Week11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n",
      "2.5.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist=keras.datasets.fashion_mnist\n",
    "(X_train_full,y_train_full),(X_test,y_test)=fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_full.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid,X_train=X_train_full[:5000]/255.0,X_train_full[5000:]/255.0\n",
    "y_valid,y_train=y_train_full[:5000],y_train_full[5000:]\n",
    "X_test=X_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "hidden2 (Dense)              (None, 100)               30100     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=keras.models.Sequential([keras.layers.Flatten(input_shape=[28,28],name='flatten'),\n",
    "                              keras.layers.Dense(300,activation='relu',name='hidden1'),\n",
    "                              keras.layers.Dense(100,activation='relu',name='hidden2'),\n",
    "                              keras.layers.Dense(10,activation='softmax',name='output')])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.7069 - accuracy: 0.7696 - val_loss: 0.5008 - val_accuracy: 0.8324\n",
      "Epoch 2/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4851 - accuracy: 0.8315 - val_loss: 0.4410 - val_accuracy: 0.8488\n",
      "Epoch 3/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4398 - accuracy: 0.8466 - val_loss: 0.4298 - val_accuracy: 0.8536\n",
      "Epoch 4/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4128 - accuracy: 0.8552 - val_loss: 0.4216 - val_accuracy: 0.8548\n",
      "Epoch 5/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3909 - accuracy: 0.8620 - val_loss: 0.3914 - val_accuracy: 0.8636\n",
      "Epoch 6/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3765 - accuracy: 0.8673 - val_loss: 0.3804 - val_accuracy: 0.8650\n",
      "Epoch 7/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3631 - accuracy: 0.8731 - val_loss: 0.3642 - val_accuracy: 0.8742\n",
      "Epoch 8/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3512 - accuracy: 0.8767 - val_loss: 0.3559 - val_accuracy: 0.8740\n",
      "Epoch 9/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3417 - accuracy: 0.8782 - val_loss: 0.3593 - val_accuracy: 0.8710\n",
      "Epoch 10/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3317 - accuracy: 0.8816 - val_loss: 0.3368 - val_accuracy: 0.8804\n",
      "Epoch 11/30\n",
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.3233 - accuracy: 0.8847 - val_loss: 0.3349 - val_accuracy: 0.8800\n",
      "Epoch 12/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3164 - accuracy: 0.8876 - val_loss: 0.3278 - val_accuracy: 0.8818\n",
      "Epoch 13/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3078 - accuracy: 0.8894 - val_loss: 0.3295 - val_accuracy: 0.8844\n",
      "Epoch 14/30\n",
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.3008 - accuracy: 0.8926 - val_loss: 0.3244 - val_accuracy: 0.8840\n",
      "Epoch 15/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2939 - accuracy: 0.8942 - val_loss: 0.3224 - val_accuracy: 0.8818\n",
      "Epoch 16/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2874 - accuracy: 0.8959 - val_loss: 0.3186 - val_accuracy: 0.8870\n",
      "Epoch 17/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2816 - accuracy: 0.8982 - val_loss: 0.3092 - val_accuracy: 0.8902\n",
      "Epoch 18/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2755 - accuracy: 0.9010 - val_loss: 0.3215 - val_accuracy: 0.8866\n",
      "Epoch 19/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2703 - accuracy: 0.9019 - val_loss: 0.3028 - val_accuracy: 0.8922\n",
      "Epoch 20/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2666 - accuracy: 0.9040 - val_loss: 0.3073 - val_accuracy: 0.8894\n",
      "Epoch 21/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2615 - accuracy: 0.9053 - val_loss: 0.3080 - val_accuracy: 0.8880\n",
      "Epoch 22/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2557 - accuracy: 0.9066 - val_loss: 0.3106 - val_accuracy: 0.8860\n",
      "Epoch 23/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2522 - accuracy: 0.9084 - val_loss: 0.2966 - val_accuracy: 0.8962\n",
      "Epoch 24/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2473 - accuracy: 0.9110 - val_loss: 0.3237 - val_accuracy: 0.8856\n",
      "Epoch 25/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2417 - accuracy: 0.9135 - val_loss: 0.2955 - val_accuracy: 0.8902\n",
      "Epoch 26/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2384 - accuracy: 0.9143 - val_loss: 0.2987 - val_accuracy: 0.8930\n",
      "Epoch 27/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2348 - accuracy: 0.9152 - val_loss: 0.2999 - val_accuracy: 0.8894\n",
      "Epoch 28/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2306 - accuracy: 0.9159 - val_loss: 0.2957 - val_accuracy: 0.8928\n",
      "Epoch 29/30\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 0.2266 - accuracy: 0.9187 - val_loss: 0.2984 - val_accuracy: 0.8926\n",
      "Epoch 30/30\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.2220 - accuracy: 0.9207 - val_loss: 0.3330 - val_accuracy: 0.8784\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train,y_train,epochs=30,validation_data=(X_valid,y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aslAPgAVzehqOkYVknT0G+6RSHFNnY5s4ZUBP5lAmE+a0NMxpaRx7+1UIIYYGCeMk0+Ewwe0bCXy0isDmDQR27CKwv5pgbVtn16/S2DxhTFaNMmmUxYTJZkdlOlCOdGOAA6cH5UpDuTJQnkyUJxvlcLGzsoIp552Hffz4k+IkGiGEGIokjI9R/HKIjkfQmBIOdV/fWo+u2020eieBHTsI7D1AoKqZYH0IHe1oxWpsaRHsWYr009KxlxRhH3cKtvFTULmjIb0Y0oqMgSD6eXnEhvJy3PPnH78KEEIIccwkjHswRqypI1xbSyQ2Yk24ts6Y1tURrqs1Rq1pbEQHAn2OOtMXa5rClu/CM3kE9rGjsZ86GduUOZgKxoEzc2A/lBBCiEFtyIZxuLER75vLaHv3XcLV1fGwTTQ4AkphzsqKXy9pHzUKc5oLFW5FBRtRgQaUrxblq0GFWo2uZJMxMIPKLEZlj0DllKByR6MKxmGbPAdzevJGehFCCDG4DKkwDlVV0br0DVqXLqV99WqIRrHk52MbORL7qRNx5+bFA9eSZ0zNOblYsrNQZhNUrIHtr8H2/8DB9cZObYAnDfImQP4nIW8i5J1qLGcMl9ulCSGEOKyUD+PArt20Ll1K6+uv419vBKjtlLHkXHctaZ/8JI5Jk3ofnq69AXa+CStegx1Lwddg3ApuxFw450dQNM0I3/RiCV0hhBBHLeXCWGuNf9MmWl9/ndalSwnuMAaRcJSWkvetb5H2iU9gHzO6txdDzSbYFmv97n/PGNXJlQPjzoXx58LYRcZ1t0IIIcQASYkw1pEI1u3bqV65ktbXlxI6cABMJlxz5pB1xZWkfeIcrEVFiV8cbIfdy2Pdz69Dc+zeLIVT4cz/gnHnQfFM48bmQgghxHGQEmHsLS8n+/4HaLRacS9YQO6NN+JZtBBL1mFasDvegGeuhlCbcQP1sQvhrO8YreD0XsJbCCGEGGApEcbu+fNp+tpXOe36GzB73P17Ue02+NsSyCqB834KJQuMmxUIIYQQJ1hKhLHJ6SQwe3b/g7i9Af56BZit8IVnIHPk8S2gEEII0YeUCOMjEgnB3xZDcwUs/qcEsRBCiKQbemH86m3GCVuffhhGnp7s0gghhBCYkl2AE+r938GaP8D8m2H6F5JdGiGEEALoZxgrpc5XSm1VSu1QSn0vwfMZSql/KqU+UkptVEotGfiiHqOdbxqt4vHnwyfuSnZphBBCiLjDhrFSygw8BFwATAKuUkpN6rHZjcAmrfU0oAy4XyllG+CyHr26HfC3LxtDVH7u93LNsBBCiEGlPy3j04AdWutdWusg8DRwaY9tNJCmjHElPUADEB7Qkh4tX6Nx5rTJAlf91bj9oBBCCDGIKK113xsodRlwvtb6a7Hlq4G5WutvdNkmDXgJmAikAVdorV9OsK/rgOsACgoKZj399NMD9Tnwer14PJ7u7xeNULr+v8ls2shH035Mc+bkAXu/k0WiehFSL72ReklM6iUxqZfE+qqXhQsXrtVaz+65vj9nUye6A0LPBD8PWAcsAsYCryul3tZat3R7kdaPAo8CzJ49W5eVlfXj7funvLycQ/b3yneg8SO45NfMmHn1gL3XySRhvQipl15IvSQm9ZKY1EtiR1Mv/emmrgBGdFkeDhzosc0S4O/asAPYjdFKTp7Vf4D3H4V534AhGsRCCCFODv0J49XAOKXU6NhJWVdidEl3tQ84B0ApVQBMAHYNZEGPyK63jFbxuHPhkz9OWjGEEEKI/jhsN7XWOqyU+gbwGmAG/qi13qiUuj72/CPA3cBjSqn1GN3at2mt645juXtXvxOevQZyx8Hn/iBnTgshhBj0+jUCl9b6FeCVHuse6TJ/ADh3YIt2FHxN8NQVoExw1dPgSE92iYQQQojDSpnhMFU0As99BRp3wzUvQvboZBdJCCGE6JeUCeOxO/8ElW/AxQ/CqDOSXRwhhBCi31JjbOotLzO88p8w9waYtTjZpRFCCCGOSGqE8SmfZPsp18K5P0l2SYQQQogjlhphbLFROfwiMKdMr7sQQoghJDXCWAghhDiJSRgLIYQQSSZhLIQQQiSZhLEQQgiRZBLGQgghRJJJGAshhBBJJmEshBBCJJmEsRBCCJFkEsZCCCFEkkkYCyGEEEkmYSyEEEIkmYSxEEIIkWQSxkIIIUSSpUQYa62p80UJRaLJLooQQghxxFIijN/YXMO33/LxcUVzsosihBBCHLGUCONpIzIBWLOnIbkFEUIIIY5CSoRxXpqdApdizd7GZBdFCCGEOGIpEcYA47LMrN3biNY62UURQgghjkjqhHGmiYa2ILvq2pJdFCGEEOKIpE4YZ5kBWLtHuqqFEEKcXFImjIvciiyXldVyEpcQQoiTTMqEsVKKWSVZrJWTuIQQQpxkUiaMAWaPymZXXRv13kCyiyKEEEL0W2qFcUkWgFziJIQQ4qSSUmFcOjwDm8UkXdVCCCFOKikVxnaLmanFGXISlxBCiJNKSoUxwKxRWWyobMYfiiS7KEIIIUS/pFwYzynJJhTRctMIIYQQJ42UC+NZsZO4pKtaCCHEySLlwjjLbeOUfI+cxCWEEOKkkXJhDMYlTmv2NBCNyk0jhBBCDH4pGcazSrJo8YfZUetNdlGEEEKIw0rJMJ4zKhuQ48ZCCCFODikZxiU5LnI9NrmDkxBCiJNCSoaxUorZJdkyLKYQQoiTQkqGMcDsUVnsa2inpsWf7KIIIYQQfUrZMJ4lN40QQghxkkjZMJ48LAOH1SQncQkhhBj0UjaMbRYT04ZnyuAfQgghBr2UDWMwLnHaeKCF9mA42UURQgghetWvMFZKna+U2qqU2qGU+l4v25QppdYppTYqpd4a2GIenVmjsohENev2NSW7KEIIIUSvDhvGSikz8BBwATAJuEopNanHNpnAb4BLtNaTgcsHvqhHbubILJSSk7iEEEIMbv1pGZ8G7NBa79JaB4GngUt7bPMF4O9a630AWuuagS3m0clwWplQkCYncQkhhBjU+hPGxcD+LssVsXVdjQeylFLlSqm1SqlrBqqAx2pWSRYf7msiIjeNEEIIMUhZ+rGNSrCuZ7JZgFnAOYATWKmUWqW13tZtR0pdB1wHUFBQQHl5+REXuDderzfh/ty+MN5AmCf/9SYj080D9n4ni97qZaiTeklM6iUxqZfEpF4SO5p66U8YVwAjuiwPBw4k2KZOa90GtCmllgPTgG5hrLV+FHgUYPbs2bqsrOyICtuX8vJyEu1vbEM7j368DPLGUjZv1IC938mit3oZ6qReEpN6SUzqJTGpl8SOpl760029GhinlBqtlLIBVwIv9djmReBMpZRFKeUC5gKbj6gkx8nwLCeF6Q7WyE0jhBBCDFKHbRlrrcNKqW8ArwFm4I9a641Kqetjzz+itd6slPo38DEQBX6vtd5wPAveX0opZo3KYo2cxCWEEGKQ6k83NVrrV4BXeqx7pMfyfcB9A1e0gTO7JIuXP66isslHcaYz2cURQgghuknpEbg6zBmVDSCtYyGEEIPSkAjjiYVpuGxmGadaCCHEoDQkwthiNjFzZJacxCWEEGJQGhJhDMbgH1sOttDqDyW7KEIIIUQ3QyaM54zKJqrhQ7lphBBCiEFmyITx9JGZmJScxCWEEGLwGTJh7LFbOLUoXe7gJIQQYtAZMmEMRlf1uv1NhCLRZBdFCCGEiBtSYTyrJIv2YITNVS3JLooQQggRN6TCePaoLAC5xEkIIcSgMqTCuCjDSXGmkzV75SQuIYQQg8eQCmMwWsdr9jSidc9bMgshhBDJMQTDOJua1gAVjb5kF0UIIYQAhmIYlxjHjVfL9cZCCCEGiSEXxuML0khzWOR6YyGEEIPGkAtjs0nFbhohLWMhhBCDw5ALY4A5o7LYVu2luV1uGiGEECL5hmQYzyrJBuCDfdJVLYQQIvmGZBhPH5GJxaTkJC4hhBCDwpAMY6fNzOTiDDmJSwghxKCQMmFcE6o5ou1nl2Tx0f4mgmG5aYQQQojkSokwLt9fzk8O/IR/7/l3v18zZ1QWgXCUDQeaj1/BhBBCiH5IiTCeN2weo+2juePtO1hXs65fr+k4iUsucRJCCJFsKRHGdrOda/OupdBdyM1v3sz+lv2HfU1emp2SHJfcwUkIIUTSpUQYA3jMHn7zid8QJcrX3/g6zYHDdz/PLslm7V65aYQQQojkSpkwBihJL+GXC39JpbeSW5fdSijS96Aes0dlUd8W5P3d0lUthBAieVIqjAFmFczi7gV3s6Z6DXe+e2efrd5PTiqgONPJksdWs2zLkZ2NLYQQQgyUlAtjgE+N+RQ3Tr+Rf+76J498/Eiv2+V67Lxw43zG5Ln52hNr+Ov7+05gKYUQQghDSoYxwP+b+v+4ZOwl/Gbdb/jnzn/2ul1+moNnrpvHmeNyuf3v6/nFa1vlGLIQQogTKmXDWCnFXfPuYk7hHO58907WHFzT67Zuu4XfXzObK+eM4NfLdvBfz34kg4EIIYQ4YVI2jAGsZiv/W/a/FHuKuWXZLexu3t3rthaziZ99tpT/+uR4/v5hJUsee58Wv9zVSQghxPGX0mEMkGHP4Def+A0Wk4Ub37iRRn/v1xUrpbjpnHHcf/k03tvVwOcfWUlVs+8EllYIIcRQlPJhDDAibQQPLnqQmvYabll2C4FIoM/tPzdrOI8tOY2KRh+feehdNle1nKCSCiGEGIqGRBgDTMubxk/P+Ckf1nzID1f8kKju+5jwGeNy+dv18wD4/CMreWdH3YkophBCiCFoyIQxwHmjzuPWmbfy6p5X+fWHvz7s9qcWpfPCjfMZlulk8R/f5/m1FSeglEIIIYaaIRXGAF+Z8hU+N+5z/G7973hh+wuH3b4ow8nfbpjHaaOz+a+/fcSv3tgulz4JIYQYUEMujJVS3HH6HcwrmsePV/6YVVWrDvuadIeVx5acxmdmFHP/69u4/e/rCUfk0ichhBADw5LsAiSD1WTl/rL7uebVa7h12a2cW3IuU/OmMjVvKmMzxmI2mQ95jc1i4oHPT6M408mvl+3gYIufez83lfx0RxI+gRBCiFQyJMMYIM2Wxm/O+Q33vH8Pb+5/kxd2GF3WLouL0txSSvNKmZo7ldK8UnKduYDRqv72eRMYlunkRy9u4Kz7lvHl+aO5/uwxZLpsyfw4QgghTmJDNowBijxF/GrRr9Bas691Hx/XfsxHtR+xvm49j214jLAOA1DsKWZq3lSm5U2jNLeUy2ZPZMEpOfzf0u38dvlOnnxvL//vrDEsWTAat31IV6kQQoijIMmB0eItSS+hJL2Ei8deDIAv7GNz/WbW163no9qP+KD6A17d/SpgdHOfmn0qY4eP5YaRw3h/m4X7l1Xzp3d38Y2F4/nC3JHYLYd2dQshhBCJSBj3wmlxMrNgJjMLZsbXVbdVs75uPR/XfsyG+g0sr1hOvb8eAM8pENIW7tuQy4PrC5k3YhIXTJjKuOyxlKSX4LQ4k/VRhBBCDHISxkegwF1AgbuAT5R8Ir6uOdDM7ubd8ceaA1vYXL+D5bUf8XbdUwAoFMM8wxiVMYrxWeP5wsQvUOguTNbHEEIIMchIGB+jDHsG0/OnMz1/enyd1pqXN+znF2++Q4V3D4W5LRTmtFPffoA/V/2Zp7c8zddKv8biyYuxm+3JK7wQQohBQcL4OFBKcVHpSC6YPIIX11XywOvbKN/t47TR2dxzdjr/qfo9v/rwV/x9+9/57pzvsnDEQpRSyS62EEKIJOnXoB9KqfOVUluVUjuUUt/rY7s5SqmIUuqygSviyctsUnx25nDe/K8y7r50Mrvr2rjhsT3s2XwZXx5zD3aznVuW3cINS29gV/OuZBdXCCFEkhw2jJVSZuAh4AJgEnCVUmpSL9v9D/DaQBfyZGezmLh63ije+k4Zd148iab2EL962UTlxq8zN2MJ62o/4nMvfo5frP4F3qA32cUVQghxgvWnZXwasENrvUtrHQSeBi5NsN1NwPNAzQCWL6W4bBaWLBjNG986m8eWzKF0WBZLV02gfss3yVMLeGLTE1z0wkX8Y8c/DntXKSGEEKmjP8eMi4H9XZYrgLldN1BKFQOfARYBcwasdCnKZFKUTcinbEI+u+vaeGLlHp5bk0GbKsU28hV++M4PeXrLM9wx9/uU5pUmu7hCCCGOM3W4OxAppS4HztNafy22fDVwmtb6pi7b/A24X2u9Sin1GPAvrfVzCfZ1HXAdQEFBwaynn356wD6I1+vF4/EM2P5ONH9Y886BMEv3Bqg1f4iz4FWwtDLDMZfLci8h3Zx+VPs92evleJF6SUzqJTGpl8SkXhLrq14WLly4Vms9u+f6/oTxPOAurfV5seXbAbTWP+uyzW6g43TgXKAduE5r/Y/e9jt79my9Zs2aPt/7SJSXl1NWVjZg+0sWrTXv7KjnD+9u5t36Z7Bmr8CibHxm9GIWT72IkoyRR3TmdarUy0CTeklM6iUxqZfEpF4S66telFIJw7g/3dSrgXFKqdFAJXAl8IWuG2itR3d5o8cwWsb/6G/BRSelFGeMy+WMcWeyv2EWD61YycuVj/DcbuNhxUOJZyILRszk9OIZlOaWkmHPSHaxhRBCHIPDhrHWOqyU+gbGWdJm4I9a641Kqetjzz9ynMs4ZI3IdvHzS87hzmAZf167iqW732dr40a2+vewvXUtj282ejUKnCOYXTiNaflTmZo7lfFZ47GarUkuvRBCiP7q16AfWutXgFd6rEsYwlrrLx97sURXTpuZ6+Yt4Lp5C4hGNZsPtvD6ln28sXMN25s3UeHYx8HWt3h5978AsJpsTMo5lal5U7G0WZgRmCGtZyGEGMRkBK6TjMmkmDwsg8nDSrl1USneQJiVO+sp31pN+c5tVAe2E3TuZ6O/kvW1TxMlxBPP/Jk5hbNZNHIRC0cslHGxhRBikJEwPsl57BY+OamAT04qQOtS9tS3s3xbLW9tq2XljhoClr3Y0jazNrKFVVX3cM979zApezKfKDmHRSMXMSZjjAzFKYQQSSZhnEKUUozOdTM6183i+aMIhCP84cVy2j3n8O7OOj6u2Y7JvZENvk1saniQBz98kALncM4f/Qk+UXIOU/OmYlL9GiFVCCHEAJIwTmF2i5lJOWbKyiYAE2j1n8bqPQ28s6Oet3ftZHf7+1SmbeKx9j/z+KbHcJkzmVd4Fp+dcB6nDzsdm9mW7I8ghBBDgoTxEJLmsLJoYgGLJhYAk6j3nsuqXQ2U79jHO5UraNAfsDT4Gm9UvoQJG3n2kYzPGsv0wgmckjWWsZljGe4ZjtlkTvZHEUKIlCJhPITleOx8amoRn5paBMzlQJOP5dureGXHCjY0vE+l9wBV3pW8fbDz3h8WZaUkfRTjssYyJmMMYzLHMCZjDCXpJb22pEOREM3BZlqCLbQEWoxpj/mojnLB6AuYljftBH16IYQYPCSMRdywTCdXzhnDlXPGANdQ2eRjzZ4GVu6u5P3KLez37sZkq2Fbay17G9cQNr0GGNc6m5WZEWkjKE4rxh/2dwtbX9jX5/s6LU601jy5+Umm5k7lS5O+xCdKPoHVJNdKCyGGBglj0aviTCfF04u5dHoxcBot/hAf7mti7Z4GVu9pZF1FLQF1EJO9hsyMBtpUIzsC1WQ6PAzzDGdyTgbptnTjYU9PPG9Lx2q20h5q58WdL/Lk5if57vLvku/K56qJV3H5+MvlGmkhRMqTMBb9lu6wcvb4PM4enwdAKBJlc1ULq/c0snavEdC1rQEAlIKSbBenFqVzalE6uUXpnJqVRnGmM+GlVC6ri6smXsUVE65gReUK/rzpz/zyg1/y249+y8VjL+ZLp36JMZljTujnFUKIE0XCWBw1q9nE1OGZTB2eyVfPGI3WmopGH5urWthc1crmqhY2VbXw6oaD8dekOyxMLEpnUlE6pxalcWpROuML0nBYjZPCTMrEWcPP4qzhZ7GtcRtPbX6KF3e8yN+2/Y0FwxbwpUlfYv6w+XIJlhAipUgYiwGjlGJEtosR2S7Ondw5ypc3EGbrwRY2xQJ6c1ULz67ZT3swAoDZpBiT62bysHSmFGcYI4wVpzM+azx3zb+Lm2fezN+2/o2ntz7NDUtvYHTGaL506pe4aMxFuKyuZH1cIYQYMBLG4rjz2C3MKslmVkl2fF00qtnX0B4P501VLby3u4F/rDsQ36Ykx8WUWDBPGfZZ/nr+F1ldu4w/b/ozd6+6m19+8EsuGXsJBa4C7BY7DrMDh8WB3WzHYXHgMDuwW+w4zU7sFjt2sx2nxYndbE9GNQghRK8kjEVSmEyKUbluRuW6uaC0KL6+zhtg44EWNlQ2s/FAM+srm3l5fVX8+WEZLiYN+w6fyj3A/shrPLXlr0R15Ijf36qsFL9QzMj0kYxIG9HtUewplgFPhBjiDngP8M+d/+S6qdedkCGDJYzFoJLrsXc7SQyguT3ExqpmNla2sOFAMxsqm3ljiwWtPwWcT5pTMyrPRkmulRHZVoqyzeSnm0hzQDAawB/x4w/7CUQCBCIBfGEfm3dtxpRpYn/rftYcXEN7uD3+fgpFkbuIEWkjGJ42vFtgF7oKiegIwUgwvr+OR891XZejOkq2I5s8Zx65rlzynHlkO7KxmOS/oBCDidaa57c/z32r7wPgwtEXMiJ9xHF/X/klEINehsvK/LG5zB+bG1/XFgjHu7e3V3vZXtPKqm1t/MvrjW/jtJo5Jd/DuPwsTinwMC4/jVMKPIzMdvF241uUlZUBxn++en89Fa0V7G/dz/7W/exr3cf+1v0s27+MBn/DcflcJmXqDGhnLnkuY5rvzI8Hdo4zB5fFFe9+lxPXhDh+DrYd5M537+TdA+8yt3Au/73gvyn2FJ+Q95YwFiclt93C7FHZzB6V3W19Y1uQHbXeeEDvqPGyclc9f/+wMr6NzWIi36GZtG8NJTkuRma7GJnjZmT2OCaVTMVm6R543qA3HtI17TVYTBbsZjs2sw2H2YHNbIsv283GsemOY9Qd6xSKBn8Dte211Ppq49M6Xx017TXU+erY3LCZBn8DUR3t9XN3PR7utDjj892mFuO5DHsGmfbM+LTrvMfqSerdurTW7GvdR6W3kpn5M3FYHEkrSyqobqvmkY8fYV3NOq6YcAWfG/85GTTnCGit+ceOf3Dv6nuJ6Ag/mPsDLp9w+Qn941fCWKSULLeNOe5s5vQI6RZ/iJ01XrbXeNlZ42XV5r3srmvjrW21BMKd4WdSUJThNAI628XIWFiX5BRxWv5YMpzWow6xQnfhYe8lHY6GjdD21VLXXke9vx5f2BfvZveH/cZyxE8gHMAXMZ7zh/00BZqM+YiftlAb3qAXHRshrSeLspBuTz8kpL2NXry7vEzImsCojFED9oMeiUbY3rSdtdVrWVu9lg+qP6DeXw+Ax+rhkyWf5OKxFzOrYJa0/o9Ao7+RP6z/A09vfZqIjjA2Yyw/fe+nPLn5SW6deSuLRi6SW6QeRnVbNXetvIsVlSuYXTCbHy/4MSPSjn+3dE8SxmJISHdYmTEyixkjswAod1VTVnY20aim1htgX0M7e+vb2dfQzr76NvY1tPPGlhrqvIEe+7FwSr6H8QVp8em4Ag+F6Y4B+dGzmCzku/LJd+VDzrHtKxKN0BJsoSnQRHOgmaZAU6/zFd4KNtZtpN5Xz+tvvw6A1WTllMxTGJc1jglZE5iQPYEJWRPIdGQe9r2DkSAb6zfGw3ddzTq8IeMQQpG7iNOHnc6sglkUuAr4z57/8Nqe13hhxwsUuYu4aMxFXDT2IsZkyCAvvWkLtfHEpid4fOPjtIfauXjsxdww7QaKPcUsr1jOA2sf4NbyW5mRP4NvzfoW0/OnJ7vIg47Wmpd2vsT/vP8/hKIhvnfa97hq4lVJ+2NQwlgMaSaToiDdQUG645DWNBjHpvc3trMvFtS769rYUePlP5uqeXr1/vh2aXYLpxR4GJ9vhHNHUBdlDExIHw2zyUyWI4ssR1a/X/PGsjcYOX0kWxu3sq1hG1sbt/LugXd5aedL8W3ynfmMzx4fD+jxWePJc+WxoXYDa2uM8N1Qt4FAxPhDZkzGGM4ffT6zCmYxK38WRZ6ibu951vCzuOP0O1i2bxkv7XqJP2z4A79b/zsm50zm4rEXc/6o88lxHuNfJsdRMBKkPdROW7iNtlCbMR9qoz3cjkIxPX86uc7cw++oHwKRAM9seYbfr/89jYFGzhl5Dt+Y/g1OyTolvs3ZI85mQfEC/rHjHzy07iGufvVqPlnySW6ZeQsl6SUDUo6TXU17DT9e+WPeqniLmfkzuXvB3YxMH5nUMkkYC9EHt93CxMJ0JhamH/JcvTfAtmovO2pa2V7jZVt1K29sqeaZNZ0h7bEbLelT8j2MynFRkuOmJMdFSbabDNfgO6ZnVmbGZY1jXNY46NIwrffVdwvobY3bWHVgFWEdPuT1E7Mn8vkJn2dW/ixmFMwg23HoHzk9OS1OLhxzIReOuZA6Xx2v7HqFf+36Fz9//+fct/o+FhQv4OIxF1M2oqxfx5e11vjCPup99dT7jUeDv4HmQDPhaJiojhKOhonoSHw+qqNEdMR4RCPd5qtqq3jqP0/RFjbCtmv4hqPhw5ZnQtYE5g2bx7xh847qGHk4GualnS/x8EcPc7DtIHOL5nLLjFsozStNuL3FZOGy8Zdx4egLeXzT4/xpw59Ytm8Zl0+4nOunXd+vf5NUpLXm5d0v87P3fkYgEuC7c77LFyZ+YVDcFlbCWIijlOOxM89jZ97Y7q22hrYg26tb2VbjZUd1K9uqvby1rZbnWrt3eWc4rfETyEbluBmZ46Ik2wjs/DQ7JtPgOdaX48xhvnM+84fNj68LRULsat7F1sat1LTXMCl7EtPyp+G2uo/pvXKduVwz+RqumXwNOxp38M9d/+TlXS+zvGJ5/Phy2Ygy/GE/Df6GeNDW+4xpx7w/4u/zfczKjEmZsJgs3eZNyoRZmbvNB0IB8sJ5uC1u8p35uKwuXBYXbqsblzU2tbji8x3L/oif96veZ2XVSv6y+S88tvExbCYbMwtmMn/YfOYNm8f4rPG9do1GdZTX977Orz/8NXta9lCaW8rdC+7m9KLT+1WXLquLG6bdwOXjL+fhdQ/z7NZneWnnS3x1ylf50qQv4bQ4j/jf52RV56vj7pV38+b+N5mWN427F9zN6IzRyS5WnNI68Qkex9vs2bP1mjVrBmx/5eXl8UtVRCepl8SSUS/twXDnsen6dvbEjk3vrW+nsslHJNr5f9FuMcWC2h07gcwI7RHZLoZnOeNjeQ+0wfp9iUQjrKlewz93/pPX977e7bpwszKT7cgmx5lDtiPbmHfkkO2MTR3Z8flMe2Y8fI/k8MFA1Et7qJ211Wt598C7rKpaxY6mHQBkO7KZWzTXCOeieRS4C9Ba886Bd3jwgwfZ3LCZsRljuWnmTSwacWwnZO1q2sX/ffB/LNu/jHxXPt+Y/g0uGXvJEbcMQ5EQLcEW3lzxJjPnzCQcDROKhoxHJNR9OfYIR8OEIsa8RpNpz4wfRsm2Z5PpyDwufxyEIiGW7lvKPe/dQ3uonZtm3MTVk64+rq3hvr4vSqm1WuvZPddLy1iIE8Rl673LOxSJcqDJx976dvbGTiLbEwvtd3bU4Qt1jjKmFBSmOxgRO+O7JHbW94jYfLbblnJn0JpNZuYWzWVu0VzuOP0OtjZsJd2WTrYjm3R7+klxBrbL6uLM4Wdy5vAzAeO45aqqVUY4H1jFq7tfBYxj7B6bh49rP6bYU8xPz/gpnxr9qQEJjzGZY3hw0YOsObiGB9Y+wI/e/RF/3vxnvjLlK1hMFlqDrd0eLcGWQ9a1Blu79zq8eMzFinNanGTZs+Ih3XU+25GNy+rCF/IZVwuEvLSF2g6Z77iSoGM+GA0CUJpbyk8W/GTQ3v1NwliIQcBqNsWOJx/axau1ps4bNM70bmhjX72PvQ1t7G9o5+3ttTzX0r37220zx2/YMTzLyfCsjqkxn+EcfMeqj4TT4kyJs4PzXflcMvYSLhl7CVpr4zh8LJyr2qr4/tzvc9m4y7CaB/7fa3bhbJ688Ele2/sav1z7S25/+/Zuz1uUhTRbWrdHviufdFt6t3X7duxj2uRpWE1WrGYrFmXBarYayyYrFpMlPm81dy5rrWkKNNEYaKTRH3sEGmnwN9Dkb6IhYBxu2Nm0k0Z/Y6+HHBxmBy6rC4/VEz88UOgqxJ3pxm1x47a58Vg9FLmLuGD0BYN6xLvBWzIhBGDcDSsvzU5emp1ZJYeeGe0LRqhobO92edb+hs5WdcfdsTqkOSzxgB7RI6jbQhqtdcq1rAc7pZRx6Vj2BBZPXnzC3vP8Uedzzohz2NKwBafFGQ9ZpyXxfcd7Kj9YTtnosqN6/0xHJqMY1a9tfWEfjf5G2kJtncfkra6UGthEwliIk5zTZmZcQRrjCtIOeU5rTVN7iIpGH/sb26lobKei0UdFo4+99W2s2N69CxzA/fZrFGU6KcpwMCzDSVGmg6IMB0UZToZlGlO3XX46UoXVbO31rOzBwmlx4vSk9slm8j9KiBSmlCLLbSPLbaN0eMYhz2utaWwPUdHYzv4GH2+tXY8rp5iqZh9VzX62HGyltsdZ4GAMflIUD2onwzIcDMt0UpzlpDjTSWGGA6t58B/HFWKwkDAWYghTSpHttpHttjF1eCbuhq2UlU3utk0wHKW6xU9Vs5+qZh8HmvzxsK5q9rG+opn6tmCP/UJBmoNhmQ6Ks1wMy3QwPNPJsNijOMtJuiN1uhiFOFYSxkKIPtkspvgJYb3xhyJUNfs50OSjstFHZZPxONDkY31FE69t8BOMdL8BRprdQlGmIz4CWkG6nYJ0B/lpdvJj6/I89kNu3CFEKpIwFkIcM4fVzOhcN6NzEw/4EY1q6toCHGjyU9lohHRHWFe3BthZU0dNa4Bw9NBxD3LcNvJjId01sDNdNjJdVrJcNjKcVjJdVjx2i5x8Jk5KEsZCiOPOZFLkpznIT3MwfURmwm2iUU1De5DqFj81LQFj2mpMq1sC1LT62XKwhdrWAAkyGwCLSZHpspLhNELamLeR5TLCOtNli4W60fLO9diwyLFtMQhIGAshBgWTSZHrsZPrsTN5WO/bRaKahrYgTe1BmnwhmtpDNLYHaY5NjXVBmtpDVDb52XSghSZf6JBLvMA4tp3jtsdb3flpRnd5XrqDglho56fbE7bYhRhIEsZCiJOK2dR53fWR8IciNLWHqGntbGlXtwSo7bK84UALdd4APUcJVkDeyqXGJV/pDooyjcu+CjOMk9QKM5wUpNmllS2OmoSxEGJIcFjNFGaYKczo+45J4UiU+rYu3eWtft77eCuOrDyqmv1sr2ll+fbaQ1raJgV5aXbjkq/YddmFGXYynTYyYl3nmS6rsey04rCa5Pi2iBtUYRwKhaioqMDv7/tuK4lkZGSwefPm41Cqk9ux1IvD4WD48OFYrXIJihg6LGZT/Jhyh2LfbsrKpsWXtda0+MMcbPZzoNnHwWY/VU0+DjT7OdjsZ2t1K+Vbaw8ZUKUrm8VkBLSzM6gzYkGd5bLGW/95aUb3eY7HJtdup7BBFcYVFRWkpaUxatSoI/6LsbW1lbS0Q0cgGuqOtl601tTX11NRUcHo0YPnNmNCDAZKKTJiITqhMPH/L6013kCYpvYQzT7j0THf5Asa69o711U2+dlc1UpTe5C2BMe3AbLdxgloXYM6z2NcCmZM7RSmO2SEtJPQoPoX8/v9RxXEYuAppcjJyaG2tjbZRRHipKSUIs1hJc1hZcQRvjYQjlDnDVLbGqCmxU+tN2DMtxrT2tYAu2rbqG0NHHL9NnSOkFaYYQxlWpBuTAvj3ecO0h1yGdhgMqjCGJAvxyAi/xZCJIfdYqY40xhatC9aa1p8YWpa/dS2Gse3DzYHOBgbIe1gi59NVYlPSnPZzJ1hneYg3WklPdbaT3dYjGVHbNlpLHtsFkwm+V04HgZdGCebx+PB6/UmuxhCCHFYSinj5DCXNeGNQjoEw1FqWo3j2VXNXaYtRmi/t7uBFn+IVn+4z/czKUhzxMLZYSXq9/FMxdr48W7jBLXux78zY9d4O61m+QO/DxLGQgiR4mwWU+y2mb0PaQrGNdxef5gWv3Ecu8UfosUXosUX7pz3h43nfCH2tXvZUeONX9sdivR+PbbNbCKjS1hnuWzkeGyxsdHt5Lht8XHScz12stxW7BbzQFfFoCVh3AutNd/97nd59dVXUUrxgx/8gCuuuIKqqiquuOIKWlpaCIfDPPzww8yfP5+vfvWrrFmzBqUUX/nKV/jmN7+Z7I8ghBBHxGzqbGn35zh3eXk5ZWVnA8Zvpi92LXdTu3GSWouvY77jRLVgfJCWPfVtfLCvkYa2YK8jqqXZLWTFArprWGe5bWTHRljrWO4YFtV8knajD9ow/u9/bmTTgZZ+bx+JRDCb+/4ratKwdO68eHKf23T4+9//zrp16/joo4+oq6tjzpw5nHXWWTz11FOcd9553HHHHUQiEdrb21m3bh2VlZVs2LABgKampn6XWwghUoFSCpfNgstmYdhhjnV3FY1qmn0h6tuCNLQFaWgLGPPeYJd1QQ40+9lwoJnGtlDCk9aMMkCG00q2qyOgjRZ4R2s7x2OLj/KWm2YE+mAZqGXQhnGyrVixgquuugqz2UxBQQFnn302q1evZs6cOXzlK18hFArx6U9/munTpzNmzBh27drFTTfdxKc+9SnOPffcZBdfCCFOCiZT5z23+0NrTXswQmN7kMa2EA3txtCoDW1BGtuCNLYb6xrbglQ2+dlQ2UJDWzBhgCsFWS4buV1DOhbaebHAXnBK7gnpLh+0YdzfFmyHgb7OWPc89TDmrLPOYvny5bz88stcffXVfOc73+Gaa67ho48+4rXXXuOhhx7i2Wef5Y9//OOAlUUIIYRBKYXbbsFttzA8q3+v0VrTGghT1xqgzhukzhuIPWLzrcbyuv1N1HsD3a7zXn/XuUM7jJPtrLPO4re//S2LFy+moaGB5cuXc99997F3716Ki4u59tpraWtr44MPPuDCCy/EZrPxuc99jrFjx/LlL3852cUXQggRo5Qi3WFcqjUm7/DbtwfD1HuD1HoDeE7QACoSxr34zGc+w8qVK5k2bRpKKe69914KCwt5/PHHue+++7BarXg8Hp544gkqKytZsmQJ0ajRDfKzn/0syaUXQghxtFw2C65sCyOy+z77fCD1K4yVUucDvwTMwO+11j/v8fwXgdtii17gBq31RwNZ0BOl4xpjpRT33Xcf9913X7fnFy9ezOLFiw953QcffHBCyieEECL1HPY0MqWUGXgIuACYBFyllJrUY7PdwNla66nA3cCjA11QIYQQIlX155zu04AdWutdWusg8DRwadcNtNbvaq0bY4urgOEDW0whhBAidfWnm7oY2N9luQKY28f2XwVeTfSEUuo64DqAgoICysvLuz2fkZFBa2trP4p0qEgkctSvTWXHWi9+v/+Qf6dU4PV6U/JzHSupl8SkXhKTeknsaOqlP2GcaDiThNf9KKUWYoTxGYme11o/SqwLe/bs2bqsrKzb85s3bz7qy5PkFoqJHWu9OBwOZsyYMYAlGhyMkYPKkl2MQUfqJTGpl8SkXhI7mnrpTxhXQLeR0YYDB3pupJSaCvweuEBrXX9EpRBCCCGGsP4cM14NjFNKjVZK2YArgZe6bqCUGgn8Hbhaa71t4IsphBBCpK7Dtoy11mGl1DeA1zAubfqj1nqjUur62POPAD8CcoDfxG6RFdZazz5+xRZCCCFSR7+uM9ZavwK80mPdI13mvwZ8bWCLltrC4TAWi4y5IoQQon/d1EPOpz/9aWbNmsXkyZN59FHjkul///vfzJw5k2nTpnHOOecAxhlzS5YsobS0lKlTp/L8888D4PF44vt67rnn4sNjfvnLX+Zb3/oWCxcu5LbbbuP9999n/vz5zJgxg/nz57N161bAOAP629/+dny/v/rVr3jjjTf4zGc+E9/v66+/zmc/+9kTUR1CCCGOs8HbNHv1e3Bwfb83d0bCYD7MxykshQt+3vc2wB//+Eeys7Px+XzMmTOHSy+9lGuvvZbly5czevRoGhoaALj77rvJyMhg/XqjnI2NjX3tFoBt27axdOlSzGYzLS0tLF++HIvFwtKlS/n+97/P888/z6OPPsru3bv58MMPsVgsNDQ0kJWVxY033khtbS15eXn86U9/YsmSJYevGCGEEIPe4A3jJHrwwQd54YUXANi/fz+PPvooZ511FqNHjwYgOzsbgKVLl/L000/HX5eVdfhbiFx++eXx+y43NzezePFitm/fjlKKUCgU3+/1118f78bueL+rr76av/zlLyxZsoSVK1fyxBNPDNAnFkIIkUyDN4z70YLtyjdA1xmXl5ezdOlSVq5cicvloqysjGnTpsW7kLvSWhM7Ya2bruv8fn+359xud3z+hz/8IQsXLuSFF15gz5498evSetvvkiVLuPjii3E4HFx++eVyzFkIIVKEHDPuobm5maysLFwuF1u2bGHVqlUEAgHeeustdu/eDRDvpj733HP59a9/HX9tRzd1QUEBmzdvJhqNxlvYvb1XcXExAI899lh8/bnnnssjjzxCOBzu9n7Dhg1j2LBh/OQnP5HbNAohRAqRMO7h/PPPJxwOM3XqVH74wx9y+umnk5eXx6OPPspnP/tZpk2bxhVXXAHAD37wAxobG5kyZQrTpk1j2bJlAPz85z/noosuYtGiRRQVFfX6Xt/97ne5/fbbWbBgAZFI582sv/a1rzFy5EimTp3KtGnTeOqpp+LPffGLX2TEiBFMmtTzXh1CCCFOVtLP2YPdbufVVxMOrc0FF1zQbdnj8fD4448fst1ll13GZZdddsj6rq1fgHnz5rFtW+cYKXfffTcAFouFBx54gAceeOCQfaxYsYJrr732sJ9DCCHEyUPC+CQya9Ys3G43999/f7KLIoQQYgBJGJ9E1q5dm+wiCCGEOA7kmLEQQgiRZBLGQgghRJJJGAshhBBJJmEshBBCJJmEsRBCCJFkEsbHoOvdmXras2cPU6ZMOYGlEUIIcbKSMBZCCCGSbNBeZ/w/7/8PWxq29Hv7SCQSvxtSbyZmT+S2027r9fnbbruNkpISvv71rwNw1113oZRi+fLlNDY2EgqF+MlPfsKll17a73KBcbOIG264gTVr1sRH11q4cCEbN25kyZIlBINBotEozz//PMOGDePzn/88FRUVRCIRfvjDH8aH3xRCCJGaBm0YJ8OVV17JrbfeGg/jZ599ln//+99885vfJD09nbq6Ok4//XQuueSShHdV6s1DDz0EwPr169myZQvnnnsu27Zt45FHHuGWW27hi1/8IsFgkEgkwiuvvMKwYcN4+eWXAeNmEkIIIVLboA3jvlqwibQOwC0UZ8yYQU1NDQcOHKC2tpasrCyKior45je/yfLlyzGZTFRWVlJdXU1hYWG/97tixQpuuukmACZOnEhJSQnbtm1j3rx5/PSnP6WiooLPfvazjBs3jtLSUr797W9z2223cdFFF3HmmWce02cSQggx+Mkx4x4uu+wynnvuOZ555hmuvPJKnnzySWpra1m7di3r1q2joKDgkHsUH47WOuH6L3zhC7z00ks4nU7OO+883nzzTcaPH8/atWspLS3l9ttv58c//vFAfCwhhBCD2KBtGSfLlVdeybXXXktdXR1vvfUWzz77LPn5+VitVpYtW8bevXuPeJ9nnXUWTz75JIsWLWLbtm3s27ePCRMmsGvXLsaMGcPNN9/Mrl27+Pjjj5k4cSLZ2dl86UtfwuPxHHKnJyGEEKlHwriHyZMn09raSnFxMUVFRXzxi1/k4osvZvbs2UyfPp2JEyce8T6//vWvc/3111NaWorFYuGxxx7DbrfzzDPP8Je//AWr1UphYSE/+tGPWL16Nd/5zncwmUxYrVYefvjh4/AphRBCDCYSxgmsX78+Pp+bm8vKlSsTbuf1envdx6hRo9iwYQMADocjYQv39ttv5/bbb++27rzzzuO88847ilILIYQ4WckxYyGEECLJpGV8jNavX8/VV1/dbZ3dbue9995LUomEEEKcbCSMj1FpaSnr1q1LdjGEEEKcxKSbWgghhEgyCWMhhBAiySSMhRBCiCSTMBZCCCGSTML4GPR1P2MhhBCivySMU0A4HE52EYQQQhyDQXtp08F77iGwuf/3Mw5HIjQc5n7G9lMnUvj97/f6/EDez9jr9XLppZcmfN0TTzzBL37xC5RSTJ06lT//+c9UV1dz/fXXs2vXLgAefvhhhg0bxkUXXRQfyesXv/gFXq+Xu+66i7KyMubPn88777zDJZdcwvjx4/nJT35CMBgkJyeHJ598koKCArxeLzfffDNr1qxBKcWdd95JU1MTGzZs4H//938B+N3vfsfmzZt54IEHDl/RQgghBtygDeNkGMj7GTscDl544YVDXrdp0yZ++tOf8s4775Cbm0tDQwMAN998M2effTYvvPACkUgEr9dLY2Njn+/R1NTEW2+9BUBjYyOrVq1CKcXvf/977r33Xu6//37uvfdeMjIy4kN8NjY2YrPZmDp1Kvfeey9Wq5U//elP/Pa3vz3W6hNCCHGUBm0Y99WCTWSw3c9Ya833v//9Q1735ptvctlll5GbmwtAdnY2AG+++SZPPPEEAGazmYyMjMOG8RVXXBGfr6io4IorrqCqqopgMMjo0aMBKC8v59lnn41vl5WVBcCiRYv417/+xamnnkooFKK0tPQIa0sIIcRAGbRhnCwd9zM+ePDgIfcztlqtjBo1ql/3M+7tdVrrw7aqO1gsFqLRaHy55/u63e74/E033cS3vvUtLrnkEsrLy7nrrrsAen2/r33ta9xzzz1MnDiRJUuW9Ks8Qgghjg85gauHK6+8kqeffprnnnuOyy67jObm5qO6n3FvrzvnnHN49tlnqa+vB4h3U59zzjnx2yVGIhFaWlooKCigpqaG+vp6AoEA//rXv/p8v+LiYgAef/zx+PpFixbx61//Or7c0dqeO3cu+/fv56mnnuKqq67qb/UIIYQ4DiSMe0h0P+M1a9Ywe/ZsnnzyyX7fz7i3102ePJk77riDs88+m2nTpvGtb30LgF/+8pcsW7aM0tJSZs2axcaNG7FarfzoRz9i7ty5XHTRRX2+91133cXll1/OmWeeGe8CB/jOd75DY2MjU6ZMYdq0aSxbtiz+3Oc//3kWLFgQ77oWQgiRHNJNncBA3M+4r9ctXryYxYsXd1tXUFDAiy++eMi2N998MzfffPMh68vLy7stX3rppQnP8vZ4PN1ayl2tWLGCb37zm719BCGEECeItIyHoKamJsaPH4/T6eScc85JdnGEEGLIk5bxMToZ72ecmZnJtm3bkl0MIYQQMRLGx0juZyyEEOJYDbpuaq11sosgYuTfQgghToxBFcYOh4P6+noJgUFAa019fT0OhyPZRRFCiJQ3qLqphw8fTkVFBbW1tUf8Wr/fL8GRwLHUi8PhYPjw4QNcIiGEED31K4yVUucDvwTMwO+11j/v8byKPX8h0A58WWv9wZEWxmq1xodxPFLl5eXMmDHjqF6byqRehBBi8DtsN7VSygw8BFwATAKuUkpN6rHZBcC42OM64OEBLqcQQgiRsvpzzPg0YIfWepfWOgg8DfQcXeJS4AltWAVkKqWKBrisQgghRErqTxgXA/u7LFfE1h3pNkIIIYRIoD/HjBPdYqjn6c792Qal1HUY3dgAXqXU1n68f3/lAnUDuL9UIfWSmNRLYlIviUm9JCb1klhf9VKSaGV/wrgCGNFleThw4Ci2QWv9KPBoP97ziCml1mitZx+PfZ/MpF4Sk3pJTOolMamXxKReEjuaeulPN/VqYJxSarRSygZcCbzUY5uXgGuU4XSgWWtddSQFEUIIIYaqw7aMtdZhpdQ3gNcwLm36o9Z6o1Lq+tjzjwCvYFzWtAPj0ia5W70QQgjRT/26zlhr/QpG4HZd90iXeQ3cOLBFO2LHpfs7BUi9JCb1kpjUS2JSL4lJvSR2xPWiZOhJIYQQIrkG1djUQgghxFCUEmGslDpfKbVVKbVDKfW9ZJdnsFBK7VFKrVdKrVNKrUl2eZJFKfVHpVSNUmpDl3XZSqnXlVLbY9OsZJYxGXqpl7uUUpWx78w6pdSFySxjMiilRiillimlNiulNiqlbomtH9LfmT7qZUh/Z5RSDqXU+0qpj2L18t+x9Uf0fTnpu6ljw3VuAz6JcYnVauAqrfWmpBZsEFBK7QFma62H9HWASqmzAC/GKHFTYuvuBRq01j+P/QGXpbW+LZnlPNF6qZe7AK/W+hfJLFsyxUYPLNJaf6CUSgPWAp8GvswQ/s70US+fZwh/Z2L3ZnBrrb1KKSuwArgF+CxH8H1JhZZxf4brFEOY1no50NBj9aXA47H5xzF+VIaUXuplyNNaV3Xc6EZr3QpsxhhRcEh/Z/qolyEtNgy0N7ZojT00R/h9SYUwlqE4e6eB/yil1sZGPxOdCjquhY9N85NcnsHkG0qpj2Pd2EOqK7YnpdQoYAbwHvKdietRLzDEvzNKKbNSah1QA7yutT7i70sqhHG/huIcohZorWdi3FXrxli3pBB9eRgYC0wHqoD7k1qaJFJKeYDngVu11i3JLs9gkaBehvx3Rmsd0VpPxxh98jSl1JQj3UcqhHG/huIcirTWB2LTGuAFjC59YajuuLNYbFqT5PIMClrr6tgPSxT4HUP0OxM79vc88KTW+u+x1UP+O5OoXuQ700lr3QSUA+dzhN+XVAjj/gzXOeQopdyxkyxQSrmBc4ENfb9qSHkJWBybXwy8mMSyDBo9bn36GYbgdyZ2Qs4fgM1a6we6PDWkvzO91ctQ/84opfKUUpmxeSfwCWALR/h9OenPpgaInUr/f3QO1/nT5JYo+ZRSYzBaw2CMtPbUUK0XpdRfgTKMO6lUA3cC/wCeBUYC+4DLtdZD6mSmXuqlDKO7UQN7gP831MaZV0qdAbwNrAeisdXfxzg+OmS/M33Uy1UM4e+MUmoqxglaZowG7rNa6x8rpXI4gu9LSoSxEEIIcTJLhW5qIYQQ4qQmYSyEEEIkmYSxEEIIkWQSxkIIIUSSSRgLIYQQSSZhLIQQQiSZhLEQQgiRZBLGQgghRJL9f74TMTMBlQsNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.3664 - accuracy: 0.8669\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3663526475429535, 0.8669000267982483]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.   0.   0.   0.   0.   0.   0.01 0.   0.99]\n",
      " [0.   0.   0.92 0.   0.07 0.   0.01 0.   0.   0.  ]\n",
      " [0.   1.   0.   0.   0.   0.   0.   0.   0.   0.  ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Ankle boot', 'Pullover', 'Trouser'], dtype='<U11')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "X_new=X_test[:3]\n",
    "y_prob=model.predict(X_new)\n",
    "print(y_prob.round(2))\n",
    "y_pred=np.argmax(model.predict(X_new),axis=1)\n",
    "np.array(class_names)[y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ID:1912360\n",
    "## NAME: ANCHAERIN\n",
    "plt.figure(figsize=(10,10))\n",
    "for img in range(3):\n",
    "    plt.subplot(1,3,img+1)\n",
    "    plt.grid(False)\n",
    "    plt.imshow(X_new[img])\n",
    "    plt.xlabel(class_names[y_pred[img]])"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}