exam16_classfication_fashion_MNIST_CNN.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"OPqOHE19XpR2"},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n","from tensorflow.keras import datasets\n","from keras.utils import np_utils"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1450,"status":"ok","timestamp":1648446066082,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"mVGw2HH8YJvN","outputId":"70333d43-40fc-433c-b4ef-143f12ce16fd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","32768/29515 [=================================] - 0s 0us/step\n","40960/29515 [=========================================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","26427392/26421880 [==============================] - 0s 0us/step\n","26435584/26421880 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","16384/5148 [===============================================================================================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","4423680/4422102 [==============================] - 0s 0us/step\n","4431872/4422102 [==============================] - 0s 0us/step\n","(60000, 28, 28) (60000,)\n","(10000, 28, 28) (10000,)\n"]}],"source":["#이 데이터는 아예 나눠져서 들어와있음\n","(X_train, Y_train), (X_test, Y_test) = datasets.fashion_mnist.load_data()\n","print(X_train.shape, Y_train.shape)\n","print(X_test.shape, Y_test.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_5Xqaa2F3DHf"},"outputs":[],"source":["label = ['T-shirt', 'trouser', 'pullover', 'dress', 'coat',\n","         'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":400,"status":"ok","timestamp":1648446066478,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"jdpT8_YZYu8m","outputId":"c57593fd-1584-44c7-fedd-677a2eb7aae7"},"outputs":[{"name":"stdout","output_type":"stream","text":["7\n","[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 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size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["my_sample = np.random.randint(60000)\n","plt.imshow(X_train[my_sample], cmap='gray') #이 데이터는 이미지라서\n","plt.show\n","print(Y_train[my_sample]) #정답을 확인해보자\n","print(X_train[my_sample]) \n","\n","#데이터를 보면 곱하면 커짐, 그러면 scale을 해줘야함, 0-1사이면 곱해도 커지지 않음."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1648446066479,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"M1MfO5obZYVF","outputId":"e69a0b65-0ed7-4bf2-d926-97402a9c7cfd"},"outputs":[{"name":"stdout","output_type":"stream","text":["4\n","[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n"]}],"source":["y_train = np_utils.to_categorical(Y_train)  #onehotencoding을 해줌\n","y_test = np_utils.to_categorical(Y_test)\n","print(Y_train[5000])\n","print(y_train[5000])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1648446066479,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"EcY1gpVQabb2","outputId":"05b68413-73a8-4beb-fbaf-a242e62ee911"},"outputs":[{"name":"stdout","output_type":"stream","text":["(60000, 28, 28, 1)\n","(10000, 28, 28, 1)\n","[[[0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.  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   ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]]\n","\n"," [[0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]\n","  [0.        ]]]\n"]}],"source":["#reshape없이 이미지 그대로 들어가야함\n","x_train = X_train/255  #minmax scale   255니까 0-1로 만들어줌\n","x_test = X_test/255\n","x_train = x_train.reshape(60000, 28, 28, 1)  #가로세로 컬러\n","print(x_train.shape)  #784개의 픽셀이 들어있는 데이터프레임 6만개\n","x_test = x_test.reshape(10000, 28, 28, 1)\n","print(x_test.shape)  #784개의 픽셀이 들어있는 데이터프레임 6만개\n","print(x_train[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8NKqNsdmc-o9"},"outputs":[],"source":["model = Sequential()\n","model.add(Conv2D(32, kernel_size=(3, 3),\n","                 input_shape=(28, 28, 1), padding='same',\n","                 activation='relu'))\n","#3,3 커널을 32개 만듦, 커널 안의 값은 랜덤 32개의 커널을 거쳐서 나오는 이미지는 32개\n","#padding='same' 입력이미지와 출력이미지가 같게\n","model.add(MaxPool2D(padding='same', pool_size=(2, 2)))\n","#2,2 필터상자에서 max값, 커널만큼 감 겹치지 않고, 빈칸이 더 없으면 끝 padding='same'을 주면 모자란 칸마큼 0으로채워줌\n","model.add(Conv2D(32, kernel_size=(3, 3),\n","                 padding='same',activation='relu'))\n","model.add(MaxPool2D(padding='same', pool_size=(2, 2)))\n","model.add(Flatten())\n","model.add(Dense(128, activation='relu'))\n","model.add(Dense(10, activation='softmax'))  #카테고리가 10개\n","#kerner은 필터임. 커널사이즈가 커지면출력이미지가 작아짐"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1648446069743,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"8h9L4S0YeINN","outputId":"23f7f37d-5797-47fc-fe42-c2e146c59797"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 28, 28, 32)        320       \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n"," )                                                               \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 14, 14, 32)        9248      \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0         \n"," 2D)                                                             \n","                                                                 \n"," flatten (Flatten)           (None, 1568)              0         \n","                                                                 \n"," dense (Dense)               (None, 128)               200832    \n","                                                                 \n"," dense_1 (Dense)             (None, 10)                1290      \n","                                                                 \n","=================================================================\n","Total params: 211,690\n","Trainable params: 211,690\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["model.compile(optimizer='adam', loss='categorical_crossentropy',\n","              metrics=['accuracy'])\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":83282,"status":"ok","timestamp":1648446153016,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"z9KE__K9edwj","outputId":"b2d653eb-ecf7-492c-be08-cf8d1c3a7827"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/20\n","375/375 [==============================] - 44s 115ms/step - loss: 0.5541 - accuracy: 0.8031 - val_loss: 0.3837 - val_accuracy: 0.8644\n","Epoch 2/20\n","375/375 [==============================] - 44s 116ms/step - loss: 0.3440 - accuracy: 0.8761 - val_loss: 0.3527 - val_accuracy: 0.8703\n","Epoch 3/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.2953 - accuracy: 0.8938 - val_loss: 0.2897 - val_accuracy: 0.8947\n","Epoch 4/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.2714 - accuracy: 0.9014 - val_loss: 0.2960 - val_accuracy: 0.8908\n","Epoch 5/20\n","375/375 [==============================] - 44s 116ms/step - loss: 0.2484 - accuracy: 0.9092 - val_loss: 0.2570 - val_accuracy: 0.9057\n","Epoch 6/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.2300 - accuracy: 0.9170 - val_loss: 0.2436 - val_accuracy: 0.9105\n","Epoch 7/20\n","375/375 [==============================] - 44s 116ms/step - loss: 0.2133 - accuracy: 0.9215 - val_loss: 0.2466 - val_accuracy: 0.9117\n","Epoch 8/20\n","375/375 [==============================] - 43s 115ms/step - loss: 0.1963 - accuracy: 0.9289 - val_loss: 0.2449 - val_accuracy: 0.9091\n","Epoch 9/20\n","375/375 [==============================] - 43s 115ms/step - loss: 0.1851 - accuracy: 0.9319 - val_loss: 0.2359 - val_accuracy: 0.9144\n","Epoch 10/20\n","375/375 [==============================] - 43s 115ms/step - loss: 0.1726 - accuracy: 0.9372 - val_loss: 0.2289 - val_accuracy: 0.9191\n","Epoch 11/20\n","375/375 [==============================] - 43s 115ms/step - loss: 0.1601 - accuracy: 0.9415 - val_loss: 0.2431 - val_accuracy: 0.9157\n","Epoch 12/20\n","375/375 [==============================] - 43s 114ms/step - loss: 0.1472 - accuracy: 0.9465 - val_loss: 0.2389 - val_accuracy: 0.9187\n","Epoch 13/20\n","375/375 [==============================] - 43s 114ms/step - loss: 0.1398 - accuracy: 0.9483 - val_loss: 0.2329 - val_accuracy: 0.9188\n","Epoch 14/20\n","375/375 [==============================] - 43s 115ms/step - loss: 0.1258 - accuracy: 0.9536 - val_loss: 0.2326 - val_accuracy: 0.9207\n","Epoch 15/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.1172 - accuracy: 0.9566 - val_loss: 0.2334 - val_accuracy: 0.9208\n","Epoch 16/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.1053 - accuracy: 0.9617 - val_loss: 0.2512 - val_accuracy: 0.9179\n","Epoch 17/20\n","375/375 [==============================] - 44s 118ms/step - loss: 0.0964 - accuracy: 0.9652 - val_loss: 0.2496 - val_accuracy: 0.9202\n","Epoch 18/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.0889 - accuracy: 0.9673 - val_loss: 0.2630 - val_accuracy: 0.9198\n","Epoch 19/20\n","375/375 [==============================] - 44s 117ms/step - loss: 0.0840 - accuracy: 0.9697 - val_loss: 0.2851 - val_accuracy: 0.9183\n","Epoch 20/20\n","375/375 [==============================] - 44s 118ms/step - loss: 0.0701 - accuracy: 0.9754 - val_loss: 0.2659 - val_accuracy: 0.9228\n"]}],"source":["#학습시키기\n","fit_hist = model.fit(x_train, y_train, batch_size=128,\n","                     epochs=20, validation_split=0.2, verbose=1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1172,"status":"ok","timestamp":1648446154179,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"z6J6yqRZe_Ak","outputId":"c0f5c144-f92a-4939-89b2-4159f03f5e6a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Final test set accuracy : 0.9154000282287598\n"]}],"source":["#검증하기\n","score = model.evaluate(x_test, y_test, verbose=0)\n","print('Final test set accuracy :', score[1])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"executionInfo":{"elapsed":380,"status":"ok","timestamp":1648446154558,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"akElf5k0fpwE","outputId":"a6041fe2-9a61-4f7e-b831-41ff0ac5b0ec"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8dcnG0sWlmxAQiBssrgAIohgtSCCVMW9Llitttp7S29b9f5qb71ea7fb5fbWttZeq9alKlpXrIorXr0tYMIOCUsIW0L2QFayzuf3x3cCY0xgIMtMZj7Px2MeM3POmcxnhuE9Z77ne75fUVWMMcaErohAF2CMMaZnWdAbY0yIs6A3xpgQZ0FvjDEhzoLeGGNCXFSgC2gvKSlJR48eHegyjDGmT1m3bl25qiZ3tC7ogn706NFkZ2cHugxjjOlTRGRfZ+us6cYYY0KcBb0xxoQ4C3pjjAlxFvTGGBPiLOiNMSbEWdAbY0yIs6A3xpgQF3T96I0xJlyoKmU1jewsqWVXaQ39oiK5cVZGtz+PBb0xxvQwVaWkupFdpTXsLKklz3u9q6SG6oaWo9tNzxhsQW+MMcFMVSmqamBXqQvxXd499V2ltdT4BPrQ2BjGp8Rx+dQRjE+JZ3xqHONT4kmKi+mRuizojTHmJDW3ethXUc/uslp3Ka0jr6yW3aW11DYeC/SkuBjGpcRxxdQ0JqTGMS4lngmpcSTG9evVei3ojTGmEzUNzewuq2N3qQv0PO/1vop6WjzHpmEdltCfsSmxXDU9jfGp8UxIiWN8ajxDY3tmD/1kWdAbY8JeZV0T24uq2dUu0EuqG49uExUhjE6KZVxKHAunDGNcShxjk+MYkxxLfP/oAFZ/Yhb0xpiw0dTiIb+8lu1FNeQWV7vrompKa44Feny/KMakxDF3XDJjU2IZlxzH2JQ4MoYOJDqyb/ZIt6A3xoQcVaWstvFokG8vdte7y2ppbnVNLjGREYxLiWPu+CQmDUtg4vB4JqTGkxLfDxEJ8CvoXhb0xpg+r6G5lY92lJK19xDbvXvqFXVNR9cPS+jPxOHxXHhaCpOGxzNpeAKZSbF9dg/9ZFnQG2P6JFVl3b5DvLy+kDc3H6S6oYX+0RGclhrPRZNSmTg8nonDEpg4LJ4hQXJQNFAs6I0xfcre8jpe3VDIqxsK2V9Zz4DoSBZOSeXK6enMGZtIVJjspZ8MC3pjTNA7XN/E3zYX8cr6AtbvP4wInDc2kW/PH8/C04cR18+i7Hj8endEZBHwEBAJPKaq/9lu/SjgCSAZqASWqmqBd10G8BgwElBgsaru7a4XYIwJTU0tHlbtKOXV9YV8uL2UplYPE1LjuPeSiSyZOoLhgwYEusQ+44RBLyKRwMPAAqAAyBKRFaqa47PZr4CnVfUpEZkH/Ay42bvuaeAnqvqeiMQBnm59BcaYkKGqbDhwmFfXF/LG5oMcrm8mKS6Gm2eP4sppaUwZkRByPWJ6gz979DOBPFXNBxCR5cASwDfoJwN3eW+vAl7zbjsZiFLV9wBUtbab6jbG9HFtA33ll9eyt7ye/LJaPtheyp7yOvpFRbBwyjCunJ7G+eOSrN29i/wJ+jTggM/9AmBWu202AVfhmneuBOJFJBGYABwWkVeATOB94F5VbfV9sIjcAdwBkJHR/SO3GWMCQ1WprGtiT3nd0cveijryy+rYV1HPkeZjURATFcH0jMH804VjueT0YUF/tmlf0l1HMO4Bfi8itwIfA4VAq/fvnw9MA/YDLwC3Ao/7PlhVHwUeBZgxY4ZijOlz9pbXsang8LFAL68jv7zuM6M2RkUII4cOJDMplvPGJpGZHEtmYiyZybEMT+hPRIQ1y/QEf4K+EHcgtU26d9lRqnoQt0ePtx3+alU9LCIFwEafZp/XgHNpF/TGmL7pSFMrb20p4oWsA3y6txIAERgxaABjkmO5YmoamUmxZCbFMjoplvQhA8LmJKVg4k/QZwHjRSQTF/DXAzf6biAiSUClqnqA7+N64LQ9drCIJKtqGTAPyO6u4o0xvU9V2VJYxQtZB1ix8SA1jS2MThzI9xZNZN7EFEYlDqR/dGSgyzQ+Thj0qtoiIsuAd3DdK59Q1W0i8iCQraorgAuBn4mI4ppuvul9bKuI3AN8IO5Q+TrgTz3zUowxPamqvpnXNhayPOsAuUXV9I+OYPHpw/nyOSOZmTnUesMEMVENribxGTNmaHa27fQbEww8HmVNfgUvZB/g7a3FNLV4OCNtENedM5LLzxrBoAF2wDRYiMg6VZ3R0To7ncwY8znFVQ28vL6AF7IOsL+ynoT+UVx/zkiumzGS09MGBbo8c5Is6I0xgJse78PtpbyYdYBVO0rxKMwek8hdCyaw6PRh1u7eh1nQGxOmPB4lt7ia1bsrWJNfwdo9ldQ0tJAS349/unAs1549ktFJsYEu03QDC3pjwoSqsrOkltW7y1ntDfbD9c0AjE4cyKVnDmf+xFQuPC3ZzkQNMRb0xoQoVWV3WR2r8ytY491rb5uMI33IABZMSmX22ERmj020AcJCnAW9MSFCVdlXUc/q/ApW765gdX4FZd65UIcP6s8FE5I5d2wis8ckMnLowABXa3qTBb0xfVhzq4dP91TyXk4J7+WUUHj4CADJ8f2YPcbtrc8ek8ioxIHWzz2MWdAb08dUNzTz0Y4y3s8pYdWOUmoaWugXFcH545P4xoVjmT0mkbHJsRbs5igLemP6gMLDR3g/p4T3c0tYk19Bc6uSGBvDoinDWDA5lfPHJzMgxro/mo5Z0BsThFSVbQerec8b7tsOVgMwJjmW2+ZksmByKtMyhhBpoz0aP1jQGxMkmlo8rN1T4cI9p4SDVQ2IwNkZQ7j3koksmJzK2OS4QJdp+iALemMCqNWjrN1TwYqNB3lrSxHVDS30j47g/PHJfGfBBOZNTCEprl+gyzR9nAW9Mb2srVnm9Y2FvLGpiOLqBgbGRLJwyjAWnzGcueOSrL3ddCsLemN6yZ7yOlZsPMjrmwrJL6sjOlK4YEIKP/jSJC6alGrhbnqMBb0xPai0uoG/bS7i9Y2FbCqoQgRmZQ7l6+eP4ZLThzF4YEygSzRhwK+gF5FFuIm/I4HHVPU/260fhZtVKhmoBJaqaoHP+gQgB3hNVZd1U+3GBKXqhmZWbi1mxcaD/GN3OR6FKSMS+MHiSVx61nAbbsD0uhMGvYhEAg8DC4ACIEtEVqhqjs9mvwKeVtWnRGQe8DPgZp/1P8LNPGVMSGpobmXV9lJe33iQD3eU0tTiYVTiQJZ9cRyXTx3BuJT4QJdowpg/e/QzgTyfCb6XA0twe+htJgN3eW+vAl5rWyEiZwOpwEqgw9lPjOmLPB7l072VvLahkDe3FFHT0EJSXD9umpXBkqlpnJU+yM5ONUHBn6BPAw743C8AZrXbZhNwFa5550ogXkQSgUPAfwFLgYu6XK0xQWBHcQ2vbihkxcZCDlY1EBsTycLTh3HF1DTOG5toQ/yaoNNdB2PvAX4vIrfimmgKgVbgn4G3VLXgeHs2InIHcAdARkZGN5VkTPcprmpgxaZCXt1wkNyiaiIjhAsmJPM974lMA2OsX4MJXv58OguBkT73073LjlLVg7g9ekQkDrhaVQ+LyGzgfBH5ZyAOiBGRWlW9t93jHwUeBTc5+Km+GGO6U9tB1dc2FLI6vwJVmDpyMD+8fApfOnO4nchk+gx/gj4LGC8imbiAvx640XcDEUkCKlXVA3wf1wMHVb3JZ5tbgRntQ96YYNLU4uF/d5bx2oZC3s8tobHFw+jEgXx7/niWTE0j06bWM33QCYNeVVtEZBnwDq575ROquk1EHgSyVXUFcCHwMxFRXNPNN3uwZmO63fbiap5bu58Vmw5yuL6ZxNgYbpiZwRXT7KCq6ftENbhaSmbMmKHZ2dmBLsOEgYbmVt7eWsRf1uxn3b5DxERFsHDKMK6alsbc8UlE20FV04eIyDpV7bBnox1BMmFnb3kdz326n79mH+BQfTOZSbHc96VJXHN2up2pakKSBb0JCy2tHt7PLeHZtfv5ZFc5kRHCxZNTWXruKM4bm2hNMyakWdCbkFZUdYTnPz3AC1n7KaluZPig/ty1YAJfPmckqQn9A12eMb3Cgt6EHI9H+SSvnL+s2ccHuSUocMGEZH58xSi+eFqyndBkwo4FvQkZFbWN/HVdAc+t3c/+ynoSY2O484Kx3HBOBhmJAwNdnjEBY0Fv+rSymkbe2VbMyq3FrM6voNWjzMwcyj0LT2PhlFT6RdkY78ZY0Js+p7iqgZVbi3hrazFZeytRhcykWO78whiumJbGhFQbKdIYXxb0pk84UFnPyq3FvL21iPX7DwMwITWOf5k3nkvOGMZpqfHWc8aYTljQm6C1p7yOt7cW8faWYrYUVgEweXgC91w8gUWnD2dcSlyAKzSmb7CgN0FlV0kNb28t5q0tRWwvrgHgrJGDufeSiVxy+jBGJdpYM8acLAt6E1CH6ppYk1/BP3ZX8Pe8cvLL6xCBszOG8O+XTmbR6cNIG2xT7xnTFRb0plfVNraQtaeSf+wu5+95FeQWV6MKsTGRzMwcyq1zRrNwyjA7mcmYbmRBb3pUQ3Mr6/cfYrV3j31TQRWtHiUmKoKzM4Zw10UTOG9cImemD7ZBxIzpIRb0plu1tHrYXFh1NNiz9x2iqcVDZIRwZvogvnHBGM4bm8TZo4bQP9r6uBvTGyzoTbfYUlDFI/+bx8c7y6ltbAFg0vAEbj53FHPGJXLO6KHE948OcJXGhCcLetMlWwqqeOiDnbyfW8qgAdEsmTqCOeOSOHdMIkNjbchfY4KBX0EvIouAh3AzTD2mqv/Zbv0o3PSByUAlsNQ7IfhU4BEgATdZ+E9U9YVurN8EyNbCKn7z/i7ezy1h0IBo7rl4ArecN9r22ntbQxXkfwR578PuVaAKqZMhZRKkTHHXyadBlM1vG85OGPQiEgk8DCwACoAsEVmhqjk+m/0KeFpVnxKRecDPgJuBeuArqrpLREYA60TkHVU93O2vxPSKrYVVPPTBLt7LKSGhfxR3L5jALXNGk2AB3zs8HijZArveg7wP4MBa0FboNwjGXOACvTTXhb6n2T1GIiFxLKRMdpdU7/WQ0RBhx0nCgT979DOBPFXNBxCR5cASwDfoJwN3eW+vAl4DUNWdbRuo6kERKcXt9VvQ9zHbDro9+LaAv2vBBG61gO8d9ZWw+0O31573AdSVuuXDz4K534FxCyD9HIj0+e/c2gwVu6F0mwv+khwo2gQ5rwPe6UOjBri9/aPhPwmGT4XYpF5/iaestQWOVEJdGdSVQ1wKJE+E3h4OQ9W9z011kDwB+g/q3ec/AX+CPg044HO/AJjVbptNwFW45p0rgXgRSVTVirYNRGQmEAPsbv8EInIHcAdARkbGydRveti2g1U89P4u3s0pIb5/FN+9aAJfnWsB36M8rXBwI+S958K9cB2oBwYMgbHzYdxFMG6+C7XOREZDykR38dVUB2XbXfCX5kJpDuz+ADY9d2yboWNg5Cz35TFylvsC6K09/6PBXe7Cu74c6ip8bnsv9d71Rw5z9IurzaAMmLDQXUafD9E9dE5G8xHY8wnsXAm73oUqn5iMH+He++RJ3uuJ7ks1QF8AJ5wcXESuARap6te8928GZqnqMp9tRgC/BzKBj4GrgdPbmmhEZDjwEXCLqq453vPZ5ODBIedgNQ99sJN3trmAv31uJl+dk8mgAUEc8FWFED0ABg4NdCUnr/kIbH8Tdrzt9t6PVAICaWe7YB+/AEZM67nAratwe/+F66EgyzUJ1ZW5dTHxkH62N/xnQvoMGDD41J+rpQkO7YWKPJ/LbnddW8LnghsAcf+usckwMAliE31uey8Dk6AyH3a+A/mroLkeogfCmAtd6I+/GBJGnHrd4D5ju96Bne+6YyMtRyA69thzxKW4L9LS7VCWC2U73TZtEtJc6LcdO0n2XvdP6FpdHH9ycH+CfjbwgKou9N7/PoCq/qyT7eOA7aqa7r2fgAv5n6rqSycq1oI+sHKLqnno/V2s3FZMfL8obpubyW1zgzzga0vhwx/DhmdAIiDzCzB5CUy8NLibIVTd3vqGv8DWV6CxyoXXuIvcZey8wH1pqcKhPXDAG/oFn0LJNvfLAnFhNfKcY+GfNP6zzSUeD1QXfDbE2y6H93v/jtfAREgc5y6D0j8b3m1hPnDoyX3JNTfA3v/zhvJK95wAw86ECYvcZcQ0iDjBSXqeVvflt3Ol+1vFW9zywRnev7MQRs3t/FeDp9U9d9l29wuqbLv30v4LIN0F/qjZ8IV/9f91+uhq0EcBO4H5QCGQBdyoqtt8tkkCKlXVIyI/AVpV9X4RiQHeBt5Q1d/4U6wFfe9rbvXw8c4ylmcd4L2ckmMBPyeTQQODOOCbG2DNH+CTX7v/NOd8ze3R57zu9uwkAkbP9Yb+ZRCfGuiKnZoS2LwcNj7n/tNHDYDJl8PUm1xTw4nCJ1Aaa1zoHfjUBf+BT6HBe7htwBDvcYIYF+yV+dDaeOyxMXGuSagt0I9exrjH9iRV9z7vXOn2xA+scV80scluL3/CQhjzxWN71Q1V7lfVznddk0x9uTugnXGud/tFLpS7chzA0wqH93n3/Lcf+yJISIMbl5/Sn+xS0Hv/wGLgN7julU+o6k9E5EEgW1VXeJt3fob7zfUx8E1VbRSRpcCfgW0+f+5WVd3Y2XNZ0PeenIPVvLy+gNc3FlJe20RibAw3zcrg9rljgjvgVWHbK/DeA1C1H05bDAt+BEnjjq0v2eoCP+d1KN8JCGTMdqE/6TIYlNa7Nbc0uT3CDc+68NBWtyc87SaYcmXQHbzzi8cDFbtc4B9YCwXZ7nUljnO9fHwDPS619w+Qdqa+0h3U3rnSHQNpOAwR0TDqPEBh3z/A0wL9B7smswmLAvvryk9dDvreZEHfs8pqGnl9YyEvry8kt6ia6Ehh/sRUrj47nQtPSw7+8WYK1sE733fBknoGLPyJ61Z4PKXbj4V+qXefI32mC/3Jl7uf4T2leCtsfBY2vwD1FRA3DM663u29J0/ouec1/mltcb9Odq50XVaRY+HevidTkLOgD3ONLa18kFvKy+sK+GhnGa0e5az0QVx9djqXnTmCIX3hDNaqAnj/h7DlRYhNgfn/7sLyZA9Olu86FvrFm92yEdOPhf7QMV2vtb4StrwEG//iujRGRMNpl8C0m92eYR8KD9N3WNCHIVVl44HDvLy+gDc2FVF1pJnUhH5cOS2dq6enMb6vzKvaWAt//w3843euSea8ZTD3u9CvG+qvzIecFS70D653y6IHunb+qAHuOnrAsWVHr9vf9t6PjHY9Mba/Ca1NMOwMmLoUzrjW9RIxpgdZ0IeRoqojvLK+kFfWF7C7rI5+UREsOn0YV09PZ864JCIjgqSd9EQ8Hte3+4MfQW0xnH41XPRAzzWzHNoHO96C6kLX1bH5iOue95nrhs8v8z3gCDBgKJx5nfu1MfzMnqnVmA4cL+jtN2QIUFXW7qnkkY928/GuMlThnNFD+Pr5Y1h85vC+d3LTnk/gnX9zTStpM+DLz8DImT37nENGwbn/dPKP87S6wG/xfgnEDYOoPtAUZsKKBX0fpqp8squc3324i6y9h0iK68e35o3n6ulpfXNu1Yrd8N79sP1vrl/x1Y+7Pflg6a3RkYhI6BfnLsYEKQv6PkhV+SC3lN+tymPTgcMMS+jPA5dN5vqZGX1zMg9VWPckrLzX9Veedx/MXubavo0xXWZB34d4PMrKbcX87sM8couqSR8ygJ9eeQZXn51Gv6g+GPDgxip549uQ85o7aeWKRyBheKCrMiakWND3AS2tHt7cUsTvP8xjV2ktY5Ji+dW1Z7Fk6ojg7/d+PAey4KXboOYgXPRDOO9fgvesUGP6MAv6INbc6uHVDYX8YVUeeyvqmZAax0PXT+XSM0f0nd4zHfF44B8PuR41g9LgqyvduCnGmB5hQR+EGlta+Wt2AY98tJvCw0eYMiKBPy6dzsWThxHRUwFfuA4++rnrvjj7mzA0s2eep6YEXr3TjS44+Qq47KGujYRojDkhC/ogcqSpleVZ+/mf/82nuLqBqSMH86MrpvDF01KQnup50lDl9qyzHnNjeez+ELIfd+OvnPcvMGJq9z1X3vvw6jfc4FiXPQTTbwnuHjXGhAgL+iCRW1TNbU9mUVTVwMzRQ/nltWcyd1xSzwW8Kmx92fVXryuDWXfCF3/gJqZY8wfI/rNbP+ZCmPMdd32qtbQ0wYc/gn/81o2/fcsbbjxuY0yvsDNjg8Da/Aq+9nQ2sTFR/PeXpzJ7bA+fLl+xG9682zWfjJgGl/63u/bVUAXZT8CaR9xkEMPPgjnfhklLTm6slso98PLtrmloxm2w8KfWbdKYHmBDIASxd7cVs+z5DaQPGcAzt88ibXAPhmBLI/zfb+CT/3KTSM+/34Xv8QYGa2mETcvd3nhFnptQevYyd4p/zMDjP9/Wl+GN7wACl/8WplzRna/GGOPDgj5IvZh1gHtf2cwZaYP481dnMrQnR5HM/8jtxVfkubNNF/4U4of5/3iPB3a86b4oCrPdrECzvuEm+2g/TndTPaz8Hqx/2g0HfPVjbogBY0yPsaAPMqrKH/83n5+v3M7545P449Kzie3XQ4dLakrg3R/Alr/CkEz40n+5iaVPlXonZvj7Q24ijeiBMP0rrqfO4Aw33dxfv+om+5j7Xfjiv7lRHY0xParLg5qJyCLgIdwMU4+p6n+2Wz8KeAJIBiqBpapa4F13C3Cfd9Mfq+pTp/QqQoTHo/z0rVwe+789XHrmcH593VRionrgJCFPK6z7M7z/oJtm74J7XfB2Nrelv0Rg9Bx3Kdnmhg/Oegw+/ZObZi1/FfRLgJtfhbFf7J7XYozpEn/mjI3EzRm7ACjAzRl7g6rm+GzzV+BvqvqUiMwDvqqqN4vIUCAbmIGbZnAdcLaqHurs+UJ5j7651cP3XtrMKxsKuWX2KP7jsik90y++aBP87bvuAGjmBfClXx+bZq8nHD7gDtpu+IsbZfKKP0BcSs89nzHmc7q6Rz8TyFPVfO8fWw4sAXJ8tpkM3OW9vQp4zXt7IfCeqlZ6H/sesAh4/mRfRF9X39TCN59dz6odZdy9YALL5o3r/q6TjTWw6qew9o+uDf2qx+CMa3q+r/rgkbDop25aP+sXb0zQ8Sfo04ADPvcLgFntttkEXIVr3rkSiBeRxE4e+7lZmUXkDuAOgIyMHpy/M0AO1zdx25NZbDxwmJ9ceTo3zerigcnGWqjc7Q6sVvhcl+2AplrXk2b+v8OAId3zAvxlIW9MUOquI4D3AL8XkVuBj4FCoNXfB6vqo8Cj4JpuuqmmoFBUdYSvPP4p+yrqefjG6Vxyhp8jM7Y0waG93hD3Xirz3XVN0We3TUiHxLHemY1uhPQOf70ZY8KUP0FfCIz0uZ/uXXaUqh7E7dEjInHA1ap6WEQKgQvbPfajLtQb3Iq3ukmsPc3Q2kzx4Vqe+Ggn5zU38j9z0hhTtxf+3gyeFndpbT66LZ4W12e9qsCF+eF9oJ5jf3tgkgvzsfPcdeI4dxmSeeL+7MaYsOZP0GcB40UkExfw1wM3+m4gIklApap6gO/jeuAAvAP8VETa2hAu9q4PPasfdsMJ+BgG/ABAgE87eVxEtOt+2HY9KM2dpXrGtcfCPHFM7zfDGGNCxgmDXlVbRGQZLrQjgSdUdZuIPAhkq+oK3F77z0REcU033/Q+tlJEfoT7sgB4sO3AbMhQhQ9/DJ/8CiZdDnO/w/qCWh54cydxA/vzi+tmkJ6Y4A3zqGOhHhHlzki1dm1jTA+zE6a6wtMKb/2rG+1x2s1w2UO8saWEu17cyNjkOJ6+bSYpCV3st26MMX7o8glTpgMtTW5c9W2vuMG+LvohT6/Zx3+s2MY5o4byp1tmMGiAnRFqjAk8C/pT0VQHL37Fja9+0Q9h7nd4eFUev3xnBxdNSuX3N07rm5N0G2NCkgX9yTpyCJ77MhRkwWW/Rad/hV+u3M4fPtrNldPS+OU1ZxLVl+dxNcaEHAv6k1FTDM9cBRW74Non8Uy8nAffyOHJf+zlhpkZ/OSK03tuqj9jjDlFFvT+qtwDz1wBtWVw44u0Zl7Iv72yhReyD3DbnEz+/dJJPTcblDHGdIEFvT9KtsEzV0JrE9yygubh07n7hY2s2HSQf5k3ju8umGAhb4wJWhb0J7J/LTx3rRt3/atv0zh0AsueXc97OSV8b9FE/unCsYGu0BhjjsuC/njy3ocXbnYzMd38Gkdi07njqWw+2VXODy+fwi3njQ50hcYYc0IW9J3Z+jK8ciekTISlr1ATNYTbn/iU7H2V/OKaM7luxsgT/w1jjAkCFvQdyXrcza+aMRtuXM5hzwBueWwt2w5W89D107jsrBGBrtAYY/xmQe9L1Y1Z8+GPYfxCuPZJyhojufnxNeSX1fHI0rNZMDk10FUaY8xJsaBvowrv3gerfw9nXAdX/IGi2hZuemw1RYcbeOLWc5g7PinQVRpjzEmzoG+z6icu5GfeAYt+zv5DDdz42BoO1zfz9O0zOWf00EBXaIwxp8SCHmDt/8DHv3QjUF7yC/LK6lj62FoaWlp57uuzODN9cKArNMaYU2ZBv/VlePt7cNqX4NLfkFtcw9LH1iICy+84l4nDEgJdoTHGdEl4B/3uVa4LZcZsuOZxNh6s5ZYnPmVgTCTPfm0WY5LjAl2hMcZ0mV/DLIrIIhHZISJ5InJvB+szRGSViGwQkc0isti7PFpEnhKRLSKSKyLBM41g4Xp4YSkkTYAbnqdeo/nK42sZNCCaF++cbSFvjAkZJwx6EYkEHgYuASYDN4jI5Hab3Qe8qKrTcHPK/sG7/Fqgn6qeAZwN3Ckio7un9C6o2A3PXgsDh8LSl2HAYHKLqqluaOG+L01i5FCbbNsYEzr82aOfCeSpar6qNgHLgSXttlGgrTF7EHDQZ3msiEQBA4AmoLrLVXdFdZEbhRKFpa9CwnAAcopqAJg8wtrkjTGhxZ+gTwMO+Nwv8C7z9QCwVEQKgLeAb3mXvwTUAUXAfuBXHU0OLiJ3iEi2iGSXlZWd3Cs4GUcOw1+uhvpKuOklSBp3dFVuUamjKrYAAA/bSURBVDUJ/aNIGzyg557fGGMCoLumQroBeFJV04HFwDMiEoH7NdAKjAAygbtFZEz7B6vqo6o6Q1VnJCcnd1NJ7TQfgedvgPKd8OVnIG36Z1bnFlUzcXiCDTdsjAk5/gR9IeA7gle6d5mv24EXAVR1NdAfSAJuBFaqarOqlgJ/BzqcpbxHtbbAy1+D/avhyj/C2HmfWe3xKDuKa5g83JptjDGhx5+gzwLGi0imiMTgDrauaLfNfmA+gIhMwgV9mXf5PO/yWOBcYHv3lO4nVXjzu7D9b3DJz+GMaz63yb7KeuqbWpk0PL5XSzPGmN5wwqBX1RZgGfAOkIvrXbNNRB4Ukcu9m90NfF1ENgHPA7eqquJ668SJyDbcF8afVXVzT7yQTn34Y1j/NJx/D8y6s8NNcovc8eFJtkdvjAlBfp0wpapv4Q6y+i673+d2DjCng8fV4rpYBsba/3GjUU6/Bebd1+lmuUXVRAhMSLU9emNM6Omug7HBZ8tLbmiDiZfCl34NxznImltUzZjkOPpHR/ZigcYY0ztCM+h3fwivfgNGnQdXPw6Rx//hkltUY802xpiQFXpBX7geli+F5NPg+ucguv9xN6+qb6bw8BE7EGuMCVmhFfTlefDsNRCbeHRogxPJLbYDscaY0BY6QV9dBM9cCQjc/BrED/PrYW09bqwPvTEmVIVO0McMhNQpsPQlSBzr98Nyi6oZGhtDSny/HizOGGMCJ3TGo+8/CG5cftIPcwdi423oA2NMyAqdPfpT0NLqYUdJDZNsFiljTAgL66DfU15HU4vHDsQaY0JaWAd9jg19YIwJA2Ed9LlFNURHCuNSbNpAY0zoCvOgr2ZschwxUWH9NhhjQlxYJ1xuUbX1nzfGhLywDfqK2kZKaxqtfd4YE/LCNuhzbTJwY0yY8CvoRWSRiOwQkTwRubeD9RkiskpENojIZhFZ7LPuTBFZLSLbRGSLiBx/lLFeYpONGGPCxQnPjBWRSNxMUQuAAiBLRFZ4Jxtpcx9u5qlHRGQybpKS0SISBfwFuFlVN4lIItDc7a/iFOQWVZOa0I+hsTGBLsUYY3qUP3v0M4E8Vc1X1SZgObCk3TYKtO0aDwIOem9fDGxW1U0Aqlqhqq1dL7vrcoqqbW/eGBMW/An6NOCAz/0C7zJfDwBLRaQAtzf/Le/yCYCKyDsisl5E/l9HTyAid4hItohkl5WVndQLOBVNLR52l9Va0BtjwkJ3HYy9AXhSVdOBxcAzIhKBaxqaC9zkvb5SROa3f7CqPqqqM1R1RnJycjeV1Lm80lqaW9WC3hgTFvwJ+kJgpM/9dO8yX7cDLwKo6mqgP5CE2/v/WFXLVbUet7c/vatFd9WxMehtViljTOjzJ+izgPEikikiMcD1wIp22+wH5gOIyCRc0JcB7wBniMhA74HZC4AcAiy3qJp+URGMTowNdCnGGNPjTtjrRlVbRGQZLrQjgSdUdZuIPAhkq+oK4G7gTyLyXdyB2VtVVYFDIvJr3JeFAm+p6ps99WL8lVtczWnD4omKDNvTCIwxYcSviUdU9S1cs4vvsvt9bucAczp57F9wXSyDgqqSW1TDgkmpgS7FGGN6Rdjt0pbWNFJZ18Qka583xoSJsAt6G4PeGBNuwi7o23rcTLSgN8aEiTAM+hrSBg9g0IDoQJdijDG9IgyD3oY+MMaEl7AK+obmVvLLau1EKWNMWAmroN9ZUoNH7UCsMSa8hFXQ2xj0xphwFGZBX0NsTCQZQwcGuhRjjOk1YRX0OUVu6IOICAl0KcYY02vCJujd0AfW48YYE37CJugLDx+hpqHFgt4YE3bCJuhzi2oAOxBrjAk/YRT01YjAxGHWh94YE17CKuhHDR1IbD+/RmY2xpiQEVZBb802xphw5FfQi8giEdkhInkicm8H6zNEZJWIbBCRzSKyuIP1tSJyT3cVfjLqGlvYV1lvQW+MCUsnDHoRiQQeBi4BJgM3iMjkdpvdB7yoqtNwc8r+od36XwNvd73cU7O9uAa1oQ+MMWHKnz36mUCequarahOwHFjSbhsF2lJ0EHCwbYWIXAHsAbZ1vdxTc2zoAzsQa4wJP/4EfRpwwOd+gXeZrweApSJSgJtb9lsAIhIHfA/44fGeQETuEJFsEckuKyvzs3T/5RRVk9A/irTBA7r9bxtjTLDrroOxNwBPqmo6sBh4RkQicF8A/62qtcd7sKo+qqozVHVGcnJyN5V0TG5RNROHJyBiQx8YY8KPP30NC4GRPvfTvct83Q4sAlDV1SLSH0gCZgHXiMgvgMGAR0QaVPX3Xa7cTx6PsqO4hutmjDzxxsYYE4L8CfosYLyIZOIC/nrgxnbb7AfmA0+KyCSgP1Cmque3bSAiDwC1vRnyAPsq66lvarX2eWNM2Dph042qtgDLgHeAXFzvmm0i8qCIXO7d7G7g6yKyCXgeuFVVtaeKPhk2Br0xJtz5dZqoqr6FO8jqu+x+n9s5wJwT/I0HTqG+LsstqiYyQpiQanv0xpjwFPJnxuYWVTMmKZb+0ZGBLsUYYwIiDIK+xpptjDFhLaSDvqq+mcLDRyzojTFhLaSDPrfYzog1xpjQDnpvj5vJtkdvjAljIR/0ibExJMf3C3QpxhgTMCEe9O5ArA19YIwJZyEb9C2tHnaU1Fj7vDEm7IVs0O8pr6OpxWM9bowxYS9kgz7Hhj4wxhgghIM+t6iG6EhhbHJcoEsxxpiACuGgr2ZcSjwxUSH7Eo0xxi8hm4K5RdV2INYYYwjRoK+obaS0ptFOlDLGGEI06HOLagA7EGuMMRCyQW89bowxpo1fQS8ii0Rkh4jkici9HazPEJFVIrJBRDaLyGLv8gUisk5Etniv53X3C+hIblE1qQn9GBob0xtPZ4wxQe2EM0yJSCTwMLAAKACyRGSFd1apNvfhphh8REQm42ajGg2UA5ep6kEROR03HWFaN7+Gz8kpqra9eWOM8fJnj34mkKeq+araBCwHlrTbRoG2ZB0EHARQ1Q2qetC7fBswQER6dISxphYPu8tqLeiNMcbLn6BPAw743C/g83vlDwBLRaQAtzf/rQ7+ztXAelVtbL9CRO4QkWwRyS4rK/Or8M7kldbS3KoW9MYY49VdB2NvAJ5U1XRgMfCMiBz92yIyBfg5cGdHD1bVR1V1hqrOSE5O7lIhx8agtz70xhgD/gV9ITDS5366d5mv24EXAVR1NdAfSAIQkXTgVeArqrq7qwWfSG5RNf2iIhidGNvTT2WMMX2CP0GfBYwXkUwRiQGuB1a022Y/MB9ARCbhgr5MRAYDbwL3qurfu6/szuUWV3PasHiiIkOy56gxxpy0E6ahqrYAy3A9ZnJxvWu2iciDInK5d7O7ga+LyCbgeeBWVVXv48YB94vIRu8lpUdeiavVTTYyzNrnjTGmzQm7VwKo6lu4g6y+y+73uZ0DzOngcT8GftzFGv1WWtNIZV2TjXFjjDE+Qqp9w8agN8aYzwupoG/rcTPRgt4YY44KsaCvIW3wAAYNiA50KcYYEzRCLOht6ANjjGkvZIK+obmV/LJaO1HKGGPaCZmgr21s4dIzRzAzMzHQpRhjTFDxq3tlX5AU14/f3jAt0GUYY0zQCZk9emOMMR2zoDfGmBBnQW+MMSHOgt4YY0KcBb0xxoQ4C3pjjAlxFvTGGBPiLOiNMSbEiZsfJHiISBmwrwt/Igko76ZyeoLV1zVWX9dYfV0TzPWNUtUOJ90OuqDvKhHJVtUZga6jM1Zf11h9XWP1dU2w19cZa7oxxpgQZ0FvjDEhLhSD/tFAF3ACVl/XWH1dY/V1TbDX16GQa6M3xhjzWaG4R2+MMcaHBb0xxoS4Phn0IrJIRHaISJ6I3NvB+n4i8oJ3/VoRGd2LtY0UkVUikiMi20Tk2x1sc6GIVInIRu/l/t6qz6eGvSKyxfv82R2sFxH5rfc93Cwi03uxttN83puNIlItIt9pt02vvoci8oSIlIrIVp9lQ0XkPRHZ5b0e0sljb/Fus0tEbunF+n4pItu9/36visjgTh573M9CD9b3gIgU+vwbLu7kscf9/96D9b3gU9teEdnYyWN7/P3rMlXtUxcgEtgNjAFigE3A5Hbb/DPwR+/t64EXerG+4cB07+14YGcH9V0I/C3A7+NeIOk46xcDbwMCnAusDeC/dzHuZJCAvYfAF4DpwFafZb8A7vXevhf4eQePGwrke6+HeG8P6aX6LgaivLd/3lF9/nwWerC+B4B7/Pj3P+7/956qr936/wLuD9T719VLX9yjnwnkqWq+qjYBy4El7bZZAjzlvf0SMF9EpDeKU9UiVV3vvV0D5AJpvfHc3WwJ8LQ6a4DBIjI8AHXMB3aralfOlu4yVf0YqGy32Pdz9hRwRQcPXQi8p6qVqnoIeA9Y1Bv1qeq7qtrivbsGSO/u5/VXJ++fP/z5/95lx6vPmx3XAc939/P2lr4Y9GnAAZ/7BXw+SI9u4/2gVwG9Pmu4t8loGrC2g9WzRWSTiLwtIlN6tTBHgXdFZJ2I3NHBen/e595wPZ3/Bwv0e5iqqkXe28VAagfbBMv7eBvuF1pHTvRZ6EnLvE1LT3TS9BUM79/5QImq7upkfSDfP7/0xaDvE0QkDngZ+I6qVrdbvR7XFHEW8Dvgtd6uD5irqtOBS4BvisgXAlDDcYlIDHA58NcOVgfDe3iUut/wQdlXWUR+ALQAz3aySaA+C48AY4GpQBGueSQY3cDx9+aD/v9SXwz6QmCkz/1077IOtxGRKGAQUNEr1bnnjMaF/LOq+kr79aparaq13ttvAdEiktRb9Xmft9B7XQq8ivuJ7Muf97mnXQKsV9WS9iuC4T0EStqas7zXpR1sE9D3UURuBS4FbvJ+GX2OH5+FHqGqJaraqqoe4E+dPG+g378o4Crghc62CdT7dzL6YtBnAeNFJNO7x3c9sKLdNiuAtt4N1wAfdvYh727e9rzHgVxV/XUn2wxrO2YgIjNx/w69+UUUKyLxbbdxB+22tttsBfAVb++bc4Eqn2aK3tLpnlSg30Mv38/ZLcDrHWzzDnCxiAzxNk1c7F3W40RkEfD/gMtVtb6Tbfz5LPRUfb7HfK7s5Hn9+f/eky4CtqtqQUcrA/n+nZRAHw0+lQuuR8hO3NH4H3iXPYj7QAP0x/3czwM+Bcb0Ym1zcT/hNwMbvZfFwDeAb3i3WQZsw/UgWAOc18vv3xjvc2/y1tH2HvrWKMDD3vd4CzCjl2uMxQX3IJ9lAXsPcV84RUAzrp34dtxxnw+AXcD7wFDvtjOAx3wee5v3s5gHfLUX68vDtW+3fQ7beqKNAN463mehl+p7xvvZ2owL7+Ht6/Pe/9z/996oz7v8ybbPnM+2vf7+dfViQyAYY0yI64tNN8YYY06CBb0xxoQ4C3pjjAlxFvTGGBPiLOiNMSbEWdAbY0yIs6A3xpgQ9/8BCgVRNoMlhLkAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(fit_hist.history['accuracy'])\n","plt.plot(fit_hist.history['val_accuracy'])\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":375},"executionInfo":{"elapsed":523,"status":"ok","timestamp":1648446338621,"user":{"displayName":"최하진","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13771240898172212384"},"user_tz":-540},"id":"2Bqy_4vSf3AE","outputId":"5b35f6ea-864b-467e-a424-8331852800a6"},"outputs":[{"name":"stdout","output_type":"stream","text":["2\n","pullover\n","[[5.73265561e-06 1.44709835e-08 1.44558549e-01 9.58237152e-08\n","  8.15756381e-01 1.13711673e-09 3.96789797e-02 2.30560389e-08\n","  1.84244470e-07 1.39263747e-07]]\n","coat\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["my_sample = np.random.randint(10000) #테스트 안한곳에서 랜덤하게\n","plt.imshow(X_test[my_sample], cmap='gray')\n","print(Y_test[my_sample])\n","print(label[Y_test[my_sample]])\n","pred = model.predict(x_test[my_sample].reshape(-1, 28,28,1))\n","print(pred)\n","print(label[np.argmax(pred)])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XmDDmE9oDOCF"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lVghQYWBgpNo"},"outputs":[],"source":[""]}],"metadata":{"accelerator":"GPU","colab":{"name":"exam16_classfication_fashion_MNIST_CNN.ipynb","provenance":[{"file_id":"1Xu2iZyU2AHf6Ml-G4C_RZmWiXN2ZMkWF","timestamp":1648444557975},{"file_id":"1PVfCjws12OvzFKkyTO6v8eM__XtHfmnI","timestamp":1648442859356}],"authorship_tag":"ABX9TyNv5Jp7U13wOayXNR4Bw/a6"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}