CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense,Conv2D,MaxPool2D,Flatten\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train,y_train), (X_test,y_test)= mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train= X_train/255\n",
    "X_test= X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model= Sequential([Conv2D(32,(3,3),input_shape= (28,28,1),activation='relu'),\n",
    "                   MaxPool2D(pool_size=(2,2),padding='same'),\n",
    "                   Conv2D(32,(3,3),activation='relu'),\n",
    "                   MaxPool2D(pool_size=(2,2),padding='same'),\n",
    "                   Flatten(),\n",
    "                   Dense(1152, activation='relu'),\n",
    "                   Dense(64, activation='relu'),\n",
    "                   Dense(32, activation='relu'),\n",
    "                   Dense(10, activation='softmax')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 13, 13, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 6, 6, 32)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1152)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1152)              1328256   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                73792     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                330       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1414026 (5.39 MB)\n",
      "Trainable params: 1414026 (5.39 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss= 'sparse_categorical_crossentropy',metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1500/1500 [==============================] - 82s 52ms/step - loss: 0.1511 - accuracy: 0.9522 - val_loss: 0.0636 - val_accuracy: 0.9809\n",
      "Epoch 2/10\n",
      "1500/1500 [==============================] - 75s 50ms/step - loss: 0.0491 - accuracy: 0.9849 - val_loss: 0.0521 - val_accuracy: 0.9848\n",
      "Epoch 3/10\n",
      "1500/1500 [==============================] - 78s 52ms/step - loss: 0.0346 - accuracy: 0.9895 - val_loss: 0.0492 - val_accuracy: 0.9852\n",
      "Epoch 4/10\n",
      "1500/1500 [==============================] - 76s 50ms/step - loss: 0.0255 - accuracy: 0.9920 - val_loss: 0.0467 - val_accuracy: 0.9869\n",
      "Epoch 5/10\n",
      "1500/1500 [==============================] - 71s 48ms/step - loss: 0.0199 - accuracy: 0.9941 - val_loss: 0.0523 - val_accuracy: 0.9878\n",
      "Epoch 6/10\n",
      "1500/1500 [==============================] - 51s 34ms/step - loss: 0.0163 - accuracy: 0.9950 - val_loss: 0.0601 - val_accuracy: 0.9866\n",
      "Epoch 7/10\n",
      "1500/1500 [==============================] - 46s 31ms/step - loss: 0.0144 - accuracy: 0.9955 - val_loss: 0.0439 - val_accuracy: 0.9892\n",
      "Epoch 8/10\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.0119 - accuracy: 0.9964"
     ]
    }
   ],
   "source": [
    "history= model.fit(X_train, y_train,epochs=10,verbose='auto',validation_split=0.2,use_multiprocessing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step - loss: 0.0362 - accuracy: 0.9911\n",
      "Test Loss: 0.03615691885352135\n",
      "Test Accuracy: 0.991100013256073\n"
     ]
    }
   ],
   "source": [
    "Loss, Accuracy =model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", Loss)\n",
    "print(\"Test Accuracy:\", Accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='Trainig Accuracy')\n",
    "plt.plot(history.history['val_accuracy'],label='Validaton Accuracy')\n",
    "# Adding labels and title\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "# Adding a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABvV0lEQVR4nO3dd3hT5d8G8DtJ23Sn0L1oKZTdAXQIKKBWyxAFUYEXpSCiKEOs+hOUrVKQYRWQ4QAXgqgMkWGpgAgoe+/ZUugCundy3j8OSRvaQvdJk/tzXbloTs74hhZy9znPkAmCIICIiIjIhMilLoCIiIiooTEAERERkclhACIiIiKTwwBEREREJocBiIiIiEwOAxARERGZHAYgIiIiMjkMQERERGRyGICIiIjI5DAAERmY4cOHw9fXt0bHTp8+HTKZrG4LMjBXr16FTCbDypUrG/zaMpkM06dP1z1fuXIlZDIZrl69+sBjfX19MXz48DqtpzY/K0SmjgGIqIpkMlmVHjt37pS6VJM3fvx4yGQyXLx4sdJ9PvjgA8hkMhw/frwBK6u+GzduYPr06Th69KjUpehoQ+i8efOkLoWoxsykLoCosfj+++/1nn/33XeIi4srt71t27a1us6XX34JjUZTo2MnT56MiRMn1ur6xmDo0KFYuHAhVq1ahalTp1a4z08//YSAgAAEBgbW+DovvfQSBg8eDKVSWeNzPMiNGzcwY8YM+Pr6Ijg4WO+12vysEJk6BiCiKnrxxRf1nv/777+Ii4srt/1eeXl5sLa2rvJ1zM3Na1QfAJiZmcHMjP+sw8PD0bJlS/z0008VBqB9+/bhypUrmD17dq2uo1AooFAoanWO2qjNzwqRqeMtMKI61LNnT3To0AGHDh1C9+7dYW1tjffffx8AsGHDBvTt2xceHh5QKpVo0aIFPvzwQ6jVar1z3Nuvo+zthuXLl6NFixZQKpUIDQ3FgQMH9I6tqA+QTCbD2LFjsX79enTo0AFKpRLt27fH1q1by9W/c+dOhISEwNLSEi1atMCyZcuq3K9o9+7deP7559GsWTMolUp4e3vjrbfeQn5+frn3Z2tri6SkJPTv3x+2trZwdnbGO++8U+7vIiMjA8OHD4dKpYKDgwOioqKQkZHxwFoAsRXo7NmzOHz4cLnXVq1aBZlMhiFDhqCoqAhTp05F586doVKpYGNjg0ceeQQ7dux44DUq6gMkCAI++ugjeHl5wdraGo8++ihOnTpV7tjbt2/jnXfeQUBAAGxtbWFvb4/evXvj2LFjun127tyJ0NBQAMCIESN0t1m1/Z8q6gOUm5uLt99+G97e3lAqlWjdujXmzZsHQRD09qvOz0VNpaamYuTIkXB1dYWlpSWCgoLw7bfflttv9erV6Ny5M+zs7GBvb4+AgAB89tlnuteLi4sxY8YM+Pv7w9LSEo6Ojnj44YcRFxdXZ7WS6eGvikR17NatW+jduzcGDx6MF198Ea6urgDED0tbW1tER0fD1tYWf/31F6ZOnYqsrCzMnTv3geddtWoVsrOz8dprr0Emk+GTTz7Bs88+i8uXLz+wJeCff/7Bb7/9hjfeeAN2dnb4/PPPMXDgQCQkJMDR0REAcOTIEfTq1Qvu7u6YMWMG1Go1Zs6cCWdn5yq977Vr1yIvLw+vv/46HB0dsX//fixcuBDXr1/H2rVr9fZVq9WIjIxEeHg45s2bh+3bt2P+/Plo0aIFXn/9dQBikHjmmWfwzz//YPTo0Wjbti3WrVuHqKioKtUzdOhQzJgxA6tWrUKnTp30rv3zzz/jkUceQbNmzZCeno6vvvoKQ4YMwahRo5CdnY2vv/4akZGR2L9/f7nbTg8ydepUfPTRR+jTpw/69OmDw4cP48knn0RRUZHefpcvX8b69evx/PPPo3nz5khJScGyZcvQo0cPnD59Gh4eHmjbti1mzpyJqVOn4tVXX8UjjzwCAOjatWuF1xYEAU8//TR27NiBkSNHIjg4GNu2bcO7776LpKQkfPrpp3r7V+Xnoqby8/PRs2dPXLx4EWPHjkXz5s2xdu1aDB8+HBkZGXjzzTcBAHFxcRgyZAgef/xxzJkzBwBw5swZ7NmzR7fP9OnTERMTg1deeQVhYWHIysrCwYMHcfjwYTzxxBO1qpNMmEBENTJmzBjh3n9CPXr0EAAIS5cuLbd/Xl5euW2vvfaaYG1tLRQUFOi2RUVFCT4+PrrnV65cEQAIjo6Owu3bt3XbN2zYIAAQfv/9d922adOmlasJgGBhYSFcvHhRt+3YsWMCAGHhwoW6bf369ROsra2FpKQk3bYLFy4IZmZm5c5ZkYreX0xMjCCTyYRr167pvT8AwsyZM/X27dixo9C5c2fd8/Xr1wsAhE8++US3raSkRHjkkUcEAMKKFSseWFNoaKjg5eUlqNVq3batW7cKAIRly5bpzllYWKh33J07dwRXV1fh5Zdf1tsOQJg2bZru+YoVKwQAwpUrVwRBEITU1FTBwsJC6Nu3r6DRaHT7vf/++wIAISoqSretoKBAry5BEL/XSqVS7+/mwIEDlb7fe39WtH9nH330kd5+zz33nCCTyfR+Bqr6c1ER7c/k3LlzK90nNjZWACD88MMPum1FRUVCly5dBFtbWyErK0sQBEF48803BXt7e6GkpKTScwUFBQl9+/a9b01E1cVbYER1TKlUYsSIEeW2W1lZ6b7Ozs5Geno6HnnkEeTl5eHs2bMPPO+gQYPQpEkT3XNta8Dly5cfeGxERARatGihex4YGAh7e3vdsWq1Gtu3b0f//v3h4eGh269ly5bo3bv3A88P6L+/3NxcpKeno2vXrhAEAUeOHCm3/+jRo/WeP/LII3rvZfPmzTAzM9O1CAFin5tx48ZVqR5A7Ld1/fp1/P3337ptq1atgoWFBZ5//nndOS0sLAAAGo0Gt2/fRklJCUJCQiq8fXY/27dvR1FREcaNG6d323DChAnl9lUqlZDLxf+C1Wo1bt26BVtbW7Ru3bra19XavHkzFAoFxo8fr7f97bffhiAI2LJli972B/1c1MbmzZvh5uaGIUOG6LaZm5tj/PjxyMnJwa5duwAADg4OyM3Nve/tLAcHB5w6dQoXLlyodV1EWgxARHXM09NT94Fa1qlTpzBgwACoVCrY29vD2dlZ14E6MzPzgedt1qyZ3nNtGLpz5061j9Uerz02NTUV+fn5aNmyZbn9KtpWkYSEBAwfPhxNmzbV9evp0aMHgPLvz9LSstyttbL1AMC1a9fg7u4OW1tbvf1at25dpXoAYPDgwVAoFFi1ahUAoKCgAOvWrUPv3r31wuS3336LwMBAXf8SZ2dn/PHHH1X6vpR17do1AIC/v7/edmdnZ73rAWLY+vTTT+Hv7w+lUgknJyc4Ozvj+PHj1b5u2et7eHjAzs5Ob7t2ZKK2Pq0H/VzUxrVr1+Dv768LeZXV8sYbb6BVq1bo3bs3vLy88PLLL5frhzRz5kxkZGSgVatWCAgIwLvvvmvw0xeQ4WMAIqpjZVtCtDIyMtCjRw8cO3YMM2fOxO+//464uDhdn4eqDGWubLSRcE/n1ro+tirUajWeeOIJ/PHHH3jvvfewfv16xMXF6Trr3vv+GmrklIuLC5544gn8+uuvKC4uxu+//47s7GwMHTpUt88PP/yA4cOHo0WLFvj666+xdetWxMXF4bHHHqvXIeazZs1CdHQ0unfvjh9++AHbtm1DXFwc2rdv32BD2+v756IqXFxccPToUWzcuFHXf6l37956fb26d++OS5cu4ZtvvkGHDh3w1VdfoVOnTvjqq68arE4yPuwETdQAdu7ciVu3buG3335D9+7ddduvXLkiYVWlXFxcYGlpWeHEgfebTFDrxIkTOH/+PL799lsMGzZMt702o3R8fHwQHx+PnJwcvVagc+fOVes8Q4cOxdatW7FlyxasWrUK9vb26Nevn+71X375BX5+fvjtt9/0bltNmzatRjUDwIULF+Dn56fbnpaWVq5V5ZdffsGjjz6Kr7/+Wm97RkYGnJycdM+rM7O3j48Ptm/fjuzsbL1WIO0tVm19DcHHxwfHjx+HRqPRawWqqBYLCwv069cP/fr1g0ajwRtvvIFly5ZhypQpuhbIpk2bYsSIERgxYgRycnLQvXt3TJ8+Ha+88kqDvScyLmwBImoA2t+0y/5mXVRUhC+++EKqkvQoFApERERg/fr1uHHjhm77xYsXy/Ubqex4QP/9CYKgN5S5uvr06YOSkhIsWbJEt02tVmPhwoXVOk///v1hbW2NL774Alu2bMGzzz4LS0vL+9b+33//Yd++fdWuOSIiAubm5li4cKHe+WJjY8vtq1AoyrW0rF27FklJSXrbbGxsAKBKw//79OkDtVqNRYsW6W3/9NNPIZPJqtyfqy706dMHycnJWLNmjW5bSUkJFi5cCFtbW93t0Vu3bukdJ5fLdZNTFhYWVriPra0tWrZsqXudqCbYAkTUALp27YomTZogKipKt0zD999/36C3Gh5k+vTp+PPPP9GtWze8/vrrug/SDh06PHAZhjZt2qBFixZ45513kJSUBHt7e/z666+16kvSr18/dOvWDRMnTsTVq1fRrl07/Pbbb9XuH2Nra4v+/fvr+gGVvf0FAE899RR+++03DBgwAH379sWVK1ewdOlStGvXDjk5OdW6lnY+o5iYGDz11FPo06cPjhw5gi1btui16mivO3PmTIwYMQJdu3bFiRMn8OOPP+q1HAFAixYt4ODggKVLl8LOzg42NjYIDw9H8+bNy12/X79+ePTRR/HBBx/g6tWrCAoKwp9//okNGzZgwoQJeh2e60J8fDwKCgrKbe/fvz9effVVLFu2DMOHD8ehQ4fg6+uLX375BXv27EFsbKyuheqVV17B7du38dhjj8HLywvXrl3DwoULERwcrOsv1K5dO/Ts2ROdO3dG06ZNcfDgQfzyyy8YO3Zsnb4fMjHSDD4javwqGwbfvn37Cvffs2eP8NBDDwlWVlaCh4eH8L///U/Ytm2bAEDYsWOHbr/KhsFXNOQY9wzLrmwY/JgxY8od6+PjozcsWxAEIT4+XujYsaNgYWEhtGjRQvjqq6+Et99+W7C0tKzkb6HU6dOnhYiICMHW1lZwcnISRo0apRtWXXYId1RUlGBjY1Pu+Ipqv3XrlvDSSy8J9vb2gkqlEl566SXhyJEjVR4Gr/XHH38IAAR3d/dyQ881Go0wa9YswcfHR1AqlULHjh2FTZs2lfs+CMKDh8ELgiCo1WphxowZgru7u2BlZSX07NlTOHnyZLm/74KCAuHtt9/W7detWzdh3759Qo8ePYQePXroXXfDhg1Cu3btdFMSaN97RTVmZ2cLb731luDh4SGYm5sL/v7+wty5c/WG5WvfS1V/Lu6l/Zms7PH9998LgiAIKSkpwogRIwQnJyfBwsJCCAgIKPd9++WXX4Qnn3xScHFxESwsLIRmzZoJr732mnDz5k3dPh999JEQFhYmODg4CFZWVkKbNm2Ejz/+WCgqKrpvnUT3IxMEA/oVlIgMTv/+/TkEmYiMDvsAEZHOvctWXLhwAZs3b0bPnj2lKYiIqJ6wBYiIdNzd3TF8+HD4+fnh2rVrWLJkCQoLC3HkyJFyc9sQETVm7ARNRDq9evXCTz/9hOTkZCiVSnTp0gWzZs1i+CEioyP5LbDFixfD19cXlpaWCA8Px/79+yvd99SpUxg4cCB8fX0hk8kqHFoKAElJSXjxxRfh6OgIKysrBAQE4ODBg/X0DoiMx4oVK3D16lUUFBQgMzMTW7du1VtIlIjIWEgagNasWYPo6GhMmzYNhw8fRlBQECIjI5Gamlrh/nl5efDz88Ps2bPh5uZW4T537txBt27dYG5uji1btuD06dOYP39+uWnoiYiIyHRJ2gcoPDwcoaGhukm7NBoNvL29MW7cOEycOPG+x/r6+mLChAnlFhmcOHEi9uzZg927d9dX2URERNTISdYHqKioCIcOHcKkSZN02+RyOSIiImo0A6vWxo0bERkZieeffx67du2Cp6cn3njjDYwaNarK59BoNLhx4wbs7OyqNQ09ERERSUcQBGRnZ8PDw6PcQrz3kiwApaenQ61Ww9XVVW+7q6urbq2Ymrh8+TKWLFmC6OhovP/++zhw4ADGjx8PCwsLvcX1yiosLNSbUj0pKQnt2rWrcQ1EREQkncTERHh5ed13H6MbBabRaBASEoJZs2YBADp27IiTJ09i6dKllQagmJgYzJgxo9z2xMRE2Nvb12u9REREVDeysrLg7e2ttxhwZSQLQE5OTlAoFEhJSdHbnpKSUmkH56pwd3cv13rTtm1b/Prrr5UeM2nSJERHR+uea/8C7e3tGYCIiIgamap0X5FsFJiFhQU6d+6M+Ph43TaNRoP4+Hh06dKlxuft1q0bzp07p7ft/Pnz8PHxqfQYpVKpCzsMPURERMZP0ltg0dHRiIqKQkhICMLCwhAbG4vc3FyMGDECADBs2DB4enoiJiYGgNhx+vTp07qvk5KScPToUdja2qJly5YAgLfeegtdu3bFrFmz8MILL2D//v1Yvnw5li9fLs2bJCIiIoMj+VIYixYtwty5c5GcnIzg4GB8/vnnCA8PBwD07NkTvr6+WLlyJQDg6tWraN68eblz9OjRAzt37tQ937RpEyZNmoQLFy6gefPmiI6OrtYosKysLKhUKmRmZrI1iIiIqJGozue35AHIEDEAERHVLY1Gg6KiIqnLoEbO3NwcCoWi0ter8/ltdKPAiIjIsBQVFeHKlSvQaDRSl0JGwMHBAW5ubrWep48BiIiI6o0gCLh58yYUCgW8vb0fODkdUWUEQUBeXp5uuSx3d/danY8BiIiI6k1JSQny8vLg4eEBa2trqcuhRs7KygoAkJqaChcXl/veDnsQRnEiIqo3arUagDj1CVFd0Abp4uLiWp2HAYiIiOod11WkulJXP0sMQERERGRyGICIiIgagK+vL2JjY6u8/86dOyGTyZCRkVFvNZkyBiAiIqIyZDLZfR/Tp0+v0XkPHDiAV199tcr7d+3aFTdv3oRKparR9bRkMhnWr19fq3MYI44Ca2DJmQUoKFbD18lG6lKIiKgCN2/e1H29Zs0aTJ06VW+NSVtbW93XgiBArVbDzOzBH6fOzs7VqsPCwqJWi4PT/bEFqAF9u/cqHoqJx5ytZ6UuhYiIKuHm5qZ7qFQqyGQy3fOzZ8/Czs4OW7ZsQefOnaFUKvHPP//g0qVLeOaZZ+Dq6gpbW1uEhoZi+/bteue99xaYTCbDV199hQEDBsDa2hr+/v7YuHGj7vWKboF9+eWX8Pb2hrW1NQYMGIAFCxbAwcGhxu9Vo9Fg5syZ8PLyglKpRHBwMLZu3ap7vaioCGPHjoW7uzssLS3h4+OjW59TEARMnz4dzZo1g1KphIeHB8aPH1/jWhoaW4AaUCtXOwDA8euZEldCRCQNQRCQX6yW5NpW5oo6G0E0ceJEzJs3D35+fmjSpAkSExPRp08ffPzxx1Aqlfjuu+/Qr18/nDt3Ds2aNav0PDNmzMAnn3yCuXPnYuHChRg6dCiuXbuGpk2bltt3z549GD16NObMmYOnn34a27dvx5QpU2r1Pj777DPMnz8fy5YtQ8eOHfHNN9/g6aefxqlTp+Dv74/PP/8cGzduxM8//4xmzZohMTERiYmJAIBff/0Vn376KVavXo327dsjOTkZx44dq1U9DYkBqAEFeKkgkwFJGflIzymEk61S6pKIiBpUfrEa7aZuk+Tap2dGwtqibj72Zs6ciSeeeEL3vGnTpggKCtI9//DDD7Fu3Tps3LgRY8eOrfQ8w4cPx5AhQwAAs2bNwueff479+/ejV69e5fZduHAhevfujXfeeQcA0KpVK+zduxebNm2q8fuYN28e3nvvPQwePBgAMGfOHOzYsQOxsbFYvHgxEhIS4O/vj4cffhgymQw+Pj66YxMSEuDm5oaIiAiYm5ujWbNmCAsLq3EtDY23wBqQrdIMLZzFe8cn2ApERNRohYSE6D3PycnBO++8g7Zt28LBwQG2trY4c+YMEhIS7nuewMBA3dc2Njawt7fXLfVwr3PnzpULGLUJHFlZWbhx4wa6deumt71bt244c+YMADGgHT16FK1bt8b48ePx559/6vZ7/vnnkZ+fDz8/P4waNQrr1q1DSUlJjetpaGwBamCBnipcTM3BsesZeLSNi9TlEBE1KCtzBU7PjJTs2nXFxkZ/IMs777yDuLg4zJs3Dy1btoSVlRWee+45FBUV3fc85ubmes9lMplBLRrbqVMnXLlyBVu2bMH27dvxwgsvICIiAr/88gu8vb1x7tw5bN++HXFxcXjjjTcwd+5c7Nq1q9z7MkQMQA0s0EuF344ksR8QEZkkmUxWZ7ehDMmePXswfPhwDBgwAIDYInT16tU6vUbr1q1x4MABvW33Pq8Oe3t7eHh4YM+ePejRo4du+549e/Raluzt7TFo0CAMGjQIzz33HHr16oXbt2+jadOmsLKyQr9+/dCvXz+MGTMGbdq0wYkTJ9CpU6ca19VQjO+n0MAFejsAEDtCC4LA6eGJiIyAv78/fvvtN/Tr1w8ymQxTpkyp85accePGoXv37liwYAH69euHv/76C1u2bKnS58iVK1dw9OjRcjW/++67mDZtGlq0aIHg4GCsWLECR48exY8//ggAWLBgAdzd3dGxY0fI5XKsXbsWbm5ucHBwwMqVK6FWqxEeHg5ra2v88MMPsLKy0usnZMgYgBpYO3d7mMllSM8pxM3MAng4WEldEhER1dKCBQvw8ssvo2vXrnBycsJ7772HrKysOr1Gt27dsHTpUsyYMQOTJ09GZGQk3nrrLSxatOiBx0ZHR5fbtnv3bowfPx6ZmZl4++23kZqainbt2mHjxo3w9/cHANjZ2eGTTz7BhQsXoFAoEBoais2bN0Mul8PBwQGzZ89GdHQ01Go1AgIC8Pvvv8PR0bFO33d9kQmCIEhdhKHJysqCSqVCZmYm7O3t6/z8fT7bjdM3s7D0xU7o1cG9zs9PRGQoCgoKcOXKFTRv3hyWlpZSl2N0Ro0ahbNnz2L37t1Sl9Jg7vczVZ3Pb44Ck0CQtzitOfsBERFRdcybNw/Hjh3DxYsXsXDhQnz77beIioqSuqxGibfAJBDg6YCfkMgARERE1bJ//3588sknyM7Ohp+fHz7//HO88sorUpfVKDEASSDQS9sClMGO0EREVGU///yz1CUYDd4Ck0BrNztYmMmRVVCCa7fypC6HiIjI5DAAScBcIUc7d7Fz1rHrGdIWQ0REZIIYgCQS5MWO0ERERFJhAJJIoJcDAK4JRkREJAUGIIloO0KfvJEJtYZTMRERETUkBiCJ+DnbwsZCgbwiNS6m5khdDhERkUlhAJKIQi5DB0+xFYgdoYmIjE/Pnj0xYcIE3XNfX1/Exsbe9xiZTIb169fXa10kYgCSkPY2GPsBEREZjn79+qFXr14VvrZ7927IZDIcP3682uc9cOAAXn311dqWp2f69OkIDg6u03NWZufOnZDJZMjIyGiQ69U3BiAJaTtCH2cLEBGRwRg5ciTi4uJw/fr1cq+tWLECISEhCAwMrPZ5nZ2dYW1tXRclUh1gAJJQ0N0AdOZmNopKNNIWQ0REAICnnnoKzs7OWLlypd72nJwcrF27FiNHjsStW7cwZMgQeHp6wtraGgEBAfjpp5/ue957b4FduHAB3bt3h6WlJdq1a4e4uLhyx7z33nto1aoVrK2t4efnhylTpqC4uBgAsHLlSsyYMQPHjh2DTCaDTCbT1ZyQkIBnnnkGtra2sLe3xwsvvICUlBTdebUtR99//z18fX2hUqkwePBgZGdn1+wvDcCdO3cwbNgwNGnSBNbW1ujduzcuXLige/3atWvo168fmjRpAhsbG7Rv3x6bN2/WHTt06FA4OzvDysoK/v7+WLFiRY1rqQouhSEh76ZWcLA2R0ZeMc4lZyPg7i0xIiKjJQhAsUQz4JtbA1VYesjMzAzDhg3DypUr8cEHH+iWK1q7di3UajWGDBmCnJwcdO7cGe+99x7s7e3xxx9/4KWXXkKLFi0QFhb2wGtoNBo8++yzcHV1xX///YfMzEy9/kJadnZ2WLlyJTw8PHDixAmMGjUKdnZ2+N///odBgwbh5MmT2Lp1K7Zv3w4AUKlU0Gg0uvCza9culJSUYMyYMRg0aBB27typO/elS5ewfv16bNq0CXfu3MELL7yA2bNn4+OPP67a3+c9hg8fjgsXLmDjxo2wt7fHe++9hz59+uD06dMwNzfHmDFjUFRUhL///hs2NjY4ffo0bG1tAQBTpkzB6dOnsWXLFjg5OeHixYvIz8+vUR1VxQAkIZlMhgBPFXZfSMex6xkMQERk/IrzgFke0lz7/RuAhU2Vdn355Zcxd+5c7Nq1Cz179gQg3v4aOHAgVCoVVCoV3nnnHd3+48aNw7Zt2/Dzzz9XKQBt374dZ8+exbZt2+DhIf59zJo1C71799bbb/LkybqvfX198c4772D16tX43//+BysrK9ja2sLMzAxubm66/eLi4nDixAlcuXIF3t7eAIDvvvsO7du3x4EDBxAaGgpADGErV66EnZ0dAOCll15CfHx8jQKQNvjs2bMHXbt2BQD8+OOP8Pb2xvr16/H8888jISEBAwcOREBAAADAz89Pd3xCQgI6duyIkJAQ3Xutb7wFJrEg9gMiIjI4bdq0QdeuXfHNN98AAC5evIjdu3dj5MiRAAC1Wo0PP/wQAQEBaNq0KWxtbbFt2zYkJCRU6fxnzpyBt7e3LvwAQJcuXcrtt2bNGnTr1g1ubm6wtbXF5MmTH3gN7bm14QcA2rVrBwcHB5w5c0a3zdfXVxd+AMDd3R2pqalVqr+ia5qZmSE8PFy3zdHREa1bt9Zdc/z48fjoo4/QrVs3TJs2Ta8j+euvv47Vq1cjODgY//vf/7B3794a1VEdbAGSWACXxCAiU2JuLbbESHXtahg5ciTGjRuHxYsXY8WKFWjRogV69OgBAJg7dy4+++wzxMbGIiAgADY2NpgwYQKKiorqrNx9+/Zh6NChmDFjBiIjI6FSqbB69WrMnz+/Ts5vbm6u91wmk0Gjqb/+qK+88goiIyPxxx9/4M8//0RMTAzmz5+PcePGoXfv3rh27Ro2b96MuLg4PP744xgzZgzmzZtXb/WwBUhi2hagC6k5yC9SS1sMEVF9k8nE21BSPKrQ/6esF154AXK5HKtWrcJ3332Hl19+WdcfaM+ePXjmmWfw4osvIigoCH5+fjh//nyVz922bVskJibi5s2bum3//vuv3j579+6Fj48PPvjgA4SEhMDf3x/Xrl3T28fCwgJqtf5nh/bciYmJum2nT59GRkYG2rVrV+Uaq6Nt27YoKSnBf//9p9t269YtnDt3Tu+a3t7eGD16NH777Te8/fbb+PLLL3WvOTs7IyoqCj/88ANiY2OxfPnyeqlVyyAC0OLFi+Hr6wtLS0uEh4dj//79le576tQpDBw4EL6+vpDJZA+cVGr27NmQyWQVdi4zBG4qS7jYKaHWCDh1g61ARESGwtbWFoMGDcKkSZNw8+ZNDB8+XPeav78/4uLisHfvXpw5cwavvfaa3iirB4mIiECrVq0QFRWFY8eOYffu3fjggw/09vH390dCQgJWr16NS5cu4fPPP8e6dev09vH19cWVK1dw9OhRpKeno7CwEBEREQgICMDQoUNx+PBh7N+/H8OGDUOPHj10fWxq48SJEzh69KjucezYMfj7++OZZ57BqFGj8M8//+DYsWN48cUX4enpiWeeeQYAMGHCBGzbtg1XrlzB4cOHsWPHDrRt2xYAMHXqVGzYsAEXL17EqVOnsGnTJt1r9UXyALRmzRpER0dj2rRpOHz4MIKCghAZGVnpfci8vDz4+flh9uzZep2+KnLgwAEsW7asRvM1NKRA3gYjIjJII0eOxJ07dxAZGanXX2fy5Mno1KkTIiMj0bNnT7i5uaF///5VPq9cLse6deuQn5+PsLAwvPLKK+U6Hz/99NN46623MHbsWAQHB2Pv3r2YMmWK3j4DBw5Er1698Oijj8LZ2Rk//fQTZDIZNmzYgCZNmqB79+6IiIiAn58f1qxZU6u/C63u3bujY8eOukfnzp0BiJ3EO3fujKeeegpdunSBIAjYvHmz7labWq3GmDFj0LZtW/Tq1QutWrXCF198AUBsyZo0aRICAwPRvXt3KBQKrF69uk7qrYxMEARJV+IMDw9HaGgoFi1aBEDsle7t7Y1x48Zh4sSJ9z3W19cXEyZMqLB1JycnB506dcIXX3yBjz76CMHBwQ9sLdLKysqCSqVCZmYm7O3tq/uWqu3z+AtYEHce/YM9EDu4Y71fj4iooRQUFODKlSto3rw5LC0tpS6HjMD9fqaq8/ktaQtQUVERDh06hIiICN02uVyOiIgI7Nu3r1bnHjNmDPr27at37soUFhYiKytL79GQ2AJERETUsCQNQOnp6VCr1XB1ddXb7urqiuTk5Bqfd/Xq1Th8+DBiYmKqtH9MTIxuXgeVSqU3dLAhaJfEuJyei6yC4ga9NhERkSmSvA9QXUtMTMSbb76JH3/8scrNrZMmTUJmZqbuUbbnfENoamMBryZWAICTbAUiIiKqd5IGICcnJygUinI951NSUh7Ywbkyhw4dQmpqKjp16gQzMzOYmZlh165d+Pzzz2FmZlZuuCAAKJVK2Nvb6z0amnY4/DEGICIiononaQCysLBA586dER8fr9um0WgQHx9f4YyYVfH444+XG6IXEhKCoUOH4ujRo1AoFHVVfp3SToh4IilD2kKIiOqBxONtyIjU1c+S5DNBR0dHIyoqCiEhIQgLC0NsbCxyc3MxYsQIAMCwYcPg6emp689TVFSE06dP675OSkrC0aNHYWtri5YtW8LOzg4dOnTQu4aNjQ0cHR3LbTck2o7QxxLZAkRExkP7S2dRURGsrKwkroaMQV6euJjuvTNZV5fkAWjQoEFIS0vD1KlTkZycjODgYGzdulXXMTohIQFyeWlD1Y0bN9CxY+lQ8Xnz5mHevHno0aOH3iq3jU2ApxiAkjLycSunEI62SokrIiKqPTMzM1hbWyMtLQ3m5uZ6/58TVYcgCMjLy0NqaiocHBxqfUdH8nmADFFDzwOk9dj8nbiclosVI0LxaGuXBrsuEVF9KioqwpUrV+p1nSkyHQ4ODnBzc9MtS1JWdT6/JW8BolJBXg64nJaL44mZDEBEZDQsLCzg7+9fpwuFkmkyNzevs768DEAGJNBLhXVHknD8eobUpRAR1Sm5XM6ZoMmg8GasAdHNCJ2UyRETRERE9YgByIC0c1dBIZchLbsQyVkFUpdDRERktBiADIiVhQKtXO0AcDg8ERFRfWIAMjCBnpwQkYiIqL4xABmYQG+uDE9ERFTfGIAMjHZNsOPX2RGaiIiovjAAGZhWrnawUMiRmV+MhNt5UpdDRERklBiADIyFmRxtPcTZK7kyPBERUf1gADJAQdr5gBIzpC2EiIjISDEAGSDtwqjHk9gCREREVB8YgAxQkLcDAOBkUibUGnaEJiIiqmsMQAaohbMtrC0UyCtS41JajtTlEBERGR0GIAOkkMvQwYPzAREREdUXBiADpVsYlSvDExER1TkGIAMVeLcfEIfCExER1T0GIAOlXRPszM0sFJVoJK6GiIjIuDAAGSgfR2uorMxRVKLB+ZRsqcshIiIyKgxABkomk+n6AR1jPyAiIqI6xQBkwLQTIp5gPyAiIqI6xQBkwALvrgzPjtBERER1iwHIgAV5iy1A51OykV+klrgaIiIi48EAZMDc7C3hZKuEWiPg9M0sqcshIiIyGgxABkwmk5WuDM+O0ERERHWGAcjAafsBcUkMIiKiusMAZOC4JAYREVHdYwAycNoAdDk9F9kFxRJXQ0REZBwYgAyco60Sng5WEATgRBJvgxEREdUFBqBGQNsKxAkRiYiI6gYDUCPAjtBERER1iwGoEQjimmBERER1igGoEWh/d02w63fycTu3SOJqiIiIGj8GoEZAZWUOPycbABwOT0REVBcYgBqJ0vmA2A+IiIiotgwiAC1evBi+vr6wtLREeHg49u/fX+m+p06dwsCBA+Hr6wuZTIbY2Nhy+8TExCA0NBR2dnZwcXFB//79ce7cuXp8B/UvgB2hiYiI6ozkAWjNmjWIjo7GtGnTcPjwYQQFBSEyMhKpqakV7p+Xlwc/Pz/Mnj0bbm5uFe6za9cujBkzBv/++y/i4uJQXFyMJ598Erm5ufX5VuoV1wQjIiKqOzJBEAQpCwgPD0doaCgWLVoEANBoNPD29sa4ceMwceLE+x7r6+uLCRMmYMKECffdLy0tDS4uLti1axe6d+/+wJqysrKgUqmQmZkJe3v7Kr+X+pRfpEb7aVuhEYB/Jz0ON5Wl1CUREREZlOp8fkvaAlRUVIRDhw4hIiJCt00ulyMiIgL79u2rs+tkZoq3jZo2bVrh64WFhcjKytJ7GBorCwVaudoBYCsQERFRbUkagNLT06FWq+Hq6qq33dXVFcnJyXVyDY1GgwkTJqBbt27o0KFDhfvExMRApVLpHt7e3nVy7brGjtBERER1Q/I+QPVtzJgxOHnyJFavXl3pPpMmTUJmZqbukZiY2IAVVp12RmhOiEhERFQ7ZlJe3MnJCQqFAikpKXrbU1JSKu3gXB1jx47Fpk2b8Pfff8PLy6vS/ZRKJZRKZa2vV990a4IlZUIQBMhkMokrIiIiapwkbQGysLBA586dER8fr9um0WgQHx+PLl261Pi8giBg7NixWLduHf766y80b968LsqVXBs3e1go5MjIK0bi7XypyyEiImq0JG0BAoDo6GhERUUhJCQEYWFhiI2NRW5uLkaMGAEAGDZsGDw9PRETEwNA7Dh9+vRp3ddJSUk4evQobG1t0bJlSwDiba9Vq1Zhw4YNsLOz0/UnUqlUsLKykuBd1g0LMznautvh2PVMHLuegWaO1lKXRERE1ChJHoAGDRqEtLQ0TJ06FcnJyQgODsbWrVt1HaMTEhIgl5c2VN24cQMdO3bUPZ83bx7mzZuHHj16YOfOnQCAJUuWAAB69uypd60VK1Zg+PDh9fp+6luAlwrHrmfiRFIm+gV5SF0OERFRoyT5PECGyBDnAdL6+WAi/vfLcYQ3b4o1r9X8NiEREZGxaTTzAFH1Bd0dCXYyKRNqDbMrERFRTTAANTItnG1gZa5AbpEal9NypC6HiIioUWIAamTMFHJ08BSb9TghIhERUc0wADVCgbqV4TMkrYOIiKixYgBqhLQTIh5jCxAREVGNMAA1QtoWoNM3s1Cs1khbDBERUSPEANQI+Tpaw87SDEUlGpxLzpa6HCIiokaHAagRkslkXBmeiIioFhiAGintbbATSRmS1kFERNQYMQA1UkHajtCJbAEiIiKqLgagRirgbgvQuZRsFBSrpS2GiIiokWEAaqQ8VJZwsrWAWiPg9M0sqcshIiJqVBiAGimxI7QDAOB4YoaktRARETU2DECNWIAnR4IRERHVBANQIxbkfTcAJTEAERERVQcDUCMW4OkAALiUloOcwhJpiyEiImpEGIAaMWc7JTxUlhAE4ARvgxEREVUZA1AjxwkRiYiIqo8BqJEL9ObK8ERERNXFANTIBd7tB3T8eoakdRARETUmDECNXMDdJTESb+fjTm6RxNUQERE1DgxAjZzKyhzNnWwAcDg8ERFRVTEAGQHdhIicEZqIiKhKGICMQKAXJ0QkIiKqDgYgIxDk7QCAHaGJiIiqigHICLT3sIdcBqRkFSIlq0DqcoiIiAweA5ARsLYwg7+LHQAujEpERFQVDEBGQtcPiLfBiIiIHogByEhoAxBnhCYiInowBiAjoVsT7HoGBEGQthgiIiIDxwBkJNq428FcIcOdvGJcv5MvdTlEREQGjQHISCjNFGjjZg8AOMZ+QERERPfFAGREtP2ATrAfEBER0X0xABmRoLv9gNgCREREdH8MQEZEuzL8yaQsaDTsCE1ERFQZgwhAixcvhq+vLywtLREeHo79+/dXuu+pU6cwcOBA+Pr6QiaTITY2ttbnNBb+LrawNJcjp7AEl9NzpS6HiIjIYEkegNasWYPo6GhMmzYNhw8fRlBQECIjI5Gamlrh/nl5efDz88Ps2bPh5uZWJ+c0FmYKOTp4cEJEIiKiB5E8AC1YsACjRo3CiBEj0K5dOyxduhTW1tb45ptvKtw/NDQUc+fOxeDBg6FUKuvknMYkQDcjNDtCExERVUbSAFRUVIRDhw4hIiJCt00ulyMiIgL79u0zmHM2JtqO0GwBIiIiqpykASg9PR1qtRqurq56211dXZGcnNxg5ywsLERWVpbeo7HSDoU/dSMLxWqNxNUQEREZJslvgRmCmJgYqFQq3cPb21vqkmrM19EGdkozFJZocD4lW+pyiIiIDJKkAcjJyQkKhQIpKSl621NSUirt4Fwf55w0aRIyMzN1j8TExBpd2xDI5TJdPyBOiEhERFQxSQOQhYUFOnfujPj4eN02jUaD+Ph4dOnSpcHOqVQqYW9vr/dozAJ1EyIyABEREVXETOoCoqOjERUVhZCQEISFhSE2Nha5ubkYMWIEAGDYsGHw9PRETEwMALGT8+nTp3VfJyUl4ejRo7C1tUXLli2rdE5jF+jFofBERET3I3kAGjRoENLS0jB16lQkJycjODgYW7du1XViTkhIgFxe2lB148YNdOzYUfd83rx5mDdvHnr06IGdO3dW6ZzGThuAziVno6BYDUtzhcQVERERGRaZIAhcM+EeWVlZUKlUyMzMbJS3wwRBQMhH23Ertwjr3uiKjs2aSF0SERFRvavO5zdHgRkhmUzGCRGJiIjugwHISAXqJkRkACIiIroXA5CRCmJHaCIiokoxABkp7S2wi2k5yCkskbgaIiIiw8IAZKRc7CzhrrKEIACnkngbjIiIqCwGICMWyI7QREREFWIAMmKlM0JnSFoHERGRoWEAMmLaFqATvAVGRESkhwHIiAV6OgAArt3KQ0ZekbTFEBERGRAGICOmsjaHj6M1APYDIiIiKosByMhp+wHxNhgREVEpBiAjp50Q8VhihrSFEBERGRAGICMX4Mmh8ERERPdiADJyHTxVkMuA5KwCpGYVSF0OERGRQWAAMnI2SjO0dLEFwFYgIiIiLQYgExBwdzg8F0YlIiISMQCZgCDvu/2AOBKMiIgIAAOQSdAOhT9+PROCIEhbDBERkQFgADIBbdzsYCaX4XZuEa7fyZe6HCIiIskxAJkAS3MF2rjbAeCEiERERAADkMngyvBERESlGIBMRKB2QsREtgARERExAJkIbQvQyaRMaDTsCE1ERKaNAchEtHK1hdJMjuzCEly5lSt1OURERJJiADIRZgo52nvYA+CEiERERAxAJkTXEZr9gIiIyMQxAJkQ7YzQHApPRESmrkYBKDExEdevX9c9379/PyZMmIDly5fXWWFU97Rrgp26kYkStUbaYoiIiCRUowD0f//3f9ixYwcAIDk5GU888QT279+PDz74ADNnzqzTAqnu+DnZwE5phoJiDc6n5EhdDhERkWRqFIBOnjyJsLAwAMDPP/+MDh06YO/evfjxxx+xcuXKuqyP6pBcLkMHT+1tsAxpiyEiIpJQjQJQcXExlEolAGD79u14+umnAQBt2rTBzZs36646qnOBXmIAOnad/YCIiMh01SgAtW/fHkuXLsXu3bsRFxeHXr16AQBu3LgBR0fHOi2Q6lbpyvAZktZBREQkpRoFoDlz5mDZsmXo2bMnhgwZgqCgIADAxo0bdbfGyDBpW4DOJWejoFgtcTVERETSMKvJQT179kR6ejqysrLQpEkT3fZXX30V1tbWdVYc1T2vJlZoYm2OO3nFOJucjWBvB6lLIiIianA1agHKz89HYWGhLvxcu3YNsbGxOHfuHFxcXOq0QKpbMpmMt8GIiMjk1SgAPfPMM/juu+8AABkZGQgPD8f8+fPRv39/LFmypE4LpLoXdPc22HF2hCYiIhNVowB0+PBhPPLIIwCAX375Ba6urrh27Rq+++47fP7559U+3+LFi+Hr6wtLS0uEh4dj//79991/7dq1aNOmDSwtLREQEIDNmzfrvZ6Tk4OxY8fCy8sLVlZWaNeuHZYuXVrtuoxVAFuAiIjIxNUoAOXl5cHOzg4A8Oeff+LZZ5+FXC7HQw89hGvXrlXrXGvWrEF0dDSmTZuGw4cPIygoCJGRkUhNTa1w/71792LIkCEYOXIkjhw5gv79+6N///44efKkbp/o6Ghs3boVP/zwA86cOYMJEyZg7Nix2LhxY03ertHRtgBdTM1BbmGJxNUQERE1vBoFoJYtW2L9+vVITEzEtm3b8OSTTwIAUlNTYW9vX61zLViwAKNGjcKIESN0LTXW1tb45ptvKtz/s88+Q69evfDuu++ibdu2+PDDD9GpUycsWrRIt8/evXsRFRWFnj17wtfXF6+++iqCgoIe2LJkKlzsLeFmbwmNAJy6kSV1OURERA2uRgFo6tSpeOedd+Dr64uwsDB06dIFgNga1LFjxyqfp6ioCIcOHUJERERpQXI5IiIisG/fvgqP2bdvn97+ABAZGam3f9euXbFx40YkJSVBEATs2LED58+f1wW1exUWFiIrK0vvYewCdP2AMqQthIiISAI1CkDPPfccEhIScPDgQWzbtk23/fHHH8enn35a5fOkp6dDrVbD1dVVb7urqyuSk5MrPCY5OfmB+y9cuBDt2rWDl5cXLCws0KtXLyxevBjdu3ev8JwxMTFQqVS6h7e3d5XfQ2MVxBmhiYjIhNVoHiAAcHNzg5ubm25VeC8vL4OZBHHhwoX4999/sXHjRvj4+ODvv//GmDFj4OHhUa71CAAmTZqE6Oho3fOsrCyjD0HaofAn2AJEREQmqEYtQBqNBjNnzoRKpYKPjw98fHzg4OCADz/8EBqNpsrncXJygkKhQEpKit72lJQUuLm5VXiMm5vbfffPz8/H+++/jwULFqBfv34IDAzE2LFjMWjQIMybN6/CcyqVStjb2+s9jF3A3UVRr97KQ2ZescTVEBERNawaBaAPPvgAixYtwuzZs3HkyBEcOXIEs2bNwsKFCzFlypQqn8fCwgKdO3dGfHy8bptGo0F8fLyuX9G9unTporc/AMTFxen2Ly4uRnFxMeRy/bemUCiqFc6MXRMbCzRrKs7afZwrwxMRkYmp0S2wb7/9Fl999ZVuFXgACAwMhKenJ9544w18/PHHVT5XdHQ0oqKiEBISgrCwMMTGxiI3NxcjRowAAAwbNgyenp6IiYkBALz55pvo0aMH5s+fj759+2L16tU4ePAgli9fDgCwt7dHjx498O6778LKygo+Pj7YtWsXvvvuOyxYsKAmb9doBXqpkHA7D8evZ+IRf2epyyEiImowNQpAt2/fRps2bcptb9OmDW7fvl2tcw0aNAhpaWmYOnUqkpOTERwcjK1bt+o6OickJOi15nTt2hWrVq3C5MmT8f7778Pf3x/r169Hhw4ddPusXr0akyZNwtChQ3H79m34+Pjg448/xujRo2vydo1WoJcKm47f5EgwIiIyOTJBEITqHhQeHo7w8PBysz6PGzcO+/fvx3///VdnBUohKysLKpUKmZmZRt0f6N/LtzB4+b9wV1li36THpS6HiIioVqrz+V2jFqBPPvkEffv2xfbt23V9b/bt24fExMRyy1KQ4ergqYJMBtzMLEBqdgFc7CylLomIiKhB1KgTdI8ePXD+/HkMGDAAGRkZyMjIwLPPPotTp07h+++/r+saqZ7YKs3QwtkWAHCC8wEREZEJqdEtsMocO3YMnTp1glqtrqtTSsJUboEBQPTPR/Hb4SSMf9wf0U+0krocIiKiGqvO53eNWoDIeARxQkQiIjJBDEAmrnRNsEzUYWMgERGRQWMAMnHt3O1hJpfhVm4RkjLypS6HiIioQVRrFNizzz5739czMjJqUwtJwNJcgdZudjh1IwsnrmfCq4m11CURERHVu2oFIJVK9cDXhw0bVquCqOEFeqlw6kYWjl3PRO8Ad6nLISIiqnfVCkArVqyorzpIQoFeDvhpfyJnhCYiIpPBPkCEwLsdoU8kZUKjYUdoIiIyfgxAhFaudlCayZFdUIKrt3KlLoeIiKjeMQARzBVytPMQJ4w6zhmhiYjIBDAAEYDSCREZgIiIyBQwABEAIMBTOyFihrSFEBERNQAGIAIABHmLAejkjUyUqDUSV0NERFS/GIAIAODnZAsbCwUKijW4mJYjdTlERET1igGIAAByuQwdtLfBEtkPiIiIjBsDEOkEeTsAAI6xHxARERk5BqCGlnkdMNBV18tOiEhERGTMGIAaUsK/wJKuwPbpUldSoUBPBwDAmZtZKCxRS1sMERFRPWIAaki3LgEFmcCeWODfJVJXU453Uys0sTZHsVrA2ZvZUpdDRERUbxiAGlLHocBjU8Svt04ETvwibT33kMlkCNBOiMjbYEREZMQYgBraI28DYa+KX68bDVzeKWk59wrUjQTLkLYQIiKiesQA1NBkMqDXbKBdf0BTDKx+Ebh5TOqqdLQdobkkBhERGTMGICnIFcCAZYDvI0BRNvDDc8DtK1JXBaB0KPyF1GzkFZVIWwwREVE9YQCSirklMPhHwDUAyE0FfngWyEmTuiq42lvCxU4JjQCcupEldTlERET1ggFISpYq4MVfAIdmwO3LwI/PAYXSj74KvNsR+hj7ARERkZFiAJKanRvw4jrA2hG4eRT4eRhQUiRpSUGcEJGIiIwcA5AhcGoJ/N9awNwauPQXsGEMoJFuRfYAdoQmIiIjxwBkKLw6Ay98D8jNgBM/A3FTJCtFewvsSnouMvOLJauDiIiovjAAGRL/COCZxeLX+xYBexdKUkZTGwt4N7UCAJzkbTAiIjJCDECGJmgwEDFD/PrPycCxNZKUoV0XjCvDExGRMWIAMkTd3gQeekP8esMbwMXtDV6CbkLERLYAERGR8WEAMkQyGfDkx0CH5wBNCbBmGJB0qEFL0PYD4kgwIiIyRgxAhkouB/ovAfx6AsW5wI8viKvJN5AOnvaQyYCkjHyk5xQ22HWJiIgagkEEoMWLF8PX1xeWlpYIDw/H/v3777v/2rVr0aZNG1haWiIgIACbN28ut8+ZM2fw9NNPQ6VSwcbGBqGhoUhISKivt1A/zCyAQT8A7kFAXjrw/QAgO6VBLm1naQ4/JxsAwHH2AyIiIiMjeQBas2YNoqOjMW3aNBw+fBhBQUGIjIxEampqhfvv3bsXQ4YMwciRI3HkyBH0798f/fv3x8mTJ3X7XLp0CQ8//DDatGmDnTt34vjx45gyZQosLS0b6m3VHaUdMPQXoElzIOMa8ONAoKBhlqgIunsbjPMBERGRsZEJgiBIWUB4eDhCQ0OxaNEiAIBGo4G3tzfGjRuHiRMnltt/0KBByM3NxaZNm3TbHnroIQQHB2Pp0qUAgMGDB8Pc3Bzff/99jWrKysqCSqVCZmYm7O3ta3SOOnf7MvD1k0BuGtC8uxiKzJT1eskVe65gxu+n8VgbF3wzPLRer0VERFRb1fn8lrQFqKioCIcOHUJERIRum1wuR0REBPbt21fhMfv27dPbHwAiIyN1+2s0Gvzxxx9o1aoVIiMj4eLigvDwcKxfv77SOgoLC5GVlaX3MDhN/YChawELW+DK38C61+p9tuhAXQtQBiTOyURERHVK0gCUnp4OtVoNV1dXve2urq5ITk6u8Jjk5OT77p+amoqcnBzMnj0bvXr1wp9//okBAwbg2Wefxa5duyo8Z0xMDFQqle7h7e1dB++uHnh0BAZ9D8jNgVPrgG2TgHoMJu097KGQy5CeU4SbmQX1dh0iIqKGJnkfoLqmudsq8swzz+Ctt95CcHAwJk6ciKeeekp3i+xekyZNQmZmpu6RmJjYkCVXT4vHgAF338d/S4E9sfV2KUtzBVq52gFgR2giIjIukgYgJycnKBQKpKToj2xKSUmBm5tbhce4ubndd38nJyeYmZmhXbt2evu0bdu20lFgSqUS9vb2eg+DFvAcEDlL/Hr7dODoqnq7lHZl+GPsCE1EREZE0gBkYWGBzp07Iz4+XrdNo9EgPj4eXbp0qfCYLl266O0PAHFxcbr9LSwsEBoainPnzuntc/78efj4+NTxO5BQlzFA1/Hi1xvGAuf/rJfL6CZEZAAiIiIjYiZ1AdHR0YiKikJISAjCwsIQGxuL3NxcjBgxAgAwbNgweHp6IiYmBgDw5ptvokePHpg/fz769u2L1atX4+DBg1i+fLnunO+++y4GDRqE7t2749FHH8XWrVvx+++/Y+fOnVK8xfoTMQPISQWOrwbWRgFRvwNeIXV6Cd2SGHc7Qstksjo9PxERkRQkD0CDBg1CWloapk6diuTkZAQHB2Pr1q26js4JCQmQy0sbqrp27YpVq1Zh8uTJeP/99+Hv74/169ejQ4cOun0GDBiApUuXIiYmBuPHj0fr1q3x66+/4uGHH27w91ev5HLgmUXiJIkXtwM/Pg+M/BNw8q+zS7R2s4OFmRxZBSW4eisPze9OjkhERNSYST4PkCEyyHmA7qcwB/i2H3DjMKBqJoYge/c6O33/xXtwNDEDnw0OxjPBnnV2XiIiorrUaOYBojqitBXnCGraAshMAH4YCORn1NnpS2+DsR8QEREZBwYgY2HjBLz0G2DrCqSeAlYPBYrrZu6eshMiEhERGQMGIGPSxFdcIsPCDrj2D/DbKECjrvVptUPhTyZloURdv7NPExERNQQGIGPjHggMWQUoLIAzG4Et/6v1bNF+zrawtlAgv1iNS2m5dVQoERGRdBiAjFHz7sCzywHIgANfAX/Pq9XpFHIZOnhqJ0TMqH19REREEmMAMlbtBwC9PxG/3vERcOjbWp0uqMx8QERERI0dA5AxC38VeORt8etNE4Czm2t8qgDOCE1EREaEAcjYPTYFCH4REDTALyOAhP9qdBptC9CZm9koKmFHaCIiatwYgIydTAb0+wzwjwRKCoBVLwCpZ6t9mmZNraGyMkeRWoOzyVn1UCgREVHDYQAyBQoz4PmVgFcoUJAB/PAskJlUrVPIZDJOiEhEREaDAchUWFgD//cz4NQKyEq6O1v0nWqdIpAdoYmIyEgwAJkS66bAi78Bdu5A2hngpyFAcX6VDy+dEZotQERE1LgxAJkaB2/gxV8BpQpI2Af8MhJQl1TpUG0L0PmUbOQX1X6GacndugT8tww4ta7KfwdERGQczKQugCTg2h4Y8hPw/QDg3B/A5reBp2LFDtP34WZvCWc7JdKyC3HqRiZCfJs2TL11RRCA1DPAmd/FWbJTTpa+5tgS6P4u0OE5sc8UEREZNbYAmSrfbsBzXwMyOXBoJbBz9gMPkclkuuHwxxrLbTBBAJIOA9tnAItCgCVdgJ2zxPAjUwC+jwBWTYFbF4F1rwGLw4Bjq9kiRERk5Pirrilr2w/oMw/4IxrYNRuwdQFCR973kABPB2w/k4oThtwRWqMBru8HTm8UW3syE0pfU1gALR4D2j4NtO4t9osqzAb2fwnsXQjcviQGoV1zgO7/AwKeZ4sQEZER4v/spi50JJCTIn7gb35HDEFt+1W6e6C3gQ6FV5cA1/4RQ8/ZTeJ70jK3BvyfEEOP/5OApb3+sUo74JFoIOxV4MCXwJ7PgduXgfWjgb8/EW+NBbzAIEREZERkglDLpcKNUFZWFlQqFTIzM2Fvb//gAxo7QRCXyji0ElAogZfWibfIKnArpxCdP9oOADg27UmorMwbrs57lRQCl3eKoefcH/rD+pUqoHUvMcy1eFycBqCqCnPERWT3fg7k3RK3NWkuBqHAQQxCREQGqjqf3wxAFTC5AASILSg/DxODhFIFvLxF7CxdgW6z/0JSRj5WvRKOri2dGrbOolzg4nYx9JzfBhRll75m7Qi06Su29DTvAZhZ1O5ahTnAwa+BPZ/dE4TeuRuEJAx/RERUDgNQLZlkAALEOYG+HyAOj7fzAEb+KQ6bv8cbPx7C5hPJeK9XG7zes0X911WQKYadMxuBC9uBkjJzF9m5i608bfsBzbrWT+uMLgh9DuSli9ua+JZpEWIQIjJY1w8Bez4FLv4l9vmz97j78Lz78Cj909aVLbyNHANQLZlsAALE20jf9BYnSnRqBby8TfxPo4yluy5h9pazeKyNC76OCoHsAcPnayT3FnBusxh6Lu8E1EWlrzk0E1t52j0DeIYA8gYazFiUCxzQtgjdDUIOPmIQChrMIEQiQQDSzor/lrzDAblC6opMjyAAl/4C/vkUuLq76sfJFICd2z0hSfu1l/innRv/rRswBqBaMukABIjrhH39hLhkhlcoMGyjXh+aQ9duY+CSfQCAbi0d8eEzHeDnbFv762bdFDswn9kIXN0DCGUmW3RqdTf0PA24BT5wzqJ6VZQLHPxGDEK5aeI2Bx/x1ljQEP7naIq0c0ydWgecXg+knxe3O/gA4a8BHV8ELFWSlmgS1CXAmQ3AP7FA8nFxm9xMbKkNHSl+nzKvA1k3xP/fsm6UPrJvAJqqTH8hE1uKtMFI5VU+LNm5A2bK+nynVAkGoFoy+QAEiCvGfxMpLp7aqhcw6Ee9puFluy5hQdx5FJZoYKGQ4/WeLfB6zxawNK/mb7t3rpVOTJi4H0CZH0e3wNLQ49y6Tt5WnSrKuxuEYssEoWbAI+8Awf/HIGTsBAFIPQ2cWq8fegBxugUzK6Dw7mhJC1vxZyJ8NODYALeNTU1xAXD0R3Hgwp2r4jZzG6DzcKDLG2JIeRCNWvx3rBeMksRfCMsGJk1x1WqycamgFenu1ypPsZuBuWVN3zFVggGolhiA7kr4D/juaaCkQPwN9ulFei0vCbfyMGXDSew6L374+zpa48P+HfCIv/P9z5t+ATi9QQw9N4/pv+YVKoaetv2Aps3r+h3VD10Q+gzITRW3OTQDHnkbCPq/2nfGJsNRNvScWgfculD6msJCHHHYfoA4AlFuDhxfA/y3VLwlpuUfCTw0GvB7VNqWTGOQnyH2z/t3aem/PaumwEOvA6GvlLt9X2sajTggIuu6fki6NzCpC6t2PmvHyvsjaf+szghWYgCqLQagMs5uBtYMBQSN2LLx+BS9lwVBwOYTyZjx+ymkZov/6J8O8sDkp9rCxc5Su5M48/LpjWLoKfthIJMDPt3uhp6nxH/wjVVRHnBohdj8rv3PWNUM6M4g1KgJApBySmzlObW+fOhpGQG06y+GnopucwkCcHmH+CF9YVvpduc24u2xwMH8kKuu7GTg3y+AA9+UjgRVeQNdx4m/rFnYSFebIAB5t8sEo7J/3v06M0l/MMf9WDUp3xfJuRXg0g5o6sc+ZvdgAKolBqB7HPoW+H28+HXvuUD4q+V2yS4oxvw/z+O7fVehEQB7SznmPFSCSNl+yM/+Dty5Urqz3Bzw6yGGnjZ9AZsGHkpf34ryxDmV9sSWTsio8hZbhIKHMgg1BnqhZ524VIpWVUJPZbQL8B79ESjKEbdZNQE6RQFho6p2q8aU3boktrQe+6l0YIRLO6DbBKDDs43ntrMgiN0LtGHo3lYk7Z/an5HKmFmK/SNd2wMubcW/C5d2YlAy0dZFBqBaYgCqwK65wI6PAMiA51eIzfz30qhx+dB2nIr/Hp3z98BDdrv0NTNL8UOj7dNAq0jAyqGhKpdOcb4YhP759J4gFA0Ev8ggZGi0oUfbkVkv9CjFn9/2/cU+cffOJl5dBZnAkR/F22MZ18RtMoV46/eh18XRYyb6AVahpMPiLxSnN0LXT7BZF+Dht8TZ3Y3x70oQgMKs8rfZMhLEDvdpZ4HivIqPtVTdDUNlQpFL27q/JWiAGIBqiQGoAoIAbH5XXCpCYQG8+CvQvDugLgau7BI7Mp/9o7QzMIBcwRLxmo7YpgmDV+gzGNc7CLZKE5xjQxeEYoGcZHGbvZcYhDq+yNEiUtLentV2ZK7P0FMRjRo4vxX4d4n+cG2PjkD46+IvGqYalAVBnALjn0/F/2O0WvUGHp4ANHtIqsoMg0YDZFwFUk6LgSj1lPhn+gX9EbRl2bmXD0XObYzqFiwDUC0xAFVCowbWDhf78SjtxQ+FC9vE32a1LB10szGnOD+ED7dexqbjNwEAbvaWmNavHXp1cKufuYMMXXG+eDvxn0/vCUJvAR1fYhBqKGVDz6l14gK4WrrQM0BsqayP0FOZ5JPAf0uA42tLO9HaugIhI4GQlwHbBwwuMBYatfh/zD+xwM2j4jaZQlyYuNubgGs7KaszfCWFYggqG4pSTusvCq1HJg44KRuKXNsDTVs0ykkhGYBqiQHoPooLgB8GiguPatm4iB2Y2/YDfB8pdx9+1/k0TFl/Egm3xebaR1s7Y+YzHeDd1Hh+66iW4gLg8LfA7gVlgpCn2JzfaRiDUH0QBCD5RGlH5ntDj/8TYp+ehg49FclNFzvTH/gayBZ/eYDCQgwA4aMB90Bp66svJYVi3549n4mLEQPiVAKdo4AuY8SRlVRzBVlA2rkyoeiUOKJRu8zPvRQWgFPruy1GbUv7Gam8DfqWIwNQLTEAPUB+BvDnB2IrUNt+VZrttqBYjcU7LmLprksoVguwNJdj/OP+eOVhP1iYNdBMzoamuAA4/B3wz4LSDzoGobqjF3rWlX6oAqWhR9vSo7STrMxKlRSJLSH/fgEkHSrd7tNN7CfUuo9xjAAqyBKnkfj3i9K+clZNgLDXgLBXARtHaeszdjmpYhDShaIz4qM4t+L9LezKhyKX9gbzfWIAqiUGoPpzMTUHk9efwL+XxQ7S/i62+HhAAMKaG3/nvEoVFwBHvhdbhLJviNvsPO72EXqJk6VVhzb0aDsyN7bQU5nEA+LtsdMbSmcrdmgmBoSOLzXOQQXZKeJ7OvBN6YSR9p53h7K/BCjrYHZ5qhmNRrxlpheKTouTfVY2W7aNi3h7Utf5ur04gW0Dfx8ZgGqJAah+CYKAdUeS8PEfZ3ArVxzK+nxnL0zq0xZNbUy0wydQSRByBx6OFluEGIQqJgjisgfajsxlQ4929GFjDD0VyUwCDnwldqrPvzvK0twGCB4i3h5z8pe0vCq5dQnYuxA4uqq0r5NTa7Fjc4fnTLfTd2NQUiQOFNC2GKWeFh/a2bcr0sS3/Ig0J/96m7KAAaiWGIAaRkZeEeZsPYef9oud85pYm2NSn7Z4rpMX5HLDvcdc70oKxVtj5YLQW+J8MQxCDw49Zfv0NPbQU5HifOD4z+Iw+tTTpdtbPiHOMt3iccPrp3HzmNix+fR6cWJVAPAKE3+uW/VquEWNqe4V5lTQv+hM6YSw95KbiyGoTV/gscl1WgoDUC0xADWsQ9du44N1J3E2WZzRNcy3KT4a0AGtXI3wg6s6SgpLW4SyksRtdu7ipG+dowBzK0nLa3CCIH6Iajsyl51cUy/09DKd2yeCAFz5WxxGf34rdHPkOLUSW4SCBks/K/LV3eLIx0t/lW73f1IMPs26GF5Qo7qTm15x/yLt7N3BQ4H+X9TpJRtdAFq8eDHmzp2L5ORkBAUFYeHChQgLC6t0/7Vr12LKlCm4evUq/P39MWfOHPTp06fCfUePHo1ly5bh008/xYQJE6pUDwNQwytWa7BizxV8GncB+cVqmMllGNXdD+Mf84eVhRF09KyNkkLgyA93g9B1cZutm/gBYuxBqCqhp/0AcX0tUwk9lbl1Cdj/pfizov2AsVTdnWX6VcDBu+Fq0WiAs5vE4HPjsLhNphBna+72JuAW0HC1kGERBCAzUQxC1k6AV+c6PX2jCkBr1qzBsGHDsHTpUoSHhyM2NhZr167FuXPn4OLiUm7/vXv3onv37oiJicFTTz2FVatWYc6cOTh8+DA6dOigt++6deswY8YMpKWl4d1332UAagSSMvIxfeMpxJ0WR4N4NbHCzGfa47E2rhJXZgAqDEKud4PQcOMJQtrQo+3IXLZ/gZnV3dDTn6GnMgVZ4lIb/y0rDYwyuThiM/x1cQLB+mp1KSkUF4Dd83npmmlmlmKn5q5jxf4gRPWoUQWg8PBwhIaGYtGiRQAAjUYDb29vjBs3DhMnTiy3/6BBg5Cbm4tNmzbptj300EMIDg7G0qVLdduSkpIQHh6Obdu2oW/fvpgwYQIDUCPy56lkTN94CjcyCwAAvdq7YdrT7eCuMpIP+dooKRQ/4P6erx+E2vYD5GZigNDeCtH98y77vCavofR5hcdV4bWqXj/5RCWhZ4B464Shp2o0auDCn+LtsbIzKbsHiUGow7N1N9VCYTZwcIU4lF07pYOlSmx5CnvNdCZxJMlV5/Nb0mkei4qKcOjQIUyaNEm3TS6XIyIiAvv27avwmH379iE6OlpvW2RkJNavX697rtFo8NJLL+Hdd99F+/btH1hHYWEhCgsLdc+zsrKq+U6orj3Z3g3dWjrhs/gL+PqfK9h6Khm7L6Qh+snWiOriAzOFCXeYNFOKMwMHvygGod3zxSblA19JXVndMbMCWj0p9ulh6KkZuQJo3Vt8pJwSO0wf/1lsXVs/GoibCoRqZ5ku39peJTlp4nkPfFk6I7ydO9BlrHh71hg7oJPRkDQApaenQ61Ww9VV//aGq6srzp49W+ExycnJFe6fnJysez5nzhyYmZlh/PjxVaojJiYGM2bMqGb1VN9slGZ4v09bDOjoiQ/WncDhhAx8uOk0fj10HbOeDUCwt4PUJUrLzAIIGSF2JDz5qzhHh0wG4O7tDd1tjrLP7/fa3ee6uyO1PU9NXoPYmtXycWk77xob1/bA0wuBx6cDh1cC+78SRxjujBEDdIeBYqdpj+Cqne/OVXEo+5EfgBKxlRaO/mL/nsAXOIknNQqNb6GPBzh06BA+++wzHD58uMrrTU2aNEmvVSkrKwve3g3YYZDuq627PX4Z3RVrDiZi9pazOH0zCwO+2IOh4c3wbmQbqKzqZz6JRsPMQpwHhuhBbByBR94Guo4XJ1X8bylw/YC4BMWxn4BmXcVh9K37VrwOVPIJcSj7qXWlC256dhb7obXuy6Hs1KhIGoCcnJygUCiQkpKitz0lJQVubm4VHuPm5nbf/Xfv3o3U1FQ0a1a6boxarcbbb7+N2NhYXL16tdw5lUollEr+xmLI5HIZhoQ1wxPtXDFr8xn8djgJP/ybgK0nUzDlqbZ4OsjDNBdYJaoJhTkQ8Jz4uH5InJH51DogYa/4UHmL/Xc6vSQucHxtjxh8LsaVnqPF42Lw8X2YQ9mpUTKITtBhYWFYuHAhALH/TrNmzTB27NhKO0Hn5eXh999/123r2rUrAgMDsXTpUty6dQs3b97UOyYyMhIvvfQSRowYgdatWz+wJnaCNnx7L6Vj8vqTuJwmrlfzcEsnfNi/A5o78bYJUY1k3RAXYD20onSBTHNrcVXwlBPic5lc7Ize7U2xMzWRgWlUo8DWrFmDqKgoLFu2DGFhYYiNjcXPP/+Ms2fPwtXVFcOGDYOnpydiYmIAiMPge/TogdmzZ6Nv375YvXo1Zs2aVeEweC1fX1+OAjNChSVqLN91GQt3XERRiQYWZnK80bMFXu/ZAkozE587iKimivOBE7+It8dSTorbFEqg41Bxna6mftLWR3QfjWYUGCC26KSlpWHq1KlITk5GcHAwtm7dquvonJCQAHmZ+8pdu3bFqlWrMHnyZLz//vvw9/fH+vXrKw0/ZLyUZgqMe9wf/YI8MGXDSey+kI7Y7Rew8egNfNi/A7q1dJK6RKLGx9xKvPXV8UXg6j/i2k9t+tZ8pBiRgZK8BcgQsQWo8REEAX+cuIkZv59GWrY4pcEzwR6Y3LcdnO3Yv4uIyBRU5/ObXfbJKMhkMjwV6IH4t3sgqosPZDJgw9EbeGz+Tvzw7zVoNMz5RERUii1AFWALUON3/HoG3l93AieTxEktg70d8PGADmjvoZK4MiIiqi9sASKTF+jlgA1jHsb0fu1gqzTD0cQM9Fv4Dz7cdBo5hSVSl0dERBJjACKjpZDLMLxbc8S/3QN9A9yhEYCv/7mCJxbswtaTyWDjJxGR6WIAIqPnam+JxUM7YcWIUHg3tcLNzAKM/uEQXvn2IBJv50ldHhERSYABiEzGo61dEPdWD4x9tCXMFTLEn03FE5/uwmfbLyAzv1jq8oiIqAGxE3QF2Ana+F1MzcYH607ivyu3AQB2SjMM6+qDl7s1h6Mth80TETVGjWomaEPEAGQaBEHAxmM3sOivi7iQmgMAsDJXYEhYM7za3Q9uKkuJKyQioupgAKolBiDTotEI+PN0ChbvuIgTSZkAAAuFHAM7e2F0Dz/4OHJ9MSKixoABqJYYgEyTIAj4+0I6Fv91EfuvirfG5DLg6SAPvPFoS7RytZO4QiIiuh8GoFpiAKL9V25j8Y6L2HU+Tbctsr0rxj7qjwAvTqZIRGSIGIBqiQGItE5cz8TiHRex9VSyblv3Vs4Y+2hLhDVvKmFlRER0LwagWmIAontdSMnGFzsvYeOxG1DfXVcs1LcJxjzaEj1aOUMmk0lcIRERMQDVEgMQVSbhVh6W7LqEXw9dR5FaAwAI8FRhzKMt8GQ7N8jlDEJERFJhAKolBiB6kOTMAiz/+zJW7b+GgmIxCPm72OKNR1ugX6AHzBScY5SIqKExANUSAxBV1a2cQqzYcxXf7r2K7LuLrDZrao3RPVpgYGdPKM0UEldIRGQ6GIBqiQGIqiuroBjf77uGr/+5gtu5RQAAV3slRj3ih/8LbwZrCzOJKyQiMn4MQLXEAEQ1lVdUgp/2J+LLvy8jOasAANDUxgIvd/PFS118obIyl7hCIiLjxQBUSwxAVFuFJWr8djgJS3ZeQsLdFee53hgRUf1iAKolBiCqKyVqDTYdv4nFO0rXG7M0l+vWG3NXWUlcIRGR8WAAqiUGIKprFa03Zq6Q4bnOXhjdowXXGyMiqgMMQLXEAET1heuNERHVHwagWmIAoobA9caIiOoWA1AtMQBRQ+J6Y0REdYMBqJYYgEgKXG+MiKh2GIBqiQGIpJRwKw9L/76EXw5yvTEioupgAKolBiAyBMmZBfhy92Ws+i8B+cVqAFxvjIjofhiAaokBiAwJ1xsjIqoaBqBaYgAiQ8T1xoiI7o8BqJYYgMiQ5Rep8dP+BCy/Z72xAR090drNDv4utmjpYgs7S647RkSmhQGolhiAqDGoaL2xstzsLdHybhhq6WKrC0Zch4yIjBUDUC0xAFFjUqLWYOupZBy4chsXUnNwMTUHqdmFle7f1MYCLZ1t0dLVFi2dbeHvKgYjN3tLDrUnokaNAaiWGICoscvML8bF1BxcTM3GxdQcXTC6fie/0mNslWZoUaalSPunVxNrKDj0nogaAQagWmIAImOVV1SCy2m5uKANRik5uJiWg2u38nSTL95LaSaHn7N+KPJ3sYWPow0szDgUn4gMBwNQLTEAkakpKtHg6q1cMRCliqHoQko2LqfnoqhEU+ExZnIZfByt7wYiO11foxbOtrCy4NB8Imp4jS4ALV68GHPnzkVycjKCgoKwcOFChIWFVbr/2rVrMWXKFFy9ehX+/v6YM2cO+vTpAwAoLi7G5MmTsXnzZly+fBkqlQoRERGYPXs2PDw8qlQPAxCRSK0RkHg7T+82mva2Wm6RusJjZDLAq4nV3f5FdqX9jVxsYc+RaURUjxpVAFqzZg2GDRuGpUuXIjw8HLGxsVi7di3OnTsHFxeXcvvv3bsX3bt3R0xMDJ566imsWrUKc+bMweHDh9GhQwdkZmbiueeew6hRoxAUFIQ7d+7gzTffhFqtxsGDB6tUEwMQ0f0JgoCbmQXlgtGF1Bxk5BVXepyrvVLXYlS2v5GjjQU7YBNRrTWqABQeHo7Q0FAsWrQIAKDRaODt7Y1x48Zh4sSJ5fYfNGgQcnNzsWnTJt22hx56CMHBwVi6dGmF1zhw4ADCwsJw7do1NGvW7IE1MQAR1YwgCLiVW6QLRpfuhqMLqdlIyap8ZJqDtbkuDLVwLh2676Gy4tpnRFRl1fn8lnTq2KKiIhw6dAiTJk3SbZPL5YiIiMC+ffsqPGbfvn2Ijo7W2xYZGYn169dXep3MzEzIZDI4ODjURdlEVAmZTAYnWyWcbJV4yM9R77WsAu3ItNLHhdRsXL+Tj4y8Yhy4egcHrt7RO8baQgE/ZxvxNlqZOY18HG1gzrXQiKgWJA1A6enpUKvVcHV11dvu6uqKs2fPVnhMcnJyhfsnJydXuH9BQQHee+89DBkypNI0WFhYiMLC0t9Os7KyqvM2iKgK7C3N0alZE3Rq1kRve36RGpfScnApLUevE/bV9FzkFalxMikLJ5P0/02W7YCtezjboYWLDZcEIaIqMer/KYqLi/HCCy9AEAQsWbKk0v1iYmIwY8aMBqyMiLSsLBTo4KlCB0+V3vZitQbXbokdsC+llbYaXUrLQV6RGpfScnEpLRfbTqXoHefpYIUWLrblWo2a2lg05NsiIgMnaQBycnKCQqFASor+f2ApKSlwc3Or8Bg3N7cq7a8NP9euXcNff/1133uBkyZN0rutlpWVBW9v7+q+HSKqQ+YKuS68lKXRCLiZVaDrX3Txbji6lJqDW7lFSMrIR1JGPv4+n6Z3XBNrc72h+uxnRGTaJA1AFhYW6Ny5M+Lj49G/f38AYifo+Ph4jB07tsJjunTpgvj4eEyYMEG3LS4uDl26dNE914afCxcuYMeOHXB0dKzgTKWUSiWUSq6PRNQYyOUyeDpYwdPBCt1bOeu9die3SBeIyj6SMvJxp5J+RlbmCrRwYT8jIlMj+S2w6OhoREVFISQkBGFhYYiNjUVubi5GjBgBABg2bBg8PT0RExMDAHjzzTfRo0cPzJ8/H3379sXq1atx8OBBLF++HIAYfp577jkcPnwYmzZtglqt1vUPatq0KSws2AxOZKya2Fgg1KYpQn2b6m3XzoCtF4zu9jPKL2Y/IyJTJPm/4kGDBiEtLQ1Tp05FcnIygoODsXXrVl1H54SEBMjlpb+Fde3aFatWrcLkyZPx/vvvw9/fH+vXr0eHDh0AAElJSdi4cSMAIDg4WO9aO3bsQM+ePRvkfRGR4bC2MKu0n1HC3YketbfRLqaJf+ZWsZ9R2dYjR1u2JBM1FpLPA2SIOA8QkWkrO9FjRf2MKlO2n1FLFzu0cbNDazc7ODEYETWIRjURoiFiACKiytyvn1FlnGwt0NrNDq1ctaHIHq1cbXkrjaiOMQDVEgMQEVVX2fmMLqbm4HxKNs4lZ+Pa7TxU9L+sTAY0a2qN1mVCUWs3O/g6WsOMna+JaoQBqJYYgIioruQVleBCSg7OJWfjbHI2zqVk4VxyNtJzKr6VZmEmh7+LLVq7lQajNm52cLFTcr00ogdgAKolBiAiqm/pOYWloShZDEXnU3KQX6yucH8Ha/N7Wots0crVDnaW5g1cOZHhYgCqJQYgIpKCRiMg8U7e3VCUfTcgZeFKei40lfxP7elgpetsLbYa2cPPmXMYkWliAKolBiAiMiQFxWpcTBVvo51LKW01SskqrHB/c4UMLZxty4QisdXIQ2XJ22hk1BrNavBERPRgluYVr5d2J7cI51Ky9W6lnU/JQU5hCc7e3VaWnaUZWrvqh6LWbnZQWfE2GpketgBVgC1ARNRYCYKA63fyy7UWXU7LRUkl99HcVZZ6rUWtXO3Q0sUWSjNFA1dPVDu8BVZLDEBEZGyKSjS4nF5mNNrdR2XzFynkMjR3Eme5bmJjDnsrc6jKPBysLPSe21macVFZkhwDUC0xABGRqcgqKMb5e0LR2eQsZBWUVOs8MhlgpzSDylo/KKmsyoene4MUwxPVFfYBIiKiKrG3NEeIb1OElFlAVhAEJGcV4GxyNq6l5yIjvxiZdx9ZZb7WPgqKNRAEIKugBFkFJUhE5bNiV6Sm4UlseTKHguGJaoABiIiI9MhkMrirrOCusgJaP3j/whJ1xeEorxiZ+SV62+7dJ79YXevwZKs0E1uTrCsPT3aW5rBQyKCQy2Eml8FMIYNCLoOZXA6FXAbzMs/NFDKYySt+bq6Q390u44i6Ro4BiIiIakVppoCLnQIudpbVPrawRI2s/BJk5hfVODxlF5Qgu6AE1+9ULzzVllwGmCnkZcKSTO952bCkuOe18vuWhrN7A5nibmAzk8tgoVDAx9Ea/q62aOFsC0tzdlSvKQYgIiKSjNJMAWc7BZztlNU+tqhEc9+AVPaRXVAMtUZAsVqAWiOgRCOgRK3RfS2+ptF7Tbu9stFzGkGsoeJFTeqfXAb4ONrA38UW/q7izOD+Lnbwc7ZhMKoCdoKuADtBExGRliAIekHp3vBUohZQoqn4eWngqvi5uK8AtUajF84qe65ddPd8SnalHdXlMsDX0UYXilq6iH/6OdsY/dQG7ARNRERUR2Syu7egDCg7CIKAtOxCnE8Rw9CF1Gzd19kFJbicnovL6bnYdipFd4xCLoOPozVaudihlastWrqKfzZ3Mv5gVBG2AFWALUBERNQYCYKA1OxCnE8RA9GFlGwxIKXkILuw4hYjhVwGX0dr8Raaqx3877YYNXeygYVZ41pTjvMA1RIDEBERGRNBEJCSpQ1GYiC6kHr/YGQml8HXyQatXG3h72Knu6Xm62i4wYgBqJYYgIiIyBRo53zSay1KzcGFu2vKVcTs7izhYouRGI5audrC18kG5gppgxEDUC0xABERkSkTBAE3Mwv0WovOp+TgYmrlwchcIQYjf1c7tNK1GNnCx7HhghEDUC0xABEREZUnCAJu6IKRGI7Op+bgYko2covUFR5jrpDBz6nsUH1b+LvawdfRGmZ1HIwYgGqJAYiIiKjqNBoBNzLz9VqLLty9nZZXSTDq3soZ370cVqd1cBg8ERERNRi5XAavJtbwamKNR9u46LZrNAKSMvJ1Ha7Pl+l87edkI2HFDEBERERUT+RyGbybWsO7qTUea+Oq267RCCgs0UhYGWCY49iIiIjIaMnlMlhZSDv5IgMQERERmRwGICIiIjI5DEBERERkchiAiIiIyOQwABEREZHJYQAiIiIik8MARERERCaHAYiIiIhMDgMQERERmRwGICIiIjI5DEBERERkchiAiIiIyOQwABEREZHJMZO6AEMkCAIAICsrS+JKiIiIqKq0n9vaz/H7YQCqQHZ2NgDA29tb4kqIiIiourKzs6FSqe67j0yoSkwyMRqNBjdu3ICdnR1kMlmdnjsrKwve3t5ITEyEvb19nZ6bqo/fD8PC74dh4ffD8PB7cn+CICA7OxseHh6Qy+/fy4ctQBWQy+Xw8vKq12vY29vzh9eA8PthWPj9MCz8fhgefk8q96CWHy12giYiIiKTwwBEREREJocBqIEplUpMmzYNSqVS6lII/H4YGn4/DAu/H4aH35O6w07QREREZHLYAkREREQmhwGIiIiITA4DEBEREZkcBiAiIiIyOQxADWjx4sXw9fWFpaUlwsPDsX//fqlLMlkxMTEIDQ2FnZ0dXFxc0L9/f5w7d07qsgjA7NmzIZPJMGHCBKlLMWlJSUl48cUX4ejoCCsrKwQEBODgwYNSl2WS1Go1pkyZgubNm8PKygotWrTAhx9+WKX1rqhyDEANZM2aNYiOjsa0adNw+PBhBAUFITIyEqmpqVKXZpJ27dqFMWPG4N9//0VcXByKi4vx5JNPIjc3V+rSTNqBAwewbNkyBAYGSl2KSbtz5w66desGc3NzbNmyBadPn8b8+fPRpEkTqUszSXPmzMGSJUuwaNEinDlzBnPmzMEnn3yChQsXSl1ao8Zh8A0kPDwcoaGhWLRoEQBxvTFvb2+MGzcOEydOlLg6SktLg4uLC3bt2oXu3btLXY5JysnJQadOnfDFF1/go48+QnBwMGJjY6UuyyRNnDgRe/bswe7du6UuhQA89dRTcHV1xddff63bNnDgQFhZWeGHH36QsLLGjS1ADaCoqAiHDh1CRESEbptcLkdERAT27dsnYWWklZmZCQBo2rSpxJWYrjFjxqBv3756/05IGhs3bkRISAief/55uLi4oGPHjvjyyy+lLstkde3aFfHx8Th//jwA4NixY/jnn3/Qu3dviStr3LgYagNIT0+HWq2Gq6ur3nZXV1ecPXtWoqpIS6PRYMKECejWrRs6dOggdTkmafXq1Th8+DAOHDggdSkE4PLly1iyZAmio6Px/vvv48CBAxg/fjwsLCwQFRUldXkmZ+LEicjKykKbNm2gUCigVqvx8ccfY+jQoVKX1qgxAJHJGzNmDE6ePIl//vlH6lJMUmJiIt58803ExcXB0tJS6nII4i8FISEhmDVrFgCgY8eOOHnyJJYuXcoAJIGff/4ZP/74I1atWoX27dvj6NGjmDBhAjw8PPj9qAUGoAbg5OQEhUKBlJQUve0pKSlwc3OTqCoCgLFjx2LTpk34+++/4eXlJXU5JunQoUNITU1Fp06ddNvUajX+/vtvLFq0CIWFhVAoFBJWaHrc3d3Rrl07vW1t27bFr7/+KlFFpu3dd9/FxIkTMXjwYABAQEAArl27hpiYGAagWmAfoAZgYWGBzp07Iz4+XrdNo9EgPj4eXbp0kbAy0yUIAsaOHYt169bhr7/+QvPmzaUuyWQ9/vjjOHHiBI4ePap7hISEYOjQoTh69CjDjwS6detWblqI8+fPw8fHR6KKTFteXh7kcv2Pa4VCAY1GI1FFxoEtQA0kOjoaUVFRCAkJQVhYGGJjY5Gbm4sRI0ZIXZpJGjNmDFatWoUNGzbAzs4OycnJAACVSgUrKyuJqzMtdnZ25fpe2djYwNHRkX2yJPLWW2+ha9eumDVrFl544QXs378fy5cvx/Lly6UuzST169cPH3/8MZo1a4b27dvjyJEjWLBgAV5++WWpS2vUOAy+AS1atAhz585FcnIygoOD8fnnnyM8PFzqskySTCarcPuKFSswfPjwhi2GyunZsyeHwUts06ZNmDRpEi5cuIDmzZsjOjoao0aNkrosk5SdnY0pU6Zg3bp1SE1NhYeHB4YMGYKpU6fCwsJC6vIaLQYgIiIiMjnsA0REREQmhwGIiIiITA4DEBEREZkcBiAiIiIyOQxAREREZHIYgIiIiMjkMAARERGRyWEAIiKqAplMhvXr10tdBhHVEQYgIjJ4w4cPh0wmK/fo1auX1KURUSPFtcCIqFHo1asXVqxYobdNqVRKVA0RNXZsASKiRkGpVMLNzU3v0aRJEwDi7aklS5agd+/esLKygp+fH3755Re940+cOIHHHnsMVlZWcHR0xKuvvoqcnBy9fb755hu0b98eSqUS7u7uGDt2rN7r6enpGDBgAKytreHv74+NGzfW75smonrDAERERmHKlCkYOHAgjh07hqFDh2Lw4ME4c+YMACA3NxeRkZFo0qQJDhw4gLVr12L79u16AWfJkiUYM2YMXn31VZw4cQIbN25Ey5Yt9a4xY8YMvPDCCzh+/Dj69OmDoUOH4vbt2w36PomojghERAYuKipKUCgUgo2Njd7j448/FgRBEAAIo0eP1jsmPDxceP311wVBEITly5cLTZo0EXJycnSv//HHH4JcLheSk5MFQRAEDw8P4YMPPqi0BgDC5MmTdc9zcnIEAMKWLVvq7H0SUcNhHyAiahQeffRRLFmyRG9b06ZNdV936dJF77UuXbrg6NGjAIAzZ84gKCgINjY2ute7desGjUaDc+fOQSaT4caNG3j88cfvW0NgYKDuaxsbG9jb2yM1NbWmb4mIJMQARESNgo2NTblbUnXFysqqSvuZm5vrPZfJZNBoNPVREhHVM/YBIiKj8O+//5Z73rZtWwBA27ZtcezYMeTm5upe37NnD+RyOVq3bg07Ozv4+voiPj6+QWsmIumwBYiIGoXCwkIkJyfrbTMzM4OTkxMAYO3atQgJCcHDDz+MH3/8Efv378fXX38NABg6dCimTZuGqKgoTJ8+HWlpaRg3bhxeeukluLq6AgCmT5+O0aNHw8XFBb1790Z2djb27NmDcePGNewbJaIGwQBERI3C1q1b4e7urretdevWOHv2LABxhNbq1avxxhtvwN3dHT/99BPatWsHALC2tsa2bdvw5ptvIjQ0FNbW1hg4cCAWLFigO1dUVBQKCgrw6aef4p133oGTkxOee+65hnuDRNSgZIIgCFIXQURUGzKZDOvWrUP//v2lLoWIGgn2ASIiIiKTwwBEREREJod9gIio0eOdfCKqLrYAERERkclhACIiIiKTwwBEREREJocBiIiIiEwOAxARERGZHAYgIiIiMjkMQERERGRyGICIiIjI5DAAERERkcn5f4woDyxbN5ucAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Trainig Loss')\n",
    "plt.plot(history.history['val_loss'],label='Validaton Loss')\n",
    "# Adding labels and title\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "# Adding a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred= model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred_classes = y_pred.argmax(axis=1)\n",
    "y_test_classes = y_test\n",
    "\n",
    "\n",
    "cm = confusion_matrix(y_test_classes, y_pred_classes)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False, xticklabels=range(10), yticklabels=range(10))\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix for MNIST')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Label: 2\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(2,2))\n",
    "plt.imshow(X_test[1],cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "predicted_label=y_pred[1].argmax()\n",
    "print('Predicted Label:',predicted_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "\n",
    "image_indices = [1503, 100, 155,200,886,302,400,500,\n",
    "                 550,247,384,1202,1521,2000,2504,3000]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "for i, index in enumerate(image_indices, 1):\n",
    "    plt.subplot(4, 4, i)\n",
    "    plt.imshow(X_test[index], cmap='gray')\n",
    "    plt.axis('off')\n",
    "    predicted_label = predictions[index].argmax()\n",
    "    plt.title(f'Predicted: {predicted_label}')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}