15. CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9e0b3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30b1f80f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cov1d(x,w,p = 0,s=1):#1차원 합성곱\n",
    "    w_rot = np.array(w[::-1])#필터 뒤집기\n",
    "    x_padded = np.array(x)\n",
    "    \n",
    "    if p>0:\n",
    "        zero_pad = np.zeros(shape=p)#제로 패딩\n",
    "        x_padded = np.concatenate([zero_pad,\n",
    "                                    x_padded,\n",
    "                                    zero_pad])#입력에 패딩 씌움(앞 뒤로 패딩 추가)\n",
    "        res = []\n",
    "        for i in range(0,int(len(x)/s),s):#스트라이드 만큼 옮김\n",
    "            res.append(np.sum(x_padded[i:i+w_rot.shape[0]]*\n",
    "                             w_rot))#곱 계산\n",
    "        return np.array(res)#배열로 반환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5386c286",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 테스트\n",
    "x = [1,3,2,4,5,6,1,3]\n",
    "w= [1,0,3,1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f15cc25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov1d 구현: [ 5. 14. 16. 26. 24. 34. 19. 22.]\n"
     ]
    }
   ],
   "source": [
    "print('Cov1d 구현:',\n",
    "     cov1d(x,w,p=2,s=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9172e20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "넘파이 결과: [ 5 14 16 26 24 34 19 22]\n"
     ]
    }
   ],
   "source": [
    "print('넘파이 결과:',\n",
    "     np.convolve(x,w,mode='same'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6467884b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52f29107",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cov2d(X,W,p=(0,0),s=(1,1)):#2D 합성곱 계산\n",
    "    W_rot = np.array(W)[::-1,::-1]\n",
    "    X_orig = np.array(X)\n",
    "    n1 = X_orig.shape[0]+2*p[0]#(원본+패딩) 세로 길이\n",
    "    n2 = X_orig.shape[1]+2*p[1]#(원본+패딩) 가로 길이\n",
    "    X_padded = np.zeros(shape=(n1,n2))#제로 패딩 생성\n",
    "    X_padded[p[0]:p[0]+X_orig.shape[0],\n",
    "            p[1]:p[1]+X_orig.shape[1]]=X_orig#입력에 패딩 씌움\n",
    "    res=[]\n",
    "    for i in range(0,int((X_padded.shape[0]-\n",
    "                         W_rot.shape[0])/s[0])+1,s[0]):#스트라이드만큼 세로로 움직임\n",
    "        res.append([])#row 곱\n",
    "        for j in range(0,int((X_padded.shape[1]-W_rot.shape[1])/s[1])+1,s[1]):#스트라이드 만큼 가로로 움직임\n",
    "            X_sub = X_padded[i:i+W_rot.shape[0],j:j+W_rot.shape[1]]\n",
    "            res[-1].append(np.sum(X_sub*W_rot))#합성곱 계싼(마지막 row에 현재 곱 append)\n",
    "    return(np.array(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4155ace8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [[1,3,2,4],[5,6,1,3],[1,2,0,2],[3,4,3,2]]\n",
    "W = [[1,0,3],[1,2,1],[0,1,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d1d6731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov2d 구현:\n",
      " [[11. 25. 32. 13.]\n",
      " [19. 25. 24. 13.]\n",
      " [13. 28. 25. 17.]\n",
      " [11. 17. 14.  9.]]\n"
     ]
    }
   ],
   "source": [
    "print('Cov2d 구현:\\n',\n",
    "     cov2d(X,W,p=(1,1),s=(1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5cc1b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "사이파이 결과:\n",
      " [[11 25 32 13]\n",
      " [19 25 24 13]\n",
      " [13 28 25 17]\n",
      " [11 17 14  9]]\n"
     ]
    }
   ],
   "source": [
    "print('사이파이 결과:\\n',\n",
    "     scipy.signal.convolve2d(X,W,mode='same'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0476501b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imageio\n",
      "  Downloading imageio-2.9.0-py3-none-any.whl (3.3 MB)\n",
      "Requirement already satisfied: pillow in c:\\anaconda3\\envs\\dsfs\\lib\\site-packages (from imageio) (8.2.0)\n",
      "Requirement already satisfied: numpy in c:\\anaconda3\\envs\\dsfs\\lib\\site-packages (from imageio) (1.19.5)\n",
      "Installing collected packages: imageio\n",
      "Successfully installed imageio-2.9.0\n"
     ]
    }
   ],
   "source": [
    "#파이썬에서 이미지 읽기\n",
    "!pip install imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a186d28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#파이썬에서 이미지 읽기\n",
    "import imageio\n",
    "img = imageio.imread('./example-image.png',pilmode='RGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e76ca7ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "이미지 크기: (252, 221, 3)\n"
     ]
    }
   ],
   "source": [
    "print('이미지 크기:',img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2508082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "채널 수: 3\n"
     ]
    }
   ],
   "source": [
    "print('채널 수:',img.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "072975b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "이미지 데이터 타입: uint8\n"
     ]
    }
   ],
   "source": [
    "print('이미지 데이터 타입:',img.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a8d7b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[179 134 110]\n",
      "  [182 136 112]]\n",
      "\n",
      " [[180 135 111]\n",
      "  [182 137 113]]]\n"
     ]
    }
   ],
   "source": [
    "print(img[100:102, 100:102, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d8ebbd4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#가져옴\n",
    "import struct\n",
    " \n",
    "def load_mnist(path, kind='train'):\n",
    "    \"\"\"`path`에서 MNIST 데이터 적재하기\"\"\"\n",
    "    labels_path = os.path.join(path, \n",
    "                               '%s-labels-idx1-ubyte' % kind)\n",
    "    images_path = os.path.join(path, \n",
    "                               '%s-images-idx3-ubyte' % kind)\n",
    "        \n",
    "    with open(labels_path, 'rb') as lbpath:\n",
    "        magic, n = struct.unpack('>II', \n",
    "                                 lbpath.read(8))\n",
    "        labels = np.fromfile(lbpath, \n",
    "                             dtype=np.uint8)\n",
    "\n",
    "    with open(images_path, 'rb') as imgpath:\n",
    "        magic, num, rows, cols = struct.unpack(\">IIII\", \n",
    "                                               imgpath.read(16))\n",
    "        images = np.fromfile(imgpath, \n",
    "                             dtype=np.uint8).reshape(len(labels), 784)\n",
    "        images = ((images / 255.) - .5) * 2\n",
    " \n",
    "    return images, labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3bb17bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mnist 폴더 없어서\n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a819f726",
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_X, train_y), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "409541f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(train_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7d3e9d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "31e405d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train_X.reshape([60000,28*28])\n",
    "X_test = X_test.reshape([10000,28*28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "de5223ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(train_X.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2c0e8b0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_y.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "50de80ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = train_X[:50000,:],train_y[:50000]\n",
    "X_valid, y_valid = train_X[50000:,:], train_y[50000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bf168c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트: (50000, 784) (50000,)\n"
     ]
    }
   ],
   "source": [
    "print('훈련 세트:',X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e140b519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "검증 세트: (10000, 784) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('검증 세트:',X_valid.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ca815c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 세트: (10000, 784) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('테스트 세트:',X_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "87cb1af4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_vals = np.mean(X_train, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "62256633",
   "metadata": {},
   "outputs": [],
   "source": [
    "std_val = np.std(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5dd5c837",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_centered = (X_train - mean_vals)/std_val\n",
    "X_valid_centered = (X_valid - mean_vals)/std_val\n",
    "X_test_centered = (X_test - mean_vals)/std_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "95a08c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_centered = X_train_centered.reshape((-1,28,28,1))\n",
    "X_valid_centered = X_valid_centered.reshape((-1,28,28,1))\n",
    "X_test_centered = X_test_centered.reshape((-1,28,28,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ff1f6dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train)\n",
    "y_valid_onehot = to_categorical(y_valid)\n",
    "y_test_onehot = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8f40c0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(y_train[0],y_train_onehot[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e9ec595e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트: (50000, 28, 28, 1) (50000, 10)\n",
      "검증 세트: (10000, 28, 28, 1) (10000, 10)\n",
      "테스트 세트: (10000, 28, 28, 1) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('훈련 세트:',X_train_centered.shape, y_train_onehot.shape)\n",
    "print('검증 세트:',X_valid_centered.shape, y_valid_onehot.shape)\n",
    "print('테스트 세트:',X_test_centered.shape, y_test_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1f1a5288",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1ee32a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "22ed5f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Conv2D(32,(5,5),padding = 'valid',\n",
    "                      activation = 'relu', input_shape = (28,28,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b2980060",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.MaxPool2D(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5676ae85",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Conv2D(64,(5,5),padding = 'valid',\n",
    "                       activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f9368951",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.MaxPool2D(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a5871b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6bf7b107",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Dense(1024, activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9f11336e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Dropout(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "02f2c256",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Dense(10,activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d957d7fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 8, 64)          51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 1,111,946\n",
      "Trainable params: 1,111,946\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b10dc061",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'categorical_crossentropy',\n",
    "             optimizer = 'adam', metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e17abcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4727d0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "callback_list = [ModelCheckpoint(filepath = 'cnn_checkpoint.h5',\n",
    "                                monitor = 'val_loss',\n",
    "                                save_best_only=True),\n",
    "                TensorBoard(log_dir = 'logs/{}'.format(str(time.asctime())[:5]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "039e30e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "782/782 [==============================] - 40s 49ms/step - loss: 0.1391 - acc: 0.9573 - val_loss: 0.0546 - val_acc: 0.9842\n",
      "Epoch 2/20\n",
      "782/782 [==============================] - 38s 49ms/step - loss: 0.0495 - acc: 0.9849 - val_loss: 0.0446 - val_acc: 0.9865\n",
      "Epoch 3/20\n",
      "782/782 [==============================] - 38s 48ms/step - loss: 0.0355 - acc: 0.9889 - val_loss: 0.0363 - val_acc: 0.9897\n",
      "Epoch 4/20\n",
      "782/782 [==============================] - 38s 48ms/step - loss: 0.0292 - acc: 0.9911 - val_loss: 0.0455 - val_acc: 0.9886\n",
      "Epoch 5/20\n",
      "782/782 [==============================] - 38s 48ms/step - loss: 0.0235 - acc: 0.9927 - val_loss: 0.0454 - val_acc: 0.9880\n",
      "Epoch 6/20\n",
      "782/782 [==============================] - 40s 52ms/step - loss: 0.0202 - acc: 0.9936 - val_loss: 0.0414 - val_acc: 0.9908\n",
      "Epoch 7/20\n",
      "782/782 [==============================] - 49s 63ms/step - loss: 0.0168 - acc: 0.9946 - val_loss: 0.0385 - val_acc: 0.9910\n",
      "Epoch 8/20\n",
      "782/782 [==============================] - 54s 70ms/step - loss: 0.0169 - acc: 0.9946 - val_loss: 0.0372 - val_acc: 0.9905\n",
      "Epoch 9/20\n",
      "782/782 [==============================] - 49s 63ms/step - loss: 0.0162 - acc: 0.9952 - val_loss: 0.0373 - val_acc: 0.9920\n",
      "Epoch 10/20\n",
      "782/782 [==============================] - 46s 58ms/step - loss: 0.0165 - acc: 0.9949 - val_loss: 0.0421 - val_acc: 0.9920\n",
      "Epoch 11/20\n",
      "782/782 [==============================] - 55s 70ms/step - loss: 0.0117 - acc: 0.9962 - val_loss: 0.0407 - val_acc: 0.9915\n",
      "Epoch 12/20\n",
      "782/782 [==============================] - 45s 58ms/step - loss: 0.0132 - acc: 0.9959 - val_loss: 0.0523 - val_acc: 0.9916\n",
      "Epoch 13/20\n",
      "782/782 [==============================] - 40s 51ms/step - loss: 0.0127 - acc: 0.9962 - val_loss: 0.0485 - val_acc: 0.9904\n",
      "Epoch 14/20\n",
      "782/782 [==============================] - 39s 50ms/step - loss: 0.0085 - acc: 0.9973 - val_loss: 0.0629 - val_acc: 0.9875\n",
      "Epoch 15/20\n",
      "782/782 [==============================] - 38s 49ms/step - loss: 0.0140 - acc: 0.9958 - val_loss: 0.0517 - val_acc: 0.9905\n",
      "Epoch 16/20\n",
      "782/782 [==============================] - 38s 49ms/step - loss: 0.0114 - acc: 0.9966 - val_loss: 0.0589 - val_acc: 0.9912\n",
      "Epoch 17/20\n",
      "782/782 [==============================] - 39s 49ms/step - loss: 0.0095 - acc: 0.9972 - val_loss: 0.0627 - val_acc: 0.9914\n",
      "Epoch 18/20\n",
      "782/782 [==============================] - 41s 52ms/step - loss: 0.0131 - acc: 0.9968 - val_loss: 0.0856 - val_acc: 0.9890\n",
      "Epoch 19/20\n",
      "782/782 [==============================] - 40s 51ms/step - loss: 0.0109 - acc: 0.9970 - val_loss: 0.0756 - val_acc: 0.9898\n",
      "Epoch 20/20\n",
      "782/782 [==============================] - 40s 51ms/step - loss: 0.0079 - acc: 0.9979 - val_loss: 0.0695 - val_acc: 0.9889\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_centered, y_train_onehot,\n",
    "                   batch_size=64, epochs=20,\n",
    "                   validation_data=(X_valid_centered,y_valid_onehot),\n",
    "                   callbacks=callback_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7854dcf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "epochs = np.arange(1,21)\n",
    "plt.plot(epochs, history.history['loss'])\n",
    "plt.plot(epochs, history.history['val_loss'])\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3f9e2010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, history.history['acc'])\n",
    "plt.plot(epochs, history.history['val_acc'])\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b9589c15",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('cnn_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0d698d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "restored_model = load_model('cnn_model.h5')\n",
    "restored_modell.load_weights('cnn_checkpoint.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "eeeb21d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 8ms/step - loss: 0.0326 - acc: 0.9892\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.032633304595947266, 0.9891999959945679]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restored_model.evaluate(X_test_centered, y_test_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "208dd892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=int64)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(restored_model.predict(X_test_centered[:10]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d3d7166a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=uint8)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "537acc83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "for i in range(10):\n",
    "    fig.add_subplot(2,5,i+1)\n",
    "    plt.imshow(X_test_centered[i].reshape(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "1fe8ae85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x000001E80435EE80>\n"
     ]
    }
   ],
   "source": [
    "#활성화 출력과 필터 시각화\n",
    "\n",
    "first_layer = model.layers[0]\n",
    "print(first_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "a65af3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KerasTensor(type_spec=TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='conv2d_6_input'), name='conv2d_6_input', description=\"created by layer 'conv2d_6_input'\")\n"
     ]
    }
   ],
   "source": [
    "print(model.input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "a79027cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_activation = models.Model(inputs = model.input,\n",
    "                               outputs = first_layer.output)\n",
    "\n",
    "activation = first_activation.predict(X_test_centered[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "4168c124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 24, 24, 32)\n"
     ]
    }
   ],
   "source": [
    "print(activation.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "cb8dd833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J0hnqn9LfIunbkmbk1Nv71X+Y8TlJ66p/rk+ov6TyTDnL1PNMLcvU80w5yxTzTDnL1PNMLcvU82xllpyJHAAAIBJD5AAAAJFYQAEAAERiAQUAABCJBRQAAEAkFlAAAACRWEABAABEqmsBZWbXmdkmM9tqZnleSRsNQJ7FQZbFQp7FQZbFMerzQJlZSf0n97pG/SfSelLSjSGEDYM9Z5x1hx5NHNX2UL8TOqpT4aSd7r7YPMkyX43MUiLPvLFvFgf7ZrEMlWdnHa97maStIYSXJMnMvi7pBkmDfiP0aKIut6vr2CTqsSasHuruqDzJMl+NzFIiz7yxbxYH+2axDJVnPR/hzZe0fUC9o3qbY2Y3m9laM1vbq5N1bA5NNmyeZNk22DeLhX2zONg3C6SeI1AjEkJYKWmlJE2xGVw3po2RZbGQZ3GQZbGMuTzNf0Jm48b5+8tlV4a+vmZ3NCL1HIHaKWnhgHpB9Ta0J/IsDrIsFvIsDrIskHoWUE9KWmJmZ5vZOEmflHR/Y9pCDsizOMiyWMizOMiyQEb9EV4Ioc/MPivpQUklSXeGENY3rDO0FHkWB1kWC3kWB1kWS10zUCGEByQ90KBekDPyLA6yLBbyLI6iZ1maeYarw4I5NY85MWeCq3snl/xzOvxMVOfxiqu7D5xyddeug64uv7rDv16TZqY4EzkAAEAkFlAAAACRWEABAABEavp5oAAAQDGVzj/X1Ycu8jNQB5f4+SZJOjnDzzRVuv2prsyf9kmdR/xrjN/nZ6gm7ex29eQp/n7bss1v7+jRmp5GgyNQAAAAkVhAAQAARGIBBQAAECnpGahDn7rC1VPvfjynTgAMZMsudHVY+0JOnQAYqHPeXFf37dnnH1DJDBjVqW/mJFcfOsfPKx2/4ETNc8b19Lo6BH/ep94TfmnSJ39tPNvlHz9hl99GZd2GITpuHI5AAQAARGIBBQAAEIkFFAAAQKSkZqDs0ne4+shZfn03tZXNABgUM09Amvp27W7p9uwH61w9/wct3XyuOAIFAAAQiQUUAABAJBZQAAAAkZKagQrP/NDVC56uDPLI9tV59iJX9728bZBHtrfSTH89pFf+zfmuPjXVZ1ue5OsZT/tzicxec9DVlWc3Dr39WbP86+/bN8gjR+74DZe5evx9T9T9mkXVMSFzLaoF81xdnjHR1cfmjXf1xL97xtWh91QDuwOA+nEECgAAIBILKAAAgEgsoAAAACIlNQPV6Gv01Kujp8fVlRO11/QZzuY/8XMz5y7Z5bdxdXxf7aC8/3VXL/ztR+t6vTd/6nJXH/hNf86w47v99ZiuXubPU3SyMsU//6SfwZGkTvNzWFPGHXf1ny9a6eoPlm92dc/fMRP1I5Vjx/wNm190Zd+HL3X1/nf6mbej//JsVx8+4meqSq/4ffOM50JtD5n/ux2b6/+9eMu/utfVN0315885/85/4+rF/+Gxmm0AGLs4AgUAABCJBRQAAEAkFlAAAACR0pqBSsxoZp5K5/rZjfuu+x+u/rn/+auunq/t8Y2NQRPuXZOph378q8O+4uGaW3oz9fFMfa0udnWPmHkarc7vPOXqs74z9ONnDX33iBz6zfe6+nf+98dd/Y1bHnf1Yo2NmafXfs2/L2f+Xn3zikhbx0Q//7nv5y5y9bE55urKOD9fWO72rzdxp3/85B1+lnnS5kO1TXT6YzevfnSaq4+f6V9j4f/2PfR8K43/93IECgAAIBILKAAAgEgsoAAAACIxA9Vg23/KX/Prbw6929Xzv8x8AZCHs36Lfe906p15Cu+72NVH/sMRV0/v8dOE5Q+95uoHX1vn6mvP9K+HBuvwx02mvuSvM3l8ph9y6vOXqVRl9klXH57U5epDF2TOyfZj/hx8kjR/8X5Xr7/oj1392Z3+vH8v3vE230PNK8bJzoFVjh4d3evU2QcAAMCYwwIKAAAgEgsoAACASMPOQJnZnZI+JmlvCOHC6m0zJP2VpMWSXpH0iRDCG81rM13lq/yM0x/c/Geu/sLt/9rVs5XvHAZ5jlzngvmu7tuxM6dOTo8si6Vd87QfrHP1lI/6+4e7wmkRZ55SzrJyxM+odW874OpZPTNdfXS2XyaETX5GyjIBd/T5+o231/Ywe6nv4Re2/TNXv3i7f9Kkdf48gLE6LrrA1ZXnfljX6731uiN4zCpJ12VuWyFpdQhhiaTV1RrtYZXIsyhWiSyLZJXIsyhWiSwLb9gFVAjh+5IOZG6+QdJd1a/vkvTxxraFZiHP4iDLYiHP4iDLsWG0pzGYE0LYVf16t6Q5gz3QzG6WdLMk9WjCKDeHJhtRnmTZFtg3i4V9szjYNwum7vNAhRCCmYUh7l8paaUkTbEZgz6uXS28bYurD5QnuXr2H7fXuWeGyrPoWXbO9f8/S23mKdZY3zeLZizvm0WT1L7ZPc5v2/y17Uqn/OZDKXOtvJJ/uRMzMvcvyl5VVHrnVH8usK8+6Gegzvnr+q5DaZe8w9Whszm/LzfaV91jZvMkqfr33sa1hByQZ3GQZbGQZ3GQZcGMdgF1v6Tl1a+XS7qvMe0gJ+RZHGRZLORZHGRZMMMuoMzsa5Iek3S+me0ws5sk3SbpGjPbIukj1RptgDyLgyyLhTyLgyzHhmFnoEIINw5y19UN7iV5b37iiprb/r+z/sTVH/6Fm1zdpaea2lMs8uzXuXBBzW3l2VP9Dbv3tKib0SHLYiHP4kg5S+vKzDxlZpqsEjK1f37ZnwZKpyb75x+f459/8Vnba3r49q7zXX32fbVzUjGy86u73+Ovvzf327tcnTlV1ahxJnIAAIBILKAAAAAisYACAACIxAIKAAAgUt0n0hxLjvzcoZrbPr9rmau7vp3W0DhO79jSuTW3jXtwbQ6dAEDrWFfmx35mSLzjlL+h1OuPs/T1+KFxZcreGf7qwsf6/NC6JO1f64e+J/2gvhNn7r7hHFd3nMo84Fh9Q+qD4QgUAABAJBZQAAAAkVhAAQAARGIGagh9V1/q6k+c84Oaxzz64fmZW16vb6OZCzkqcF3QZmDeCcCYVPEzTnbSDwx1HvNnyqx0+eMsocP/jOrNnEhz3PQTrt64OfszUnr7//uiq8s1jxhauPJdrj4+2/ew4DvHXF05fCRyCyPDESgAAIBILKAAAAAisYACAACIxAzUANbtP/vd9+/856h3PnxVzXOW7H+8rm2WpvkL2JYP1p5rCgCARqic6nV15wk/A1U66uuuUuY4S6Y8Od3fcOqg/zl67ldrL91b3rN3JK0OaseHJ7q6601/f+cb/md3+TjngQIAAEgCCygAAIBILKAAAAAiMQM1wNbfucTVHSf856hLbqlv3um0OomgFUrTp7u6/MYbOXWC0SjNmuXq8r59OXUCtLlK5qxLfZkZpXLm3IOZcxEeWtTl6hPz/PPPWOt/pnV878n4HjMO/fwVmW36/4YzVvvH2xH/s7tZ51PkCBQAAEAkFlAAAACRWEABAABEGtMDOK/fdKWr//W133b19376IlfHXq/ndDoX+OsC9e3Y2YBXRefCBa7u277D1cw8tTdmnoAmKZVcGXr8suDkGeNcffBifx6p7l1+JuqMP3u07pY6LrrA1Xs+4H/69rzmtznxFX/+xPLu+s4zNVIcgQIAAIjEAgoAACASCygAAIBIY3oGqvOn/VzFXd+4xtULN9f/WW5pzmxXl/cwy9EUnaXhHwMA8DLXuuub7Gee9r3LLxNs3ElXz3q2EdPB3g8/N8nfcMpcOecJP4fV8eJ2V5d7/fX8moUjUAAAAJFYQAEAAERiAQUAABBpTM1A7b7lva5+1/QXXL3nt7fWvY3SlCmuLu9pzfkoxrq+l7fl3QIAtJ9Tfp7o+Cw/A3XibD/zNPm5bldPuLf+WeHtv+F/NuuUv77e7Mf9jOv47z/n6vLRo3X3MBocgQIAAIjEAgoAACDSsAsoM1toZg+b2QYzW29mt1Rvn2FmD5nZlurf05vfLupRUUVkWRzsm8XBvlks7Jtjw0hmoPok3RpCeNrMJkt6yswekvSLklaHEG4zsxWSVkj6QvNajVc6723+hg/566GtX/UOV8/UY3Vvs3z4cN2v0WRtmSVOq233TZwWWRZH2+ybodfPQJ2c6s+5NPUZP/N05lc3uboRZ4GasDu4etoWf2xn8jeecHWl0vhzT43GsEegQgi7QghPV78+ImmjpPmSbpB0V/Vhd0n6eJN6RIN0qENkWRzsm8XBvlks7JtjQ9Rv4ZnZYkmXSFojaU4IYVf1rt2S5gzynJsl3SxJPZow6kbRWGRZLORZHGRZLORZXCMeIjezSZLukfT5EIL7nCqEECSF0z0vhLAyhLAshLCsS92newhajCyLhTyLgyyLhTyLbURHoMysS/3fBHeHEO6t3rzHzOaFEHaZ2TxJyZ3waOMX/Hzeogn7Xd39pxtb2U4S2jVLnB55FgdZFkvb5Jk5D9T0H55wdcc/PePqZkwfzfq6Pydj5ciRJmyl8UbyW3gm6Q5JG0MItw+4635Jy6tfL5d0X+PbQyOF/n/skGVBsG8WB/tmsbBvjg0jOQL1PkmflvS8ma2r3vYlSbdJ+oaZ3SRpm6RPNKVDNEy5/98OZFkc7JsFwb5ZOOybY8CwC6gQwiOSbJC7r25sO2imTnUqhECWBcG+WRzsm8XCvjk2FOpaeJ1z/S803PSeR1z9V3/5YVefqVea3RIapO/Dl7q68ztP5dQJABRH9tyF2ZmnVmiXmacsLuUCAAAQiQUUAABAJBZQAAAAkQo1AyXzM3t3PnyVq5f83qNN36bCac+Lhkid8+a6uuPJza6uDPP80pzZri7vyf90Kxi90rlnu3rnx+a5eu4fNmHfBoAhcAQKAAAgEgsoAACASCygAAAAIllo4cyOme1T/9lXZ0raP8zD81TU/haFEGY1ogGybJjcs5TIs4Fyz5MsGyb3LCXybKCG59nSBdRbGzVbG0JY1vINjxD9jVxKvZwO/cVJrZ8s+hu5lHo5HfqLk1o/WWOxPz7CAwAAiMQCCgAAIFJeC6iVOW13pOhv5FLq5XToL05q/WTR38il1Mvp0F+c1PrJGnP95TIDBQAA0M74CA8AACASCygAAIBILV1Amdl1ZrbJzLaa2YpWbnswZnanme01sxcG3DbDzB4ysy3Vv6fn1NtCM3vYzDaY2XozuyWx/pLKM+Usq70km2dqWUpp55lyltU+ksoz5SyrvSSbZ2pZSmnn2cosW7aAMrOSpK9I+qikpZJuNLOlrdr+EFZJui5z2wpJq0MISyStrtZ56JN0awhhqaQrJH2m+p7l3l+iea5SullKieaZaJZS2nkmmaWUbJ6rlG6WUqJ5JpqllHaercsyhNCSP5KulPTggPqLkr7Yqu0P09tiSS8MqDdJmlf9ep6kTXn3WO3lPknXpNBfqnm2S5Yp5Zlqlu2UZypZppxnu2SZUp6pZtlOeTYzy1Z+hDdf0vYB9Y7qbSmaE0LYVf16t6Q5eTYjSWa2WNIlktYojf7aJc8U3qsaieXZLllK+b9XNRLLUmqfPFN4r2oklme7ZCnl/17VaHaWDJEPI/QvV3M914OZTZJ0j6TPhxAOD7wvhf7aRSrvFXk2RgrvFVk2RirvFXk2RgrvVSuybOUCaqekhQPqBdXbUrTHzOZJUvXvvXk1YmZd6v8muDuEcG9C/bVLnim8V29JNM92yVLK/716S6JZSu2TZwrv1VsSzbNdspTyf6/e0qosW7mAelLSEjM728zGSfqkpPtbuP0Y90taXv16ufo/Q205MzNJd0jaGEK4fcBdKfTXLnmm8F5JSjrPdslSyv+9kpR0llL75JnCeyUp6TzbJUsp//dKUouzbPEw1/WSNkt6UdKv5z1cVu3pa5J2SepV/+fLN0k6Q/1T+lskfVvSjJx6e7/6DzM+J2ld9c/1CfWXVJ4pZ5l6nqllmXqeKWeZYp4pZ5l6nqllmXqercySS7kAAABEYogcAAAgEgsoAACASCygAAAAIrGAAgAAiMQCCgAAIFJdC6gUrxKN0SPP4iDLYiHP4iDL4hj1aQyqV4nerP6L9O1Q/wm/bgwhbBjsOeOsO/Ro4qi2h/qd0FGdCiftdPfF5kmW+WpklhJ55o19szjYN4tlqDw763jdyyRtDSG8JElm9nVJN0ga9BuhRxN1uV1dxyZRjzVh9VB3R+VJlvlqZJYSeeaNfbM42DeLZag86/kIb0RXiTazm81srZmt7dXJOjaHJhs2T7JsG+ybxcK+WRzsmwXS9CHyEMLKEMKyEMKyLnU3e3NoIrIsFvIsDrIsljGfZ0fJ/0lUPQuodrpKNIZHnsVBlsVCnsVBlgVSzwKqna4SjeGRZ3GQZbGQZ3GQZYGMeog8hNBnZp+V9KCkkqQ7QwjrG9YZWoo8i4Msi4U8i4Msi6We38JTCOEBSQ80qBfkjDyLgyyLZSzlad21Mz9W8nMwlWPHWtVOwxU9y9L06f6GM2fXPObULH9ahr4JPt/Q4c8a0Hms7Opx+4+62rbvcXX5jTdG1Gu9OBM5AABAJBZQAAAAkVhAAQAARKprBgoAgBgdF17g6sNvn+rq3gm1/67v6POXHOs+VHH1+J2ZmZitr7q6cuRIdJ8YGbv0Ha7e964prj6yuPY5p6b5/MI4n6+d8jNQXYfGuXrCbj8nN/Xlya6e+MN9ru576ZXaJhqAI1AAAACRWEABAABEYgEFAAAQiRkoABhDrMvPk4TeU63d/km/vYmv+Yvllo6cqH3OscwFdV8/6MrseX/8RA2aKTzlzwM668T5rrbyjJrn9E70533Kzrj1vOFnpCZt9zNuHes2u7pywn/P9A3RbyNxBAoAACASCygAAIBILKAAAAAiMQMFAGNIq2eesspbXnK1bfH3++kXpK507tmuPvz2aa4+Ot+f00lSzZDa+P2+7ihn6uO9rs7OPOWFI1AAAACRWEABAABEYgEFAAAQiRkoAAAwItbplw19c/y1DMfv8zN2ncdqlxmlE37SrXutH4QLZT8EVTnqzwOVCo5AAQAARGIBBQAAEIkFFAAAQKS2moE69dAiV4+7ZltOnQAAMPaEPn+lOfvBOl9nHt89gtcsD/+QJHEECgAAIBILKAAAgEgsoAAAACK11QzUwb+d7+rZYgYKAFqp4+Klrj4xd6Krxz/j/79c3rO34T3sXPFeV8+/7dGGbwMYDkegAAAAIrGAAgAAiMQCCgAAIFJbzUDN/gqfcwNAng4vmezqA+8ouXrc+ee6uqPX1zPXH/fPP7/H1cfmZc8kJP3kxx9x9a9N+VNX/95t7xyiYwzl0M9f4eoDS/37P3FnJo9MWToZXN153NfBavMM/ltGBy70zznnXv89Yo8+W/Ma9SgtOcfV5S0vjep1OAIFAAAQiQUUAABAJBZQAAAAkYadgTKzOyV9TNLeEMKF1dtmSPorSYslvSLpEyGEN5rXZnFs/ctLXH3uzz/T0u2TZ3GQZXMd+6nLXT3h3jVN3V675Dll82FXdx/y54GyvsxMzJunXN1xyl/5zMp+Bqr3/GM12/zdOc+5+tozLx5Rr3lJOcvKB/zPoDfn++Mo5bP8/NHUZb7Fo6fGuXr/jmmuLk3xeU+Z5F9Pki6ctcvVT+7w17k9ssh/T02pc/z56M/4fXnqI6/U94JVIzkCtUrSdZnbVkhaHUJYIml1tUZ7WCXyLIpVIssiWSXyLIpVIsvCG3YBFUL4vqQDmZtvkHRX9eu7JH28sW2hWcizOMiyWMizOMhybBjtaQzmhBB+dAxut6Q5gz3QzG6WdLMk9WjCKDeHJhtRnmTZFtg3i4V9szjYNwum7vNAhRCCmYUh7l8paaUkTbEZgz5urAhvjBv+QTkaKk+ybC/sm3G2f/NCVy/8mebOPMVKZt8M/uVLJzMzTZkZqPKELlcfOs/Ptxyf7c8T9Nl3fbdmk+d+9xdd/TatG0GjA3RkTjxUKZ/+cS3Syn3Turtd3TfR/9gv+7vV0TH05iZ1n3R16Sx/oG3iOD8D9ctnfa/mNS4Yt8fVP/Xy/+XqKV97fMgehlPKnIvs5BT/YVvfbr/90Rrtb+HtMbN5klT9u/FXi0QrkWdxkGWxkGdxkGXBjHYBdb+k5dWvl0u6rzHtICfkWRxkWSzkWRxkWTDDLqDM7GuSHpN0vpntMLObJN0m6Roz2yLpI9UabYA8i4Msi4U8i4Msx4ZhZ6BCCDcOctfVDe6lkB58bZ2rrz0znz5+hDyLgyzrc/AXrnR1z+qcGqlqlzytNzPz1FtxdXm8/7FyfJafgTo61/+7/fhc//yLe16t2ebbfm5dVI8dPf7cUpUTJ6KeX6+UsuyY4IfQ+8b7978ybuiZp96Kf/yErl5Xz514xNWXTX/F1Z+YdKjmNf/D3ve4uufZxg7Kb/mXs1x99n215xZrBM5EDgAAEIkFFAAAQCQWUAAAAJFYQAEAAESq+0SaGNqFj3/K1fO1PqdOAAy05rb/6erUL1CbCnvTXxy2o8efHLhvgv+xUsn8lOnInMPyootfdvWtG36mZpsztTmqx445foi4sm171POLxHr8mTLL4yzzAF+G4G/oLfuTkHb3+PzfNmmfq//F1Kdc/dTJ2pNH3/24/wWOt/+p/7kYe5rTvZ99r6srC3yPnYf9LxE06jSqHIECAACIxAIKAAAgEgsoAACASMxANdjf7HjC1T+5IKdGADi7bvVzEh/4zOWunqC0Lh6cqsqBN1xd6vI/RjozJ9LsecP/O/3geX7G5lcXPujq3znn4vimLnunK/ueeD7+NYpqnD+RaXbmyTIDQeWTfubpZG9mpi0zIzW3258o821dk1x97caP1bR05mr/GuWDtSfbHEppyhRXz/vpV1zdtWqRf8LubVGvP1IcgQIAAIjEAgoAACASCygAAIBIzEA12PU3f8bV3Xoyp04ADHTsUn9B0Xl/sC6fRtpc5ehRV9sRfyHYzsxMVKXbz9Rc8EF/seAv/ftfdvXEUcyiHXiHn7uZ8cQgD4Q6+vzFg0vH/XEUO+bzOpY5j9Sb4309p9PPL91+4BxXv/zEwpoezv7rx0bW7CC2rlzs6tkn33T15B2nXB1OnKxre4PhCBQAAEAkFlAAAACRWEABAABEYgaqTuUPvdvV3Q8w8wSk4NR173H1ef/pgKsbdT2sMS9UfF3x9cu/5GduKhv9OXrOuyd+5umV3/HXUjvnd5/z24h+xQIr+3ej83imzsw8ld70x1X6Muf1Gt/Z6+p/fN2fg+up717g6nP/W+11DGP3vfJV/ufs71/6dVf/+68vd/XiI34mKju31ygcgQIAAIjEAgoAACASCygAAIBIzEDVqfuHr7m6L6c+gLEse20sSTpwgb8G2Nx/fLFV7RSadfvzANlkfw6mI0tnuvrmix929XfeOTF6myc+dpmru1/311Jr1oxLIZT9xFHHycwM1Ak/o9bhR5xqrp33tin7Xf0PL7zD1XPX+9cr7399hI0O7to/+p6rnzm22NXTN/ltdu477Opm/VzmCBQAAEAkFlAAAACRWEABAABEYgYqwuGfu6LmtilffTyHTgAMtOfGd9TcNvcPH82hk+LryMybHX37bFfPvPVlVz/07z7g6pKejt7m3uXHXX3Wzz4f/RpjVkfmWneZk2Rl65BZFUya7q8h+Y+Pv8vV8x7xQ1KT/6r+n4lb//ISV98/faWrl37j37n6nG3+Wnd9L2+ru4eR4AgUAABAJBZQAAAAkVhAAQAARGIGaggdkye7mnknIA37f9lfC238fq5+lpedV/kfIxPK/vxbpe/GzTzt/cx7a25b9PNPuTrUPAKN0jfR70tzJ/j5s3EvTHX15L96rO5tVj7gZ542fMjPPN207RpXL/iO77HrWX+Ot1Zd55IjUAAAAJFYQAEAAEQadgFlZgvN7GEz22Bm683slurtM8zsITPbUv17evPbRT0qqogsi4N9szjYN4uFfXNsGMkMVJ+kW0MIT5vZZElPmdlDkn5R0uoQwm1mtkLSCklfaF6rrVc5ciTvFpphTGZZUGN23zzwbn91q/N++cmcOmmo9shy5jRXhgV+Rqb8IX990Fizv1J7/q42nHlKZt8MJ/05kjp6h54XtLI/r9PBB+e5ev6ag65uxPRh328ecPUfvH6hq7f+8QWunvFPG11dPuyvfdcqwx6BCiHsCiE8Xf36iKSNkuZLukHSXdWH3SXp403qEQ3SoQ6RZXGwbxYH+2axsG+ODVG/hWdmiyVdImmNpDkhhF3Vu3ZLmjPIc26WdLMk9WjCqBtFY5FlsZBncZBlsZBncY14iNzMJkm6R9LnQwjueFkIIWiQo6whhJUhhGUhhGVd6q6rWTQGWRYLeRYHWRYLeRbbiI5AmVmX+r8J7g4h3Fu9eY+ZzQsh7DKzeZL2NqtJNA5ZFstYzbMgM09Ou2b5tp9bl3cLSUolz/L+113d9foZvs++HldP3eRnoOb9/auu7tu+o4Hd9du5f5qr71/1Id/T3f4cjK06z9NwRvJbeCbpDkkbQwi3D7jrfknLq18vl3Rf49tDI4X+f+yQZUGwbxYH+2axsG+ODSM5AvU+SZ+W9LyZrave9iVJt0n6hpndJGmbpE80pUM0TLl/3U6WxcG+WRDsm4XDvjkGDLuACiE8IskGufvqxraDZupUp0IIZFkQ7JvFwb5ZLOybYwPXwgMAjFh545a8W0Adyhs2u3pW+Vx//6atrvZnXGuOc//T0UwPz7Zgq/XjUi4AAACRWEABAABEYgEFAAAQiRkoAADGqOzM01jtYTQ4AgUAABCJBRQAAEAkFlAAAACRrP96hi3amNk+9Z99daak/S3bcLyi9rcohDCrEQ2QZcPknqVEng2Ue55k2TC5ZymRZwM1PM+WLqDe2qjZ2hDCspZveITob+RS6uV06C9Oav1k0d/IpdTL6dBfnNT6yRqL/fERHgAAQCQWUAAAAJHyWkCtzGm7I0V/I5dSL6dDf3FS6yeL/kYupV5Oh/7ipNZP1pjrL5cZKAAAgHbGR3gAAACRWEABAABEaukCysyuM7NNZrbVzFa0ctuDMbM7zWyvmb0w4LYZZvaQmW2p/j09p94WmtnDZrbBzNab2S2J9ZdUnilnWe0l2TxTy1JKO8+Us6z2kVSeKWdZ7SXZPFPLUko7z1Zm2bIFlJmVJH1F0kclLZV0o5ktbdX2h7BK0nWZ21ZIWh1CWCJpdbXOQ5+kW0MISyVdIekz1fcs9/4SzXOV0s1SSjTPRLOU0s4zySylZPNcpXSzlBLNM9EspbTzbF2WIYSW/JF0paQHB9RflPTFVm1/mN4WS3phQL1J0rzq1/Mkbcq7x2ov90m6JoX+Us2zXbJMKc9Us2ynPFPJMuU82yXLlPJMNct2yrOZWbbyI7z5krYPqHdUb0vRnBDCrurXuyXNybMZSTKzxZIukbRGafTXLnmm8F7VSCzPdslSyv+9qpFYllL75JnCe1UjsTzbJUsp//eqRrOzZIh8GKF/uZrruR7MbJKkeyR9PoRweOB9KfTXLlJ5r8izMVJ4r8iyMVJ5r8izMVJ4r1qRZSsXUDslLRxQL6jelqI9ZjZPkqp/782rETPrUv83wd0hhHsT6q9d8kzhvXpLonm2S5ZS/u/VWxLNUmqfPFN4r96SaJ7tkqWU/3v1llZl2coF1JOSlpjZ2WY2TtInJd3fwu3HuF/S8urXy9X/GWrLmZlJukPSxhDC7QPuSqG/dskzhfdKUtJ5tkuWUv7vlaSks5TaJ88U3itJSefZLllK+b9XklqcZYuHua6XtFnSi5J+Pe/hsmpPX5O0S1Kv+j9fvknSGeqf0t8i6duSZuTU2/vVf5jxOUnrqn+uT6i/pPJMOcvU80wty9TzTDnLFPNMOcvU80wty9TzbGWWXMoFAAAgEkPkAAAAkVhAAQAARGIBBQAAEIkFFAAAQCQWUAAAAJHqWkCleJVojB55FgdZFgt5FgdZFseoT2NQvUr0ZvVfpG+H+k/4dWMIYUPj2kOrkGdxkGWxkGdxkGWxdNbx3MskbQ0hvCRJZvZ1STdIGvQbYZx1hx5NrGOTqMcJHdWpcNIGuTsqT7LMVyOzlMgzb+ybxcG+WSxD5VnPAup0V4m+fKgn9GiiLrer69gk6rEmrB7q7qg8yTJfjcxSIs+8sW8WB/tmsQyVZz0LqBExs5sl3SxJPZrQ7M2hiciyWMizOMiyWMizPdQzRD6iq0SHEFaGEJaFEJZ1qbuOzaHJhs2TLNsG+2axsG8WB/tmgdSzgGqnq0RjeORZHGRZLORZHGRZIKP+CC+E0Gdmn5X0oKSSpDtDCOsb1hlaijyLgyyLhTyLgyyLpa4ZqBDCA5IeaFAvyBl5FgdZFgt5FgdZFgdnIgcAAIjEAgoAACASCygAAIBILKAAAAAiNf1EmgCA9mVd41x97McvdvXxGSVXj3vTX191wq6T/v6X9ri6cvCQr48eHU2bQMtxBAoAACASCygAAIBILKAAAAAitdUM1ImPXebqcUd6Xd3xvWda2Q4idC6Y7+q+HTWXfwKQIOvyPyZ2XeFnnuZc7Geals161dWTSn4G6pVjZ7j6zd5Jrj5wYvawPR083uPqI3v9a3Tv9j13vWmunvpS2ddrd7m67xX/39BOTl27zNWHF3W5+o33nnJ1OOmPo5z1Lf9e9fzdEw3srlg4AgUAABCJBRQAAEAkFlAAAACRkp6BOvLJK1x98Fy/3uud5D+LP+d7TW8Jo8TM09hSvurdrg4dfq6i8ztPtbId1KFy7Jirz1nx2JCP33jZO129+ZfGu/p9F2129ZfO+jtXL8zMTEnSjFK3q7vNz/Xc8+YUV//Nfv/919XhZ572nfAzU+s3LHT13EfOdPWUrz5e01OqTk73P9bf9P9p+lfvfsTVX5q5ydX7r/fn4Xrg9xe5+pt7/IzV+ZP9DNz0Tv/9Mrl0oqbH3uB/dp+o+DxndL7p6j29U1399XuucvXi31/n6uz3bLNwBAoAACASCygAAIBILKAAAAAiJTUDZd3+c+4pm464etrT/rPU8uYXm94T8tHxrre7um+KP+/LuJ1v+PtfeqXZLWGAN5Zf6eqP/ur3XT1/3D+6+h/3vcPV637ycleHCX5GpfN1PxPRddjPUJl/uKwydL+SdGq6v0Zb6W1+zuLk3gmuvuAr/nusvMHP7mAQTzzvyvMypxHal3n4b+g90ZsoTfMzMeXM9fSkg0M+v/yhJa4e/z7/o/BY5lRU0yZPdnXliP/ZlJLJX/fzWpMz93/vN/xM2vd08dAvmJlpO3i+nx97YdliV3fO8fNHs6b5/ex09r3hu/zU0iddXZHf/zMjVDp+lf//S88e30N4av2wPYwGR6AAAAAisYACAACIxAIKAAAgUlIzUOFk5vwfz/jPLTNjD23p6E/72Y835/sPc8/82hZXl/dlJwbGho7XD/v62Y2u7mtlM1Vv/qzP7shCn9282x8d8vmlM2a4urJ4nqub9Tl9M8y8b4OrH7+rK/OI7PXM/PfxeZ2ZGbYPXOTq197vZx6mv3+3qxdN9s+/cPJrNT1+bMqzrr5onJ+j++Udfo5rw1d9D9mZp9JMfw23o1e8zdVcM6x1amee4pQeftrVCx/O3J+dsUp45qnpMjNt0zLf5tP+ov5NTMrUj2rckI9fJP//2tLb/Uzbm+dNd/X4Jp12jiNQAAAAkVhAAQAARGIBBQAAECmpGagi6jxnsauPLT/o6qMb/VzMWJ15si7/mXcK186zZRe6etcH/P1LPjf0zFPWi79yvqsX/4ehrymWsnpnUEKfn2IbbiYl6/VM/T2Nr3nM93RFzW3ecVdN0JohHx3mznI1M0/FVe/3N1qrvOklV4/f2JqJaY5AAQAARGIBBQAAEIkFFAAAQCRmoJpswwo/NzFd/vxG53yhfedgGin0nsq7hRoHLvTXZ1ryubisyle929W9U0dwwTYkq/LCD/NuIRfZ819pxjRfH/LnSCrv2dvchoCsSj5nieQIFAAAQCQWUAAAAJGGXUCZ2Z1mttfMXhhw2wwze8jMtlT/nj7UayAd5FkcZFks5FkcZDk2jGQGapWkP5L05wNuWyFpdQjhNjNbUa2/0Pj22k/vRy519d9c80eu/rmnbmplO6ezSuR5Wp3zz3T1rO/vcnXs9feu+h/+PFHjPuivfdeAT+1XiSyLZJUSyLNz0UJXH1/ir2t4dF7mnG3j/f0dvf4agePe9LN/HZkdySrB1RNe8+fnkqTw5PM1tyVulRLIcqzoXHyWq/teebUl2x32CFQI4fuSDmRuvkHSXdWv75L08ca2hWYhz+Igy2Ihz+Igy7FhtDNQc0IIP/rn+W5JcxrUD/JBnsVBlsVCnsVBlgVT9xB5CCFICoPdb2Y3m9laM1vbq5P1bg5NNlSeZNle2DeLhX2zONg3i2G054HaY2bzQgi7zGyepEFP/BFCWClppSRNsRmDfsMUxUduf8TV3eYnXRb+zAtK0IjyLHqWYaK/nlp584tRz3/lt6909YJjfm6j/MYbo2ssDvtmsbR+3yyV/Gt3+n9nB3+3Kp3m6y5/f9/EzOtl/tleqfkpVHtdw9pb2hL7ZpO0auYpa7RHoO6XtLz69XJJ9zWmHeSEPIuDLIuFPIuDLAtmJKcx+JqkxySdb2Y7zOwmSbdJusbMtkj6SLVGGyDP4iDLYiHP4iDLsWHYj/BCCDcOctfVDe4FLUCexUGWxUKexUGWYwPXwqvToU9d4eoF477p6uu//TlXn6e1Te8JoxM785T1wC/8V1f/u3deX9frAXkIpczMk2Xuz3xuETI/RcrdmXpcph7vR3oqmftnbGTkB+2BS7kAAABEYgEFAAAQiQUUAABAJBZQAAAAkRgir1P5Rn+5o7/de4mrz/tXDI0X1eY7lrn6n699h6sXHF7fynaApshe7DdzbuCaIfPsiTH7Jvvn9071L1B6059os3TSX3wY6Jg40dWVo0dz6sTjCBQAAEAkFlAAAACRWEABAABEYgYqQvakmZJ0w1nfdfW3fv9Drp6mfc1sCTn68gf+2tV3nHd2Tp0AjWNlP4NUOuXrzhN+pql83D+/Mi47FOXL7MzT5Jf940sn+mp76vJn2wy9p2oeM1BHT49v4cSJIR+PtKUy85TFESgAAIBILKAAAAAisYACAACIxAxUhI994bs1t9296T2uPuvPH2tRN4hROmOGq8uvHxjkkYPb/s0LXf2lJ/05v96mZ+IbQ8t0nr3I1X0vb8upk8RYdmbJDy11nPDnbRr3ZuZEUOZnmixzLWAr+9ef8qp//vg9fp6p46kf1rQ43MxT6by3ubreC4Nj9ErTp7u6/MYb0a9hnX5pEvpq5+JSwBEoAACASCygAAAAIrGAAgAAiMQM1BB23uuvbbb9xEs1jznrX2xoVTuI0Dlvrqv7du2Of42FC1z97JV3ufpj8y+Nbwwtc+ynLnf1hHvX5NRJ2qyzy9/Q4f9dbcEPNXVkrlXX1eFnnLIzT2c8e8zVna/5+cO+HTtdnRmhOr2OzNzVkTTPEzQWjWbmKSvVmacsjkABAABEYgEFAAAQiQUUAABAJGagBjj6M35m4nNv/5arv/yP/7zmOedWHm9qTxidcPz48A8axn/8/t+4+vx7PuvqJWKmJhWV919cc9uUp15zdXtMVbSejfMzUKHHX3eu3NOZqf38UaXLzzxlr5WnJ553ZUNyqPhzScXOOHZMnuxf7siRulvC2MMRKAAAgEgsoAAAACKxgAIAAIjEDNQA4V/tc/WfbPmAq8/9FeadUtVx0QWuLj9Xez2t4Rz9x3Nc/fwJP1ex5HPMPKWiNGuWq09O6ap5TMe27a1qp61ZKTPTNN6/l72T/Y+J4zP844MvNW1z/fOHjVY692x/w/74a2ECWRyBAgAAiMQCCgAAIBILKAAAgEhjegbqpa9e7Oppff6aTXM+5c8j468AhTxZlz9XTSVy5ik7MyVJj1z0dVf/+Ps/nnnEK1HbQPOU9/l5xe4H9g3ySAwnnDrlb8j8j65vvP939snp/rxPXW/68z5ZeURXsxtUaeYZNbdZp/9R1bd7z9CvscTPM6ozM7d1qnd0zQEDcAQKAAAg0rALKDNbaGYPm9kGM1tvZrdUb59hZg+Z2Zbq39Ob3y7qUVFFZFkc7JvFwb5ZLOybY8NIjkD1Sbo1hLBU0hWSPmNmSyWtkLQ6hLBE0upqjfSRZXGwbxYLWRYH++YYMOwMVAhhl6Rd1a+PmNlGSfMl3SDpqurD7pL0XUlfaEqXDZL9XPwPL/MzL1/4s19ydeXI5qb31Eod6lAI4Wmp/bOsuX5X76lBHnl6P/zcpJrbLvmdf+vq2S89Gt9YCxVp3xzr8tw3KydOuLrz4Ju+PjbR1V1H/QzUpNf81e06ev116oabiCpf9W5Xv/KR7prHTNzh63n/6B8TujPX75s+wdWd2/b6+4/5eddGK9K+2fuRS13dvfeoq4ebPz3xE5fV3NbzrSfqbywBUTNQZrZY0iWS1kiaU/0mkaTdkuY0tjU0E1kWC3kWB1kWC3kW14gXUGY2SdI9kj4fQjg88L4QQtAg/9Aws5vNbK2Zre3VybqaRWOQZbGQZ3GQZbGQZ7GNaAFlZl3q/ya4O4Rwb/XmPWY2r3r/PEl7T/fcEMLKEMKyEMKyLtUemkVrkWWxkGdxkGWxkGfxDTsDZWYm6Q5JG0MItw+4635JyyXdVv37vqZ02EDz/sJ/r/74BP/Z/1dWbnS1/yS//YX+f+wUIsvK0aPDP2gIPdvH1dw2+ytpzzxlFWnfHOtS2jf7XnnV1RMn9Pj6Zf/48vpNro49C9Shs/0CYdwhq3nM8cwHXacW+nNFWcVvteu1N1zdt8tf17LZ2nnffOW3r3R157HMzNsO///OrvMud/WUz/hrUG7fVvuTdMm36ukwHSM5keb7JH1a0vNmtq5625fU/w3wDTO7SdI2SZ9oSodomHL/kpAsi4N9syDYNwuHfXMMGMlv4T0iqfafBP2ubmw7aKZOdSqEQJYFwb5ZHOybxcK+OTZwJnIAAIBIhb4W3rb//F5X3zjtm67e0efPd1J+w39ujuKoOZcJUbe1N37Rz2mMf712zqIo55ppueDnibIzTo024/97zN9gtQduOufMdnWY4s/jZsf8PGvfjp2NaW4MKE2b6urszNPMF/x5viY9t8vVP/z8fFdfNd3PQJ36z3PrbTFZHIECAACIxAIKAAAgEgsoAACASIWegTq5wF8f7euvvcfVv7XhJ129RGua3hPyMeU3/MW0jv6vcwZ5JPJw/AZ/vaztH/X3T557xNd3V1zNvFOBhNozSfXt3uNvyNYYtfLBQ65e8F+GPh9eX6Y+91f8zNOTKrm6pKdH3VvqOAIFAAAQiQUUAABAJBZQAAAAkSyc5vPmpm3MbJ/6T18/U9L+lm04XlH7WxRCmNWIBsiyYXLPUiLPBso9T7JsmNyzlMizgRqeZ0sXUG9t1GxtCGFZyzc8QvQ3cin1cjr0Fye1frLob+RS6uV06C9Oav1kjcX++AgPAAAgEgsoAACASHktoFbmtN2Ror+RS6mX06G/OKn1k0V/I5dSL6dDf3FS6ydrzPWXywwUAABAO+MjPAAAgEgtXUCZ2XVmtsnMtprZilZuezBmdqeZ7TWzFwbcNsPMHjKzLdW/p+fU20Ize9jMNpjZejO7JbH+ksoz5SyrvSSbZ2pZSmnnmXKW1T6SyjPlLKu9JJtnallKaefZyixbtoAys5Kkr0j6qKSlkm40s6Wt2v4QVkm6LnPbCkmrQwhLJK2u1nnok3RrCGGppCskfab6nuXeX6J5rlK6WUqJ5plollLaeSaZpZRsnquUbpZSonkmmqWUdp6tyzKE0JI/kq6U9OCA+ouSvtiq7Q/T22JJLwyoN0maV/16nqRNefdY7eU+Sdek0F+qebZLlinlmWqW7ZRnKlmmnGe7ZJlSnqlm2U55NjPLVn6EN1/SwMs276jelqI5IYRd1a93S5qTZzOSZGaLJV0iaY3S6K9d8kzhvaqRWJ7tkqWU/3tVI7EspfbJM4X3qkZiebZLllL+71WNZmfJEPkwQv9yNddfVTSzSZLukfT5EMLhgfel0F+7SOW9Is/GSOG9IsvGSOW9Is/GSOG9akWWrVxA7ZS0cEC9oHpbivaY2TxJqv69N69GzKxL/d8Ed4cQ7k2ov3bJM4X36i2J5tkuWUr5v1dvSTRLqX3yTOG9ekuiebZLllL+79VbWpVlKxdQT0paYmZnm9k4SZ+UdH8Ltx/jfknLq18vV/9nqC1nZibpDkkbQwi3D7grhf7aJc8U3itJSefZLllK+b9XkpLOUmqfPFN4ryQlnWe7ZCnl/15JanGWLR7mul7SZkkvSvr1vIfLqj19TdIuSb3q/3z5JklnqH9Kf4ukb0uakVNv71f/YcbnJK2r/rk+of6SyjPlLFPPM7UsU88z5SxTzDPlLFPPM7UsU8+zlVlyJnIAAIBIDJEDAABEYgEFAAAQiQUUAABAJBZQAAAAkVhAAQAARGIBBQAAEKmuBZSZXWdmm8xsq5nleSVtNAB5FgdZFgt5FgdZFseozwNlZiX1n9zrGvWfSOtJSTeGEDYM9pxx1h16NHFU20P9TuioToWTdrr7YvMky3w1MkuJPPPGvlkc7JvFMlSenXW87mWStoYQXpIkM/u6pBskDfqN0KOJutyurmOTqMeasHqou6PyJMt8NTJLiTzzxr5ZHOybxTJUnvV8hDdf0vYB9Y7qbY6Z3Wxma81sba9O1rE5NNmweZJl22DfLBb2zeJg3yyQpg+RhxBWhhCWhRCWdam72ZtDE5FlsZBncZBlsZBne6hnAbVT0sIB9YLqbWhP5FkcZFks5FkcZFkg9SygnpS0xMzONrNxkj4p6f7GtIUckGdxkGWxkGdxkGWBjHqIPITQZ2aflfSgpJKkO0MI6xvWGVqKPIuDLIuFPIuDLIulnt/CUwjhAUkPNKgX5Iw8i4Msi4U8i4Msi4MzkQMAAERiAQUAABCJBRQAAECkumagAABj28nr3+Pq7geezKkToLU4AgUAABCJBRQAAEAkFlAAAACRmIEC0HQnfuIyV+96X8k/YNExV9rWCa6e/UzF1VOe2e3qyv4DNdu0M+e4unfuZFefnNbl6o7e4OqJz/orbPTtfK1mGxh+5ql03ttcXZky3t+/c7+r+3b5bCWpdMYMV9vkSa4Ox0+4urxnr3+BDv/9Vjp3sX/85hdrtgkMhyNQAAAAkVhAAQAARGIBBQAAEIkZKABN1/OtJ1x99rfqe72+7A2ZGRdJ6jzV6+rszNOhc/z//kLmn5Pl7oWuPj5zkasPXOznss771XX+9U6erOlpLBpuvqgmy9O9xuuZGbdsPZxKOaqnIjv081e4esaTfgatvGlrK9tpaxyBAgAAiMQCCgAAIBILKAAAgEjtPQNl5srS0vNcXV6/qZXdAMhLZsZFkvpe3ubq8dk6chMTMvUZl73T1Ts/d6mrz/yvj0ZuAWi+qV/15+2qlPz8oF3yDlfvu2yKq8vj/M/d+d98ydWnO49Xo3W86+2u3n/pNFfPuPOxpvcgcQQKAAAgGgsoAACASCygAAAAIrXXDFRm5unwjZe7eu/1/rwrX7rUfxZ701Rff/n1Ja7+0+992NWdb/r15cRX/fb7e/LlGev9NZlK33269jkDVN5/sau7Nr7q6prznwA56LjoAldv/pfTXL3w234Gqfvvh74+WiE88bwrz3xikMcl7qXfu9LV5/xa3PxIaeYZri7vf73untBEmXnBkJ0ffGa9K2c+M/TLjeQ8Xo225eenuXrrp/6nq6+98+KW9MERKAAAgEgsoAAAACKxgAIAAIjUXjNQIbhyylcfz9T+4d/Q3CHr0jvOd/WM9/r15LG5fsDpzUV++5JUOuEf07GuUvOYgV6/yc8bHDnH3z9rrp/LmvjNNUO+HtLVufgsV/e98uogj0xf5bkfunrK1ve6eucH/X5gH/Df5zPX+X3n4BK/r03Y7e8/48+afx6XjouXurqybkPTt5mis//22NAPuOIiVx76Tf/4xy/+pqsveOTTrl70CT8rBtRryW+94OoPXXyDq8fJn/OtWTgCBQAAEIkFFAAAQCQWUAAAAJHaawaqwbLXyjvDn/5C/uwmjXHGHX62oxnbQBo2ffZMV7/t/27fGais2V/x13mbHfn8yZm6Zl4sc791+v9Vhb74s8/Ypf4aX//wrbtd/dGP3ujqyrMbo7fRjuzRZ4d+wOPPuXLq9f7ua3WxqxcpbuapNCfz3TM1+90hlTe/OPRrzJqVeY1J/vlbX47qKfu9Ep5aP8gjkYty5rxzv+bzrp1Wbg6OQAEAAERiAQUAABCJBRQAAECkYWegzOxOSR+TtDeEcGH1thmS/krSYkmvSPpECOGN5rWJRiHP5rHubleXpzX3KlFFynK4c2SNZuap5jUycyw//h4/zFPZme/MU5HyjFHes9ffkK1H8hr79vkbsnWkemeexmqWrVI5ljl32TP5zKiN5AjUKknXZW5bIWl1CGGJpNXVGu1hlcizKFaJLItklcizKFaJLAtv2AVUCOH7kg5kbr5B0l3Vr++S9PHGtoVmIc/iIMtiIc/iIMuxYbSnMZgTQthV/Xq3pDmDPdDMbpZ0syT1aMIoN4cmG1GeZNkW2DeLhX2zONg3C6bu80CFEIKZDXrahRDCSkkrJWmKzWjV6RkwSkPlSZZDO3a/P+/TedeuzamTfuybQ+vb+VreLURh32yeg7/gr9047c+bey1G9s1iGO1v4e0xs3mSVP07fuoPKSHP4iDLYiHP4iDLghntAup+ScurXy+XdF9j2kFOyLM4yLJYyLM4yLJghl1AmdnXJD0m6Xwz22FmN0m6TdI1ZrZF0keqNdoAeRYHWRYLeRYHWY4Nw85AhRBuHOSuqxvcC1qAPBvHLvHXy9qxyV+PaYnirr8ViyyLhTzz0+iZJ7IcGzgTOQAAQCQWUAAAAJFYQAEAAERiAQUAABCp7hNpAu2oNG1qzW3lg4eiXqP3vx5x9ZKP5HNBS5xe+ap3u7r03afzaQTJef0mf+LMM+5o7okzEef4xy9z9fi/fSKnTobGESgAAIBILKAAAAAisYACAACIxAwUxqTYeSdJeunLfm6i4/Wjrl6sV+vqCY01budBV5fzaQMJsO5uVzPzlLZUZ56yOAIFAAAQiQUUAABAJBZQAAAAkZiBwpjQefYiV/e9vC36NbZ8+n+6+tozL66nJTRZectLebeARHQsXujq8qatOXWCIuEIFAAAQCQWUAAAAJFYQAEAAERiBgqFVJo1y9WjmXl69Tff6+ol373Y1edoXfRrAmg9Zp7QDByBAgAAiMQCCgAAIBILKAAAgEjMQKGQyvv21f0as963y9Xjr3257tcE0HzZGchG/P8AyOIIFAAAQCQWUAAAAJFYQAEAAERiBgqo2n2LP+/Tmf/+gKsrrWwGwKgx85SOzoULam7r274jh04ajyNQAAAAkVhAAQAARGIBBQAAEIkZKIxJr/3ae2tum/ian3KqPPfDVrWDjM4F813dt2NnTp0AqEdR5p1OhyNQAAAAkVhAAQAARBp2AWVmC83sYTPbYGbrzeyW6u0zzOwhM9tS/Xt689tFPSqqiCyLg32zONg3i4V9c2wYyQxUn6RbQwhPm9lkSU+Z2UOSflHS6hDCbWa2QtIKSV9oXqtoELKUdOzM2rM6nfl7j+fQSV0Ku29u+9QiV8//8tAzUOHKd9XcZo8929CeWqCQWTZDael5ri5v2JxTJ4Mq7L45nNI7znd1ef2mnDppvmGPQIUQdoUQnq5+fUTSRknzJd0g6a7qw+6S9PEm9YgG6VCHyLI42DeLg32zWNg3x4ao38Izs8WSLpG0RtKcEMKPLle/W9KcQZ5zs6SbJalHE0bdKBqLLIuFPIuDLIuFPItrxEPkZjZJ0j2SPh9CODzwvhBCkBRO97wQwsoQwrIQwrIuddfVLBqDLIuFPIuDLIuFPIttREegzKxL/d8Ed4cQ7q3evMfM5oUQdpnZPEl7m9UkGmesZrnjS/68T/Mf7supk8Yqap7zv/xo1OM7N2+vua3cqGZapKhZNsLE789y9dF/ltzMU42xkuf+X77S1cdnmasXrm9lN601kt/CM0l3SNoYQrh9wF33S1pe/Xq5pPsa3x4aKfT/Y4csC4J9szjYN4uFfXNsGMkRqPdJ+rSk581sXfW2L0m6TdI3zOwmSdskfaIpHaJhyv3/JifL4mDfLAj2zcJh3xwDhl1AhRAekWSD3H11Y9tBM3WqUyEEsiwI9s3iYN8sFvbNsYFr4WFMOH7+CVeP/92nc+oEzVB+/UDeLaCJjv6zfXm3gEHMevpNV2/9lVJOnbQel3IBAACIxAIKAAAgEgsoAACASMxAYUxY8pXevFvAAJ0L5ru6b8fQ17oDkKaOg0ddPetbs12dvW6hlOS1C0eFI1AAAACRWEABAABEYgEFAAAQyfqvZ9iijZntU//ZV2dK2t+yDccran+LQgizhn/Y8MiyYXLPUiLPBso9T7JsmNyzlMizgRqeZ0sXUG9t1GxtCGFZyzc8QvQ3cin1cjr0Fye1frLob+RS6uV06C9Oav1kjcX++AgPAAAgEgsoAACASHktoFbmtN2Ror+RS6mX06G/OKn1k0V/I5dSL6dDf3FS6ydrzPWXywwUAABAO+MjPAAAgEgsoAAAACK1dAFlZteZ2SYz22pmK1q57cGY2Z1mttfMXhhw2wwze8jMtlT/np5TbwvN7GEz22Bm683slsT6SyrPlLOs9pJsnqllKaWdZ8pZVvtIKs+Us6z2kmyeqWUppZ1nK7Ns2QLKzEqSviLpo5KWSrrRzJa2avtDWCXpusxtKyStDiEskbS6WuehT9KtIYSlkq6Q9Jnqe5Z7f4nmuUrpZiklmmeiWUpp55lkllKyea5SullKieaZaJZS2nm2LssQQkv+SLpS0oMD6i9K+mKrtj9Mb4slvTCg3iRpXvXreZI25d1jtZf7JF2TQn+p5tkuWaaUZ6pZtlOeqWSZcp7tkmVKeaaaZTvl2cwsW/kR3nxJ2wfUO6q3pWhOCGFX9evdkubk2YwkmdliSZdIWqM0+muXPFN4r2oklme7ZCnl/17VSCxLqX3yTOG9qpFYnu2SpZT/e1Wj2VkyRD6M0L9czfVcD2Y2SdI9kj4fQjg88L4U+msXqbxX5NkYKbxXZNkYqbxX5NkYKbxXrciylQuonZIWDqgXVG9L0R4zmydJ1b/35tWImXWp/5vg7hDCvQn11y55pvBevSXRPNslSyn/9+otiWYptU+eKbxXb0k0z3bJUsr/vXpLq7Js5QLqSUlLzOxsMxsn6ZOS7m/h9mPcL2l59evl6v8MteXMzCTdIWljCOH2AXel0F+75JnCeyUp6TzbJUsp//dKUtJZSu2TZwrvlaSk82yXLKX83ytJLc6yxcNc10vaLOlFSb+e93BZtaevSdolqVf9ny/fJOkM9U/pb5H0bUkzcurt/eo/zPicpHXVP9cn1F9SeaacZep5ppZl6nmmnGWKeaacZep5ppZl6nm2Mksu5QIAABCJIXIAAIBILKAAAAAisYACAACIxAIKAAAgEgsoAACASHUtoFK8SjRGjzyLgyyLhTyLgyyLY9SnMaheJXqz+i/St0P9J/y6MYSwYbDnjLPu0KOJo9oe6ndCR3UqnLTT3RebJ1nmq5FZSuSZN/bN4mDfLJah8uys43Uvk7Q1hPCSJJnZ1yXdIGnQb4QeTdTldnUdm0yQnfZ9/T8SOs/WmrB6qLuj8ixklm2kkVlK5Jk39s3iYN8slqHyrOcjvBFdJdrMbjaztWa2tlcn69gcmmzYPMmybbBvFgv7ZnGwbxZIPUegRiSEsFLSSkmaYjPSORwjybq7XV2aNdPVlRmTXd03pafmNco9JX9Dhz8iVTpednXnoeP+4fsO+m3s2edfr+Kfn6eUs0Q88iwOsiwW8mwP9RyBaqerRGN45FkcZFks5FkcZFkg9Syg2ukq0RgeeRYHWRYLeRYHWRbIqD/CCyH0mdlnJT0oqSTpzhDC+oZ1hpYiz+Igy2Ihz+Igy2KpawYqhPCApAca1EvTlc6Y4W+Y42eeTs6Z5Orjs8a5+sS02t+465vgbwuZY3qlU74ed8jPUU3Y67c5fsdU/4Ttu1xZPny4podGabc8MTiyLBbyLA6yLA7ORA4AABCJBRQAAEAkFlAAAACRmn4eqJSUXz/gb8jUHVPf5es+f/oNq9TOQFnF1yFzWqiyH6PSqcn+NTr6fASlU/6U/d3H/dyWHTvmt9fXV9MTAABoLo5AAQAARGIBBQAAEIkFFAAAQKQxNQM1HHvsWVdPyNyfrVthrE44hfdd7Gr7wbpc+gAA4HQ4AgUAABCJBRQAAEAkFlAAAACRmIFCLo588gpXH53j1/Jz//ujrWzntKy729fj/Em9KkeOuLpz4QJX98335/Dq3HPI3//ytnpbBOq27beudPWffOpPXf0rf/B/ufqXPvP3rr79e9e6+rx/+0TU9kvnnu3q8taXo57fDCn2hPRwBAoAACASCygAAIBILKAAAAAiJTUD1THRXwduz6cvcvUbF5Vdbb1+/TflRV+PO+SvZTdr9auu7tuxc1R9Il7n4rNcPf3x11w9+RWfTQrCyZND1ll923f4GzL1WD2nF9K26Dcfc/V/+U3//93Z8vOIf//Y+1390ZXPufrV785z9aH/7vf9CX+zxtWVqf4Meyc+dllNjz1/5+eqOuef6V/joJ8vrBw9WvMaMdpp5in7c7Pe/3aMHEegAAAAIrGAAgAAiMQCCgAAIFJaM1Azpru6OzPDVDru13vlSX4maum/2OTqz8xb7erz/5/jrp5Z8p8df/2I3/5zxxa6+oXD/nN3Seo038OBE/41t708y9UznvZv+cw/9fMHRdWX4IwTWqc0y+8H5X37cuoE9QrPrHf1i+/JPmKXqyZk6prXe8q/Xs8Ieujb+drwDxqgtOQcV5e3vBT1/JQx85QfjkABAABEYgEFAAAQiQUUAABApKRmoLLn0ZnytWw99PPfuOgCV3/uis+4+vC5me3N7HX1rDn+XCJnTjrs6jO6az9rLgdz9Qs7/ZxU1xv+LbaKf37l/Re7uuORdTXbGIve/NnLXd3zuj+LUvcun40qfl6uvGlrU/oayt3bf+Dqn//JX3Z1WPtCK9vJ1cu3+eur/d2Nv+/q+474cw39/WvvdPWrW+a4+vw/89cdrKzbEN3Tji+919ULfjf/6y2iNXrPnOrqPT/hvxfmr37D1ZVnNza9Jwzu5I/7wbruv38y6vm7bvX5zvuD5uzrHIECAACIxAIKAAAgEgsoAACASEnNQNWr8twPXT3TX6JJsyf4ay5ZZ2Y+abr/nPzUpGmufmWBPy+UJI3f7mdxzt7wrH/NrnGuDr2nal6jHtn/htBXjCuuTfrrNUPeXx7yXqk0Z7Z//CI/U1N6eXfta0aem+jB19a5+r8e8HM9Y2nmKevsFf78Zrf81jWurhw75upuveLq+T/l8zrwTr9v7r/1EleHcu2/BV/6sTtcvavvEVf/4u/6a7oNp/Ocxa7ue+mVqOcjP137/febhfGu3vkRfw7Aef5/42ix3Zf7n2uL/j7u+Wf+0VOuDoM8rl4cgQIAAIjEAgoAACASCygAAIBIw85Amdmdkj4maW8I4cLqbTMk/ZWkxZJekfSJEMIbg71GqmzmjKEf0Js599DrJ2oeUt6weciXaPTMU73XFCtyngOV9+z1N2Tq4WaoTqfz7EWuvmajn4nruHr7KF519Nopy+zM03Am3Otn4CZk7p/2F8O/xkc+9EuuLj38dFQPWdmZp4O/4M91Ne3P67uuZTvl2Xa2++vxnflwydWjOa/YUMgyji270NWLfrO+faljoT8fY3nry3W93qDbGcFjVkm6LnPbCkmrQwhLJK2u1mgPq0SeRbFKZFkkq0SeRbFKZFl4wy6gQgjfl3Qgc/MNku6qfn2XpI83ti00C3kWB1kWC3kWB1mODaM9jcGcEMKPjonuljRnsAea2c2SbpaknpoD8UjEiPIky7bAvlks7JvFwb5ZMHWfByqEEMxs0NMshBBWSlopSVNsRrNOxzAyJf+5d3bGKYzv9vW4Lv/0ff5aeZKUPetSabo/n4hN8Ocb6dv52gga/T86Jk/2N4TK6R/YIEPlmVSWOdj8y/5z9XN/wp8sprnJxGurfbMJ6p15yup419tdPeObrc2ffXP0Dv+Yz27q03tc3ep9d6zvm1kdh/yM5GhmVN3zmzTzlDXa38LbY2bzJKn6995hHo+0kWdxkGWxkGdxkGXBjHYBdb+k5dWvl0u6rzHtICfkWRxkWSzkWRxkWTDDLqDM7GuSHpN0vpntMLObJN0m6Roz2yLpI9UabYA8i4Msi4U8i4Msx4ZhZ6BCCDcOctfVDe6l6SpHjrg6nDjpauvKXBsvc525vsP+uneSVDrDn0vKxvlr36mrvjGzvovP9dt77Pm6Xq9IebbalMzH6rHnNlJHZgavUt8n/WTZWq99yM83zn12Y0Nfnzybp3TCjxE1+zqGZDm00jvOd3V5/ab6Xm/JOf71trxU1+uNFGciBwAAiMQCCgAAIBILKAAAgEgsoAAAACLVfSLNdpa90O9oLvxbft2frb80baq/f9fuqNfrnO9P1qhX97uyry976k40y4mPXebqmX8ad4HLei/8jHxlLxY875/8iXQLf3bDdpL9BY3MCYd7/u6JFjaD4diB2pNS16NVQ+NZHIECAACIxAIKAAAgEgsoAACASGN6BqoZygfr+2z31NmzXd3xg2cHeSSabft15uolfxf3fGae2lvvBF+Hp9bn0wiGlzkpbem8t7m6vPnFVnaDAd782ctrbpv012vqes3SuWe7ulUXD87iCBQAAEAkFlAAAACRWEABAABEYgYqZ6XzMxcL3uMvWFwOnG2mVbb8kf+sfsln6/ucHu3l8I1XuHrWn8Sd9wut07lwgav7tu9wNTNP6ah33ul08pp5yuIIFAAAQCQWUAAAAJFYQAEAAEQaUzNQHRP8iV0qx47l1Mn/0TdzkqvtB+vyaWSMOfBLV9bcNmWzneaRGCvenO//PTklpz4wvOzMU8218DLnhUJ7O/5xf13S8X+bxrUNOQIFAAAQiQUUAABAJBZQAAAAkQo9A9UxebKvp/g6jxmoznMW+xte3OXKvta1MqaUpviJlqPXH6l5zMKfeaFV7SABW/783a5e8guP5tQJhnPoU/4cXVPvftw/gJmn3HT09Li6cuJEw7eRysxTFkegAAAAIrGAAgAAiMQCCgAAIFKhZ6AqR44MWQ+nNH26q8tvvBHdg3Vm3uJeP+XUt3tP9Gsi3qavvM3V5/7MMzl1grxs/ctLXD35qZ5BHonUTPum31+5Qmg6mjHz9Op/fK+rz/rPac4ncgQKAAAgEgsoAACASCygAAAAIhV6Bqpeo5l5ygp9mZmn7DWc0BT7f9lf627SU/Vf565z0UJX923bXvdronlOXv8eV4cD/t+Lc/9bmnMVY135qnfX3HZyRperJ9y7plXtIAepzjxlcQQKAAAgEgsoAACASMMuoMxsoZk9bGYbzGy9md1SvX2GmT1kZluqf08f7rWQr4oqIsviYN8sDvbNYmHfHBtGMgPVJ+nWEMLTZjZZ0lNm9pCkX5S0OoRwm5mtkLRC0hea1yoaZExkWe72M09z/zD+M/XKB/15g/q+l9y5o9g3hzDx2Z2uXvLAazl1MmJjMsvOBfNdfXxq7Y+lve/2/9ZffG9TW2oE9s0xYNgjUCGEXSGEp6tfH5G0UdJ8STdIuqv6sLskfbxJPaJBOtQhsiwO9s3iYN8sFvbNsSHqt/DMbLGkSyStkTQnhLCretduSXMGec7Nkm6WpB5NGHWjaCyyLBbyLA6yLBbyLK4RD5Gb2SRJ90j6fAjh8MD7QghBg5xdP4SwMoSwLISwrEvddTWLxiDLYiHP4iDLYiHPYhvRESgz61L/N8HdIYQfffq8x8zmhRB2mdk8SXub1SQap6hZ2nve6eo5/6P+84h0pDfzVKOoeTZC387kZ56csZLlvvvPd/Xxp89w9Tn/a1vNc05OOaupPTXDWMlzOKVpU2tuKx88lEMnjTeS38IzSXdI2hhCuH3AXfdLWl79ermk+xrfHhop9P9jhywLgn2zONg3i4V9c2wYyRGo90n6tKTnzWxd9bYvSbpN0jfM7CZJ2yR9oikdomHKKktkWSTsmwXBvlk47JtjwLALqBDCI5IGuw7G1Y1tB83UqU6FEMiyINg3i4N9s1jYN8cGroWHQghPPp93CwBGYM2lX3V117KSq6/9TxfXPGfaX+ysuQ3t4YEN36u57dozL259I03ApVwAAAAisYACAACIxAIKAAAgUqFnoDouvMDVb7xrmqt7J/oZv5krH2t2S2iQjne93dWV537oHxBOe346FFTn/DNrbmu380AVRc2++exGV39s/qWu3vJHl7t6idY0pzGMyEtfvtLV53yhvp+LT508VdfzU8YRKAAAgEgsoAAAACKxgAIAAIhkoYWzIma2T/1nX50paX/LNhyvqP0tCiHMakQDZNkwuWcpkWcD5Z4nWTZM7llK5NlADc+zpQuotzZqtjaEsKzlGx4h+hu5lHo5HfqLk1o/WfQ3cin1cjr0Fye1frLGYn98hAcAABCJBRQAAECkvBZQK3Pa7kjR38il1Mvp0F+c1PrJor+RS6mX06G/OKn1kzXm+stlBgoAAKCd8REeAABAJBZQAAAAkVq6gDKz68xsk5ltNbMVrdz2YMzsTjPba2YvDLhthpk9ZGZbqn9Pz6m3hWb2sJltMLP1ZnZLYv0llWfKWVZ7STbP1LKU0s4z5SyrfSSVZ8pZVntJNs/UspTSzrOVWbZsAWVmJUlfkfRRSUsl3WhmS1u1/SGsknRd5rYVklaHEJZIWl2t89An6dYQwlJJV0j6TPU9y72/RPNcpXSzlBLNM9EspbTzTDJLKdk8VyndLKVE80w0SyntPFuXZQihJX8kXSnpwQH1FyV9sVXbH6a3xZJeGFBvkjSv+vU8SZvy7rHay32Srkmhv1TzbJcsU8oz1SzbKc9Uskw5z3bJMqU8U82ynfJsZpat/AhvvqTtA+od1dtSNCeEsKv69W5Jc/JsRpLMbLGkSyStURr9tUueKbxXNRLLs12ylPJ/r2oklqXUPnmm8F7VSCzPdslSyv+9qtHsLBkiH0boX67meq4HM5sk6R5Jnw8hHB54Xwr9tYtU3ivybIwU3iuybIxU3ivybIwU3qtWZNnKBdROSQsH1Auqt6Voj5nNk6Tq33vzasTMutT/TXB3COHehPprlzxTeK/ekmie7ZKllP979ZZEs5TaJ88U3qu3JJpnu2Qp5f9evaVVWbZyAfWkpCVmdraZjZP0SUn3t3D7Me6XtLz69XL1f4bacmZmku6QtDGEcPuAu1Lor13yTOG9kpR0nu2SpZT/eyUp6Syl9skzhfdKUtJ5tkuWUv7vlaQWZ9niYa7rJW2W9KKkX897uKza09ck7ZLUq/7Pl2+SdIb6p/S3SPq2pBk59fZ+9R9mfE7Suuqf6xPqL6k8U84y9TxTyzL1PFPOMsU8U84y9TxTyzL1PFuZJZdyAQAAiMQQOQAAQCQWUAAAAJFYQAEAAERiAQUAABCJBRQAAECkuhZQKV4lGqNHngAAjMyoT2NQvUr0ZvVfpG+H+k/4dWMIYUPj2kOrkCcAACPXWcdzL5O0NYTwkiSZ2dcl3SBp0B+446w79GhiHZtEPU7oqE6FkzbI3VF5kmW+hskSANBk9SygTneV6MuHekKPJupyu7qOTaIea8Lqoe6OypMs8zVMlgCAJqtnATUiZnazpJslqUcTmr05NBFZAgDQr54h8hFdJTqEsDKEsCyEsKxL3XVsDk02bJ5kCQBAv3oWUO10lWgMjzwBABihUX+EF0LoM7PPSnpQUknSnSGE9Q3rDC1FngAAjFxdM1AhhAckPdCgXpAz8gQAYGQ4EzkAAEAkFlAAAACRWEABAABEYgEFAAAQiQUUAABAJBZQAAAAkVhAAQAARGIBBQAAEIkFFAAAQCQWUAAAAJFYQAEAAERiAQUAABCprosJN1vHu97u6l0fnO7qeXc86+rK0aNN7wkAAIAjUAAAAJFYQAEAAERiAQUAABAp7Rmog2+6unein4E66+GKq//p/ve6+uRMf/+EHX69eObvP1pvixih0vnnurq8aWtOnQAAUD+OQAEAAERiAQUAABCJBRQAAECkpGeg+rZtd/WC/+Lrbb8/ztVnaa2rj193sasXfWmDq//8V9e5+htvTnX1F9f8lKtDxWp6DMf8Wzhtva8PnVf2Pbx9t6u3753h6iVfPu7qynM/rNlmO2LmCQBQJByBAgAAiMQCCgAAIBILKAAAgEhJz0ANJ/SeGvL+nm894eo93/L3X6uLh3z+uXpmNG05sy97p6v3vftMV4+f4ueqdvzYeFef+VzdLbQny8ybhZBPH0Po+/Clru78zlM5dQIAaDWOQAEAAERiAQUAABCJBRQAAECktp6BagtPPO/KWU8M8jh4DZ556pg82dWVI0eiX+ODz/lzdH1p5h2uvvbMi6NfEwDQnjgCBQAAEIkFFAAAQKRhF1BmdqeZ7TWzFwbcNsPMHjKzLdW/pze3TTQKeQIAUL+RzECtkvRHkv58wG0rJK0OIdxmZiuq9Rca3177O3Xde1w97h+fzKmTt6zSGMxzNDNPWX/25Adc/b2bxg/ySABA0Q17BCqE8H1JBzI33yDprurXd0n6eGPbQrOQJwAA9RvtDNScEMKu6te7Jc1pUD/IB3kCABCh7iHyEEKQNOjvnJvZzWa21szW9upkvZtDkw2VJ1kCANBvtOeB2mNm80IIu8xsnqS9gz0whLBS0kpJmmIz0rugWYP1fsRfHy2BmaeRGFGeYy3Loz9zuatn/RO/tAoA6Dfanwj3S1pe/Xq5pPsa0w5yQp4AAEQYyWkMvibpMUnnm9kOM7tJ0m2SrjGzLZI+Uq3RBsgTAID6DfsRXgjhxkHuurrBvaAFyBMAgPpxLbw6lc6Y4epwtC+nTtBor19YcvVZ/+nRnDoBAKSGqVgAAIBILKAAAAAisYACAACIxAIKAAAgEkPkdbKuLl8/9mxOnaBe9p53uvqcO151Nb8eAAD4EY5AAQAARGIBBQAAEIkFFAAAQCRmoOrUt3tP3i2gQV5/5yRXz3jy+Zw6AQCkjiNQAAAAkVhAAQAARGIBBQAAEIkZqAid8+bW3Na3a3cOnaBelQ9eUnPb7P/NeZ8AACPDESgAAIBILKAAAAAisYACAACIxAzUEEpzZrs6TByfUydotNLhUzW39e3YmUMnAIB2xBEoAACASCygAAAAIrGAAgAAiMQM1AClKVNcXVmYmYFa+0Ir20EDHfrUFa6eevfjOXUCACgCjkABAABEYgEFAAAQiQUUAABAJGagBjj8Y2939cRvrsmpE9Tr+A2XubohM08dJV9XyvW/JgCgLXEECgAAIBILKAAAgEgsoAAAACIxAzUAM0/FcWK6n1dqyFUMmXkCAFRxBAoAACDSsAsoM1toZg+b2QYzW29mt1Rvn2FmD5nZlurf05vfLupRUUVkCQBA/UZyBKpP0q0hhKWSrpD0GTNbKmmFpNUhhCWSVldrpI8sAQCo07AzUCGEXZJ2Vb8+YmYbJc2XdIOkq6oPu0vSdyV9oSldNkn5qne7uvTdp/NppEU61KEQwtNS8bI8+OkrXT191WM5dQIAGAuiZqDMbLGkSyStkTSnuriSpN2S5jS2NTQTWQIAMHojXkCZ2SRJ90j6fAjh8MD7QghBUhjkeTeb2VozW9urk3U1i8YgSwAA6jOiBZSZdan/B+7dIYR7qzfvMbN51fvnSdp7uueGEFaGEJaFEJZ1qbsRPaMOZAkAQP2GnYEyM5N0h6SNIYTbB9x1v6Tlkm6r/n1fUzpsoNL55/obCj7zlBX6DywVIsuOiy5w9bS/YOYJANA6IzmR5vskfVrS82a2rnrbl9T/w/YbZnaTpG2SPtGUDtEwZZUlsgQAoG4j+S28RyTZIHdf3dh20Eyd6lQIgSwBAKgTZyIHAACIVOhr4fV9+FJX7zt/nKtnbdraynbQQMfnT3Z193M5NQIAGJM4AgUAABCJBRQAAEAkFlAAAACRCj0DVe7268MJeyv+/jF2Lbwi6f6HJ/NuAQAwhnEECgAAIBILKAAAgEgsoAAAACJZCKF1GzPbp/5LhcyUtL9lG45X1P4WhRBmNaIBsmyY3LMEAMRr6QLqrY2arQ0hLGv5hkeI/kYupV5Oh/4AAM3AR3gAAACRWEABAABEymsBtTKn7Y4U/Y1cSr2cDv0BABoulxkoAACAdsZHeAAAAJFauoAys+vMbJOZbTWzFa3c9mDM7E4z22tmLwy4bYaZPWRmW6p/T8+pt4Vm9rCZbTCz9WZ2S2L9JZVnyllWe0k6TwDAyLVsAWVmJUlfkfRRSUsl3WhmS1u1/SGsknRd5rYVklaHEJZIWl2t89An6dYQwlJJV0j6TPU9y72/RPNcpXSzlBLOEwAQp5VHoC6TtDWE8FII4ZSkr0u6oYXbP60QwvclHcjcfIOku6pf3yXp463s6UdCCLtCCE9Xvz4iaaOk+Yn0l1yeKWcpJZ8nACBCKxdQ8yVtH1DvqN6WojkhhF3Vr3dLmpNnM5JkZoslXSJpjdLor13yTOG9qpFgngCACAyRDyP0/5pirr+qaGaTJN0j6fMhhMMD70uhv3aRyntFngDQ/lq5gNopaeGAekH1thTtMbN5klT9e29ejZhZl/p/2N4dQrg3of7aJc8U3qu3JJwnACBCKxdQT0paYmZnm9k4SZ+UdH8Ltx/jfknLq18vl3RfHk2YmUm6Q9LGEMLtA+5Kob92yTOF90pS8nkCACK09ESaZna9pD+UVJJ0Zwjhd1q28UGY2dckXSVppqQ9kv6jpL+V9A1JZ0naJukTIYTscHIrenu/pH+S9LykSvXmL6l/biaF/pLKM+Usq/0lnScAYOQ4EzkAAEAkhsgBAAAisYACAACIxAIKAAAgEgsoAACASCygAAAAIrGAAgAAiMQCCgAAIBILKAAAgEj/P0Q6cvD//LEJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,15))\n",
    "for i in range(32):\n",
    "    fig.add_subplot(7,5,i+1)\n",
    "    plt.imshow(activation[0,:,:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "22b67f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,15))\n",
    "for i in range(32):\n",
    "    fig.add_subplot(7,5,i+1)\n",
    "    plt.imshow(activation[3,:,:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "cbb5f242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,15))\n",
    "\n",
    "for i in range(32):\n",
    "    fig.add_subplot(7,5,i+1)\n",
    "    plt.imshow(first_layer.kernel[:,:,0,i])"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}