAJ8y1uYmcglUXImUIBkuNw9/uZaD8SlEVizDa7dEY7drbLqIiBQxh/42e6q2/2ZueznNgqr9o+Cv5UKk+FJxJVKCTPp1K8t3HaeMj4P37mpBgNPb6pRERETOiNsFC16Ev78xt20OaH43dBoJgVWszU0kH6i4Eikhft5wgPcX7wLg1ZujqVM5wOKMRERE/nXqkDn735pPwZ1hxhr1gy6joYKGr0vJoeJKpATYeugUI7/dAMD9nSK5pnGYxRmJiIgAySfhj6nmelUZyWasdndzWvWwaEtTEykIKq5Eirn45HQe+Hw1SWku2teuwIge9axOSURESru0JFj5Hix9HVL+XQC4amvoPg5qdLA2N5ECpOJKpBhzuw2e+N86Yo4lEh7sx5u3NcPLoXVARETEIq50WPsZLHwFTh8yYyENzJ6qetdoAWAp8VRciRRjby/YwbwtR/DxsjP9zuZUKOtrdUoiIlIaud2w6XtY8II5aQVAcDXznqrGN4PdYW1+IoVExZVIMbVg6xGmzNsGwPPXN6JJ1WBrExIRkdLHMGDHPJg/wZxeHcC/ojn7X4uB4KU/+knpouJKpBjaczyRR/+7FsOAO9pU45aWEVanJCIipc3eFWZRtecPc9s3ENo9AlcMBd+y1uYmYhEVVyLFTHKai/s/W01CSgZNI4J5tk+U1SmJiEhpcngTzJ8I234xtx2+0HoIdBgOZSpYm5uIxVRciRQjhmHw9Pcb+OfQKSqW9WH6nc3x9dI4dhERKQQndsOCl2DD14ABNjs0uxM6PQVBVa3OTqRIUHElUox8smw3P647gMNuY1r/5oQF+VmdkoiIlHSnj8DiV2HVDHCnm7Go66HrGKhYx9LURIoaFVcixcTKmDhemLUFgGd6NeCKSA29EBGRApQSD8veguXvQHqiGYvsYk6rHt7c2txEiigVVyLFwOGEFB78Yg0ZboProqswuH0Nq1MSEZGSKj0ZVn4AS6dA8gkzFt4Cuo2DyE7W5iZSxKm4Eini0jLcDP18NcdOp1I/NICX+zXGpkUYRUQkv7kyYN0XsPBlOHXAjFWsB93GQv1rtQCwSB6ouBIp4ib+vJk1e08S4PTi3Ttb4O+jX1sREclHbjds+RF+fx6O7zBjgVWhyyhochs49O+OSF7pt0WkCPtm1T4++3MPAFNva0qNimUszkhEREoMw4Cdv8P85+DgOjPmXwE6PgktB4O309L0RIojFVciRdTG2HhGz9wIwGPd69C1fmWLMxIRkRJj/yqYNx52LzG3fcpCu4fhigfBGWhpaiLFmYorkSLoRGIa93+2mrQMN93qV+KRrprqVkRE8sGRf+D3ifDPz+a2wwda3Qsdn4AyFa3NTaQEUHElUsS43AaPfLWW2JPJVK/gz5Rbm2K36yZiERHJgwUvgd0BnUZmjZ/cC1/dAYc2mNs2O0T3h85PQXC1ws9TpIRScSVSxLz221aWbD+Gn7eD9+5qQZCft9UpiYhIcWF3wIIXzO/bPY5PegL2uaPhrw/BcJnx+tdC17FQqb51eYqUUCquRIqQORsP8s7CnQC8clMT6odq3LuIiFyEzB6rBS/g2LWQq/atxuFOMWPB1eGmGVC1pXX5iZRwKq5EiogdR07zxP/WA3BPh5pcF13F4oxERKTYSU8xJ6fw9sO+5w/smfHo2+H66VqrSqSAqbgSKQJOp2Zw/2erSExz0aZmeZ6+RkM1RETkIrgyYMNX5j1XCfsBMAAbYDh8sN3wrqXpiZQW9gvvIiIFyTAMnvzfenYeTSQ00Mm0/s3xduhXU0RE8sAwYPP/wfR28ONDZmEVUAXq9cIGuGxe2FxpsGiS1ZmKlArquRKx2LuLdjFn0yF8HHam39mckABfq1MSEZHiYNcimD8BYleb237lzCnVUxJg8SRcVz7Nz6eiuDZgM47MSS7OnUVQRPKViisRCy3ZfpTJv/4DwPjrGtKsWjmLMxIRkSIvdo1ZVO1aaG57l4G2D5qLAK94DxZPgi6jcbd7HGbPxt3xSRyOs2YRVIElUmBUXIlYZF9cEo/8dy1uA25pWZXbW0dYnZKIiBRlx7abCwBv/tHctntDy8Fw5ZNQtpIZc7ugy2izgEpPP/PczILK7SrcnEVKGRVXIhZISXcx9IvVnEhKp0nVIJ7r2wibZnASEZGcxMfCopdh7Rf/rlVlg+jboPPTUK5G1n27jMr9OOqxEilwKq5ECplhGIyZuZGNsQmUL+PD9Dtb4PR2WJ2WiIgUNUlxsOQ1WPkBuFLNWL1e5gLAlaOszU1EcqTiSqSQfbFiL9+u3o/dBm/d3ozwYD+rUxIRkaIk9TT8+Q4sewtSE8xY9fbQfTxEtLY0NRE5PxVXIoVo9Z4TTPhpEwBPXV2f9rUrWpyRiIgUGRmpsPoTWDwZEo+asdAm0G0c1O6mBYBFigEVVyKF5MipFB78YjXpLoNejUO578pIq1MSEZGiwO2CDf+DBS9C/F4zVj4Suo6BqBvArrUPRYoLFVcihSDd5WbYF2s5nJBK7UplmXRTtCawEBEp7QwDts6G+RPh6BYzFhAGnZ6CZneCw9va/ETkoln+p5C3336bGjVq4HQ6adOmDStXrsx13/T0dJ577jlq1aqF0+kkOjqaOXPmXNYxRQrDi7O3sHJ3HGV9vXjvrhaU9dXfNURESrXdS+Gjq+Cr/mZh5QyC7hPg4TXQcpAKK5FiytLi6uuvv2b48OGMGzeONWvWEB0dTc+ePTly5EiO+48ZM4b33nuPt956i82bN/PAAw9www03sHbt2ks+pkhB+3FdLB//sRuA126JplZIWWsTEhER6xxYB5/dCJ/0hv1/gZcfdBgOj66HDo+Bj7/VGYrIZbC0uJoyZQpDhgxh0KBBREVF8e677+Lv78+MGTNy3P+zzz7jmWeeoVevXkRGRjJ06FB69erFa6+9dsnHFClIWw4m8NR3GwAY1qU2PRuGWpyRiIhY4vhO+GYQvN8Jds4Huxe0uhceXQfdx4FfOaszFJF8YNnYpLS0NFavXs2oUWcWu7Pb7XTv3p3ly5fn+JzU1FScTmeWmJ+fH0uXLr3kY2YeNzU11bOdkGBOe5qenk762aubWyDz9a3Oo6QqyPaNT07nvv+sIiXdTcfaFRjWuWap+znq/Vuw1L4FT21csEpF+546iH3Jq9jXfY7NcGFgw2jUD9eVT0G5muY+BXT+paJ9LaT2LVhFqX0vJgebYRhGAeaSqwMHDhAeHs6yZcto27atJz5y5EgWLVrEihUrsj2nf//+rF+/npkzZ1KrVi3mz59P3759cblcpKamXtIxAcaPH8+ECROyxb/88kv8/dU9LxfPbcD7/9jZctJOBV+DJxq7KKPh8yIipYZ3xmnqHJ5F5NHfcBjmhdmhwGi2hN1Mgn81i7MTkYuRlJRE//79iY+PJzAw8Lz7Fqu76qdOncqQIUOoX78+NpuNWrVqMWjQoMse8jdq1CiGDx/u2U5ISCAiIoIePXpcsAELWnp6OnPnzuWqq67C21tX5/mtoNp36vwdbDm5C18vOx8Nbk3DKta+j6yi92/BUvsWPLVxwSqR7ZuWiP2v97EvfwvbvwsAu6u2wd11LBUirqBDIaZSItu3CFH7Fqyi1L6Zo9rywrLiqmLFijgcDg4fPpwlfvjwYUJDc74vJSQkhJkzZ5KSksLx48epUqUKTz/9NJGRkZd8TABfX198fX2zxb29vS3/YWYqSrmURPnZvvM2H2bawl0AvHRjY5pWr5Avxy3O9P4tWGrfgqc2Llglon0z0mDNp7BoEiT+O4lW5UbQ7VnsdXpgt3D5jRLRvkWY2rdgFYX2vZjXt2xCCx8fH1q0aMH8+fM9Mbfbzfz587MM6cuJ0+kkPDycjIwMvvvuO/r27XvZxxTJDzHHEnn8f+sAGNC2Ojc2r2ptQiIiUrDcbnMB4LdbwewnzcKqXA248UO4fwnU7Qla11Ck1LB0WODw4cMZMGAALVu2pHXr1rzxxhskJiYyaNAgAO6++27Cw8N56aWXAFixYgWxsbE0bdqU2NhYxo8fj9vtZuTIkXk+pkhBSUzN4IHPVnMqJYOW1csxuneU1SmJiEhBMQzY/hvMfw4ObzRjZSvDlSOg+QDw8rE2PxGxhKXF1a233srRo0d59tlnOXToEE2bNmXOnDlUrlwZgL1792K3n+lcS0lJYcyYMezatYuyZcvSq1cvPvvsM4KDg/N8TJGCYBgGT323ga2HTxES4Ms7dzTHx8vyNbpFRKQg7FkO88bDvj/Nbd8g6PAotHkAfMpYmpqIWMvyCS2GDRvGsGHDcnxs4cKFWbY7derE5s2bL+uYIgXho6Ux/LzhIF52G9PvaE6lQOeFnyQiIsXLob9h/kTY/qu57eU0C6r2j4J/eWtzE5EiwfLiSqS4W7bzGC/98g8AY6+NomUN/QMrIlKixO2CBS/C398CBtgc0Pxu6DQSAqtYnZ2IFCEqrkQuw4GTyTz85VpcboMbm4Vzd9vqVqckIiL55dQhWDwZVn8C7gwz1vBG6DoGKtSyNDURKZpUXIlcotQMF0O/WMPxxDSiwgJ54YbG2DQjlIhI8Zd8Ev6YCivehfQkM1a7O3R7FsKiLU1NRIo2FVcil2j8/21m/b6TBPl5895dLfDzcVidkoiIXI60JFj5Pix9HVJOmrGqraH7OKhRmMv/ikhxpeJK5BJ8tXIv/125F5sN3ry9GRHl/a1OSURELpUrHdZ+BgtfgdOHzFhIA7Onqt41WqdKRPJMxZXIRVq37yTP/rgJgCd71KNT3RCLMxIRkUvidsOm72HBC+akFQDB1aDLaGh8M9g1IkFELo6KK5GLcOx0KkM/X02ay81VUZUZ2kk3NIuIFDuGATvmw/zx5vTqAP4Vzdn/WgwEL18rsxORYkzFlUgeZbjcPPzlWg7GpxBZsQyv3RKN3a6hIiIixcreFTB/Auz5w9z2DYR2j8AVQ8G3rLW5iUixp+JKJI8m/7qV5buO4+/j4L27WhDo9LY6JRERyavDm+H3ibB1trnt8IXWQ6DDcChTwdrcRKTEUHElkgezNhzkvcXmePxXb46mTuUAizMSEZE8ObEHFr4E67/CXADYDs3uhE5PQVBVq7MTkRJGxZXIBWw7fIoR364H4P5OkfRqHGZxRiIickGnj8DiV2HVDHCnm7Go680FgCvWsTQ1ESm5VFyJnEdCSjr3f7aapDQX7WtXYESPelanJCIi55MSD8veguXvQHqiGYvsYk6rHt7c2txEpMRTcSWSC7fbYPjX64k5lkiVICdv3tYML4fd6rRERCQn6cnw14ew5DVIPmHGwltAt3EQ2cna3ESk1FBxJZKLtxfsYN6Ww/h42Xn3rhZUKKupeUVEihxXBqz7Aha9AgmxZqxiPeg2FupfqwWARaRQqbgSycHCrUeYMm8bAM/3bUSTqsHWJiQiIlkZBmz+EX5/Ho5vN2OBVaHLKGhyGzh0iSMihU+fPCLn2Hs8iUe/WodhQP821bilVYTVKYmISCbDgF0LYN4EOLjOjPlXgI5PQsvB4O20ND0RKd1UXImcJTnNxf2fryY+OZ2mEcGM6xNldUoiIpJp/2qYPx5iFpvbPmWh3cNwxYPgDLQ0NRERUHEl4mEYBqO+38CWgwlULOvD9Dub4+vlsDotERE58o+5APA/P5vbDh9odS90fALKVLQ2NxGRs6i4klLL5TZYERPH6mM2KsTEse1IIjPXHcBhtzGtf3PCgvysTlFEpHQ7uRcWvgzr/wuG21wAOLo/dH4KgqtZnZ2ISDYqrqRUmrPxIBN+2szB+BTAwX+2r/I8Nuqa+lwRWcG65ERESrvEY+aU6n99CK40M1b/Wug6FirVtzY3EZHzUHElpc6cjQcZ+vkajFweDw9Wj5WISIFZ8BLYHdBpZPbH5k+EvX+aE1WknTZjNTpC9/FQtWVhZikicklUXEmp4nIbTPhpc66FlQ147ufN9GgYisOutVFERPKd3QELXjC/b/e4+f+MFPj6Ntgx78x+YU2h+ziI7KK1qkSk2FBxJaXKypi4f4cC5swADsansDImjra1NDRQRCTfZfZYLXgBe0Y61Y7H4fXGMEhNMOMVapvD/6L6qqgSkWJHxZWUKkdO5V5YXcp+IiJyCa4cAUf/wbFkEs0yYz4BcPWL5oQVWgBYRIopfXpJqVIpIG+LS+Z1PxERuUi7FsH8CRC72hMybA5sI7aDt+55FZHizW51AiKFqXXN8oQFOcltoIkNCAty0rpm+cJMS0Sk5ItdA/+5Hv5znVlY2b0BcNm8sBkuWPaWtfmJiOQDFVdSqjjsNsb1icpxQovMgmtcnyhNZiEikl+ObYf/3Q0fdIFdC8yiKrwluNNxXfk0PzedgevKp81JLhZNsjpbEZHLomGBUupc3SiM66LD+L/1B7PEQ4OcjOsTxdWNwizKTESkBImPhUUvw9ovwHABNoi+DfzKwZ/vQJfRuNs9DrNn4+74JA7HWbMI5jRNu4hIMaDiSkodwzDYsD8egCEdapB6eCc9Orahbe1K6rESEblcSXHmAsArPwBXqhmr18ucAbBylLnOVZfRZgGVnn7meZkFldtV+DmLiOQTFVdS6qzac4Ldx5Pw93HwUOdIFs3fQZua5VVYiYhcjtTT8Od0WPbmmWnVq7c3FwCOaH1mvy6jcj+GeqxEpJhTcSWlzjer9gHQu3EYZXz1KyAiclkyUmH1J7B4MiQeNWOhTaDbOKjdTWtViUipoitLKVWS0jKYtcG81+qmFlUtzkZEpBhzu+Dvb8z7pE7uNWPlI6HrGIi6AeyaM0tESh8VV1KqzP77EIlpLqpX8Kd1zfJkZGRYnZKISPFiGLD1F5j/HBzdYsYCwqDTU9DsTnB4W5ufiIiFVFxJqfLtanNI4E3Nq2LTUBURkYuzeynMmwD7V5rbziDoMBxa3wc+/tbmJiJSBKi4klJj7/Ek/twVh80GN2pIoIhI3h1cb/ZU7Zhnbnv5wRVDof0j5tTqIiICqLiSUuTbNfsBaF+rIuHBfhZnIyJSDBzfCb8/D5u+N7ftXtBiIFw5AgJCLU1NRKQoUnElpYLbbfDdarO4urmleq1ERM4r4SAsegXW/OfMAsCNbzanUS8faXV2IiJFloorKRX+3HWc2JPJBDi96NlQf20VEclRUhz88QaseA8yUsxYnZ7QbSyENrY0NRGR4kDFlZQK3/zba9UnugpOb4fF2YiIFDFpieYCwH+8CanxZiziCnMB4OptLU1NRKQ4uejiasuWLXz11VcsWbKEPXv2kJSUREhICM2aNaNnz57069cPX1/fgshV5JIkpKTzy0atbSUikk1GGqz5FBZNgsQjZqxyI+j2LNTpoQWARUQuUp6LqzVr1jBy5EiWLl1K+/btadOmDTfccAN+fn7ExcWxceNGRo8ezcMPP8zIkSN57LHHVGRJkTBrw0FS0t3UCilDs4hgq9MREbGe2w0bvzUXAD6x24yVqwFdxkCjfloAWETkEuW5uOrXrx8jRozg22+/JTg4ONf9li9fztSpU3nttdd45pln8iNHkcvyrWciiwitbSUipZthwPbfzGnVD280Y2Urm7P/NR8AXj7W5iciUszlubjatm0b3t4XXnW9bdu2tG3blvT09MtKTCQ/7Dx6mtV7TmC3wY3Nwq1OR0TEOnuWw/wJsHe5ue0bBB0ehTYPgE8Za3MTESkh8lxc5aWwAkhKSsLf3z/P+4sUpMxeq051Q6gU6LQ4GxERCxzaaPZUbf/V3PZyQpv7of1j4F/e0tREREqaSxpU3a1bN2JjY7PFV65cSdOmTS83J5F84XIbfL/mzJBAEZFSJS4GvhsC73YwCyubA1oMgkfWwlXPqbASESkAl1RcOZ1OmjRpwtdffw2A2+1m/PjxdOjQgV69euVrgiKXasn2oxxOSCXY35tuDSpZnY6ISOE4dQhmPQHTWsLf/wMMaHgjDPsL+rwBgVWszlBEpMS6pHWuZs2axdtvv83gwYP58ccf2b17N3v27OHnn3+mR48e+Z2jyCXJXNvq+qbh+HppbSsRKeGST8IfU2HFu5CeZMZqdzenVQ+LtjQ1EZHS4pIXEX7ooYfYv38/r7zyCl5eXixcuJB27drlZ24il+xkUhpzNx0GtLaViJRwaUmw8n1Y+jqknDRjVVtD93FQo4OlqYmIlDaXVFydOHGCe++9l/nz5/Pee++xaNEievTowaRJk3jwwQfzO0eRi/Z/6w+Q5nJTPzSAhlUCrU5HRCT/udJh7Wew8BU4fciMhTQwe6rqXaMFgEVELHBJxVWjRo2oWbMma9eupWbNmgwZMoSvv/6aBx98kFmzZjFr1qz8zlPkomhtKxEpsdxu2PS9uQBw3C4zFlwNuoyGxjeDXcOgRUSsckkTWjzwwAMsXryYmjVremK33nor69evJy0tLd+SE7kUWw+dYsP+eLzsNq5vqhu3RaSEMAzYPg/evxK+u8csrPwrwjWTYNgqiL5NhZWIiMUuqedq7NixOcarVq3K3LlzLyshkcv1zap9AHStX4kKZX0tzkZEJB/sXWEuALznD3PbJwDaPwpXDAXfstbmJiIiHnkurvbu3Uu1atXyfODY2FjCw8MvKSmRS5XucjNznbkGm9a2EpFi7/Bm+H0ibJ1tbjt8ofUQ6DAcylSwNjcREckmz8MCW7Vqxf33389ff/2V6z7x8fF88MEHNGrUiO+++y5fEhS5GAu3HuXY6TQqlvWhc70Qq9MREbk0J/bADw/A9HZmYWWzQ/O74ZE10PMFFVYiIkVUnnuutmzZwvPPP89VV12F0+mkRYsWVKlSBafTyYkTJ9i8eTObNm2iefPmTJo0SYsJiyUyhwTe0Cwcb8cl3VIoImKd00dg8auwaga4081YVF/oMgZC6lqbm4iIXFCei6v9+/czefJkXnjhBWbPns2SJUvYs2cPycnJVKxYkTvuuIOePXvSqFGjgsxXJFfHTqfy+z9HALiphYYEikgxkhIPy96C5e9AeqIZi+xiTqse3tza3EREJM/yXFw1a9aMQ4cOERISwogRI/jrr7+oUEHDEqTomLk2lgy3QZOqQdQLDbA6HRGRC0tPhr8+hCWvQfIJM1alubkAcGRnS1MTEZGLl+fiKjg4mF27dhESEsLu3btxu90FmZfIRTEM48zaVi2qWpyNiMgFuDJg3Rew6BVIMCfhoWI96DYW6l+rBYBFRIqpPBdX/fr1o1OnToSFhWGz2WjZsiUOR87raezatSvfEhTJi00HEvjn0Cl8HHaui9YslSJSRBkGbP4Rfn8ejm83Y4FVocsoaHIbOC5phRQRESki8vwp/v7773PjjTeyY8cOHnnkEYYMGUJAgIZeSdGQOZHFVQ0rE+TvbXE2IiLnMAzYtQDmTYCD68yYfwXo+CS0HAzeTkvTExGR/HFRfyK7+uqrAVi9ejWPPvqoiispElIzXPy4/gCgIYEiUgTtXw3zx0PMYnPbpyy0exiueBCcgZamJiIi+euSxh98/PHH+Z2HyCWbv+UIJ5PSCQ100rGO1rYSkSLiyD/mAsD//GxuO3yg1b3Q8QkoU9Ha3EREpEBocLcUe5lDAm9sHo7DrpvARcRiJ/fCwpdh/X/BcJsLAEffDp2fhuBqVmcnIiIFSMWVFGuHE1JYtO0oADdpSKCIWCnxmDml+l8fgivNjNW/FrqOhUr1rc1NREQKhYorKda+XxOL24AW1csRGVLW6nREpDRKPQXL3zYXAU47bcZqdITu46FqS0tTExGRwqXiSootc20rc0igJrIQkUKXngKrZsCSVyHpuBkLa/rvAsBdtFaViEgppOJKiq21+06y82giTm87vZuEWZ2OiJQWrgzY8JV5X1W8+QceKtQ2h/9F9VVRJSJSitmtTuDtt9+mRo0aOJ1O2rRpw8qVK8+7/xtvvEG9evXw8/MjIiKCxx9/nJSUFM/j48ePx2azZfmqX19j3Uuib1btB+CaRmEEOLW2lYjkkwUvwaJJ2eOGAd8Ohlfrwo8PmYVVQBXo8yY8uAIaXq/CSkSklLO05+rrr79m+PDhvPvuu7Rp04Y33niDnj17snXrVipVqpRt/y+//JKnn36aGTNm0K5dO7Zt28bAgQOx2WxMmTLFs1/Dhg2ZN2+eZ9vLSx10JU1KuouftbaViBQEuwMWvGB+3+5xAGy7F8P/DYNT5ucOfuXMKdVb3QvefhYlKiIiRY2lVceUKVMYMmQIgwYNAuDdd99l1qxZzJgxg6effjrb/suWLaN9+/b0798fgBo1anD77bezYsWKLPt5eXkRGhpa8Ccglvl10yFOpWZQtZwfV0RWsDodESlJOo00/7/gBewn99N2x2q81m40Y3Zv6PCYuQiwM8iyFEVEpGiyrLhKS0tj9erVjBo1yhOz2+10796d5cuX5/icdu3a8fnnn7Ny5Upat27Nrl27mD17NnfddVeW/bZv306VKlVwOp20bduWl156iWrVcl9bJDU1ldTUVM92QkICAOnp6aSnp1/OaV62zNe3Oo+i5uu/9gJwQ9MwXK4MXK5LO47at2CpfQuW2rcA1bsWx9/f4lj7KZnjKNxVWuC6+TMo+29E7X7Z9B4uWGrfgqX2LVhFqX0vJgebYRhGAeaSqwMHDhAeHs6yZcto27atJz5y5EgWLVqUrTcq05tvvsmTTz6JYRhkZGTwwAMPMH36dM/jv/zyC6dPn6ZevXocPHiQCRMmEBsby8aNGwkICMjxmOPHj2fChAnZ4l9++SX+/v6XeaaS3+JS4bk1DgxsPNssgwpOqzMSkZLCmRZHvUM/UO34Euy4MQAb4LY5+Knpx1anJyIiFkhKSqJ///7Ex8cTGBh43n2L1c1ICxcu5MUXX+Sdd96hTZs27Nixg0cffZSJEycyduxYAK655hrP/k2aNKFNmzZUr16d//3vf9xzzz05HnfUqFEMHz7cs52QkEBERAQ9evS4YAMWtPT0dObOnctVV12Ft7cmbQB4e+EuDHbQpmY57rqx1WUdS+1bsNS+BUvtm4+S4rAvewP7ho+wucyRDO7ytbHH7cBl88JhZHBtwGbcHZ+0ONGSRe/hgqX2LVhq34JVlNo3c1RbXlhWXFWsWBGHw8Hhw4ezxA8fPpzr/VJjx47lrrvu4t577wWgcePGJCYmct999zF69Gjs9uyTHwYHB1O3bl127NiRay6+vr74+vpmi3t7e1v+w8xUlHKxkmEY/LDOvKH8lpbV8q1N1L4FS+1bsNS+lyH1NPz5jrkAcOq//3hWbw8VamFf8x9cVz7Nz6eiuDZgM47FL+NwOM7ckyX5Ru/hgqX2LVhq34JVFNr3Yl7fsqnYfXx8aNGiBfPnz/fE3G438+fPzzJM8GxJSUnZCiiHwwGYF905OX36NDt37iQsTOsglQQrY+LYczyJMj4OrmmsSUtE5BJlpMKK9+DNpubMgKkJENoY7vgOanaCNf+BLqM9PVXujk9Cl9HmvjlN0y4iIoLFwwKHDx/OgAEDaNmyJa1bt+aNN94gMTHRM3vg3XffTXh4OC+99BIAffr0YcqUKTRr1swzLHDs2LH06dPHU2Q9+eST9OnTh+rVq3PgwAHGjRuHw+Hg9ttvt+w8Jf98s9pc26p3kzD8fYrVqFYRKQrcLvj7G7NIOmlOjEP5SLNwangj2O2w/y9zu9PIrJNWZPZYuS9xBh0RESnxLL06vfXWWzl69CjPPvsshw4domnTpsyZM4fKlSsDsHfv3iw9VWPGjMFmszFmzBhiY2MJCQmhT58+vPDCC5599u/fz+23387x48cJCQmhQ4cO/Pnnn4SEhBT6+Un+SkzNYPbfBwG4uWWExdmISLFiGLD1F5j/HBzdYsbKhkLnp6DZXeA4a8hHl1E5HwM0JFBERM7L8j/9Dxs2jGHDhuX42MKFC7Nse3l5MW7cOMaNG5fr8b766qv8TE+KkNl/HyQpzUXNimVoWb2c1emISHGxeynMmwD7V5rbziDoMBxa3wc+mhFWRETyj+XFlUheZQ4JvKlFVWw2m8XZiEiRd3C92VO1Y5657eUHVwyF9o+An/5AIyIi+U/FlRQLe44nsjImDpsNbmgWbnU6IlKUHd8Jvz8Pm743t+1e0GIgXDkCAjQRjoiIFBwVV1IsfPdvr1WH2hWpEuxncTYiUiQlHIRFr5gz/Rn/TjrR+Gbo8ow5aYWIiEgBU3ElRZ7bbfDdmlhAE1mISA6S4uCPN8yp1TNSzFidntBtrDm9uoiISCFRcSVF3rKdx4k9mUyA04seUZWtTkdEioq0RPhzOvzxJqTGm7GIK6D7OKjeztrcRESkVFJxJUXeN6v3AXBddBWc3g6LsxERy2WkwZpPzcV8E4+YsUoNzaKqTg/QhDciImIRFVdSpCWkpDNn4yFAQwJFSj23GzZ+BwuehxO7zVhwdeg6BhrdZC4ALCIiYiEVV1Kk/bz+IKkZbupUKkt01SCr0xERKxgGbP/NnFb98EYzVqaSuaBv8wHg5WNtfiIiIv9ScSVFWuaQwJtbam0rkVJpz3KYPwH2Lje3fYPMdaquGAo+ZazNTURE5BwqrqTI2nHkFGv3nsRht3G91rYSKV0ObTR7qrb/am57OaHN/dD+MfAvb2lqIiIiuVFxJUXWt6vN6dc71w2hUoDT4mxEpFDExcCCF+HvbwADbA5ofrc5BDCwitXZiYiInJeKKymSMlxuvl9jLhx8c8uqFmcjIgXu1CFYPBlWfwLuDDPW8EboMhoq1rY0NRERkbxScSVF0pLtxzhyKpVy/t50ra+1rURKrOST8MdUWPEupCeZsVrdoNuzUKWplZmJiIhcNBVXUiRlTmTRt2k4Pl6aXlmkxElLgpXvw9LXIeWkGavaCrqNg5odLU1NRETkUqm4kiLnRGIa8zabC4NqSKBICeNKh7Wfw6JX4NRBMxbSALqNhXq9tACwiIgUayqupMj5v/UHSHO5iQoLpGEVrW0lUiK43bD5B/j9eYjbZcaCqkGXZ6DJLWB3WJufiIhIPlBxJUXO2WtbiUgxZxiwY765VtWhDWbMv6I5+1+LgeDla2l6IiIi+UnFlRQpWw4msDE2AW+Hjb5NtbaVSLG2byXMmwB7lprbPgFnFgD2DbA2NxERkQKg4kqKlG9Xm9Ovd6tfmfJlfCzORkQuyeHN8PtE2Drb3Hb4Qush0GE4lKlgbW4iIiIFSMWVFBnpLjcz15oLB2tIoEgxdGIPLHwJ1n+FuQCwHZreAZ2fhiD9TouISMmn4kqKjN//OcLxxDQqlvWlU90Qq9MRkbw6fQQWvwqrZoA73YxF9YUuYyCkrrW5iYiIFCIVV1JkfLPKHBJ4Y/NwvBxa20qkyEuJh2VvwfJ3ID3RjEV2NhcADm9haWoiIiJWUHElRcLRU6ks2Prv2lYtNHxIpEhLT4a/PoQlr0HyCTNWpTl0H2cWVyIiIqWUiispEn5cF4vLbRAdEUydyppFTKRIcmXAui/MBYATzPsjqVgXuo6FBn20ALCIiJR6Kq7EcoZheIYEqtdKpAgyDNj8o7kA8PHtZiywKnQZBU1uA4f+KREREQEVV1IE/B0bz9bDp/DxstOnSRWr0xGRs+1cYC4AfGCtue1fATo+AS3vAW+ntbmJiIgUMSquxHKZa1v1bBhKkL+3xdmICAD7V8P88RCz2Nz2KQtth0Hbh8AZaGlqIiIiRZWKK7FUSrqLH9cdADQkUKRIOLrVXAB4y0/mtsMHWt1r9laVqWhtbiIiIkWciiux1Lwth4lPTicsyEn72rpwE7HMyX2w8GVY/yUYbnMB4OjbzQWAg6tZnZ2IiEixoOJKLHX22lYOu2YaEyl0icfMKdX/+hBcaWas/rXmDICV6lubm4iISDGj4koscyg+hSXbjwJwU4sIi7MRKWVST8Hyt81FgNNOm7EaHaHbOIhoZW1uIiIixZSKK7HM92v34zagVY1y1KxYxup0REqH9BRYNQOWvApJx81YWLRZVNXqqrWqRERELoOKK7GEYRh861nbSr1WIgXOlQEbvjLvq4rfZ8Yq1IauY6BBX7Dbrc1PRESkBFBxJZZYs/cEu44l4uftoFeTMKvTESm5DAP++RnmT4RjW81YQBVzooqmd2gBYBERkXykf1XFEplrW13TOJSyvnobihSImMUwbzzErja3/cpBh+HQegh4+1mamoiISEmkq1opdMlpLn5afxDQkECRAnFgLcybALsWmNve/ubiv+0eBmeQtbmJiIiUYCqupNDN2XSQ06kZRJT3o03N8lanI1K8LHgJ7A7oNDL7Y788BTsXnBn+Z/eGloPhyiehbKXCzVNERKQUUnElhS5zSGC/5lWxa20rkYtjd8CCF8zv2z1u/j/hAHx7Nxxc9+9ONmhyK3QZBeVqWJCkiIhI6aTiSgrV/hNJLNtpTv/cr3lVi7MRKYYye6wWvIA95TRRsdvwmjYIDJcZr9fLnAGwckPrchQRESmlVFxJofpudSyGAe1qVSCivL/V6YgUT1c8CLuX4lg+lTqZsaAI6PcRVGtjZWYiIiKlmoorKTRut8G3a8z1dW5uqV4rkYuWkQqrP4HFkyHxqCds2L2wPfa3FgAWERGxmFaNlEKzcncc++KSKevrxdUNtbaVSJ65XbD+K5jWEn4ZaRZWzmAAXDYvbO4Ms+ASERERS6nnSgrNN6vMiSyubRKGn4/D4mxEigHDgK2/wPzn4OgWM1Y2FKo0hW1zcF35ND+fiuLagM04Mie5yGkWQRERESkUKq6kUJxOzWD23/+ubaUhgSIXtvsPcwHg/SvNbWcQdHgc0hLNXqouo3G3exxmz8bd8UkcjrNmEVSBJSIiYgkVV1IoZm84SHK6i8iQMjSvVs7qdESKroMbzJ6qHXPNbS8/uGIotH8E/MqZ61x1GW0WUOnpZ56XWVC5XYWfs4iIiAAqrqSQZK5tdVOLqth0071Idsd3mj1PG78zt+1e0HyAWTQFhJ7Zr8uo3I+hHisRERFLqbiSArf7WCIrd8dht8GNzTQkUCSLhIOw6BVY+xm4M8xY45uhyzNQPtLa3EREROSiqLiSApfZa9WxTgihQU6LsxEpIpJPwNI3YMV7kJFsxur0hG5jIbSxpamJiIjIpVFxJQXK5Tb4bo1ZXGkiCxHMCSlWvAtLp0JqvBmLuAK6j4Pq7azNTURERC6LiispUMt2HuNgfApBft50b1DZ6nRErJORBms+NWf6O33YjFVqaBZVdXpoAWAREZESQMWVFKjMta2ui66C01trW0kp5Habk1QseB5O7DZjwdWh6xho1A/s+r0QEREpKVRcSYGJT07n102HAA0JlFLIMGD7b+a06oc3mrEylcwZ/ZoPAC8fa/MTERGRfKfiSgrMT+sPkJrhpl7lABqHB1mdjkjh2bMc5k+AvcvNbd9AaP+ouV6VTxlrcxMREZECo+JKCkzmLIE3t9TaVlJKHNpo9lRt/9Xc9nJCm/uh/WPgX97S1ERERKTgqbiSArHjyCnW7TuJw26jb9Nwq9MRKVhxMbDgRfj7G8AAmwOa3wWdnoLAKlZnJyIiIoVExZUUiMyJLLrUq0RIgK/F2YgUkFOHYfEkWP3JmQWAG94IXUZDxdqWpiYiIiKFT8WV5LsMl5vv18YCmshCSqjkk7DsTfhzOqQnmbFa3aDbs1ClqZWZiYiIiIVUXEm+W7z9KEdPpVKhjA9d61eyOh2R/JOWBCvfh6WvQ8pJM1a1FXQbBzU7WpqaiIiIWE/FleS7zCGBfZuG4+2wW5yNSD5wpcPaz2HRK3DqoBkLaQDdxkK9XloAWERERAAVV5LP4hLTmLflMKAhgVICuN2w+Qf4/XmI22XGgqpBl2egyS1aAFhERESyUHEl+erHdbGkuwwahQfSICzQ6nRELo1hwI755lpVhzaYMf+K5gLALQaClyZpERERkexUXEm+8qxt1SLC4kxELtG+lTBvAuxZam77BED7R8wFgH0DrM1NREREijQVV5JvNh9IYNOBBHwcdq6L1to+Uswc3gy/T4Sts81thy+0HgIdhkOZCtbmJiIiIsWCiivJN9+s3gdA96hKlCvjY3E2Inl0Yg8sfAnWf4W5ALAdmt4BnZ+GIN03KCIiInmn4kryRVqGmx/XHQA0JFCKidNHYPGrsGoGuNPNWFRf6DIGQupam5uIiIgUSyquJF/8/s8R4hLTqBTgS8c6Fa1ORyR3KfGw7C1Y/g6kJ5qxyM7mAsDhLSxNTURERIo3FVeSL779d0jgDc3D8dLaVlIUpafAXx/Aktcg+YQZq9Icuo8ziysRERGRy2T5VfDbb79NjRo1cDqdtGnThpUrV553/zfeeIN69erh5+dHREQEjz/+OCkpKZd1TLk8R06lsGDrUUBDAqUIcmXAmv/AW83/v707D4+qPvs//pmZ7CRhz8oOsoWwJYKAiCyCqBQeleKOoPZpCxWkagFFQDa1z0OxVUF9cPlBKQoCWoqyBBKgIEtYBFF22QIYtqyQTGbO748D0TRBthnOZPJ+XRdXe76ZzHxyJ72aO+c731ta9rLZWNVoLP16lvTMShorAADgMZY2V5988olGjBihsWPHasuWLWrVqpV69eqlH3/8sczHz5kzRyNHjtTYsWP13XffaebMmfrkk080evTo635O3LhFW4/J5TbUpk4VNYoKtzoOYDIM6dtF0ju3SV/8Qco+JkXWkvq+Lf1uvdT8V5LNZnVKAADgRyxtrqZOnapnnnlGgwYNUvPmzTVjxgyFhYXpgw8+KPPx69atU6dOnfTII4+oXr166tmzpx5++OESd6au9TlxYwzDYLYVfM/+VdL7XaV5A6XTe6Ww6lKvydIf0qU2j0kOdkQDAADPs+w3jMLCQqWnp2vUqFHFa3a7XT169ND69evL/JyOHTtq9uzZ2rhxo9q1a6cDBw5oyZIlevzxx6/7OSWpoKBABQUFxdfZ2dmSJKfTKafTeUNf54269PpW57icb45mac/JXAUH2HV38xo+m/NyfL2+5d3Nrq/t2BbZUyfI/sMaSZIRVEnu9r+Xu/3vfxoA7Effa35+vY8aexf19S7q613U17t8qb7XksGy5urUqVNyuVyKjo4usR4dHa3vv/++zM955JFHdOrUKd1+++0yDENFRUX67W9/W7wt8HqeU5KmTJmi8ePHl1pftmyZwsLCrvVL84rly5dbHaFMnx6wS7KrRZUirVnpmxmvhq/W1194u77hF46pWcZnisvaLEly2QL0Q43u2hPdR4W5kVLKGq++vtX4+fU+auxd1Ne7qK93UV/v8oX65ufnX/Vjy9XemNTUVE2ePFnvvPOO2rdvr3379mnYsGGaMGGCxowZc93PO2rUKI0YMaL4Ojs7W7Vr11bPnj0VGRnpiejXzel0avny5brrrrsUGBhoaZb/VOB0acwbaZKK9If7blWnhtWtjnTNfLm+/sDr9c06KsfqN2T7fq5shluGzS4jcYDcd7yoOpVrq47nX9Gn8PPrfdTYu6ivd1Ff76K+3uVL9b20q+1qWNZc1ahRQw6HQydPniyxfvLkScXExJT5OWPGjNHjjz+up59+WpKUmJiovLw8/eY3v9FLL710Xc8pScHBwQoODi61HhgYaPk38xJfynLJV7sylX2hSHGVQ3RH42jZ7eX3cABfrK8/8Xh9806ZR6pv+j/JVWiuNb1Ptm4vyxbVzPpjUG8yfn69jxp7F/X1LurrXdTXu3yhvtfy+pb9DhIUFKSkpCSlpKQUr7ndbqWkpKhDhw5lfk5+fr7s9pKRHQ6HJPNghet5Tly/eRcPsnggqVa5bqxQjhTkSKmvSW+2kr5+x2ys6nWWnlohPfR3KaqZ1QkBAEAFZum2wBEjRmjgwIFKTk5Wu3btNG3aNOXl5WnQoEGSpCeeeELx8fGaMmWKJKlPnz6aOnWq2rRpU7wtcMyYMerTp09xk3Wl54RnHM86rzV7zdlWDybVsjgN/J7zgrT5A2nN/0j5p8212FZS97FSw24cqQ4AAHyCpc3VgAEDlJmZqVdeeUUnTpxQ69at9dVXXxUfSHH48OESd6pefvll2Ww2vfzyyzp27Jhq1qypPn36aNKkSVf9nPCMBVuOyTCkdvWrqW71SlbHgb9yFUnfzDXvVmUdMdeqN5K6vSw16yvZK9oGQAAA4MssP9Bi6NChGjp0aJkfS01NLXEdEBCgsWPHauzYsdf9nLhxJWdbcdcKXmAY0veLpZQJ0qnd5lpEnHTnSKn1o8ypAgAAPonfUHDN0g+d1cFTeQoLcuiexFir48DfHFwtrRgnHUs3r0OrSrePkNo9IwWGWhoNAADgl9Bc4ZrN22zetbonMVaVgvkRgodkbJVWjJcOrDKvA8OkDkOkjn+QQipbmw0AAOAq8Jsxrkl+YZEWf5MhiS2B8JBTe6WVE6Vdi8xre6CUPFi643kpPMrSaAAAANeC5grX5KudJ5RX6FKdamFqV7+a1XFQnmUdk9Jek7b+XTJckmxSywFS11FS1XpWpwMAALhmNFe4Jpe2BD6YVEs2jr9GWVZNkewOqcuLpT+W9oZUkGs2Uxvfl1wF5nqTe8wTAKMTbm5WAAAAD6K5wlU7ciZf6w+cls1mDg4GymR3SKsujkfo+NxP6ykTzDlVjuCfmqo6HaUe46Q67W96TAAAAE+jucJV+2yLedeqU8Maiq/CqW24jEt3rFZNkt3lkt19ixxzH5L2rzDXXQVSTKI5ALhRDwYAAwAAv0Fzhavidv9stlUyd61wBV1elAy3HKlTdJ+k4vapan1z+1/C/QwABgAAfofmClfl64OndfTseUUEB6hn8xir48CXGYa0+0vp20WSzMbKkGS7d6rU9gnJEWhlOgAAAK+hucJVmX/xIIv7WsUpNMhhcRr4rB/WmrOqjm4sXnLLLrvcUv5pGisAAODXaK5wRTkXnFqy87gktgTiMo5vl1JelfZdfF+VPUByF8nVcbgWn2+r+yJ2yXHpkIuyThEEAADwAzRXuKIlO47rgtOthjUrqU3tKlbHgS85vd88GXDnZ+a1PUCKaSllbJG6viR3x+ekJUvk7vy8HI6fnSJIgwUAAPwQzRWu6KfZVrWZbQVT9nEp7XVp6yzJXWSuJfaXuo6Wtn8iNeltNlBO50+fc6mhcrtufl4AAICbgOYKv+hAZq42Hzoru026v2281XFgtfNnpbXTpA3vSkXnzbVbekndx5jHq0tS11GX/3zuWAEAAD9Gc4VfdGm2VZfGNRUdGWJxGlimME/aMENa+6ZUkGWu1b5N6jFWqtvR2mwAAAA+guYKl+VyG/os/ZgkqX9ybYvTwBJFhdKWj6XVf5ZyT5prUQlmU3VLTwYAAwAA/AzNFS5r7b5TOpF9QVXCAtW9WZTVcXAzud3mIRWrJkpnfzDXqtQ1BwC3eECycxw/AADAf6K5wmXN23xEktS3VZyCA/hlukIwDGnvMvNY9ZM7zbVKUeZ7pdoOlAKCrM0HAADgw2iuUKasfKeW7TK3gbElsII4tF5KGS8dXm9eB0dKnYZJt/1OCqpkbTYAAIBygOYKZfrimwwVFrnVNCZCCXGRVseBN53Yad6p2rvUvA4Ikdr/t9RpuBRWzdJoAAAA5QnNFco0/+KWwAeTajHbyl+dOSitmiztmCfJkGwOqe3jUpc/SZFxVqcDAAAod2iuUMqekznafjRLAXab/qsNs638Ts5JafUbUvpHPw0ATrhf6vqSVKORpdEAAADKM5orlDI/3Zxt1a1plKqHB1ucBh5z/py07q/S19MlZ7651rC71P0VKa61lckAAAD8As0VSnC63FqwhdlWfqUwX9r4nrT2L9KFc+ZarVul7mOl+p0tjQYAAOBPaK5QQtruTJ3KLVCN8CDd2aSm1XFwI1xOaetsKe11Kee4uVazqXmnqsk9DAAGAADwMJorlDAv3TzIol/reAU67BanwXVxu6VdC6WVE6UzB8y1ynWkrqOklgMYAAwAAOAlNFcodjq3QCnf/SiJLYHlkmFI+1LMWVUnvjHXwmpId7wgJQ+SAnj/HAAAgDfRXKHY59syVOQ21LJWZTWJibA6Dq7FkY3SivHSobXmdVCE1OlZcwBwMN9LAACAm4HmCsXmXTwl8MGkWhYnwVU7uUtaOUHavcS8dgRL7Z6Rbh8hVapubTYAAIAKhuYKkqSdx7L03fFsBTns+lUrBsj6vLOHpNQp0va5MgcA26XWj0p3jpQq0xwDAABYgeYKkn6abXVXQrSqhAVZnAaXlZsprfkfadNMye0015r3lbq+LNVsbG02AACACo7mCioscuvzbRdnW7El0DddyJLWvSWtf1ty5plrDe40j1WPT7I0GgAAAEw0V1DKdyd1Nt+p6Mhgdb6F2VY+xXlB2vS+tGaqdP6MuRbXVuox1myuAAAA4DNorlB8kMX9bWvJYWewrE9wFUnb50ipr0nZ5l1F1WgsdRsjNevDAGAAAAAfRHNVwf2YfUFpezIlsSXQJxiGtOtzcwDw6b3mWmStiwOAH5Ic/E8WAADAV/GbWgW3cOsxudyGkupWVYOa4VbHqdj2rzIHAGdsNa9Dq0l3PC8lPyUFhlibDQAAAFdEc1WBGYbBbCtfcDRdShknHVxtXgeFSx2GSh2GSCGRlkYDAADA1aO5qsC2HTmnfT/mKiTQrvtaxlodp+LJ3G0OAP7un+a1I8i8S9X5j1I4B4sAAACUNzRXFdil2Va9W8QqIiTQ4jQVyLkj5kEV2+dIhtscANzqYXMAcJU6VqcDAADAdaK5qqAuOF36YnuGJA6yuGnyTplHqm96X3IVmmtN75O6vSxFNbM2GwAAAG4YzVUFtfTbE8q5UKT4KqG6rUF1q+P4t4Icc/jvurekwhxzrV5nqftYqfat1mYDAACAx9BcVVCXtgQ+kFRLdmZbeUdRgbT5A2n1n6X80+ZabCuzqWrYjVlVAAAAfobmqgLKOHdea/edksSWQK9wu6Ttc6XUKVLWEXOteiNz+1+zvpLdbm0+AAAAeAXNVQW0YMtRGYZ0W4Nqql0tzOo4/sMwpO8XmwOAM7831yLizIMqWj/KAGAAAAA/x297FYxhGMVbAh9Mqm1xGj9ycLW0Ypx0LN28DqliHqne7hkpMNTKZAAAALhJaK4qmE0/nNUPp/NVKcihexJjrI5T/mVslVaMlw6sMq8Dw6Tbfi91/IMUWsXSaAAAALi5aK4qmPnp5nuA7m0Zq7Agvv3X7dRec/vfrkXmtT1QSh4kdX5eioi2NBoAAACswW/XFUh+YZH+9c1xSVL/ZLYEXpesY1Laa9LWv0uGS5JNajlA6jpKqlrP6nQAAACwEM1VBbJkxwnlFbpUr3qYkutWtTpO+ZJ/Rlo7VdrwnuQqMNca95a6j5GiE6zNBgAAAJ9Ac1WBzNtsbgl8MKmWbMxYujoFudLX06V1f5UKss21Oh2lHuOkOu0tjQYAAADfQnNVQRw+na8NB8/IZpPub8tsqysqKpTSP5JWvyHlZZprMYnmAOBGPRgADAAAgFJoriqI+VvM49dvb1RDcVUq+NHgq6ZIdofU5cXSH0t9TTqxQzrxjXTusLlWtb45ADjhfgYAAwAA4LJorioAt9vQZ8WzrbhrJbtDWjXJ/O8dnzP/0zCk+U9LO+f99LjwaKnLn6S2T0iOwJufEwAAAOUKzVUF8PWB0zp27rwiQgLUK4HZVsV3rFZNkt3lUvVcuxx/GyXlHDPXQypLtz8ntftvKSjMupwAAAAoV2iuKoB5F+9a/apVnEICHRan8RFdXpRyTsix+jXdfmnNHmAO/+00TArlNEUAAABcG5orP5d9wakvdzLbqoTT+81tgTs/K14ybHbZnvtWiuDOHgAAAK4P7873c//65rguON1qFBWuVrUqWx3HWtnHpcXPSW+3K9FYuW0O2Qy3tOX/WRgOAAAA5R13rvzc/ItbAvtX5NlW589Ka6dJG96Vis6ba9UaSmf2y3XHSC3Oaa77InbJcemQi7JOEQQAAACugObKj+3PzFX6obNy2G36r7bxVse5+QrzpQ0zpH9Pky5kmWu1b5Nq3CJtnSV1fUnujs9JS5bI3fl5ORw/O0WQBgsAAADXiObKj126a3Vn45qKigixOM1N5HJKWz6W0t6Qck+aa1EJUvdXpMa9zFlWXV8yGyin86fPu9RQuV03PzMAAADKPZorP+VyG1qwpYLNtnK7zfdSrZoknT1orlWpaw4AbvGAOd9KkrqOuvxzcMcKAAAA14nmyk+t2Zupk9kFqhoWqO7Noq2O412GIe1dLqW8Kp3cYa5VijIbpbYDpYAga/MBAACgQqC58lOXZlv1bR2voAA/PhTy0HopZbx0eL15HRxpzqm67XdSUCVrswEAAKBCobnyQ+fyC7X8W/O9Rv2T/XRL4Imd0soJ0p6vzOuAEKndb6Tbn5PCqlmbDQAAABUSzZUf+mJ7hgpdbjWLjVRCnJ/NtjpzUFo1WdoxT5Ih2RxS28elLn+SIuOsTgcAAIAKjObKD/18tpXfyDkprX5DSv9IcheZawn/JXV9WarRyNJoAAAAgERz5Xd2n8jRN0ezFOiwqV8bP5htdf6ctO6v0tfTJWe+udawu3mselxrK5MBAAAAJfjESQdvv/226tWrp5CQELVv314bN2687GPvvPNO2Wy2Uv/uvffe4sc8+eSTpT5+991334wvxXLzNh+RJHVvGq1qlcrxKXnO89K/35TebCWt+V+zsap1qzRwsfT4AhorAAAA+BzL71x98sknGjFihGbMmKH27dtr2rRp6tWrl3bv3q2oqKhSj1+wYIEKCwuLr0+fPq1WrVqpf//+JR53991368MPPyy+Dg4O9t4X4SOcLrcWbTsmqRzPtnI5pa2zpbTXpZzj5lrNpuadqib3SDabtfkAAACAy7C8uZo6daqeeeYZDRo0SJI0Y8YM/etf/9IHH3ygkSNHlnp8tWolT4KbO3euwsLCSjVXwcHBiomJ8V5wH5S6O1OncgtVIzxYdzapaXWca+N2S7sWSisnSWf2m2uV65gDf1sO+GkAMAAAAOCjLG2uCgsLlZ6erlGjRhWv2e129ejRQ+vXr7+q55g5c6YeeughVapUcqZRamqqoqKiVLVqVXXr1k0TJ05U9erVy3yOgoICFRQUFF9nZ2dLkpxOp5xO57V+WR516fWvJsenmw5Lkvq2ipHhdsnpdnk1m0cYhmwHVsmxaoJsFwcAG2E15L59hNxtBkoBwZLLbf7zgmupL64d9fUu6ut91Ni7qK93UV/vor7e5Uv1vZYMNsMwDC9m+UUZGRmKj4/XunXr1KFDh+L1F198UWlpadqwYcMvfv7GjRvVvn17bdiwQe3atStev3Q3q379+tq/f79Gjx6t8PBwrV+/Xg5H6Tsg48aN0/jx40utz5kzR2FhYTfwFd48OU7plXSH3IZNI1sVKbYcxK6at0/NMuapZu53kiSnPUT7ou/RgZq9VOQItTgdAAAAIOXn5+uRRx5RVlaWIiMjf/Gxlm8LvBEzZ85UYmJiicZKkh566KHi/56YmKiWLVuqYcOGSk1NVffu3Us9z6hRozRixIji6+zsbNWuXVs9e/a8YgG9zel0avny5brrrrsUGBh42cd9uO6Q3MZutYyP1FMP3nYTE16HzO/lSJ0k+54vJUmGI1ju5MFSx+FqFFZdN/Ng9autL64P9fUu6ut91Ni7qK93UV/vor7e5Uv1vbSr7WpY2lzVqFFDDodDJ0+eLLF+8uTJK75fKi8vT3PnztWrr756xddp0KCBatSooX379pXZXAUHB5d54EVgYKDl38xLfimLYRhasDVDktT/1jo+k7mUs4ek1CnS9rkyBwDbpdaPyNZlpBxVasvKd1X50vfaH1Ff76K+3keNvYv6ehf19S7q612+UN9reX1Lj2IPCgpSUlKSUlJSitfcbrdSUlJKbBMsy7x581RQUKDHHnvsiq9z9OhRnT59WrGxsTec2Rd9m5Gt70/kKCjArl+1jLM6Tmm5mdKXf5L+liRt/4ckQ2r2K+n3X0t935aq1LY6IQAAAHDDLN8WOGLECA0cOFDJyclq166dpk2bpry8vOLTA5944gnFx8drypQpJT5v5syZ6tevX6lDKnJzczV+/Hg98MADiomJ0f79+/Xiiy+qUaNG6tWr1037um6mS7OteiXEqHKYD/3l5EKWtO4taf3bkjPPXGtwp3msenySpdEAAAAAT7O8uRowYIAyMzP1yiuv6MSJE2rdurW++uorRUdHS5IOHz4su73kDbbdu3dr7dq1WrZsWannczgc+uabb/Txxx/r3LlziouLU8+ePTVhwgS/nHVVUOTS59vNLYE+M9vKeUHa9H/m8N/zZ8y1uDZS97FSw67WZgMAAAC8xPLmSpKGDh2qoUOHlvmx1NTUUmtNmjTR5Q45DA0N1dKlSz0Zz6elfPejzuU7FVs5RLc3qmFtGFeRtH2OlPqalG0OM1aNxlK3MVKzPgwABgAAgF/zieYK1+/SlsD728bLYbeoeTEM6bsvpJQJ0um95lpkLenOkVKrhyUHP2YAAADwf/zWW46dzL6gtD2ZkqQHkyw6FGL/KillvJSx1bwOrSbd8byU/JQUGGJNJgAAAMACNFfl2IItx+Q2pOS6VVW/RqWb++LH0qUV46WDaeZ1ULjUYajUYYgUYu1sMAAAAMAKNFfllGEYmp9ubgnsn3wTD7LI3C2tnCB990/z2hFk3qXq/EcpvObNywEAAAD4GJqrcmrrkXPan5mn0ECH7r0Zs63OHTEPqtg+RzLc5gDglg+Z76uqWtf7rw8AAAD4OJqrcmre5qOSpN6JMQoP9uK3Me+UtGaqtOl9yVVorjW9T+r2shTVzHuvCwAAAJQzNFfl0PlClxZ7e7ZVQY45/HfdW1JhjrlWr7M5q6r2rd55TQAAAKAco7kqh5btOqGcgiLVqhqq2+pX9+yTFxVImz+QVv9Zyj9trsW2ujgAuBuzqgAAAIDLoLkqhy5tCXwwqZbsnppt5XZJ2+dKqVOkLPOgDFVraG7/a95Psts98zoAAACAn6K5KmeOnTuvf+8/JUl6oK0HtgQahvT9v8wTADO/N9ciYs2DKlo/KjkCb/w1AAAAgAqA5qqc+Sz9qAxD6tCgumpXC7uxJzu42pxVdWyzeR1SxTxSvd0zUmDoDWcFAAAAKhKaq3LEnG1lbgm8odlWGVullFel/SvN68Aw6bbfSx3/IIVWufGgAAAAQAVEc1WObDx4RofP5Cs8OEC9W8Re+xOc2iutnCjtWmRe2wOl5EFS5+eliGiPZgUAAAAqGpqrcmTexbtW97WMVWiQ4+o/MeuYlPa6tHW2ZLgk2aSWv5buHCVVq++dsAAAAEAFQ3NVTuQVFGnJjuOSrmG2Vf4Zae1UacN7kqvAXGvcW+o+RopO8FJSAAAAoGKiuSonvvr2pPILXWpQo5KS6lb95QcX5EpfT5fW/VUqyDbX6nSUeoyV6tzm/bAAAABABURzVU58tjVDkvRAUi3ZLjfIt6hQSv9IWv2GlJdprkUnmk1Vox4MAAYAAAC8iObKh7nchjYcPKPUDJs2HTormy4z28rtknbMl1ZNlM4dNteq1jcHACfczwBgAAAA4CagufJRX+08rvH/3KXjWRckmYdXBAbYte3IWd1d+eJJgYYh7fnKPFb9x13mWni01OVPUtsnGAAMAAAA3EQ0Vz7oq53H9bvZW2T8x3phkVu/m71F0x9rq7vDD0gp46UjG8wPhlSWbn9OavffUtANDhcGAAAAcM1ornyMy23o6MJXNNTh1t9c95f6+KsBHyhh/ghJJ8yFgFDptt9KnYZJoVc46AIAAACA19Bc+ZiNB88o64JbfwycL0nFDVZd2wm9G/gXNbUfkSS5bQGyJw2UurwoRcRYlhcAAACAiebKx/yYc6G4ofpj4HxV0gWF287rYcdKOWzmRsHPXR1VqdcY9bi9o5VRAQAAAPwMzZWPiYoIkWTesQqQS8MCFxZ/7IA7RkOdz2qXUU//iG1iVUQAAAAAZeCMbh/Trn41xVYOkU3SX1z95TLMb5HTcKhb4VR9Z9RTbOUQtatfzdqgAAAAAEqgufIxDrtNY/s0lyQ961ggh82tAiNAgTaXnnUskCSN7dNcDjsDgQEAAABfwrZAH3R3i1gta/u1btk1X//rfFB/c92vPzgW6I+B89WnVZxuaXGv1REBAAAA/AeaK1+U9oZu2fVXue8crfZxTyprzQa17zxZ7ozGuiV1spQWYZ4SCAAAAMBn0Fz5IrdL6vqS7F1eVHunU6e/M9S+fjXZG/9JstnMjwMAAADwKTRXvqjrqMt/jDtWAAAAgE/iQAsAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAAADwgACrA/giwzAkSdnZ2RYnkZxOp/Lz85Wdna3AwECr4/gd6utd1Ne7qK/3UWPvor7eRX29i/p6ly/V91JPcKlH+CU0V2XIycmRJNWuXdviJAAAAAB8QU5OjipXrvyLj7EZV9OCVTBut1sZGRmKiIiQzWazNEt2drZq166tI0eOKDIy0tIs/oj6ehf19S7q633U2Luor3dRX++ivt7lS/U1DEM5OTmKi4uT3f7L76rizlUZ7Ha7atWqZXWMEiIjIy3/wfJn1Ne7qK93UV/vo8beRX29i/p6F/X1Ll+p75XuWF3CgRYAAAAA4AE0VwAAAADgATRXPi44OFhjx45VcHCw1VH8EvX1LurrXdTX+6ixd1Ff76K+3kV9vau81pcDLQAAAADAA7hzBQAAAAAeQHMFAAAAAB5AcwUAAAAAHkBzBQAAAAAeQHPlo1avXq0+ffooLi5ONptNixYtsjqSX5kyZYpuvfVWRUREKCoqSv369dPu3butjuU3pk+frpYtWxYP/uvQoYO+/PJLq2P5rddee002m03Dhw+3OopfGDdunGw2W4l/TZs2tTqWXzl27Jgee+wxVa9eXaGhoUpMTNTmzZutjuUX6tWrV+rn12azaciQIVZH8wsul0tjxoxR/fr1FRoaqoYNG2rChAnifDjPycnJ0fDhw1W3bl2FhoaqY8eO2rRpk9WxrlqA1QFQtry8PLVq1UqDBw/W/fffb3Ucv5OWlqYhQ4bo1ltvVVFRkUaPHq2ePXtq165dqlSpktXxyr1atWrptdde0y233CLDMPTxxx+rb9++2rp1qxISEqyO51c2bdqkd999Vy1btrQ6il9JSEjQihUriq8DAvi/S085e/asOnXqpK5du+rLL79UzZo1tXfvXlWtWtXqaH5h06ZNcrlcxdc7d+7UXXfdpf79+1uYyn+8/vrrmj59uj7++GMlJCRo8+bNGjRokCpXrqxnn33W6nh+4emnn9bOnTs1a9YsxcXFafbs2erRo4d27dql+Ph4q+NdEUexlwM2m00LFy5Uv379rI7itzIzMxUVFaW0tDTdcccdVsfxS9WqVdOf//xnPfXUU1ZH8Ru5ublq27at3nnnHU2cOFGtW7fWtGnTrI5V7o0bN06LFi3Stm3brI7il0aOHKl///vfWrNmjdVRKoThw4dr8eLF2rt3r2w2m9Vxyr377rtP0dHRmjlzZvHaAw88oNDQUM2ePdvCZP7h/PnzioiI0Oeff6577723eD0pKUm9e/fWxIkTLUx3ddgWCEjKysqSZDYA8CyXy6W5c+cqLy9PHTp0sDqOXxkyZIjuvfde9ejRw+oofmfv3r2Ki4tTgwYN9Oijj+rw4cNWR/IbX3zxhZKTk9W/f39FRUWpTZs2ev/9962O5ZcKCws1e/ZsDR48mMbKQzp27KiUlBTt2bNHkrR9+3atXbtWvXv3tjiZfygqKpLL5VJISEiJ9dDQUK1du9aiVNeGfQ6o8Nxut4YPH65OnTqpRYsWVsfxGzt27FCHDh104cIFhYeHa+HChWrevLnVsfzG3LlztWXLlnK1D728aN++vT766CM1adJEx48f1/jx49W5c2ft3LlTERERVscr9w4cOKDp06drxIgRGj16tDZt2qRnn31WQUFBGjhwoNXx/MqiRYt07tw5Pfnkk1ZH8RsjR45Udna2mjZtKofDIZfLpUmTJunRRx+1OppfiIiIUIcOHTRhwgQ1a9ZM0dHR+sc//qH169erUaNGVse7KjRXqPCGDBminTt3lpu/iJQXTZo00bZt25SVlaX58+dr4MCBSktLo8HygCNHjmjYsGFavnx5qb/u4cb9/C/QLVu2VPv27VW3bl19+umnbGv1ALfbreTkZE2ePFmS1KZNG+3cuVMzZsygufKwmTNnqnfv3oqLi7M6it/49NNP9fe//11z5sxRQkKCtm3bpuHDhysuLo6fXw+ZNWuWBg8erPj4eDkcDrVt21YPP/yw0tPTrY52VWiuUKENHTpUixcv1urVq1WrVi2r4/iVoKCg4r8yJSUladOmTXrzzTf17rvvWpys/EtPT9ePP/6otm3bFq+5XC6tXr1ab731lgoKCuRwOCxM6F+qVKmixo0ba9++fVZH8QuxsbGl/sjSrFkzffbZZxYl8k+HDh3SihUrtGDBAquj+JUXXnhBI0eO1EMPPSRJSkxM1KFDhzRlyhSaKw9p2LCh0tLSlJeXp+zsbMXGxmrAgAFq0KCB1dGuCu+5QoVkGIaGDh2qhQsXauXKlapfv77Vkfye2+1WQUGB1TH8Qvfu3bVjxw5t27at+F9ycrIeffRRbdu2jcbKw3Jzc7V//37FxsZaHcUvdOrUqdToiz179qhu3boWJfJPH374oaKiokocCoAbl5+fL7u95K/PDodDbrfbokT+q1KlSoqNjdXZs2e1dOlS9e3b1+pIV4U7Vz4qNze3xF9JDx48qG3btqlatWqqU6eOhcn8w5AhQzRnzhx9/vnnioiI0IkTJyRJlStXVmhoqMXpyr9Ro0apd+/eqlOnjnJycjRnzhylpqZq6dKlVkfzCxEREaXeH1ipUiVVr16d9w16wPPPP68+ffqobt26ysjI0NixY+VwOPTwww9bHc0vPPfcc+rYsaMmT56sX//619q4caPee+89vffee1ZH8xtut1sffvihBg4cyBgBD+vTp48mTZqkOnXqKCEhQVu3btXUqVM1ePBgq6P5jaVLl8owDDVp0kT79u3TCy+8oKZNm2rQoEFWR7s6BnzSqlWrDEml/g0cONDqaH6hrNpKMj788EOro/mFwYMHG3Xr1jWCgoKMmjVrGt27dzeWLVtmdSy/1qVLF2PYsGFWx/ALAwYMMGJjY42goCAjPj7eGDBggLFv3z6rY/mVf/7zn0aLFi2M4OBgo2nTpsZ7771ndSS/snTpUkOSsXv3bquj+J3s7Gxj2LBhRp06dYyQkBCjQYMGxksvvWQUFBRYHc1vfPLJJ0aDBg2MoKAgIyYmxhgyZIhx7tw5q2NdNeZcAQAAAIAH8J4rAAAAAPAAmisAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAAADwAJorAAAAAPAAmisAAAAA8ACaKwAAfiYzM1MxMTGaPHly8dq6desUFBSklJQUC5MBAHydzTAMw+oQAAD4kiVLlqhfv35at26dmjRpotatW6tv376aOnWq1dEAAD6M5goAgDIMGTJEK1asUHJysnbs2KFNmzYpODjY6lgAAB9GcwUAQBnOnz+vFi1a6MiRI0pPT1diYqLVkQAAPo73XAEAUIb9+/crIyNDbrdbP/zwg9VxAADlAHeuAAD4D4WFhWrXrp1at26tJk2aaNq0adqxY4eioqKsjgYA8GE0VwAA/IcXXnhB8+fP1/bt2xUeHq4uXbqocuXKWrx4sdXRAAA+jG2BAAD8TGpqqqZNm6ZZs2YpMjJSdrtds2bN0po1azR9+nSr4wEAfBh3rgAAAADAA7hzBQAAAAAeQHMFAAAAAB5AcwUAAAAAHkBzBQAAAAAeQHMFAAAAAB5AcwUAAAAAHkBzBQAAAAAeQHMFAAAAAB5AcwUAAAAAHkBzBQAAAAAeQHMFAAAAAB7w/wFuIeRLXIYjVAAAAABJRU5ErkJggg==", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sigmoid_values=[]\n", | |
| "for i in range(1,10):\n", | |
| " sigmoid_values.append(sigmoid(i))\n", | |
| "\n", | |
| "#and a linear function example\n", | |
| "\n", | |
| "def linear_function(x):\n", | |
| " return 0.03*x + 0.7\n", | |
| "linear_values=[]\n", | |
| "for i in range(1,10):\n", | |
| " linear_values.append(linear_function(i))\n", | |
| "\n", | |
| "# Prepare data for plotting\n", | |
| "x_values = list(range(1,10))\n", | |
| "sigmoid_y_values = sigmoid_values\n", | |
| "linear_y_values = linear_values\n", | |
| "\n", | |
| "# Plotting both the sigmoid and linear functions\n", | |
| "plt.figure(figsize=(10, 6))\n", | |
| "plt.plot(x_values, sigmoid_y_values, label='Sigmoid Function', marker='o')\n", | |
| "plt.plot(x_values, linear_y_values, label='Linear Function (0.03x + 0.7)', marker='x')\n", | |
| "plt.title('Comparison of Sigmoid and Linear Functions')\n", | |
| "plt.xlabel('x')\n", | |
| "plt.ylabel('f(x)')\n", | |
| "plt.legend()\n", | |
| "plt.grid(True)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e9F-4HaEZbgS" | |
| }, | |
| "source": [ | |
| "**Why is Non linearity so important ?**\n", | |
| "\n", | |
| "- Enables Complex Function Approximation: Non-linear activation functions allow neural networks to approximate virtually any complex function, a foundational aspect of universal approximation theory. This capability is crucial because real-world data often exhibit non-linear relationships, and capturing these nuances is essential for making accurate predictions or decisions.\n", | |
| "\n", | |
| "- Facilitates Deep Learning Architectures: Without non-linearities, deep neural networks would not be able to realize their potential. A network composed of linear layers, regardless of its depth, could be simplified to a single linear transformation, which severely limits its computational power. Non-linear activation functions between layers ensure that the network can create complex mappings from inputs to outputs, enabling the depth of the network to contribute to its learning capacity.\n", | |
| "\n", | |
| "**Illustrate by Example - A network composed of linear layers, regardless of its depth, could be simplified to a single linear transformation**\n", | |
| "\n", | |
| "\n", | |
| "Let's illustrate why a network composed of linear layers can be simplified to a single linear transformation with a simple example. Consider a neural network that has multiple linear layers stacked together, without any non-linear activation functions in between.\n", | |
| "\n", | |
| "Suppose we have a 3-layer neural network where each layer performs a linear transformation defined as follows:\n", | |
| "\n", | |
| "- The first layer applies a transformation $f_1(x) = a_1x + b_1,$\n", | |
| "- The second layer applies $f_2(x) = a_2x + b_2$,\n", | |
| "- The third layer applies $f_3(x) = a_3x + b_3$.\n", | |
| "\n", | |
| "The output of the entire network, when given an input $(x$, is the composition of these three functions: $f_3(f_2(f_1(x)))$.\n", | |
| "\n", | |
| "Let's compute this step by step to see how it simplifies to a single linear transformation.\n", | |
| "\n", | |
| "1. After the first layer: $y_1 = a_1x + b_1$.\n", | |
| "2. Inputting $y_1$ into the second layer: $y_2 = a_2(a_1x + b_1) + b_2 = a_2a_1x + a_2b_1 + b_2$.\n", | |
| "3. Inputting $y_2$ into the third layer results in: $y_3 = a_3(a_2a_1x + a_2b_1 + b_2) + b_3 = a_3a_2a_1x + a_3a_2b_1 + a_3b_2 + b_3$.\n", | |
| "\n", | |
| "This final expression, $y_3 = a_3a_2a_1x + a_3a_2b_1 + a_3b_2 + b_3$, is still a linear equation in terms of $x$, **with the entire network's effect being captured by a single linear transformation of the form $y = ax + b$, where $a = a_3a_2a_1$ and $b = a_3a_2b_1 + a_3b_2 + b_3$.**\n", | |
| "\n", | |
| "\n", | |
| "This simplification shows that stacking multiple linear layers, without introducing non-linearities, does not increase the model's representational power beyond that of a single linear layer. The network remains incapable of modeling complex patterns that are not linearly separable or that require capturing non-linear relationships. This is a key reason why non-linear activation functions are essential in neural network design, enabling the network to learn and model complex, non-linear mappings between inputs and outputs.\n", | |
| "\n", | |
| "*Credits ChatGPT4*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "lDXCEUlezpgd" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "np.random.seed(1)\n", | |
| "\n", | |
| "# randomly initialize our weights. Weights are the Neurons of the Neural Network\n", | |
| "# The whole point is in learning the values of weights from a random point to be able to model the input function\n", | |
| "\n", | |
| "weight1 = np.random.random((3,4))\n", | |
| "weight2 = np.random.random((4,1))\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "d1zX4wtYgzA7" | |
| }, | |
| "source": [ | |
| "\n", | |
| " ### Modeling and a very simple two layered Neural Network.\n", | |
| " The network is taken form http://iamtrask.github.io/2015/07/12/basic-python-network/\n", | |
| "\n", | |
| "\n", | |
| "#### The forward pass\n", | |
| "\n", | |
| "$$\n", | |
| "x \\rightarrow a^{l-1} \\rightarrow a^{l} \\rightarrow y\n", | |
| "$$\n", | |
| "\n", | |
| "We can write $a^l$ as\n", | |
| "\n", | |
| "$$\n", | |
| "a^{l} = \\sigma(z^l) \\quad where \\quad\n", | |
| "z^l =w^l a^{l-1} +b^l\n", | |
| "$$\n", | |
| "\n", | |
| "So forward pass is (Weight Matrix * Input) the result of which is fed into a non-linear function like Sigmoid to give an Output.\n", | |
| "\n", | |
| "Input of each layer is the output of the *non linearity* from the previous layer.\n", | |
| "\n", | |
| "Note $x$ can also be written as $a^0$\n", | |
| "\n", | |
| "Usually a softmax is applied to the last layers output to fit the weights of the last layer between 0 and 1, for classification for multi-class selection. For this toy example this is not needed\n", | |
| "\n", | |
| "Also there are LayerNoramlisation added between layers in actual netwroks which is also not shown here\n", | |
| "\n", | |
| "## The Backward pass - or back propagation\n", | |
| "\n", | |
| "This is where the magic of Neural Network happens and this magic potion is same irrespective of the depth and complexity of the Neural Network.\n", | |
| "\n", | |
| "- A bit about Back Propagation\n", | |
| " \n", | |
| " In the context of Neural Network it was this [1986 Article in Nature magazine](https://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf) that defined it in the terms we know of today. This was by David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams.\n", | |
| " This paper also refers to Yann Le Cun's paper - [Learning Process in an Asymmetric Threshold Network of 1986](https://link.springer.com/chapter/10.1007/978-3-642-82657-3_24) which speaks of Gradient Descent.\n", | |
| "\n", | |
| " The derivation of the errors with respect to inner and outer layer as described in the paper is almost exactly how we apply it here below.\n", | |
| "\n", | |
| " However before that, in modelling certain problems it was Paul J Webros who suggested using derivatives to fit a non linear equation/model - (Solow Economic forecasting model) in his PhD Theisis - [Beyond Regression -New Tools for Prediction and Analysis in Behaviour Sciences 1974](https://www.researchgate.net/publication/35657389_Beyond_regression_new_tools_for_prediction_and_analysis_in_the_behavioral_sciences). Tracing back is futile but some interesting deatils are here in [this book review](https://academic.oup.com/comjnl/article-pdf/37/8/723/1116753/370723.pdf) of \"The Roots of Backpropagation- Paul J Webros\"\n", | |
| "\n", | |
| "- What is the main idea of Back Propagation ?\n", | |
| "\n", | |
| " It is basically trying to find how to adjust weights for all the layers so that the weight in all the layers, especially the inner layers arrive at a point where the input applied to the weights give the desired output.\n", | |
| "\n", | |
| " Taking back a step - basically how to learn the weights so that the neural net can model any complex function\n", | |
| "\n", | |
| " Please refer to https://alexcpn.github.io/html/NN/ml/5_backpropogation_scalar_calculus/\n", | |
| "\n", | |
| "### **How to calculate Gradient Vector of Loss function In Output Layer**\n", | |
| "\n", | |
| " If we model our Neural Network like a Graph,the below is a representation of a single path in the last layer(l) of a neural network; and it shows how the connection from previous layer - that is the activation of the previous layer and the weight of the current layer is affecting the output; and thereby the final Cost.\n", | |
| "\n", | |
| " The central idea is how a small change in weight affects the final Cost in this chain depiction.\n", | |
| "\n", | |
| " [](https://postimg.cc/k6pmgzF9)\n", | |
| "\n", | |
| " For the last layer shown in code below as dC_dw2\n", | |
| "\n", | |
| " $$\n", | |
| " \\mathbf {\n", | |
| " \\frac {\\partial C}{\\partial w^2} = \\frac {\\partial z^2}{\\partial w^2} . \\frac {\\partial a^2}{\\partial z^2} . \\frac {\\partial C}{\\partial a^2}\n", | |
| " }\n", | |
| " $$\n", | |
| " The first term is\n", | |
| " $$\n", | |
| " \\mathbb{\n", | |
| " \\frac{\\partial z^{2} }{\\partial w^2} = \\frac{\\partial (a^1.w^2)}{\\partial w^2} =a^1 \\quad \\rightarrow (\\mathbf {1.1})\n", | |
| " }\n", | |
| " $$\n", | |
| "\n", | |
| " The second term is\n", | |
| "\n", | |
| " $$\n", | |
| " \\mathbb{\n", | |
| " \\frac{\\partial a^{2} }{\\partial z^2} = \\frac{\\partial \\sigma(z^2) }{\\partial z^2} =\\sigma' (z^{2}) \\quad \\rightarrow (\\mathbf {1.2})\n", | |
| " }\n", | |
| " $$\n", | |
| "\n", | |
| " The third term is the derivative of the loss. Note here that we are using Mean Square Error Loss ( that is (output -expected) squared so that positve and negative are treated same).\n", | |
| " \n", | |
| " $$\n", | |
| " \\mathbf{\n", | |
| " \\frac{\\partial C}{\\partial(a^2)} = \\frac {\\partial({\\frac{1}{2} \\|y-a^2\\|^2)}}{\\partial(a^2)} = \\frac{1}{2}*2*(a^2-y) =(a^2-y) \\rightarrow (1.3) }\n", | |
| " $$\n", | |
| "\n", | |
| " Note that the division by 2 is for mathematical convenience during optimization. The complete loss over a dataset would typically involve summing this term over all samples and dividing by the number of samples.\n", | |
| "\n", | |
| " Putting it all together\n", | |
| "\n", | |
| " $$\n", | |
| " \\mathbf{\n", | |
| " \\frac {\\partial C}{\\partial w^2} = a^1* \\sigma' (z^{2})*\\frac{\\partial C}{\\partial(a^2)} \\quad \\rightarrow (A_2l) }\n", | |
| " $$\n", | |
| "```\n", | |
| " # Backward Pass - Backpropagation\n", | |
| " delta2 = (a2-y)\n", | |
| " #---------------------------------------------------------------\n", | |
| " # Calcluating change of Cost/Loss wrto weight of Last layer\n", | |
| " # Eq (A) ---> dC_dw2\n", | |
| " #---------------------------------------------------------------\n", | |
| "\n", | |
| " dC_dw2_1 = delta2*derv_sigmoid(z2)\n", | |
| " dC_dw2 = a1.T.dot(dC_dw2_1)\n", | |
| "```\n", | |
| "### **How to calculate Gradient Vector of Loss function In Inner Layer**\n", | |
| "\n", | |
| "Let's do similar to above; the key is in using the calculations of above part in this layer and similarly for other layers\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^1}= \\frac {\\partial z^1}{\\partial w^1}. \\frac {\\partial a^1}{\\partial z^1}. \\frac {\\partial C}{\\partial a^1}\n", | |
| "$$\n", | |
| "\n", | |
| "The first term is similar to (1.1)\n", | |
| "$$\n", | |
| "\\mathbb{\n", | |
| "\\frac{\\partial z^{1} }{\\partial w^1} = \\frac{\\partial a^0.w^1}{\\partial w^1} =a^0 \\quad \\rightarrow (\\mathbf {2.1})\n", | |
| "}\n", | |
| "$$\n", | |
| "The second term is also similar to (1.2)\n", | |
| "$$\n", | |
| "\\mathbb{\n", | |
| "\\frac{\\partial a^{1} }{\\partial z^1} = \\frac{\\partial \\sigma(z^1) }{\\partial z^1} =\\sigma' (z^{1}) \\quad \\rightarrow (\\mathbf {2.2})\n", | |
| "}\n", | |
| "$$\n", | |
| "For the third part, we use Chain Rule to split like below, the first part of which we calculated in the earlier step.\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^1)} = \\frac{\\partial C}{\\partial(a^2)}.\\frac{\\partial(a^2)}{\\partial(a^1)}\n", | |
| "$$\n", | |
| "First term from (1.3)\n", | |
| " $$\n", | |
| " \\mathbf{\n", | |
| " \\frac{\\partial C}{\\partial(a^2)} = (a^2-y) \\rightarrow (2.3.1) }\n", | |
| " $$\n", | |
| "The second term can be re-written as\n", | |
| "\n", | |
| "$$\n", | |
| "\\begin{aligned}\n", | |
| " \\frac{\\partial(a^2)}{\\partial(a^1)} = \\frac{\\partial(a^2)}{\\partial(z^2)}. \\frac{\\partial(z2)}{\\partial(a^1)} \\\\ \\\\\n", | |
| " Note \\space that \\space a^2 = \\color{red}{\\sigma}(z^2), and \\space above \\space becomes\n", | |
| " \\\\ \\\\\n", | |
| " \\frac{\\partial \\sigma (z^2)}{\\partial(z^2)} .\\frac{\\partial(w^2.a^1)}{\\partial(a^1)} \\\\ \\\\\n", | |
| " which \\space is \\space \\\\ \\\\\n", | |
| " \\sigma'(z^2).w^2 \\\\ \\\\\n", | |
| "\\frac{\\partial(a^2)}{\\partial(a^1)} = \\sigma'(z^2).w^2 \\quad \\rightarrow (2.3.2)\\\\ \\\\\n", | |
| "\\end{aligned}\n", | |
| "$$\n", | |
| "\n", | |
| "Putting all together we get\n", | |
| "\n", | |
| "$$\n", | |
| "\\mathbf{\n", | |
| "\\frac {\\partial C}{\\partial w^1} =a^0* \\sigma'(z^1)*(a^2-y).\\sigma'(z^2).w^2\n", | |
| "\\quad \\rightarrow \\mathbb (B)\n", | |
| "\\\\ \n", | |
| "which \\space is \\space same \\space as \\\\\n", | |
| "\\\\\n", | |
| "\\frac {\\partial C}{\\partial w^1} =a^0* \\sigma'(z^1)* \\frac{\\partial C}{\\partial(a^2)} .\\sigma'(z^2).w^2\n", | |
| "}\n", | |
| "$$\n", | |
| "\n", | |
| "This is what is shown in the code snippet below\n", | |
| "\n", | |
| "```\n", | |
| "# note dC_dw2_1 = dC_da2*derv_sigmoid(z2)\n", | |
| "dC_dw1 = np.multiply(dC_dw2_1,weight2.T) * derv_sigmoid(z1)\n", | |
| "dC_dw1 = a0.T.dot(dC_dw1)\n", | |
| "\n", | |
| "for a moment if we forget vector calculus and transpises the above equation spread out matched the equation (B)\n", | |
| "\n", | |
| "dC_dw1 = a0*derv_sigmoid(z1)*dC_da2*derv_sigmoid(z2)*weight2\n", | |
| "\n", | |
| "```\n", | |
| "which is basically the re-write of the above using previous computed terms\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "vQzvHOTvgs4A", | |
| "outputId": "ffffc972-85cc-4140-c2e3-bbdc8b50b363" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Intial Ouput \n", | |
| " [[ 0.733]\n", | |
| " [ 0.777]\n", | |
| " [ 0.757]\n", | |
| " [ 0.789]]\n", | |
| "New ouput\n", | |
| " [[ 0.011]\n", | |
| " [ 0.988]\n", | |
| " [ 0.993]\n", | |
| " [ 0.010]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# set learning rate as 1 for this toy example\n", | |
| "learningRate = 1\n", | |
| "\n", | |
| "\n", | |
| "# Randomly initalised weights\n", | |
| "weight1 = np.random.random((3,4))\n", | |
| "weight2 = np.random.random((4,1))\n", | |
| "\n", | |
| "# Activation to layer 0 is taken as input x\n", | |
| "a0 = x\n", | |
| "\n", | |
| "iterations = 10000\n", | |
| "for iter in range(0,iterations):\n", | |
| "\n", | |
| " ## The forward pass\n", | |
| " z1= np.dot(x,weight1)\n", | |
| " a1 = sigmoid(z1)\n", | |
| " z2= np.dot(a1,weight2)\n", | |
| " a2 = sigmoid(z2)\n", | |
| " if iter == 0:\n", | |
| " print(\"Intial Ouput \\n\",a2)\n", | |
| "\n", | |
| " # Backward Pass - Backpropagation\n", | |
| " delta2 = (a2-y)\n", | |
| " #---------------------------------------------------------------\n", | |
| " # Calcluating change of Cost/Loss wrto weight of Last layer\n", | |
| " # Eq (A) ---> dC_dw2\n", | |
| " #---------------------------------------------------------------\n", | |
| "\n", | |
| " dC_dw2_1 = delta2*derv_sigmoid(z2)\n", | |
| " dC_dw2 = a1.T.dot(dC_dw2_1)\n", | |
| "\n", | |
| " #---------------------------------------------------------------\n", | |
| " # Calcluating change of Cost/Loss wrto weight of 2nd last layer\n", | |
| " # Eq (B)---> dC_dw1 = derv_sigmoid(z1)*delta2*derv_sigmoid(z2)*weight2*a0.T\n", | |
| " # dC_dw1 = derv_sigmoid(z1)*dC_dw2*weight2_1*a0.T\n", | |
| " #---------------------------------------------------------------\n", | |
| "\n", | |
| " dC_dw1 = np.multiply(dC_dw2_1,weight2.T) * derv_sigmoid(z1)\n", | |
| " dC_dw1 = a0.T.dot(dC_dw1)\n", | |
| "\n", | |
| " #---------------------------------------------------------------\n", | |
| " #Gradinent descent\n", | |
| " #---------------------------------------------------------------\n", | |
| "\n", | |
| " weight2 = weight2 - learningRate*(dC_dw2)\n", | |
| " weight1 = weight1 - learningRate*(dC_dw1)\n", | |
| "\n", | |
| "\n", | |
| "print(\"New ouput\\n\",a2)\n", | |
| "\n", | |
| "#---------------------------------------------------------------\n", | |
| "# Training is done, weight2 and weight2 are primed for output y\n", | |
| "#---------------------------------------------------------------\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5cSRTA_HylFq" | |
| }, | |
| "source": [ | |
| "Lets test out, two ones in input and one zero, ouput should be One" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "L-S1HOTAeAg-" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def forward(x):\n", | |
| " x1 = np.array(x)\n", | |
| " z1= np.dot(x1,weight1)\n", | |
| " a1 = sigmoid(z1)\n", | |
| " z2= np.dot(a1,weight2)\n", | |
| " a2 = sigmoid(z2)\n", | |
| " return a2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "pqZd-ptprFAB", | |
| "outputId": "1cbe5eb0-8400-4d89-f087-6bb31f830472" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Ouput after Training is [[ 0.993]]\n", | |
| "Ouput after Training is [[ 0.010]]\n", | |
| "Ouput after Training is [[ 0.003]]\n", | |
| "Ouput after Training is [[ 0.026]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"Ouput after Training is \",forward([[1,0,1]]))\n", | |
| "print(\"Ouput after Training is \",forward([[1,1,1]]))\n", | |
| "print(\"Ouput after Training is \",forward([[1,1,0]]))\n", | |
| "print(\"Ouput after Training is \",forward([[0,0,0]]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8CHqrhpdzky6" | |
| }, | |
| "source": [ | |
| "Note that the input `x1 = np.array([[1,1,0]])` was not in the training set; and the expected output was 1, but this network gave 0; which is wrong. When a network is not able to generalise from its inputs to give the correct output for something is has not been trained on, but for all inputs in its training set it gives the correct output, such a learned/trained network is said to overfit.\n", | |
| "\n", | |
| "This could be due to the shallow network or due to small amount of training data,as is the problem here" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "inNPx01QhOrS" | |
| }, | |
| "source": [ | |
| "## Three layered Neural net\n", | |
| "\n", | |
| "Lets add one more layer to the above Neural Network and see all the equations hold\n", | |
| "\n", | |
| "THe Network will be now where $l=3$, three layer.\n", | |
| "\n", | |
| "$$\n", | |
| "a^0 \\rightarrow a^{l-2} \\rightarrow a^{l-1} \\rightarrow a^{l} \\rightarrow y\n", | |
| "$$\n", | |
| "\n", | |
| "and the equation we derived earlier for the last layer of a two layered NN will stand except of the layer indiceds\n", | |
| "\n", | |
| "#### The Equation of the Last layer also would be similar to previous eq A for a two layered NW\n", | |
| "\n", | |
| "Equation for two layered\n", | |
| " $$\n", | |
| " \\mathbf{\n", | |
| " \\frac {\\partial C}{\\partial w^2} = a^1* \\sigma' (z^{2})*\\frac{\\partial C}{\\partial(a^2)} \\quad \\rightarrow (A_2l) }\n", | |
| " $$\n", | |
| "\n", | |
| "Modified for three layered\n", | |
| "\n", | |
| "$$\n", | |
| "\\mathbf{\n", | |
| "\\frac {\\partial C}{\\partial w^3} = a^2* \\sigma' (z^{3})*\\frac{\\partial C}{\\partial(a^3)} \\quad \\rightarrow (A_3l) }\n", | |
| "$$\n", | |
| "where\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^3)} =(a^3-y)\n", | |
| "$$\n", | |
| "\n", | |
| "#### The Equation of the inner layer also would be similar to previous\n", | |
| "\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^2}= \\frac {\\partial z^2}{\\partial w^2}. \\frac {\\partial a^2}{\\partial z^2}. \\frac {\\partial C}{\\partial a^2}\n", | |
| "$$\n", | |
| "\n", | |
| "The first term is similar to (1.1) /(2.1)\n", | |
| "$$\n", | |
| "\\mathbb{\n", | |
| "\\frac{\\partial z^{2} }{\\partial w^2} = a^1 \\quad\n", | |
| "}\n", | |
| "$$\n", | |
| "The second term is also similar to (1.2)/ (2.2)\n", | |
| "$$\n", | |
| "\\mathbb{\n", | |
| "\\frac{\\partial a^{2} }{\\partial z^2} = \\sigma' (z^{2})\n", | |
| "}\n", | |
| "$$\n", | |
| "For the third part, we split using Chain Rule like previously.\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^2)} = \\frac{\\partial C}{\\partial(a^3)}.\\frac{\\partial(a^3)}{\\partial(a^2)}\n", | |
| "$$\n", | |
| "The second term can can be reduced similar to (2.3.2) to\n", | |
| "$$\n", | |
| " \\sigma'(z^3).w^3\n", | |
| "$$\n", | |
| "\n", | |
| "So\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^2)} = \\frac{\\partial C}{\\partial(a^3)} . \\sigma'(z^3).w^3 \\quad \\rightarrow (A_3l.2)\n", | |
| "$$\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "Putting all together we get\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^2} =a^1* \\sigma'(z^2)* \\frac{\\partial C}{\\partial(a^3)} .\\sigma'(z^3).w^3 \\quad \\rightarrow (B_3l)\n", | |
| "$$\n", | |
| "\n", | |
| "#### The Equation of the first layer also would be similar to previous\n", | |
| "\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^1}= \\frac {\\partial z^1}{\\partial w^1}. \\frac {\\partial a^1}{\\partial z^1}. \\frac {\\partial C}{\\partial a^1}\n", | |
| "$$\n", | |
| "\n", | |
| "\n", | |
| "The first term is similar to (1.1)\n", | |
| "$$\n", | |
| "\\mathbb{\n", | |
| "\\frac{\\partial z^{1} }{\\partial w^1} = \\frac{\\partial a^0.w^1}{\\partial w^1} =a^0\n", | |
| "}\n", | |
| "$$\n", | |
| "The second term is also similar to (1.2)\n", | |
| "$$\n", | |
| "\\mathbb{\n", | |
| "\\frac{\\partial a^{1} }{\\partial z^1} = \\frac{\\partial \\sigma(z^1) }{\\partial z^1} =\\sigma' (z^{1})\n", | |
| "}\n", | |
| "$$\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "For the third part, we use Chain Rule to split like below, the first part of which we calculated in the earlier step.\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^1)} = \\frac{\\partial C}{\\partial(a^2)}.\\frac{\\partial(a^2)}{\\partial(a^1)}\n", | |
| "$$\n", | |
| "First term from previous layer calculation (A.2)\n", | |
| "\n", | |
| "The second term can be re-written similar to (2.3.2) to\n", | |
| "\n", | |
| "$$\n", | |
| "\\begin{aligned}\n", | |
| "= \\sigma'(z^2).w^2\n", | |
| "\\end{aligned}\n", | |
| "$$\n", | |
| "\n", | |
| "Putting all together we get\n", | |
| "\n", | |
| "$$\n", | |
| "\\mathbf{\n", | |
| "\\frac {\\partial C}{\\partial w^1} =a^0* \\sigma'(z^1)* \\frac{\\partial C}{\\partial(a^2)} .\\sigma'(z^2).w^2 \\quad \\rightarrow (C_3l)\n", | |
| "}\n", | |
| "$$\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "GPx9rmKJDhqr" | |
| }, | |
| "source": [ | |
| "So the final equations for the three layer are\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^3} = a^2* \\sigma' (z^{3})*\\frac{\\partial C}{\\partial(a^3)} \\quad \\rightarrow (A_3l)\n", | |
| "$$\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^2} =a^1* \\sigma'(z^2)* \\frac{\\partial C}{\\partial(a^2)} \\quad \\rightarrow (B_3l)\n", | |
| "$$\n", | |
| "\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial C}{\\partial w^1} =a^0* \\sigma'(z^1)* \\frac{\\partial C}{\\partial(a^1)} \\quad \\rightarrow (C_3l)\n", | |
| "$$\n", | |
| "\n", | |
| "---\n", | |
| "Where\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^3)} = (a^3-y)\n", | |
| "$$\n", | |
| "\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^2)} = \\frac{\\partial C}{\\partial(a^3)} . \\sigma'(z^3).w^3 \n", | |
| "$$\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac{\\partial C}{\\partial(a^1)} = \\frac{\\partial C}{\\partial(a^2)} . \\sigma'(z^2).w^2\n", | |
| "$$\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NN4i53dYeAg_" | |
| }, | |
| "source": [ | |
| "## Sclaar and Matrix Calculus\n", | |
| "\n", | |
| "The above equations are all in Scalar form if I can call it that.\n", | |
| "\n", | |
| "However we have weights and training data as large matrices. Matrices can be thought of as stacked vectors. If you see below the above equations are implemented with Weight Transponse. To understand this we need to understand Matrix Calculus, and things like Jacobian matrix\n", | |
| "\n", | |
| "This is described in some detail here https://alexcpn.github.io/html/NN/ml/7_backpropogation_matrix_calculus/\n", | |
| "\n", | |
| "and in more detail here https://alexcpn.github.io/html/NN/ml/8_backpropogation_full/\n", | |
| "\n", | |
| "Note that these are quite involved to understand\n", | |
| "\n", | |
| "\n", | |
| "For example\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac {\\partial a^2}{\\partial w^2} = \\sigma^{'}(z^2) * (a^{1})^T\n", | |
| "$$\n", | |
| "\n", | |
| "and\n", | |
| "\n", | |
| "$$\n", | |
| "\\begin{aligned}\n", | |
| "\\frac {\\partial C}{\\partial w^2}= (a^2-y)*\\sigma^{'}(z^2) * (a^{1})^T\n", | |
| "\\end{aligned}\n", | |
| "$$\n", | |
| "\n", | |
| "The Vector dot product $w.a$ when applied on matrices becomes the elementwise multiplication $w^2 \\otimes a^1$ (also called Hadamard product)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "G1urqSQYeAg_", | |
| "outputId": "42040476-7ed7-41bc-8326-199320e51c33" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[0 0 1]\n", | |
| " [0 1 1]\n", | |
| " [1 0 1]\n", | |
| " [1 1 1]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 263 | |
| }, | |
| "id": "0Fs3DyoahKXw", | |
| "outputId": "816b3905-8c4c-45e5-b405-4392aadfab7f" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "New ouput\n", | |
| " [[ 0.015]\n", | |
| " [ 0.986]\n", | |
| " [ 0.986]\n", | |
| " [ 0.014]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# set learning rate as 1 for this toy example\n", | |
| "learningRate = 1\n", | |
| "\n", | |
| "\n", | |
| "# Randomly initalised weights\n", | |
| "w1 = np.random.random((3,20))\n", | |
| "w2 = np.random.random((20,4))\n", | |
| "w3 = np.random.random((4,1))\n", | |
| "\n", | |
| "# Activation to layer 0 is taken as input x\n", | |
| "a0 = x\n", | |
| "\n", | |
| "iterations = 10000\n", | |
| "for iter in range(0,iterations):\n", | |
| "\n", | |
| " # Forward pass - Straight Forward\n", | |
| " z1= np.dot(a0,w1)\n", | |
| " a1 = sigmoid(z1)\n", | |
| " z2= np.dot(a1,w2)\n", | |
| " a2 = sigmoid(z2)\n", | |
| " z3= np.dot(a2,w3)\n", | |
| " a3 = sigmoid(z3)\n", | |
| "\n", | |
| "\n", | |
| " # Backward Pass - Backpropagation\n", | |
| " dC_da3 = (a3-y)\n", | |
| " #---------------------------------------------------------------\n", | |
| " # Calcluating change of Cost/Loss wrto weight of Last layer Eq (A)\n", | |
| " #---------------------------------------------------------------\n", | |
| " dC_dw3_t = dC_da3*derv_sigmoid(z3)\n", | |
| " dC_dw3 = a2.T.dot(dC_dw3_t)\n", | |
| " # Gradient descent\n", | |
| " w3 = w3 - learningRate*dC_dw3\n", | |
| " #---------------------------------------------------------------\n", | |
| " # Calcluating change of Cost/Loss wrto weight of Inner Layer Eq (B)\n", | |
| " #---------------------------------------------------------------\n", | |
| " dC_da2 = np.dot(dC_da3*derv_sigmoid(z3), w3.T)\n", | |
| " dC_dw2_t = dC_da2*derv_sigmoid(z2)\n", | |
| " dC_dw2 = a1.T.dot(dC_dw2_t)\n", | |
| " # Gradient descent\n", | |
| " w2 = w2 - learningRate*dC_dw2\n", | |
| " #---------------------------------------------------------------\n", | |
| " # Calcluating change of Cost/Loss wrto weight of First Layer Eq (C)\n", | |
| " #---------------------------------------------------------------\n", | |
| "\n", | |
| " dC_da1 = np.dot(dC_da2*derv_sigmoid(z2), w2.T)\n", | |
| " dC_dw1_t = dC_da1*derv_sigmoid(z1)\n", | |
| " dC_dw1 = a0.T.dot(dC_dw1_t)\n", | |
| " #Gradinent descent\n", | |
| " w1 = w1 - learningRate*dC_dw1\n", | |
| " #---------------------------------------------------------------\n", | |
| "\n", | |
| "\n", | |
| "print(\"New ouput\\n\",a3)\n", | |
| "\n", | |
| "#---------------------------------------------------------------\n", | |
| "# Training is done, weights are primed for output y\n", | |
| "#---------------------------------------------------------------\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "u7vR4Z_seAg_" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def forward(x):\n", | |
| " # Forward pass - Straight Forward\n", | |
| " z1= np.dot(x,w1)\n", | |
| " a1 = sigmoid(z1)\n", | |
| " z2= np.dot(a1,w2)\n", | |
| " a2 = sigmoid(z2)\n", | |
| " z3= np.dot(a2,w3)\n", | |
| " a3 = sigmoid(z3)\n", | |
| " return a3\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "4-8Rk8GLeAg_", | |
| "outputId": "7a4fbf0a-9d15-4ef2-d7b3-f45c216d9ed3" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Ouput after Training is [[ 0.986]]\n", | |
| "Ouput after Training is [[ 0.014]]\n", | |
| "Ouput after Training is [[ 0.010]]\n", | |
| "Ouput after Training is [[ 0.980]]\n", | |
| "Ouput after Training is [[ 0.986]]\n", | |
| "Ouput after Training is [[ 0.015]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"Ouput after Training is \",forward([[1,0,1]]))\n", | |
| "print(\"Ouput after Training is \",forward([[1,1,1]]))\n", | |
| "print(\"Ouput after Training is \",forward([[1,1,0]]))#wrong\n", | |
| "print(\"Ouput after Training is \",forward([[0,0,0]]))#wrong\n", | |
| "print(\"Ouput after Training is \",forward([[0,1,1]]))\n", | |
| "print(\"Ouput after Training is \",forward([[0,0,1]]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "UXsi5vXheAg_" | |
| }, | |
| "source": [ | |
| "### When to use Element Wise multiplicaion/broadcast (*) and when to use Dot Product (numpy.matmul/np.dot)\n", | |
| "\n", | |
| "\n", | |
| "**Element-wise Operation for Activation Function Derivative:**\n", | |
| "\n", | |
| "dC_dw3_t = dC_da3 * derv_sigmoid(z3): This operation applies the derivative of the sigmoid function to z3 element-wise. The derivative of the sigmoid function is applied to each neuron's output individually, which is necessary because the sigmoid function's effect is applied individually to each neuron's output during the forward pass. The multiplication with dC_da3 (the gradient of the cost with respect to the activations of the last layer) is also element-wise, correctly applying the chain rule for each neuron's contribution to the cost.\n", | |
| "\n", | |
| "\n", | |
| "**Dot Product for Weight Gradient Calculation:**\n", | |
| "\n", | |
| "dC_dw3 = a2.T.dot(dC_dw3_t): This operation is correct and necessary. Here, you're calculating the gradient of the cost with respect to the weights of the last layer (w3). The dot product between the transpose of the activations of the second layer (a2.T) and the result from the previous step (dC_dw3_t) correctly computes this gradient. This matrix multiplication aligns with the dimensions of the weights and reflects how each weight contributes to the output, factoring in the gradients from the final layer's activations.\n", | |
| "\n", | |
| "\n", | |
| "The element-wise multiplication is used where the operation is inherently element-wise, such as applying the derivative of an activation function to the outputs (activations) of a layer.\n", | |
| "The dot product (matrix multiplication) is used for calculating gradients with respect to weights and for propagating gradients through the network. This aligns with how weights influence activations (forward pass) and how changes in activations influence the cost (backward pass).\n", | |
| "\n", | |
| "**General Rule for Correction:**\n", | |
| "\n", | |
| "Use element-wise operations when dealing with activation functions and their derivatives.\n", | |
| "\n", | |
| "Use dot products (matrix multiplication) when propagating activations through the network and when calculating gradients with respect to weights.\n", | |
| "\n", | |
| "Credit ChatGPT4\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kt_0Tsy3eAg_" | |
| }, | |
| "source": [ | |
| "## Deep Neural Networks are Universal function approximators\n", | |
| "\n", | |
| "So lets build a slighly more complex Neural Net that can say model a Sine function" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "v5GyAcHceAhA" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# This is the training data for the Sine function; Since the value is know it is easy to generate it\n", | |
| "# If you need to approximate any other deistribution and then predict from it, you can see sources like Kaggle data set for training and validation data\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "# Number of data points\n", | |
| "n_points = 1000\n", | |
| "\n", | |
| "# Generate input values (angles) - let's use one full period 0 to 2pi\n", | |
| "angles = np.linspace(0, 2*np.pi, n_points)\n", | |
| "\n", | |
| "# Calculate sine values\n", | |
| "sine_values = np.sin(angles)\n", | |
| "\n", | |
| "# Prepare inputs for the network\n", | |
| "# Assuming we vary the first input and keep the others as some pattern or constant\n", | |
| "X = angles.reshape(-1,1)\n", | |
| "\n", | |
| "\n", | |
| "# Target outputs (reshape sine_values to match output shape of the network), also called validation data set\n", | |
| "Y = sine_values.reshape(-1, 1) # Shape will be (n_points, 1)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "HAcU5JBseAhA", | |
| "outputId": "a20b00db-cfb3-40e3-b277-c9f1211cafac" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(1000, 1)\n", | |
| "(1000, 1)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(X.shape)\n", | |
| "print(Y.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "mRCP6DENeAhA" | |
| }, | |
| "source": [ | |
| "The fist complexity ; A change of Activation function" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Xj3TEqTzeAhA" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Sigmoid squashes from 0 to 1; but we need to model Sine function which is from -1 to 1; So we need to use a differnt inequality like tanh\n", | |
| "sigmoid=None\n", | |
| "derv_sigmoid=None\n", | |
| "\n", | |
| "# Hyperbolic tangent activation function\n", | |
| "def tanh(x):\n", | |
| " return np.tanh(x)\n", | |
| "\n", | |
| "# Derivative of the tanh function\n", | |
| "def derv_tanh(x):\n", | |
| " return 1 - np.tanh(x)**2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "WB2rfIXseAhA", | |
| "outputId": "78658bd3-fd57-417b-82ad-a86f8944e320" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "---------------batch=(49, 50)- iter=(999, 1000)------------------\n", | |
| "x [[ 0.038]\n", | |
| " [ 0.686]\n", | |
| " [ 4.774]\n", | |
| " [ 0.214]]\n", | |
| "y (expected) [[ 0.038]\n", | |
| " [ 0.633]\n", | |
| " [-0.998]\n", | |
| " [ 0.212]]\n", | |
| "a3 (output) [[ 1.000]\n", | |
| " [ 1.000]\n", | |
| " [ 1.000]\n", | |
| " [ 1.000]]\n", | |
| "gradinnt [[ 0.096]\n", | |
| " [ 0.037]\n", | |
| " [ 0.200]\n", | |
| " [ 0.079]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# set learning rate 10e-6 was too low\n", | |
| "learningRate = 10e-4\n", | |
| "\n", | |
| "# Randomly initalised weights\n", | |
| "w1 = np.random.random((1,100))\n", | |
| "w2 = np.random.random((100,40))\n", | |
| "w3 = np.random.random((40,1))\n", | |
| "\n", | |
| "\n", | |
| "# Activation to layer 0 is taken as input x\n", | |
| "a0 = X\n", | |
| "# Number of epochs\n", | |
| "iterations = 1000\n", | |
| "\n", | |
| "# Batch size\n", | |
| "batch_size = 20\n", | |
| "n_batches = X.shape[0] // batch_size # change the code slightly for batching through the inputs\n", | |
| "epoch_losses = []\n", | |
| "a3_stddeviations = []\n", | |
| "a3_means =[]\n", | |
| "\n", | |
| "for iter in range(iterations):\n", | |
| " # Shuffle the data at the beginning of each epoch\n", | |
| "\n", | |
| " epoch_loss =0\n", | |
| " indices = np.arange(X.shape[0])\n", | |
| " np.random.shuffle(indices)\n", | |
| " X_shuffled = X[indices]\n", | |
| " Y_shuffled = Y[indices]\n", | |
| "\n", | |
| " for b in range(n_batches):\n", | |
| " # Slice out the batch\n", | |
| " start = b * batch_size\n", | |
| " end = start + batch_size\n", | |
| " a0 = X_shuffled[start:end]\n", | |
| " y_batch = Y_shuffled[start:end]\n", | |
| "\n", | |
| " # Forward pass\n", | |
| " z1 = np.dot(a0, w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2 = np.dot(a1, w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3 = np.dot(a2, w3)\n", | |
| " a3 = tanh(z3)\n", | |
| "\n", | |
| " # Calculate batch loss (MSE)\n", | |
| " batch_loss = np.mean((a3 - y_batch)**2)\n", | |
| " epoch_loss += batch_loss # Accumulate batch loss\n", | |
| "\n", | |
| " # Calculate mean and standard deviation\n", | |
| " mean_a3 = np.mean(a3)\n", | |
| " std_a3 = np.std(a3)\n", | |
| " a3_stddeviations.append(std_a3)\n", | |
| " a3_means.append(mean_a3)\n", | |
| "\n", | |
| "\n", | |
| " # Backward Pass - Backpropagation\n", | |
| "\n", | |
| " # calculate the gradient for backpropagation\n", | |
| " #dC_da3 = (a3 - y_batch) # this is the derivative for Mean Square Error Loss (See Eq 1.3)\n", | |
| " # there we have simplified a bit; Here we need to divide by the batch size\n", | |
| " # MSE derivative by batch size\n", | |
| " dC_da3 = 2 * (a3 - y_batch) / batch_size # The derivative of MSE with respect to the output\n", | |
| "\n", | |
| " if b // n_batches/2 == 0 and iter ==1 and iterations==1:#print only once\n", | |
| " print(f\"---------------batch={b,n_batches}- iter={iter,iterations}---batch-loss={batch_loss}----\")\n", | |
| " print(\"x\",a0[1:5])\n", | |
| " print(\"y (expected)\",y_batch[1:5])\n", | |
| " print(\"a3 (output)\",a3[1:5])\n", | |
| " print(\"gradinnt\",dC_da3[1:5])\n", | |
| "\n", | |
| " dC_dw3_t = dC_da3 * derv_tanh(z3)\n", | |
| " dC_dw3 = np.dot(a2.T, dC_dw3_t)\n", | |
| "\n", | |
| " dC_da2 = np.dot(dC_da3 * derv_tanh(z3), w3.T)\n", | |
| " dC_dw2_t = dC_da2 * derv_tanh(z2)\n", | |
| " dC_dw2 = np.dot(a1.T, dC_dw2_t)\n", | |
| "\n", | |
| " dC_da1 = np.dot(dC_da2 * derv_tanh(z2), w2.T)\n", | |
| " dC_dw1_t = dC_da1 * derv_tanh(z1)\n", | |
| " dC_dw1 = np.dot(a0.T, dC_dw1_t)\n", | |
| "\n", | |
| " # Update weights with L2 regularization\n", | |
| " w3 -= learningRate * dC_dw3\n", | |
| " w2 -= learningRate * dC_dw2\n", | |
| " w1 -= learningRate * dC_dw1\n", | |
| " # Calculate the average loss for the epoch\n", | |
| " epoch_average_loss = epoch_loss / n_batches\n", | |
| " #print(f\"Epoch loss Average={epoch_average_loss:.2f}\")\n", | |
| " epoch_losses.append(epoch_average_loss)\n", | |
| "\n", | |
| "print(f\"---------------batch={b,n_batches}- iter={iter,iterations}------------------\")\n", | |
| "print(\"x\",a0[1:5])\n", | |
| "print(\"y (expected)\",y_batch[1:5])\n", | |
| "print(\"a3 (output)\",a3[1:5])\n", | |
| "print(\"gradinnt\",dC_da3[1:5])\n", | |
| "\n", | |
| "#---------------------------------------------------------------\n", | |
| "# Training is done, weights are primed for output y\n", | |
| "#---------------------------------------------------------------" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YsMr5QzpeAhA" | |
| }, | |
| "source": [ | |
| "Note that I have commented out the printing of activations; For debugging you can reduce the number of iterations to a few and then print and check. The a3 activations\n", | |
| "directly jump to 1 and stay there here" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "AZB85JqveAhA", | |
| "outputId": "a0b8be99-ce3f-4298-c267-114f861e6fb1" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Ouput after Training is [[ 1.000]] Expected -0.8754699268728889\n", | |
| "Ouput after Training is [[-1.000]] Expected 0.06635854848376267\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "def forward(x):\n", | |
| " # Forward pass - Straight Forward\n", | |
| " z1= np.dot(x,w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2= np.dot(a1,w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3= np.dot(a2,w3)\n", | |
| " a3 = tanh(z3)\n", | |
| " return a3\n", | |
| "\n", | |
| "angle = 4.208\n", | |
| "x = (np.array([angle]).reshape(-1,1))\n", | |
| "print(\"Ouput after Training is \",forward(x), \"Expected\", np.sin(angle))\n", | |
| "\n", | |
| "angle = -3.208\n", | |
| "x = (np.array([angle]).reshape(-1,1))\n", | |
| "print(\"Ouput after Training is \",forward(x), \"Expected\", np.sin(angle))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QTfPT9_heAhB" | |
| }, | |
| "source": [ | |
| "This obviously has not worked\n", | |
| "\n", | |
| "## Debugging Neural Network Training problems" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "E0REG98jeAhB", | |
| "outputId": "5b6ca2af-d3c7-4f04-8a1c-6f99a4396310" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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AAEAbtGrVqpx88slZunRp6urq1npumwqnp556KkceeWS+/OUvZ+jQoZk/f37OP//87L///rnuuuvWeJ81veLUr1+/LFq0qPSHsyk0Njamvr4+Rx111LvGH/wtO0Ol7AyVsjNUys5QqdayM8uWLcu22267TuHUpt6qN378+Bx88ME5//zzkyT77LNPttpqqxx66KG59NJL06dPn9XuU1tbm9ra2tWOd+jQoVX9g93a5qH1szNUys5QKTtDpewMlar2zlTy2G3q9zitWrUq7dq1HHmLLbZIkrSSF84AAID3oKqG04oVKzJ79uzMnj07SfL8889n9uzZmTdvXpJk7NixOfXUU5vPHz58eG677bZcffXVmTt3bu6///586UtfygEHHJC+fftW4ykAAACbgaq+VW/WrFk5/PDDm78+99xzkyQjR47MhAkTMn/+/OaISpLTTjsty5cvz49+9KOcd9556datWz7ykY+4HDkAALBRVTWchgwZsta32E2YMGG1Y2effXbOPvvsjTgVAABAS23qM04AAADVIJwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASlQ1nGbMmJHhw4enb9++qampyZQpU0rv09DQkIsuuig77bRTamtrs/POO+f666/f+MMCAACbrfbVfPCVK1dm4MCBOeOMM3LCCSes030+9alPZeHChbnuuuuy6667Zv78+WlqatrIkwIAAJuzqobTsGHDMmzYsHU+/9e//nXuvffezJ07N927d0+S7LzzzhtpOgAAgLdVNZwqdccdd2S//fbL5Zdfnp///OfZaqut8rGPfSzf+ta30qlTpzXep6GhIQ0NDc1fL1u2LEnS2NiYxsbGTTL32rwzQ2uYhbbBzlApO0Ol7AyVsjNUqrXsTCWP36bCae7cuZk5c2Y6duyYyZMnZ9GiRTnzzDPz6quv5oYbbljjfcaPH59x48atdnzatGnp3Lnzxh55ndXX11d7BNoYO0Ol7AyVsjNUys5QqWrvzKpVq9b53JqiKIqNOMs6q6mpyeTJk3Pccce96zlHH3107rvvvixYsCBdu3ZNktx22235xCc+kZUrV67xVac1veLUr1+/LFq0KHV1dRv8eVSqsbEx9fX1Oeqoo9KhQ4dqj0MbYGeolJ2hUnaGStkZKtVadmbZsmXZdttts3Tp0tI2aFOvOPXp0yfbb799czQlyZ577pmiKPKXv/wlu+2222r3qa2tTW1t7WrHO3To0Kr+wW5t89D62RkqZWeolJ2hUnaGSlV7Zyp57Db1e5wOPvjgvPTSS1mxYkXzsWeffTbt2rXLDjvsUMXJAACA97KqhtOKFSsye/bszJ49O0ny/PPPZ/bs2Zk3b16SZOzYsTn11FObzz/55JPTo0ePnH766XnqqacyY8aMnH/++TnjjDPe9eIQAAAA/6iqhtOsWbMyaNCgDBo0KEly7rnnZtCgQbn44ouTJPPnz2+OqCTp0qVL6uvrs2TJkuy3334ZMWJEhg8fnh/+8IdVmR8AANg8VPUzTkOGDMnark0xYcKE1Y4NGDCg6lffAAAANi9t6jNOAAAA1SCcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEusVTi+88EL+8pe/NH/90EMPZcyYMbn22ms32GAAAACtxXqF08knn5y77747SbJgwYIcddRReeihh3LRRRflm9/85gYdEAAAoNrWK5zmzJmTAw44IEnyi1/8InvttVd++9vf5qabbsqECRM25HwAAABVt17h1NjYmNra2iTJb37zm3zsYx9LkgwYMCDz58/fcNMBAAC0AusVTh/4wAdyzTXX5L777kt9fX0++tGPJkleeuml9OjRY4MOCAAAUG3rFU7f/e538+Mf/zhDhgzJSSedlIEDByZJ7rjjjua38AEAALxXtF+fOw0ZMiSLFi3KsmXLss022zQf/8IXvpDOnTtvsOEAAABag5qiKIpK7/Taa6+lKIrmSPrzn/+cyZMnZ88998zQoUM3+JAb0rJly9K1a9csXbo0dXV1VZ3ljTeSH/zgrUyatDBvvtk7HTu2S6dOb9/22mvJ668nHTtmtWO1tUlDQ+W3bW7nt6VZKzn/tdeasmrVq+nbt0fatWu3Wf8s7MXG25nW9tz8fd60P4vXX1/zzrS252YvWs/Pora2KYsWvZIuXbZLQ0O7zfpn0ZbP31SP3alTstNOTdl999/mwgsPTMeOHVItlbTBeoXT0UcfnRNOOCGjRo3KkiVLMmDAgHTo0CGLFi3K97///fzLv/zLeg+/sbWWcLrgguSKK5KmpqqNAAAAVdWlS5H//M+anHBCdR6/kjZYr884/f73v8+hhx6aJLn11lvTq1ev/PnPf87Pfvaz/PCHP1yfb7lZueCC5HvfE00AAGzeVqxITjwxue22ak9Sbr3CadWqVdl6662TJNOmTcsJJ5yQdu3a5aCDDsqf//znDTrge80bb7z9ShMAAFCTJDnnnOStt6o8Son1Cqddd901U6ZMyQsvvJA777wzRx99dJLk5Zdfrvrnhlq7q67yShMAAPytv/wlue++ak+xdusVThdffHG+8pWvZOedd84BBxyQwYMHJ3n71adBgwZt0AHfa/7nf6o9AQAAtD7z51d7grVbr8uRf+ITn8ghhxyS+fPnN/8OpyQ54ogjcvzxx2+w4d6Ldtml2hMAAEDr06dPtSdYu/W6qt7f+stf/pIk2WGHHTbIQBtbta+q98Ybb1+C0dv1AADgbTvskPzpT8kWW2zax93oV9VramrKN7/5zXTt2jU77bRTdtppp3Tr1i3f+ta30qQI1mrLLZPzzqv2FAAA0Bq8/RrOD36w6aOpUuv1Vr2LLroo1113XS677LIcfPDBSZKZM2fmkksuyeuvv55vf/vbG3TI95rLL3/7f/0eJwAANmdbb51MmJCq/R6nSqzXW/X69u2ba665Jh/72MdaHL/99ttz5pln5sUXX9xgA25o1X6r3t96443kBz94K5MmLcybb/ZOx47t2sxvfG4L57elWSs5/7XXmrJq1avp27dH2rVrt1n/LOzFxtuZ1vbc/H3etD+L119f8860tudmL1rPz6K2timLFr2SLl22S0NDu836Z9GWz99Uj92pU7LTTk3Zffff5sILD0zHjh1SLZW0wXq94rR48eIMGDBgteMDBgzI4sWL1+dbbpa23DIZM6Ypu+/+cI455ph06LBe75xkM9PY+FamTv2tnWGd2RkqZWeo1Ns786CdYZ29vTOvtvq35/2t9drsgQMH5kc/+tFqx3/0ox9ln332+YeHAgAAaE3W6xWnyy+/PMcee2x+85vfNP8OpwceeCAvvPBCpk6dukEHBAAAqLb1esXpwx/+cJ599tkcf/zxWbJkSZYsWZITTjghTz75ZH7+859v6BkBAACqar1ecUrevkDE318977HHHst1112Xa6+99h8eDAAAoLXw6T0AAIASwgkAAKCEcAIAAChR0WecTij5lb5Lliz5R2YBAABolSoKp65du5befuqpp/5DAwEAALQ2FYXTDTfcsLHmAAAAaLV8xgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKBEVcNpxowZGT58ePr27ZuamppMmTJlne97//33p3379vmnf/qnjTYfAABAUuVwWrlyZQYOHJgrr7yyovstWbIkp556ao444oiNNBkAAMD/176aDz5s2LAMGzas4vuNGjUqJ598crbYYouKXqUCAABYH1UNp/Vxww03ZO7cubnxxhtz6aWXlp7f0NCQhoaG5q+XLVuWJGlsbExjY+NGm3NdvTNDa5iFtsHOUCk7Q6XsDJWyM1SqtexMJY/fpsLpueeey4UXXpj77rsv7duv2+jjx4/PuHHjVjs+bdq0dO7ceUOPuN7q6+urPQJtjJ2hUnaGStkZKmVnqFS1d2bVqlXrfG6bCae33norJ598csaNG5fdd999ne83duzYnHvuuc1fL1u2LP369cvRRx+durq6jTFqRRobG1NfX5+jjjoqHTp0qPY4tAF2hkrZGSplZ6iUnaFSrWVn3nk32rpoM+G0fPnyzJo1K48++mjOOuusJElTU1OKokj79u0zbdq0fOQjH1ntfrW1tamtrV3teIcOHVrVP9itbR5aPztDpewMlbIzVMrOUKlq70wlj91mwqmuri5PPPFEi2NXXXVV7rrrrtx6663p379/lSYDAADe66oaTitWrMgf//jH5q+ff/75zJ49O927d8+OO+6YsWPH5sUXX8zPfvaztGvXLnvttVeL+/fs2TMdO3Zc7TgAAMCGVNVwmjVrVg4//PDmr9/5LNLIkSMzYcKEzJ8/P/PmzavWeAAAAEmqHE5DhgxJURTvevuECRPWev9LLrkkl1xyyYYdCgAA4O+0q/YAAAAArZ1wAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABKCCcAAIASwgkAAKBEVcNpxowZGT58ePr27ZuamppMmTJlreffdtttOeqoo7Lddtulrq4ugwcPzp133rlphgUAADZbVQ2nlStXZuDAgbnyyivX6fwZM2bkqKOOytSpU/PII4/k8MMPz/Dhw/Poo49u5EkBAIDNWftqPviwYcMybNiwdT7/P/7jP1p8/Z3vfCe33357/uu//iuDBg3awNMBAAC8rarh9I9qamrK8uXL071793c9p6GhIQ0NDc1fL1u2LEnS2NiYxsbGjT5jmXdmaA2z0DbYGSplZ6iUnaFSdoZKtZadqeTxa4qiKDbiLOuspqYmkydPznHHHbfO97n88stz2WWX5emnn07Pnj3XeM4ll1yScePGrXZ84sSJ6dy58/qOCwAAtHGrVq3KySefnKVLl6aurm6t57bZcJo4cWI+//nP5/bbb8+RRx75ruet6RWnfv36ZdGiRaU/nE2hsbEx9fX1Oeqoo9KhQ4dqj0MbYGeolJ2hUnaGStkZKtVadmbZsmXZdttt1ymc2uRb9W655ZZ87nOfy6RJk9YaTUlSW1ub2tra1Y536NChVf2D3drmofWzM1TKzlApO0Ol7AyVqvbOVPLYbe73ON188805/fTTc/PNN+fYY4+t9jgAAMBmoKqvOK1YsSJ//OMfm79+/vnnM3v27HTv3j077rhjxo4dmxdffDE/+9nPkrz99ryRI0fmBz/4QQ488MAsWLAgSdKpU6d07dq1Ks8BAAB476vqK06zZs3KoEGDmi8lfu6552bQoEG5+OKLkyTz58/PvHnzms+/9tpr8+abb2b06NHp06dP859zzjmnKvMDAACbh6q+4jRkyJCs7doUEyZMaPH1Pffcs3EHAgAAWIM29xknAACATU04AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJQQTgAAACWEEwAAQAnhBAAAUEI4AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJQQTgAAACWEEwAAQAnhBAAAUEI4AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJQQTgAAACWEEwAAQAnhBAAAUEI4AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJQQTgAAACWEEwAAQAnhBAAAUEI4AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJQQTgAAACWEEwAAQAnhBAAAUEI4AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJQQTgAAACWEEwAAQAnhBAAAUEI4AQAAlBBOAAAAJYQTAABACeEEAABQQjgBAACUEE4AAAAlhBMAAEAJ4QQAAFBCOAEAAJSoajjNmDEjw4cPT9++fVNTU5MpU6aU3ueee+7Jvvvum9ra2uy6666ZMGHCRp8TAADYvFU1nFauXJmBAwfmyiuvXKfzn3/++Rx77LE5/PDDM3v27IwZMyaf+9zncuedd27kSQEAgM1Z+2o++LBhwzJs2LB1Pv+aa65J//79c8UVVyRJ9txzz8ycOTP//u//nqFDh26sMQEAgM1cVcOpUg888ECOPPLIFseGDh2aMWPGvOt9Ghoa0tDQ0Pz10qVLkySLFy9OY2PjRpmzEo2NjVm1alVeffXVdOjQodrj0AbYGSplZ6iUnaFSdoZKtZadWb58eZKkKIrSc9tUOC1YsCC9evVqcaxXr15ZtmxZXnvttXTq1Gm1+4wfPz7jxo1b7Xj//v032pwAAEDbsXz58nTt2nWt57SpcFofY8eOzbnnntv8dVNTUxYvXpwePXqkpqamipO9bdmyZenXr19eeOGF1NXVVXsc2gA7Q6XsDJWyM1TKzlCp1rIzRVFk+fLl6du3b+m5bSqcevfunYULF7Y4tnDhwtTV1a3x1aYkqa2tTW1tbYtj3bp121gjrre6ujr/oqEidoZK2RkqZWeolJ2hUq1hZ8peaXpHm/o9ToMHD8706dNbHKuvr8/gwYOrNBEAALA5qGo4rVixIrNnz87s2bOTvH258dmzZ2fevHlJ3n6b3amnntp8/qhRozJ37txccMEFefrpp3PVVVflF7/4Rb785S9XY3wAAGAzUdVwmjVrVgYNGpRBgwYlSc4999wMGjQoF198cZJk/vz5zRGVvH1Bh//+7/9OfX19Bg4cmCuuuCI//elP2/SlyGtra/ONb3xjtbcTwruxM1TKzlApO0Ol7AyVaos7U1Osy7X3AAAANmNt6jNOAAAA1SCcAAAASggnAACAEsIJAACghHCqoiuvvDI777xzOnbsmAMPPDAPPfRQtUeiSsaPH5/9998/W2+9dXr27JnjjjsuzzzzTItzXn/99YwePTo9evRIly5dcuKJJ672C6HnzZuXY489Np07d07Pnj1z/vnn580339yUT4UquOyyy1JTU5MxY8Y0H7MvrMmLL76Yz3zmM+nRo0c6deqUvffeO7NmzWq+vSiKXHzxxenTp086deqUI488Ms8991yL77F48eKMGDEidXV16datWz772c9mxYoVm/qpsAm89dZb+frXv57+/funU6dO2WWXXfKtb30rf3tdMTuzeZsxY0aGDx+evn37pqamJlOmTGlx+4baj8cffzyHHnpoOnbsmH79+uXyyy/f2E9tzQqq4pZbbim23HLL4vrrry+efPLJ4vOf/3zRrVu3YuHChdUejSoYOnRoccMNNxRz5swpZs+eXRxzzDHFjjvuWKxYsaL5nFGjRhX9+vUrpk+fXsyaNas46KCDig996EPNt7/55pvFXnvtVRx55JHFo48+WkydOrXYdttti7Fjx1bjKbGJPPTQQ8XOO+9c7LPPPsU555zTfNy+8PcWL15c7LTTTsVpp51W/O53vyvmzp1b3HnnncUf//jH5nMuu+yyomvXrsWUKVOKxx57rPjYxz5W9O/fv3jttdeaz/noRz9aDBw4sHjwwQeL++67r9h1112Lk046qRpPiY3s29/+dtGjR4/il7/8ZfH8888XkyZNKrp06VL84Ac/aD7Hzmzepk6dWlx00UXFbbfdViQpJk+e3OL2DbEfS5cuLXr16lWMGDGimDNnTnHzzTcXnTp1Kn784x9vqqfZTDhVyQEHHFCMHj26+eu33nqr6Nu3bzF+/PgqTkVr8fLLLxdJinvvvbcoiqJYsmRJ0aFDh2LSpEnN5/zhD38okhQPPPBAURRv/8urXbt2xYIFC5rPufrqq4u6urqioaFh0z4BNonly5cXu+22W1FfX198+MMfbg4n+8KafPWrXy0OOeSQd729qamp6N27d/G9732v+diSJUuK2tra4uabby6KoiieeuqpIknx8MMPN5/zq1/9qqipqSlefPHFjTc8VXHssccWZ5xxRotjJ5xwQjFixIiiKOwMLf19OG2o/bjqqquKbbbZpsX/N331q18t9thjj438jFbnrXpV8MYbb+SRRx7JkUce2XysXbt2OfLII/PAAw9UcTJai6VLlyZJunfvniR55JFH0tjY2GJnBgwYkB133LF5Zx544IHsvffe6dWrV/M5Q4cOzbJly/Lkk09uwunZVEaPHp1jjz22xV4k9oU1u+OOO7Lffvvlk5/8ZHr27JlBgwblJz/5SfPtzz//fBYsWNBib7p27ZoDDzywxd5069Yt++23X/M5Rx55ZNq1a5ff/e53m+7JsEl86EMfyvTp0/Pss88mSR577LHMnDkzw4YNS2JnWLsNtR8PPPBADjvssGy55ZbN5wwdOjTPPPNM/vrXv26iZ/O29pv00UiSLFq0KG+99VaL/2BJkl69euXpp5+u0lS0Fk1NTRkzZkwOPvjg7LXXXkmSBQsWZMstt0y3bt1anNurV68sWLCg+Zw17dQ7t/Hecsstt+T3v/99Hn744dVusy+sydy5c3P11Vfn3HPPzde+9rU8/PDD+dKXvpQtt9wyI0eObP77vqa9+Nu96dmzZ4vb27dvn+7du9ub96ALL7wwy5Yty4ABA7LFFlvkrbfeyre//e2MGDEiSewMa7Wh9mPBggXp37//at/jndu22WabjTL/mggnaGVGjx6dOXPmZObMmdUehVbqhRdeyDnnnJP6+vp07Nix2uPQRjQ1NWW//fbLd77znSTJoEGDMmfOnFxzzTUZOXJklaejNfrFL36Rm266KRMnTswHPvCBzJ49O2PGjEnfvn3tDJslb9Wrgm233TZbbLHFale4WrhwYXr37l2lqWgNzjrrrPzyl7/M3XffnR122KH5eO/evfPGG29kyZIlLc7/253p3bv3Gnfqndt473jkkUfy8ssvZ99990379u3Tvn373HvvvfnhD3+Y9u3bp1evXvaF1fTp0yfvf//7Wxzbc889M2/evCT//+/72v6/qXfv3nn55Zdb3P7mm29m8eLF9uY96Pzzz8+FF16YT3/609l7771zyimn5Mtf/nLGjx+fxM6wdhtqP1rT/18JpyrYcsst88EPfjDTp09vPtbU1JTp06dn8ODBVZyMaimKImeddVYmT56cu+66a7WXpD/4wQ+mQ4cOLXbmmWeeybx585p3ZvDgwXniiSda/Auovr4+dXV1q/3HEm3bEUcckSeeeCKzZ89u/rPffvtlxIgRzX9tX/h7Bx988Gq/5uDZZ5/NTjvtlCTp379/evfu3WJvli1blt/97nct9mbJkiV55JFHms+566670tTUlAMPPHATPAs2pVWrVqVdu5b/qbjFFlukqakpiZ1h7TbUfgwePDgzZsxIY2Nj8zn19fXZY489Nunb9JK4HHm13HLLLUVtbW0xYcKE4qmnniq+8IUvFN26dWtxhSs2H//yL/9SdO3atbjnnnuK+fPnN/9ZtWpV8zmjRo0qdtxxx+Kuu+4qZs2aVQwePLgYPHhw8+3vXF766KOPLmbPnl38+te/LrbbbjuXl95M/O1V9YrCvrC6hx56qGjfvn3x7W9/u3juueeKm266qejcuXNx4403Np9z2WWXFd26dStuv/324vHHHy8+/vGPr/HSwYMGDSp+97vfFTNnzix22203l5Z+jxo5cmSx/fbbN1+O/Lbbbiu23Xbb4oILLmg+x85s3pYvX148+uijxaOPPlokKb7//e8Xjz76aPHnP/+5KIoNsx9LliwpevXqVZxyyinFnDlziltuuaXo3Lmzy5Fvbv73//7fxY477lhsueWWxQEHHFA8+OCD1R6JKkmyxj833HBD8zmvvfZaceaZZxbbbLNN0blz5+L4448v5s+f3+L7/OlPfyqGDRtWdOrUqdh2222L8847r2hsbNzEz4Zq+Ptwsi+syX/9138Ve+21V1FbW1sMGDCguPbaa1vc3tTUVHz9618vevXqVdTW1hZHHHFE8cwzz7Q459VXXy1OOumkokuXLkVdXV1x+umnF8uXL9+UT4NNZNmyZcU555xT7LjjjkXHjh2L973vfcVFF13U4rLQdmbzdvfdd6/xv19GjhxZFMWG24/HHnusOOSQQ4ra2tpi++23Ly677LJN9RRbqCmKv/n1zwAAAKzGZ5wAAABKCCcAAIASwgkAAKCEcAIAACghnAAAAEoIJwAAgBLCCQAAoIRwAgAAKCGcAKACNTU1mTJlSrXHAGATE04AtBmnnXZaampqVvvz0Y9+tNqjAfAe177aAwBAJT760Y/mhhtuaHGstra2StMAsLnwihMAbUptbW169+7d4s8222yT5O230V199dUZNmxYOnXqlPe973259dZbW9z/iSeeyEc+8pF06tQpPXr0yBe+8IWsWLGixTnXX399PvCBD6S2tjZ9+vTJWWed1eL2RYsW5fjjj0/nzp2z22675Y477ti4TxqAqhNOALynfP3rX8+JJ56Yxx57LCNGjMinP/3p/OEPf0iSrFy5MkOHDs0222yThx9+OJMmTcpvfvObFmF09dVXZ/To0fnCF76QJ554InfccUd23XXXFo8xbty4fOpTn8rjjz+eY445JiNGjMjixYs36fMEYNOqKYqiqPYQALAuTjvttNx4443p2LFji+Nf+9rX8rWvfS01NTUZNWpUrr766ubbDjrooOy777656qqr8pOf/CRf/epX88ILL2SrrbZKkkydOjXDhw/PSy+9lF69emX77bfP6aefnksvvXSNM9TU1ORf//Vf861vfSvJ2zHWpUuX/OpXv/JZK4D3MJ9xAqBNOfzww1uEUZJ07969+a8HDx7c4rbBgwdn9uzZSZI//OEPGThwYHM0JcnBBx+cpqamPPPMM6mpqclLL72UI444Yq0z7LPPPs1/vdVWW6Wuri4vv/zy+j4lANoA4QRAm7LVVlut9ta5DaVTp07rdF6HDh1afF1TU5OmpqaNMRIArYTPOAHwnvLggw+u9vWee+6ZJNlzzz3z2GOPZeXKlc2333///WnXrl322GOPbL311tl5550zffr0TTozAK2fV5wAaFMaGhqyYMGCFsfat2+fbbfdNkkyadKk7LfffjnkkENy00035aGHHsp1112XJBkxYkS+8Y1vZOTIkbnkkkvyyiuv5Oyzz84pp5ySXr16JUkuueSSjBo1Kj179sywYcOyfPny3H///Tn77LM37RMFoFURTgC0Kb/+9a/Tp0+fFsf22GOPPP3000nevuLdLbfckjPPPDN9+vTJzTffnPe///1Jks6dO+fOO+/MOeeck/333z+dO3fOiSeemO9///vN32vkyJF5/fXX8+///u/5yle+km233Taf+MQnNt0TBKBVclU9AN4zampqMnny5Bx33HHVHgWA9xifcQIAACghnAAAAEr4jBMA7xnefQ7AxuIVJwAAgBLCCQAAoIRwAgAAKCGcAAAASggnAACAEsIJAACghHACAAAoIZwAAABK/D/BwW6Ukn1boQAAAABJRU5ErkJggg==", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Plotting the loss chart to find out learning\n", | |
| "plt.figure(figsize=(10, 6))\n", | |
| "plt.plot(epoch_losses, marker='o', linestyle='-', color='blue')\n", | |
| "plt.title('Loss Chart')\n", | |
| "plt.xlabel('Epoch')\n", | |
| "plt.ylabel('Loss')\n", | |
| "plt.ylim([1, 2])\n", | |
| "plt.grid(True)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "XlSR-w2NeAhB" | |
| }, | |
| "source": [ | |
| "This loss curve looks bad; there is no curve. Basically network is not learning anything;\n", | |
| "\n", | |
| "One possible suspect is the learning rate; If it is too large or too small; I adjusted that; It dit not help\n", | |
| "\n", | |
| "The next could be the size of the network related to the training data; I adjusted that a bit and it helped very little;\n", | |
| "\n", | |
| "Even while I was printing the first few activations (of a3) it was going from .9 to 1 in no time and staying there" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2YvJT8A3eAhB" | |
| }, | |
| "source": [ | |
| "### Visualizing Layer Activations - Activaions Saturaing problem\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Ergzai0VeAhB", | |
| "outputId": "6cc90a89-c175-4696-d335-4acd0087a15c" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x7f8c89dcab00>" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Let's plot the output of the last activation to see if we can get a hint on what is wrong\n", | |
| "\n", | |
| "# Plotting the mean and standard deviation over epochs\n", | |
| "plt.figure(figsize=(10, 6))\n", | |
| "\n", | |
| "# Plot the mean values of a3\n", | |
| "plt.plot(a3_means, linestyle='-', color='blue', label='Mean of a3')\n", | |
| "\n", | |
| "# Plot the standard deviation values of a3\n", | |
| "plt.plot(a3_stddeviations, linestyle='-', color='orange', label='Std Dev of a3')\n", | |
| "\n", | |
| "# Adding titles and labels\n", | |
| "plt.title('Mean and Standard Deviation of Last Layer Activation (a3) Over Epochs')\n", | |
| "plt.xlabel('Epoch')\n", | |
| "plt.ylabel('Value')\n", | |
| "\n", | |
| "# Show grid and legend\n", | |
| "plt.grid(True)\n", | |
| "plt.legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "sBbrBKKueAhB" | |
| }, | |
| "source": [ | |
| "We can see that our activations are directly going to 1 and staying there; that is it is saturating" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gokM0iPbeAhD" | |
| }, | |
| "source": [ | |
| "### Preventing Activation Layer Saturation via Xavier/Glorot initialization of Weights\n", | |
| " \n", | |
| "Our network is not learning anything; Loss is not decreasing and the output (a3) quickly saturates to near 1\n", | |
| "\n", | |
| "I tried to reduce the dimensions of the weights as large networks with small inputs could saturate; That is not helping\n", | |
| "I also tried decreasing the learning rate. that is also not helping\\n\",\n", | |
| "This is where things like regularization, batch-normalisation, proper weight initialization etc could be needed; lets try those\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "Instead of random weights we will use this technique of weight intialization.\n", | |
| "\n", | |
| "This follows the paper [Understanding the difficulty of training deep feedforward neural networks](https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf) by Xavier Glorot & Yoshua Bengio in 2010.\n", | |
| "\n", | |
| "*Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. .... We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation*\n", | |
| "...\n", | |
| "Then the paper goes on this simple way to intialise weights\n", | |
| "\n", | |
| "$$\n", | |
| "W_{ij} \\sim U\\left(-\\frac{1}{\\sqrt{n}}, \\frac{1}{\\sqrt{n}}\\right) \\quad (1)\n", | |
| "$$\n", | |
| "\n", | |
| "where $U[−a, a]$ is the uniform distribution in the interval $(−a, a)$ and n is the size of the previous layer (the number of columns of W)\n", | |
| "\n", | |
| "He further notes -\n", | |
| "*For tanh networks, the proposed normalized initialization can be quite helpful, presumably because the layer-to-layer transformations maintain magnitudes of activations (flowing upward) and gradients (flowing backward).*\n", | |
| "\n", | |
| "which is exactly what we are facing here\n", | |
| "\n", | |
| "More details are here https://www.deeplearning.ai/ai-notes/initialization/index.html" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "R93165zieAhE", | |
| "outputId": "9a89b357-30bc-4ee3-9d94-5c40d839bc0a" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "---------------batch=(9, 10)- iter=(999, 1000)------------------\n", | |
| "x [[ 1.151]\n", | |
| " [ 1.987]\n", | |
| " [ 0.314]\n", | |
| " [ 0.566]]\n", | |
| "y (expected) [[ 0.913]\n", | |
| " [ 0.914]\n", | |
| " [ 0.309]\n", | |
| " [ 0.536]]\n", | |
| "a3 (output) [[ 0.881]\n", | |
| " [ 0.724]\n", | |
| " [ 0.547]\n", | |
| " [ 0.778]]\n", | |
| "gradinnt [[-0.001]\n", | |
| " [-0.004]\n", | |
| " [ 0.005]\n", | |
| " [ 0.005]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# adding what is called as the L2 regularization\n", | |
| "lambda_reg = 0.01\n", | |
| "\n", | |
| "# set learning rate 10e-6 was too low\n", | |
| "learningRate = 10e-4\n", | |
| "\n", | |
| "# Xavier/Glorot initialization for tanh activation functions\n", | |
| "# for ReLu this will be Kaiming He intializtion\n", | |
| "w1 = np.random.randn(1, 150) * np.sqrt(1. / 1)\n", | |
| "w2 = np.random.randn(150, 50) * np.sqrt(1. / 150)\n", | |
| "w3 = np.random.randn(50, 1) * np.sqrt(1. / 50)\n", | |
| "# Here, np.sqrt(1. / n) is the standard deviation of the distribution used for initializing the weights,\n", | |
| "# with n being the number of input units to the layer.\n", | |
| "\n", | |
| "# Activation to layer 0 is taken as input x\n", | |
| "a0 = X\n", | |
| "# Number of epochs\n", | |
| "iterations = 1000\n", | |
| "\n", | |
| "# Batch size\n", | |
| "batch_size = 100\n", | |
| "n_batches = X.shape[0] // batch_size # change the code slightly for batching through the inputs\n", | |
| "epoch_losses = []\n", | |
| "act_means = []\n", | |
| "act_stddeviations =[]\n", | |
| "y_means=[]\n", | |
| "\n", | |
| "for iter in range(iterations):\n", | |
| " # Shuffle the data at the beginning of each epoch\n", | |
| "\n", | |
| " epoch_loss =0\n", | |
| " indices = np.arange(X.shape[0])\n", | |
| " np.random.shuffle(indices)\n", | |
| " X_shuffled = X[indices]\n", | |
| " Y_shuffled = Y[indices]\n", | |
| "\n", | |
| " for b in range(n_batches):\n", | |
| " # Slice out the batch\n", | |
| " start = b * batch_size\n", | |
| " end = start + batch_size\n", | |
| " a0 = X_shuffled[start:end]\n", | |
| " y_batch = Y_shuffled[start:end]\n", | |
| "\n", | |
| " # Forward pass\n", | |
| " z1 = np.dot(a0, w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2 = np.dot(a1, w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3 = np.dot(a2, w3)\n", | |
| " a3 = tanh(z3)\n", | |
| "\n", | |
| " # Calculate batch loss (MSE)\n", | |
| " batch_loss = np.mean((a3 - y_batch)**2)\n", | |
| " epoch_loss += batch_loss # Accumulate batch loss\n", | |
| "\n", | |
| " if b == n_batches-1: # add at end of every batch\n", | |
| " # Calculate mean and standard deviation\n", | |
| " act_means.append((np.mean(a1),np.mean(a2),np.mean(a3)))\n", | |
| " act_stddeviations.append((np.std(a1),np.std(a2),np.std(a3)))\n", | |
| " y_means.append(np.mean(y_batch))\n", | |
| "\n", | |
| "\n", | |
| " # Backward Pass - Backpropagation\n", | |
| "\n", | |
| " # calculate the gradient for backpropagation\n", | |
| " #dC_da3 = (a3 - y_batch) # this is the derivative for Mean Square Error Loss (See Eq 1.3)\n", | |
| " # there we have simplified a bit (removed the multiplicaiton by 2); Here we need to divide by the batch size\n", | |
| " # MSE derivative by batch size\n", | |
| " dC_da3 = 2 * (a3 - y_batch) / batch_size # The derivative of MSE with respect to the output\n", | |
| "\n", | |
| " if b // n_batches/2 == 0 and iter ==1 and iterations==1:#print only once\n", | |
| " print(f\"---------------batch={b,n_batches}- iter={iter,iterations}---batch-loss={batch_loss}----\")\n", | |
| " print(\"x\",a0[1:5])\n", | |
| " print(\"y (expected)\",y_batch[1:5])\n", | |
| " print(\"a3 (output)\",a3[1:5])\n", | |
| " print(\"gradinnt\",dC_da3[1:5])\n", | |
| "\n", | |
| " dC_dw3_t = dC_da3 * derv_tanh(z3)\n", | |
| " dC_dw3 = np.dot(a2.T, dC_dw3_t)\n", | |
| "\n", | |
| " dC_da2 = np.dot(dC_da3 * derv_tanh(z3), w3.T)\n", | |
| " dC_dw2_t = dC_da2 * derv_tanh(z2)\n", | |
| " dC_dw2 = np.dot(a1.T, dC_dw2_t)\n", | |
| "\n", | |
| " dC_da1 = np.dot(dC_da2 * derv_tanh(z2), w2.T)\n", | |
| " dC_dw1_t = dC_da1 * derv_tanh(z1)\n", | |
| " dC_dw1 = np.dot(a0.T, dC_dw1_t)\n", | |
| "\n", | |
| " # Update weights with L2 regularization\n", | |
| " # instead of w3 -= learningRate * dC_dw3\n", | |
| " # This term works to penalize large weights, effectively \"shrinking\" them during each update.\n", | |
| " # The strength of this penalty is controlled by the lambda_reg hyperparameter:\n", | |
| " # setting it too high can lead to underfitting (as the model becomes too simple),\n", | |
| " # while setting it too low may lead to minimal regularization effect.\n", | |
| " w3 -= learningRate * (dC_dw3 + lambda_reg * w3)\n", | |
| " w2 -= learningRate * (dC_dw2 + lambda_reg * w2)\n", | |
| " w1 -= learningRate * (dC_dw1 + lambda_reg * w1)\n", | |
| "\n", | |
| " # Calculate the average loss for the epoch\n", | |
| " epoch_average_loss = epoch_loss / n_batches\n", | |
| " #print(f\"Epoch loss Average={epoch_average_loss:.2f}\")\n", | |
| " epoch_losses.append(epoch_average_loss)\n", | |
| "\n", | |
| "print(f\"---------------batch={b,n_batches}- iter={iter,iterations}------------------\")\n", | |
| "print(\"x\",a0[1:5])\n", | |
| "print(\"y (expected)\",y_batch[1:5])\n", | |
| "print(\"a3 (output)\",a3[1:5])\n", | |
| "print(\"gradinnt\",dC_da3[1:5])\n", | |
| "\n", | |
| "#---------------------------------------------------------------\n", | |
| "# Training is done, weights are primed for output y\n", | |
| "#---------------------------------------------------------------" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "wEVlyyexeAhE", | |
| "outputId": "2939acb7-e4ff-44b2-8317-b74ed7bebc61" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Plotting the loss chart\n", | |
| "plt.figure(figsize=(10, 6))\n", | |
| "plt.plot(epoch_losses, marker='o', linestyle='-', color='blue')\n", | |
| "plt.title('Loss Chart')\n", | |
| "plt.xlabel('Epoch')\n", | |
| "plt.ylabel('Loss')\n", | |
| "plt.ylim([0, 1])\n", | |
| "plt.grid(True)\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "t0f8MH8ZeAhE" | |
| }, | |
| "source": [ | |
| "The loss has come down nicely; this hockey stick pattern is much better.\n", | |
| "\n", | |
| "Since the loss is coming down slowly after the intial steep curve, that is a good charectestics that indicates learning rate is probably okay\n", | |
| "\n", | |
| "But note that the loss is plateauing and not coming down or coming down extremely slowly." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "iLFSj7jqeAhE", | |
| "outputId": "fa1e257b-4023-49ba-b1a0-a3f2151dcde3" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x7f8c318a7be0>" | |
| ] | |
| }, | |
| "execution_count": 40, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Let's plot the output of the last activation to see if we can get a hint on what is wrong\n", | |
| "# Convert to numpy arrays for easier indexing\n", | |
| "act_means = np.array(act_means)\n", | |
| "act_stddeviations = np.array(act_stddeviations)\n", | |
| "\n", | |
| "# Plotting the mean and standard deviation over epochs\n", | |
| "plt.figure(figsize=(10, 6))\n", | |
| "\n", | |
| "# Plot the mean values of a3\n", | |
| "plt.plot(act_means[:,2], linestyle='-', color='blue', label='Mean of a3')\n", | |
| "\n", | |
| "# Plot the mean values of a3\n", | |
| "plt.plot(y_means, linestyle='-', color='red',alpha=.5, label='Mean of Expected Value')\n", | |
| "\n", | |
| "# Plot the standard deviation values of a3\n", | |
| "#plt.plot(act_stddeviations[:,2], linestyle='-', color='orange', label='Std Dev of a3')\n", | |
| "\n", | |
| "# Adding titles and labels\n", | |
| "plt.title('Mean and Standard Deviation of Last Layer Activation (a3) Over Epochs')\n", | |
| "plt.xlabel('Epoch')\n", | |
| "plt.ylabel('Value')\n", | |
| "\n", | |
| "# Show grid and legend\n", | |
| "plt.grid(True)\n", | |
| "plt.legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "buD0vr_6eAhE" | |
| }, | |
| "source": [ | |
| "Note that the layer 3 which is the last layer should have a similar output like sine function, go between 0 and 1" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "nbP2zEA3eAhE", | |
| "outputId": "334843c4-b6ea-4b0c-997e-a0c2a74d3b1b" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Ouput after Training for angle 4.208 is [[-0.601]] Expected is -0.8754699268728889\n", | |
| "Ouput after Training for angle -3.208 is [[ 0.090]] Expected is 0.06635854848376267\n", | |
| "Ouput after Training for angle 2.108 is [[ 0.673]] Expected is 0.859143018339726\n", | |
| "Ouput after Training for angle 3.141592653589793 is [[-0.042]] Expected is 1.2246467991473532e-16\n", | |
| "Ouput after Training for angle 0 is [[ 0.000]] Expected is 0.0\n", | |
| "Ouput after Training for angle 1 is [[ 0.879]] Expected is 0.8414709848078965\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "def forward(x):\n", | |
| " # Forward pass - Straight Forward\n", | |
| " z1= np.dot(x,w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2= np.dot(a1,w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3= np.dot(a2,w3)\n", | |
| " a3 = tanh(z3)\n", | |
| " return a3\n", | |
| "\n", | |
| "test_angles = [4.208,-3.208, 2.108,np.pi,0,1]\n", | |
| "\n", | |
| "for angle in test_angles:\n", | |
| " x = (np.array([angle]).reshape(-1,1))\n", | |
| " print(f\"Ouput after Training for angle {angle} is \",forward(x), \"Expected is\", np.sin(angle))\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "TdWMrBYjeAhE" | |
| }, | |
| "source": [ | |
| "Now the results looks much better.\n", | |
| "\n", | |
| "Intially the results were not too great. Then I increased the number of samples from 1000 to 10,000 in training data\n", | |
| "\n", | |
| "Also note that the NW is not perfect still" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "v1Ezeyj0eAhF", | |
| "outputId": "b6fd317a-280f-47b8-c43a-8c5c375658c4" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Ouput after Training is [[-0.902]] Expected -2.4492935982947064e-16\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "\n", | |
| "# this is failing, expected is close to zero; could be that there are very less training data for these values\n", | |
| "angle = 2*np.pi\n", | |
| "x = (np.array([angle]).reshape(-1,1))\n", | |
| "print(\"Ouput after Training is \",forward(x), \"Expected\", np.sin(angle))\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YOxrb2IFeAhF" | |
| }, | |
| "source": [ | |
| "### Visualising if the Neural Net has really approxiamted the Sine Funtion" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "F0ERXNwWeAhF", | |
| "outputId": "9ace3af3-2462-45b9-b41c-6647157367cc" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1400x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# lets plot the angles generated by this nettwok and the ones calcuated by numpy.sine function\n", | |
| "\n", | |
| "angles = np.linspace(0, 2*np.pi, n_points)\n", | |
| "calculated_angles = [forward(x) for x in angles]\n", | |
| "# Target outputs (reshape sine_values to match output shape of the network), also called validation data set\n", | |
| "Y_nn = np.array(calculated_angles).reshape(-1, 1)\n", | |
| "\n", | |
| "# Plotting the sine wave\n", | |
| "plt.figure(figsize=(14, 6))\n", | |
| "plt.plot(X, Y, label='Sine Function Expected')\n", | |
| "plt.plot(X, Y_nn, label='Sine Function Calculated' )\n", | |
| "\n", | |
| "# Customizing the plot\n", | |
| "plt.title('Sine Function')\n", | |
| "plt.xlabel('Radians')\n", | |
| "plt.ylabel('sin(x)')\n", | |
| "plt.axhline(0, color='black',linewidth=0.5)\n", | |
| "plt.axvline(0, color='black',linewidth=0.5)\n", | |
| "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n", | |
| "plt.legend()\n", | |
| "plt.show()\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "dlmH_zioeAhF" | |
| }, | |
| "source": [ | |
| "Lets see if we can improve the results\n", | |
| "\n", | |
| "1. Standerdising the weights\n", | |
| " Since inputs are angle between -2.pi and +2/pi, it is centered around zero and that should be fine\n", | |
| " We could standerdising the angles Y\n", | |
| " YN= (Y - np.mean(Y)) / np.std(Y)\n", | |
| " However this gave worse result\n", | |
| "2. Drop Out ?\n", | |
| "3. Layer Normalisation\n", | |
| "4. Bigger Network - Here have tried to increase the neurons in each layers; this helps to some extend\n", | |
| "\n", | |
| "Let's try layer normalization and see if that is going to help drastically\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "trMPbj_GeAhF" | |
| }, | |
| "source": [ | |
| "### Checking for underfitting or overfitting via Validation dataset" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "QPi639QReAhF", | |
| "outputId": "8eaad847-3750-4bad-fdec-ba5d7171f951" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "0.9999999876605268 -0.9999999876605268\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# This is the training data for the Sine function; Since the value is know it is easy to generate it\n", | |
| "# If you need to approximate any other deistribution and then predict from it, you can see sources like Kaggle data set for training and validation data\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "\n", | |
| "# Number of data points\n", | |
| "n_points = 10000\n", | |
| "\n", | |
| "# Generate input values (angles) - let's use one full period 0 to 2pi\n", | |
| "angles = np.linspace(0, 2*np.pi, n_points)\n", | |
| "\n", | |
| "# Calculate sine values\n", | |
| "sine_values = np.sin(angles)\n", | |
| "\n", | |
| "print(max(sine_values),min(sine_values))\n", | |
| "# Prepare inputs for the network\n", | |
| "# Assuming we vary the first input and keep the others as some pattern or constant\n", | |
| "X = angles.reshape(-1,1)\n", | |
| "\n", | |
| "\n", | |
| "# Target outputs (reshape sine_values to match output shape of the network), also called validation data set\n", | |
| "Y = sine_values.reshape(-1, 1) # Shape will be (n_points, 1)\n", | |
| "\n", | |
| "\n", | |
| "# Split the data into training and validation sets\n", | |
| "X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.2, random_state=42)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "PL-c1OWBeAhF", | |
| "outputId": "8d2cf606-7d13-45bc-db22-e01ea659f37d" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "8000\n", | |
| "2000\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(len(X_train))\n", | |
| "print(len(X_val))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "u7KE3yJ2eAhF" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# adding what is called as the L2 regularization\n", | |
| "lambda_reg = 0.01\n", | |
| "\n", | |
| "# set learning rate 10e-6 was too low\n", | |
| "learningRate = 10e-3\n", | |
| "\n", | |
| "# Xavier/Glorot initialization for tanh activation functions\n", | |
| "# for ReLu this will be Kaiming He intializtion\n", | |
| "w1 = np.random.randn(1, 150) * np.sqrt(1. / 1)\n", | |
| "w2 = np.random.randn(150, 50) * np.sqrt(1. / 150)\n", | |
| "w3 = np.random.randn(50, 1) * np.sqrt(1. / 50)\n", | |
| "# Here, np.sqrt(1. / n) is the standard deviation of the distribution used for initializing the weights,\n", | |
| "# with n being the number of input units to the layer.\n", | |
| "\n", | |
| "# Activation to layer 0 is taken as input x\n", | |
| "a0 = X_train\n", | |
| "# Number of epochs\n", | |
| "iterations = 1000\n", | |
| "\n", | |
| "# Batch size\n", | |
| "batch_size = 10\n", | |
| "n_batches = X_train.shape[0] // batch_size # change the code slightly for batching through the inputs\n", | |
| "train_losses = []\n", | |
| "val_losses = []\n", | |
| "\n", | |
| "for iter in range(iterations):\n", | |
| " # Shuffle the data at the beginning of each epoch\n", | |
| "\n", | |
| " epoch_loss =0\n", | |
| " indices = np.arange(X_train.shape[0])\n", | |
| " np.random.shuffle(indices)\n", | |
| " X_shuffled = X_train[indices]\n", | |
| " Y_shuffled = Y_train[indices]\n", | |
| "\n", | |
| " for b in range(n_batches):\n", | |
| " # Slice out the batch\n", | |
| " start = b * batch_size\n", | |
| " end = start + batch_size\n", | |
| " a0 = X_shuffled[start:end]\n", | |
| " y_batch = Y_shuffled[start:end]\n", | |
| "\n", | |
| " # Forward pass\n", | |
| " z1 = np.dot(a0, w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2 = np.dot(a1, w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3 = np.dot(a2, w3)\n", | |
| " a3 = tanh(z3)\n", | |
| "\n", | |
| " # Calculate batch loss (MSE)\n", | |
| " batch_loss = np.mean((a3 - y_batch)**2)\n", | |
| " epoch_loss += batch_loss # Accumulate batch loss\n", | |
| "\n", | |
| " if b == n_batches-1: # add at end of every batch\n", | |
| " # Calculate mean and standard deviation\n", | |
| " act_means.append((np.mean(a1),np.mean(a2),np.mean(a3)))\n", | |
| " act_stddeviations.append((np.std(a1),np.std(a2),np.std(a3)))\n", | |
| " y_means.append(np.mean(y_batch))\n", | |
| "\n", | |
| "\n", | |
| " # Backward Pass - Backpropagation\n", | |
| "\n", | |
| " # calculate the gradient for backpropagation\n", | |
| " #dC_da3 = (a3 - y_batch) # this is the derivative for Mean Square Error Loss (See Eq 1.3)\n", | |
| " # there we have simplified a bit (removed the multiplicaiton by 2); Here we need to divide by the batch size\n", | |
| " # MSE derivative by batch size\n", | |
| " dC_da3 = 2 * (a3 - y_batch) / batch_size # The derivative of MSE with respect to the output\n", | |
| "\n", | |
| " if b // n_batches/2 == 0 and iter ==1 and iterations==1:#print only once\n", | |
| " print(f\"---------------batch={b,n_batches}- iter={iter,iterations}---batch-loss={batch_loss}----\")\n", | |
| " print(\"x\",a0[1:5])\n", | |
| " print(\"y (expected)\",y_batch[1:5])\n", | |
| " print(\"a3 (output)\",a3[1:5])\n", | |
| " print(\"gradinnt\",dC_da3[1:5])\n", | |
| "\n", | |
| " dC_dw3_t = dC_da3 * derv_tanh(z3)\n", | |
| " dC_dw3 = np.dot(a2.T, dC_dw3_t)\n", | |
| "\n", | |
| " dC_da2 = np.dot(dC_da3 * derv_tanh(z3), w3.T)\n", | |
| " dC_dw2_t = dC_da2 * derv_tanh(z2)\n", | |
| " dC_dw2 = np.dot(a1.T, dC_dw2_t)\n", | |
| "\n", | |
| " dC_da1 = np.dot(dC_da2 * derv_tanh(z2), w2.T)\n", | |
| " dC_dw1_t = dC_da1 * derv_tanh(z1)\n", | |
| " dC_dw1 = np.dot(a0.T, dC_dw1_t)\n", | |
| "\n", | |
| " # Update weights with L2 regularization\n", | |
| " # instead of w3 -= learningRate * dC_dw3\n", | |
| " # This term works to penalize large weights, effectively \"shrinking\" them during each update.\n", | |
| " # The strength of this penalty is controlled by the lambda_reg hyperparameter:\n", | |
| " # setting it too high can lead to underfitting (as the model becomes too simple),\n", | |
| " # while setting it too low may lead to minimal regularization effect.\n", | |
| " w3 -= learningRate * (dC_dw3 + lambda_reg * w3)\n", | |
| " w2 -= learningRate * (dC_dw2 + lambda_reg * w2)\n", | |
| " w1 -= learningRate * (dC_dw1 + lambda_reg * w1)\n", | |
| "\n", | |
| " # Calculate the average loss for the epoch\n", | |
| " epoch_average_loss = epoch_loss / n_batches\n", | |
| " #print(f\"Epoch loss Average={epoch_average_loss:.2f}\")\n", | |
| " train_losses.append(epoch_average_loss)\n", | |
| " # At the end of each epoch, after training on the training set, evaluate on the validation set\n", | |
| " val_loss = 0\n", | |
| " for i in range(0, len(X_val), batch_size):\n", | |
| " # Prepare the batch from the validation set\n", | |
| " start = i\n", | |
| " end = i + batch_size\n", | |
| " a0_val = X_val[start:end]\n", | |
| " y_val_batch = Y_val[start:end]\n", | |
| "\n", | |
| " # Forward pass on the validation set (no backpropagation)\n", | |
| " z1_val = np.dot(a0_val, w1)\n", | |
| " a1_val = tanh(z1_val)\n", | |
| " z2_val = np.dot(a1_val, w2)\n", | |
| " a2_val = tanh(z2_val)\n", | |
| " z3_val = np.dot(a2_val, w3)\n", | |
| " a3_val = tanh(z3_val)\n", | |
| "\n", | |
| " # Calculate validation loss (MSE)\n", | |
| " val_batch_loss = np.mean((a3_val - y_val_batch)**2)\n", | |
| " val_loss += val_batch_loss\n", | |
| "\n", | |
| " # Calculate the average validation loss\n", | |
| " val_average_loss = val_loss / (len(X_val) / batch_size)\n", | |
| " val_losses.append(val_average_loss)\n", | |
| "\n", | |
| "\n", | |
| "#---------------------------------------------------------------\n", | |
| "# Training is done, weights are primed for output y\n", | |
| "#---------------------------------------------------------------\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rxHc4DS8eAhF" | |
| }, | |
| "source": [ | |
| "Lets plot the training and validation Loss.\n", | |
| "\n", | |
| "We can see that there is not much deviation between.\n", | |
| "\n", | |
| "So the network is neither underfitting nor overfitting. So that does not seem to be the problem.\n", | |
| "\n", | |
| "More on this here in the DeepLearning Book https://www.deeplearningbook.org/contents/ml.html\n", | |
| "\n", | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "7ID51WYUeAhG", | |
| "outputId": "2cb36490-e927-4a26-bf7c-127ff026af41" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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", | |
| "text/plain": [ | |
| "<Figure size 1000x500 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(10, 5))\n", | |
| "plt.plot(train_losses, label='Training Loss')\n", | |
| "plt.plot(val_losses, label='Validation Loss')\n", | |
| "plt.title('Training and Validation Losses Over Epochs')\n", | |
| "plt.xlabel('Epoch')\n", | |
| "plt.ylabel('Loss')\n", | |
| "plt.legend()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "QqX7cKvDeAhG", | |
| "outputId": "bb926759-936d-4e5d-9b6b-0976911e07af" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 1400x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "def forward(x):\n", | |
| " # Forward pass - Straight Forward\n", | |
| " z1= np.dot(x,w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2= np.dot(a1,w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3= np.dot(a2,w3)\n", | |
| " a3 = tanh(z3)\n", | |
| " return a3\n", | |
| "\n", | |
| "# lets plot the angles generated by this nettwok and the ones calcuated by numpy.sine function\n", | |
| "\n", | |
| "angles = np.linspace(0, 2*np.pi, n_points)\n", | |
| "calculated_angles = [forward(x) for x in angles]\n", | |
| "# Target outputs (reshape sine_values to match output shape of the network), also called validation data set\n", | |
| "Y_nn = np.array(calculated_angles).reshape(-1, 1)\n", | |
| "\n", | |
| "# Plotting the sine wave\n", | |
| "plt.figure(figsize=(14, 6))\n", | |
| "plt.plot(X, Y, label='Sine Function Expected')\n", | |
| "plt.plot(X, Y_nn, label='Sine Function Calculated' )\n", | |
| "\n", | |
| "# Customizing the plot\n", | |
| "plt.title('Sine Function')\n", | |
| "plt.xlabel('Radians')\n", | |
| "plt.ylabel('sin(x)')\n", | |
| "plt.axhline(0, color='black',linewidth=0.5)\n", | |
| "plt.axvline(0, color='black',linewidth=0.5)\n", | |
| "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n", | |
| "plt.legend()\n", | |
| "plt.show()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "V3hWL27TeAhG" | |
| }, | |
| "source": [ | |
| "This is getting better. Basically reducing the batch size alone seems to have helped fit more precisely.\n", | |
| "\n", | |
| "However looking at tha training loss steeply decreasing and then plateauing would give us an idea on having a more adataptive learning rate\n", | |
| "\n", | |
| "Lets try that" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Q3cbB-04eAhG" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# adding what is called as the L2 regularization\n", | |
| "lambda_reg = 0.01\n", | |
| "\n", | |
| "# set learning rate 10e-6 was too low\n", | |
| "learningRate = 10e-3\n", | |
| "\n", | |
| "# Adam optimizer parameters\n", | |
| "alpha = 10e-3 # learning rate\n", | |
| "beta1 = 0.9\n", | |
| "beta2 = 0.999\n", | |
| "epsilon = 1e-8\n", | |
| "\n", | |
| "# Initialize first and second moments vectors\n", | |
| "m1_w1, m1_w2, m1_w3 = 0, 0, 0\n", | |
| "v1_w1, v1_w2, v1_w3 = 0, 0, 0\n", | |
| "\n", | |
| "\n", | |
| "# Xavier/Glorot initialization for tanh activation functions\n", | |
| "# for ReLu this will be Kaiming He intializtion\n", | |
| "w1 = np.random.randn(1, 150) * np.sqrt(1. / 1)\n", | |
| "w2 = np.random.randn(150, 50) * np.sqrt(1. / 150)\n", | |
| "w3 = np.random.randn(50, 1) * np.sqrt(1. / 50)\n", | |
| "# Here, np.sqrt(1. / n) is the standard deviation of the distribution used for initializing the weights,\n", | |
| "# with n being the number of input units to the layer.\n", | |
| "\n", | |
| "# Activation to layer 0 is taken as input x\n", | |
| "a0 = X_train\n", | |
| "# Number of epochs\n", | |
| "iterations = 100\n", | |
| "\n", | |
| "# Batch size\n", | |
| "batch_size = 10\n", | |
| "n_batches = X_train.shape[0] // batch_size # change the code slightly for batching through the inputs\n", | |
| "train_losses = []\n", | |
| "val_losses = []\n", | |
| "\n", | |
| "for iter in range(iterations):\n", | |
| " # Shuffle the data at the beginning of each epoch\n", | |
| "\n", | |
| " epoch_loss =0\n", | |
| " indices = np.arange(X_train.shape[0])\n", | |
| " np.random.shuffle(indices)\n", | |
| " X_shuffled = X_train[indices]\n", | |
| " Y_shuffled = Y_train[indices]\n", | |
| "\n", | |
| " for b in range(n_batches):\n", | |
| " # Slice out the batch\n", | |
| " start = b * batch_size\n", | |
| " end = start + batch_size\n", | |
| " a0 = X_shuffled[start:end]\n", | |
| " y_batch = Y_shuffled[start:end]\n", | |
| "\n", | |
| " # Forward pass\n", | |
| " z1 = np.dot(a0, w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2 = np.dot(a1, w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3 = np.dot(a2, w3)\n", | |
| " a3 = tanh(z3)\n", | |
| "\n", | |
| " # Calculate batch loss (MSE)\n", | |
| " batch_loss = np.mean((a3 - y_batch)**2)\n", | |
| " epoch_loss += batch_loss # Accumulate batch loss\n", | |
| "\n", | |
| " if b == n_batches-1: # add at end of every batch\n", | |
| " # Calculate mean and standard deviation\n", | |
| " act_means.append((np.mean(a1),np.mean(a2),np.mean(a3)))\n", | |
| " act_stddeviations.append((np.std(a1),np.std(a2),np.std(a3)))\n", | |
| " y_means.append(np.mean(y_batch))\n", | |
| "\n", | |
| "\n", | |
| " # Backward Pass - Backpropagation\n", | |
| "\n", | |
| " # calculate the gradient for backpropagation\n", | |
| " #dC_da3 = (a3 - y_batch) # this is the derivative for Mean Square Error Loss (See Eq 1.3)\n", | |
| " # there we have simplified a bit (removed the multiplicaiton by 2); Here we need to divide by the batch size\n", | |
| " # MSE derivative by batch size\n", | |
| " dC_da3 = 2 * (a3 - y_batch) / batch_size # The derivative of MSE with respect to the output\n", | |
| "\n", | |
| " if b // n_batches/2 == 0 and iter ==1 and iterations==1:#print only once\n", | |
| " print(f\"---------------batch={b,n_batches}- iter={iter,iterations}---batch-loss={batch_loss}----\")\n", | |
| " print(\"x\",a0[1:5])\n", | |
| " print(\"y (expected)\",y_batch[1:5])\n", | |
| " print(\"a3 (output)\",a3[1:5])\n", | |
| " print(\"gradinnt\",dC_da3[1:5])\n", | |
| "\n", | |
| " dC_dw3_t = dC_da3 * derv_tanh(z3)\n", | |
| " dC_dw3 = np.dot(a2.T, dC_dw3_t)\n", | |
| "\n", | |
| " dC_da2 = np.dot(dC_da3 * derv_tanh(z3), w3.T)\n", | |
| " dC_dw2_t = dC_da2 * derv_tanh(z2)\n", | |
| " dC_dw2 = np.dot(a1.T, dC_dw2_t)\n", | |
| "\n", | |
| " dC_da1 = np.dot(dC_da2 * derv_tanh(z2), w2.T)\n", | |
| " dC_dw1_t = dC_da1 * derv_tanh(z1)\n", | |
| " dC_dw1 = np.dot(a0.T, dC_dw1_t)\n", | |
| "\n", | |
| "\n", | |
| " # Updating Adam paramters\n", | |
| " #Update the moments for each set of weights\n", | |
| " m1_w1 = beta1 * m1_w1 + (1 - beta1) * dC_dw1\n", | |
| " v1_w1 = beta2 * v1_w1 + (1 - beta2) * (dC_dw1 ** 2)\n", | |
| "\n", | |
| " m1_w2 = beta1 * m1_w2 + (1 - beta1) * dC_dw2\n", | |
| " v1_w2 = beta2 * v1_w2 + (1 - beta2) * (dC_dw2 ** 2)\n", | |
| "\n", | |
| " m1_w3 = beta1 * m1_w3 + (1 - beta1) * dC_dw3\n", | |
| " v1_w3 = beta2 * v1_w3 + (1 - beta2) * (dC_dw3 ** 2)\n", | |
| "\n", | |
| " # Compute bias-corrected moments\n", | |
| " m1_hat_w1 = m1_w1 / (1 - beta1**(iter + 1))\n", | |
| " v1_hat_w1 = v1_w1 / (1 - beta2**(iter + 1))\n", | |
| "\n", | |
| " m1_hat_w2 = m1_w2 / (1 - beta1**(iter + 1))\n", | |
| " v1_hat_w2 = v1_w2 / (1 - beta2**(iter + 1))\n", | |
| "\n", | |
| " m1_hat_w3 = m1_w3 / (1 - beta1**(iter + 1))\n", | |
| " v1_hat_w3 = v1_w3 / (1 - beta2**(iter + 1))\n", | |
| "\n", | |
| " # Update weights with Adam optimization\n", | |
| " w1 -= alpha * (m1_hat_w1 / (np.sqrt(v1_hat_w1) + epsilon) + lambda_reg * w1)\n", | |
| " w2 -= alpha * (m1_hat_w2 / (np.sqrt(v1_hat_w2) + epsilon) + lambda_reg * w2)\n", | |
| " w3 -= alpha * (m1_hat_w3 / (np.sqrt(v1_hat_w3) + epsilon) + lambda_reg * w3)\n", | |
| "\n", | |
| " # Calculate the average loss for the epoch\n", | |
| " epoch_average_loss = epoch_loss / n_batches\n", | |
| " #print(f\"Epoch loss Average={epoch_average_loss:.2f}\")\n", | |
| " train_losses.append(epoch_average_loss)\n", | |
| " # At the end of each epoch, after training on the training set, evaluate on the validation set\n", | |
| " val_loss = 0\n", | |
| " for i in range(0, len(X_val), batch_size):\n", | |
| " # Prepare the batch from the validation set\n", | |
| " start = i\n", | |
| " end = i + batch_size\n", | |
| " a0_val = X_val[start:end]\n", | |
| " y_val_batch = Y_val[start:end]\n", | |
| "\n", | |
| " # Forward pass on the validation set (no backpropagation)\n", | |
| " z1_val = np.dot(a0_val, w1)\n", | |
| " a1_val = tanh(z1_val)\n", | |
| " z2_val = np.dot(a1_val, w2)\n", | |
| " a2_val = tanh(z2_val)\n", | |
| " z3_val = np.dot(a2_val, w3)\n", | |
| " a3_val = tanh(z3_val)\n", | |
| "\n", | |
| " # Calculate validation loss (MSE)\n", | |
| " val_batch_loss = np.mean((a3_val - y_val_batch)**2)\n", | |
| " val_loss += val_batch_loss\n", | |
| "\n", | |
| " # Calculate the average validation loss\n", | |
| " val_average_loss = val_loss / (len(X_val) / batch_size)\n", | |
| " val_losses.append(val_average_loss)\n", | |
| "\n", | |
| "\n", | |
| "#---------------------------------------------------------------\n", | |
| "# Training is done, weights are primed for output y\n", | |
| "#---------------------------------------------------------------\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "o6zy0fSueAhG" | |
| }, | |
| "source": [ | |
| "Lets Plot the Train and Validation Loss and check\n", | |
| "\n", | |
| "We see a marked change now from previous charts. The main thing is that the training loss has grown very low now and reaching 0;\n", | |
| "\n", | |
| "The validation loss is jumping around a bit.\n", | |
| "\n", | |
| "Could this mean that the model is overfitting the training data. In this case it seems more likely that the validation dataset may be too small and hence causing the difference. This could be investigated further\n", | |
| "\n", | |
| "If you find that the loss increases after some eppchs it could be that we could do early stopping of the training as well.\n", | |
| "\n", | |
| "I gave the below chart to ChatGPT4 and asked its recommendataion. Here is what it came with\n", | |
| "\n", | |
| "*If the L2 regularization is set appropriately, it should help prevent overfitting by penalizing large weights. However, the observed variability in the validation loss suggests reviewing the regularization strength or considering other forms of regularization like dropout, especially if overfitting is a concern.*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "lGtAoNbTeAhG", | |
| "outputId": "390a4af9-850e-45f4-a5c6-c5841c1d76a3" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1000x500 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(10, 5))\n", | |
| "plt.plot(train_losses, label='Training Loss')\n", | |
| "plt.plot(val_losses, label='Validation Loss')\n", | |
| "plt.title('Training and Validation Losses Over Epochs')\n", | |
| "plt.xlabel('Epoch')\n", | |
| "plt.ylabel('Loss')\n", | |
| "plt.legend()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "OxMmfquGeAhG" | |
| }, | |
| "source": [ | |
| "Plotting again the expected and the generated values, we can see that this simple Neural Network has effectiverly approxiamted the sine function" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "EOperMkkeAhG", | |
| "outputId": "6e2fde68-3d22-4a89-b5d2-3f1f79002be5" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 1400x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "def forward(x):\n", | |
| " # Forward pass - Straight Forward\n", | |
| " z1= np.dot(x,w1)\n", | |
| " a1 = tanh(z1)\n", | |
| " z2= np.dot(a1,w2)\n", | |
| " a2 = tanh(z2)\n", | |
| " z3= np.dot(a2,w3)\n", | |
| " a3 = tanh(z3)\n", | |
| " return a3\n", | |
| "\n", | |
| "# lets plot the angles generated by this nettwok and the ones calcuated by numpy.sine function\n", | |
| "\n", | |
| "angles = np.linspace(0, 2*np.pi, n_points)\n", | |
| "calculated_angles = [forward(x) for x in angles]\n", | |
| "# Target outputs (reshape sine_values to match output shape of the network), also called validation data set\n", | |
| "Y_nn = np.array(calculated_angles).reshape(-1, 1)\n", | |
| "\n", | |
| "# Plotting the sine wave\n", | |
| "plt.figure(figsize=(14, 6))\n", | |
| "plt.plot(X, Y, label='Sine Function Expected')\n", | |
| "plt.plot(X, Y_nn, label='Sine Function Calculated' )\n", | |
| "\n", | |
| "# Customizing the plot\n", | |
| "plt.title('Sine Function')\n", | |
| "plt.xlabel('Radians')\n", | |
| "plt.ylabel('sin(x)')\n", | |
| "plt.axhline(0, color='black',linewidth=0.5)\n", | |
| "plt.axvline(0, color='black',linewidth=0.5)\n", | |
| "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n", | |
| "plt.legend()\n", | |
| "plt.show()" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| }, | |
| "interpreter": { | |
| "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.10.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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