{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Copy the file into the directory which allows writing into it as well\n! cp ../input/sample_submission.csv /kaggle/working/sample_submission.csv\n\n# Any results you write to the current directory are saved as output.","execution_count":23,"outputs":[{"output_type":"stream","text":"['train.csv', 'sample_submission.csv', 'test.csv']\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"# keras is an optimized deep learning library supporting Tensorflow, Theano and CNTK at its backend\nimport keras\nfrom keras import layers\n\n# Sequential model because the model built here has no skipped connections or inception modules\nfrom keras.models import Sequential\n\n# To choose the model's state when the best validation accuracy is obtained\nfrom keras.callbacks import ModelCheckpoint\n\n# To visualize the structure of the model built, layer by layer\nfrom IPython.display import SVG\nfrom keras.utils.vis_utils import model_to_dot\n\n# To visualize figures\nimport matplotlib.pyplot as plt","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Model built by adding layers to the object of Sequential class\ndef LeNet(input_shape):\n    # The sequential model, a linear class of layers, is used because the model is a sequence of layers\n    model = Sequential()\n    \n    # Padding each item of the dataset with 2 columns and 2 rows of zeros for an image to be a (32,32,1) image\n    model.add(layers.ZeroPadding2D((2,2)))\n    \n    \n    # Adding the first convolutional layer with 6 filters of size (5,5)\n    # and stride_size=1 and activation=relu\n    model.add(layers.Conv2D(filters=6, kernel_size=(5,5), strides=1, activation=\"tanh\", input_shape=input_shape))\n\n    \n    # Adding the first average pooling layer with pool_size (2,2)\n    # and stride_size=2\n    model.add(layers.AveragePooling2D(pool_size=(2,2), strides=(2,2), padding=\"valid\"))\n\n    \n    # Adding the second convolutional layer with 16 filters of size (5,5)\n    # and stride_size=1 and activation=relu\n    model.add(layers.Conv2D(filters=16, kernel_size=(5,5), strides=1, activation=\"tanh\"))\n\n    \n    # Adding the second average pooling layer with pool_size (2,2)\n    # and stride_size=2\n    model.add(layers.AveragePooling2D(pool_size=(2,2), strides=(2,2), padding=\"valid\"))\n\n    \n    # Unrolling the matrix row-wise into a vector for feeding into a fully connected layer\n    model.add(layers.Flatten())\n\n    # Feeding into a fully connected layer containing 120 neurons and activating with relu\n    model.add(layers.Dense(units=120, activation=\"relu\"))\n    \n    # Feeding into the next fully connnected layer containing 84 neurons and activating with relu\n    model.add(layers.Dense(units=84, activation=\"relu\"))\n\n    # The output is a fully connnected layer containing 10 neurons \n    # with class probabilities calculated by using softmax activation\n    model.add(layers.Dense(units=10, activation=\"softmax\"))\n\n    return model","execution_count":25,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Reading the file using pandas read_csv function\ndf = pd.read_csv(os.path.join('../input', 'train.csv'))\n#df.head(5)\n\n# Assigning the columns from index 1 to the end as the 1st column in the training set is the label(digit 0-9)\ninputs = df.iloc[:, 1:].values\n\n# Reshaping the input images into a shape accepted by the LeNet5\ninputs = np.array([i.reshape((28,28,1)) for i in inputs])\n#print(\"Input shape is\", inputs.shape)\n\n# 1st column assigned as label; the label is one-hot encoded as our predictions will be encoded(softmax) similarly\noutputs = pd.get_dummies(df.loc[:, 'label']).values\n#print(\"Output shape is\", outputs.shape)","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Call to the function with params as accepted by the input_shape paramter of a layer\nmodel = LeNet(inputs.shape[1:])","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Configure the model with an optimization process, an objective and metric(s)\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":28,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Train the model and validate it over some epochs\nepochs = 20\ncheckpoint = ModelCheckpoint('model.h5', verbose=1, monitor='val_acc', save_best_only=True, mode='max')\nhis = model.fit(x=inputs, y=outputs, validation_split=0.3, epochs=epochs, callbacks = [checkpoint], verbose=0)","execution_count":29,"outputs":[{"output_type":"stream","text":"\nEpoch 00001: val_acc improved from -inf to 0.96492, saving model to model.h5\n\nEpoch 00002: val_acc improved from 0.96492 to 0.97834, saving model to model.h5\n\nEpoch 00003: val_acc improved from 0.97834 to 0.97881, saving model to model.h5\n\nEpoch 00004: val_acc improved from 0.97881 to 0.98286, saving model to model.h5\n\nEpoch 00005: val_acc did not improve from 0.98286\n\nEpoch 00006: val_acc improved from 0.98286 to 0.98421, saving model to model.h5\n\nEpoch 00007: val_acc did not improve from 0.98421\n\nEpoch 00008: val_acc did not improve from 0.98421\n\nEpoch 00009: val_acc did not improve from 0.98421\n\nEpoch 00010: val_acc did not improve from 0.98421\n\nEpoch 00011: val_acc improved from 0.98421 to 0.98429, saving model to model.h5\n\nEpoch 00012: val_acc did not improve from 0.98429\n\nEpoch 00013: val_acc did not improve from 0.98429\n\nEpoch 00014: val_acc did not improve from 0.98429\n\nEpoch 00015: val_acc improved from 0.98429 to 0.98548, saving model to model.h5\n\nEpoch 00016: val_acc did not improve from 0.98548\n\nEpoch 00017: val_acc did not improve from 0.98548\n\nEpoch 00018: val_acc did not improve from 0.98548\n\nEpoch 00019: val_acc did not improve from 0.98548\n\nEpoch 00020: val_acc did not improve from 0.98548\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# To visualise the model thus built\nSVG(model_to_dot(model).create(prog='dot', format='svg'))","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"<IPython.core.display.SVG object>","image/svg+xml":"<svg height=\"702pt\" viewBox=\"0.00 0.00 253.00 702.00\" width=\"253pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 698)\">\n<title>G</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-698 249,-698 249,4 -4,4\" stroke=\"none\"/>\n<!-- 139853336808528 -->\n<g class=\"node\" id=\"node1\"><title>139853336808528</title>\n<polygon fill=\"none\" points=\"17,-584.5 17,-620.5 228,-620.5 228,-584.5 17,-584.5\" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"122.5\" 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28, 28, 6)         156       \n_________________________________________________________________\naverage_pooling2d_3 (Average (None, 14, 14, 6)         0         \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 10, 10, 16)        2416      \n_________________________________________________________________\naverage_pooling2d_4 (Average (None, 5, 5, 16)          0         \n_________________________________________________________________\nflatten_2 (Flatten)          (None, 400)               0         \n_________________________________________________________________\ndense_4 (Dense)              (None, 120)               48120     \n_________________________________________________________________\ndense_5 (Dense)              (None, 84)                10164     \n_________________________________________________________________\ndense_6 (Dense)              (None, 10)                850       \n=================================================================\nTotal params: 61,706\nTrainable params: 61,706\nNon-trainable params: 0\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ind is the index of the state of the model with the best validation accuracy\nind = his.history['val_acc'].index(max(his.history['val_acc']))\nprint(ind)","execution_count":32,"outputs":[{"output_type":"stream","text":"14\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# The row with the best value of validation accuracy\nkeys = ['val_acc', 'val_loss', 'acc', 'loss']\na = {str(key): his.history[key][ind] for key in keys}\nprint(a)","execution_count":33,"outputs":[{"output_type":"stream","text":"{'val_acc': 0.9854773430680105, 'val_loss': 0.06406325418741995, 'acc': 0.9964284499472771, 'loss': 0.012172954699006268}\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Test the model built, on the training set itself\nplt.subplots(2, 3, figsize=(20, 10))\nfor i in range(6):\n    # Choose any example\n    a = np.random.randint(0, 42000, 1)[0]\n    pred = model.predict(np.array([inputs[a]]))\n    plt.subplot(2,3, i+1)\n    plt.imshow(inputs[a].reshape((28,28)))\n    plt.title(\"Predicted label: {}\\n Actual label: {}\".format(np.argmax(pred, axis=1)[0], df.loc[:,'label'][a]), fontsize=16)\nplt.subplots_adjust(hspace=0.7, wspace=0.4)","execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x720 with 6 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAABCUAAAJiCAYAAADwhXh8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XmYJGWVqPH39EI3IAiItIgoOwKKbMOIIjaCiKgDuKAIDpvbBbzAqBeQQZtRZ0QBQURnUBAYcMGFRWVAaEFRQEEEZR/URhpZZJO193P/iGjJzq4vqypriayu9/c8+VRVnIgvTkZn5uk6FRFfZCaSJEmSJEmjbULTCUiSJEmSpPHJpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRGjJumRETsHxHZ8ngyIm6OiEMjYtIo7H9GRGTbsoyIGYMc5/CIePuwJleNOysizupnnel1ztO7HP/cbvPrY7yrIuKqIWy/fURcExHPRsQDEXFSRCw/XPlJGp+sNf2OO65qTcs4q0TE/fXz2nkYUpM0jllr+h3XWjPGas2Iv2h70LuA2cDK9fenAmsAn2wgl+3qXAbjcOAXwA+GP53xISI2By4HLgPeCqwLfAFYC3h3g6lJWnZYa9Tq+KYTkLRMstao1ZitNeOxKXFTZt5df/+TiNgAOIzCmzciApicmfOGO5HMvG64x9SAHEf1ofmuzJwPEBHzgLMj4vjMvLHR7CQtC6w1AiAiXgvsC3wEOKPhdCQtW6w1AsZ+rRk3l290cD2wckSsAc+djhMRB0bEHcA84C11bIWIOD4i/hQR8+qvx0TEEscxIraMiKsjYk5E3BcRxwLRvuO+TnOKiFdFxAUR8Uh9acGdEXH04tyAlwH7tJyudVbbthdHxGP1tr+MiNf1sd/D6uc5JyJu6GudgYqIXSLikvpUoWci4paI+GhETCys/4GIuLve940RsWMf67w+ImbWp6I9HRGXRcQrus2xbezJwK7A+YsbErXzqf6tdx+O/UhSG2vNOKo1LfuYDPwX8Dngj8M5tiT1wVpjrRmTtWY8ninRbl1gIfBUy7IdgS2o/qL+EDArquuzLgM2BT4N/B54NXAssBrwUYCIWB34KfAAsB8wF/g48NL+EomIbYGrgLuBI6j+mr8hsHm9yp7AJcDNwIx62V/rbbcCrgZ+C3wAeAb4MHBFRLwmM39Tr3cQcDJwFvAdYAPgW8BK/eVXsB4wk+p0sTnANnVuLwSOalt3OrA1cAzVcTkS+J+IeFVm3lnn9xbgIuDHVN0+6vWujojNM/PeUiL1h9uszJzeId/1ganALa0LM3NORPyB6t9XkoabtWZ81ZrF/h+wHPB5qlObJWkkWWusNWOz1mTmuHgA+wMJbEzVjFkV+BDVG/fClvVmUb3wX9S2/fvq7XdoW34MVddxjfrnz9Y/r92yzorAw9XhXmLbBGa0/Pxz4F5ghQ7PYxZwbh/LZwK3A8u1LJtYL7uw/nlCPf6lbdu+u87lrH6O4fR6vemFeNTH9hjgMWBCW97tx2Ul4FHgv1uW3Q3MbBt35fr4ndyy7Crgqrb1ltq2jxxfUz+HXfuI/aK/7X348OGj08NaY61pWW8D4Flg57bntXPTr1MfPnyM7Ye1xlrTst4yUWvG4+UbdwDzqV40XwHOAw5sW+e6zHygbdmuwD3ANRExafED+Akwmaq7CFV36rps6Xxl5tPADzslFRErAK8FzsvMZwbzhKKaNeL1wHeBRS25BXAFsEO96kvqx/ltQ3wfWDCYfbbse82I+K+IuIfqzTkf+AywCtWNdlq1H5cnqTqH29VjbUh1JsN5bcf4GeDalufRp8zcIDN36uZ5SNIws9ZYa74KXJSZVwzoSUrS4FlrrDXLRK0Zj5dv7El1+tCTwD2ZOaePde7vY9kaVNc9ze8jBvCC+uuatF0aUHuwn7xWper4DfautVCdZjWR6pSrY/taob4+bM2+csnMBRHxyGB3Wo95MfBiqlOb7qDq1O1B1VWc2rZJX8fgQapZL+C5N/sZ9H2Dlj8PNsc+PFZ/XbWP2GrArcOwD0my1ozjWhMRe1GdmfcPEbFKvfh59dcVI+L5mfm3oe5H0rhnrbHWLBO1Zjw2JW7J5+5SW5J9LHsE+BOwV2GbWfXX+4FpfcT7WtbqMWARz72QB+PxetvTgHP6WiEzF0XE4g+lJXKpu3YvWHqrfq1Pda3V+zLz73P1RsTbCuuXjst99feLP0COpuqEthuOOwX/geq6r81aF0bEVKrryL47DPuQJGvN+K41mwIr0Hej+0Lgb1R/eZOkobDWWGuWiVozHpsS3boUeAfwVGbe0WG9a4GPR8Tai0/piYgVgdILGoDMfCYifgHsGxH/lpnPFladCyzftu3TEXE18CrgxsxcVNh2NtW1V3sBZ7YsfwfdvRZWqL/+vcta3/11n8L6r247LitR3QH4x3X8TqoPwc0y83Nd5NOvzJwXEZcCe0XEjMxcfHrXO4EpVB1SSWqKtWZpY67WUN107aq2ZVsAXwQ+BvxqhPYrSQNhrVmataZBNiUG7jzgAGBmRJxIdafY5ai6av8E7FFfM/VF4GCquYJn8NxdaktvxlYfA34GXFvvYzbVX++3yMyP1OvcBrwuIt5KdSfchzNzFvAvVDeUuSwizqDqbK4ObAVMzMyj6q7iccDXI+IbwLepbo5yFPBEF8fkdqrr0T4bEQup3sRHdFj/QZY8LkdS3Szn01DdLSciDgEuiojlqK4Re5iq6/ga4M+ZeVJp8Ii4m+rUtf6uv5oBXAecHxGnAesAXwC+l/XdfCWpIdaapY25WlMfq1lt2y3+9ubM/EWH/CVppFlrlmatadB4vNFlVzJzPvAm4GvAB6mmsDmPanqca6hPwcnMh4GdqF50Z1OdenQpS3bwSvu4nuqmMPdSTUVzCdUbv/V6rKOpOm/nU81FPKPe9kbgH6hOFfoS1Y1qTgFeSfWmXryPM4DDgTdQTVFzALA3z91rYcAycx7VdVYPUJ1edVq9r1I38GfAicC/U03bMxV4c2be1TLmJVQ3flkR+DrVdEWfB15E1a3tZBLVNWj95X0TsAvVtWg/rvM5h+rfUpIaY63pM98xWWskqVdZa/rM11rToMjs6zIjSZIkSZKkkeWZEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhphU2KQIuKYiMiIuGAIY+wfEQcOZ16F/Uyvc53ez3pnRcSsIYy/c7c5to23Tj3e/l1uPyEijo6IWRExJyJujoh3DEdukjTarDd9jt8T9aZtrPfUY83uf21J6i3Wmj7Ht9aMMpsSg/fP9dfdIuIFXY6xPzDib9xx6NNU8xt/GXgzcB3w3YjYrcmkJKlL1pseFxGrACdTzWsvSWORtabHjYdaY1NiECJiO2Aj4BJgOWDvZjPSYhGxBvAx4HOZeUJmXpmZHwKuBD7XbHaSNDjWmzHj88DNwGVNJyJJg2WtGTOW+VpjU2Jw9gMWAh8A7q1/XkpErBsR/x0RD0TE3Ij4Y0ScUseuAl4PvLY+BSfrZUTEjIjIPsZb6hSkiDguIm6MiCci4uGI+GlEvHq4nuggx39+neNj9frntXdaI2JSfWnFHfUx+UtEnBgRU4cp5TdRfZie27b8XOCVEbHuMO1HkkaD9aZvvVBvFu/ntcC+wCHDOa4kjSJrTd+sNaNsUtMJjBURMQV4N3B5Zv4lIs4Fjo6ITTLz9pb11gV+DTwDfBL4X+ClwC71KgdT/aI8EfhQveyJLlJaC/giMBtYkerF+vOI2Dozf9/FeEMZ/2TgCqru6obAvwMvBnZsWedc4G3A8cA1wCZUl1usAxTv+xDVNWNXAgdk5lkd8t0MmAvc3bb81vrrpsCfOmwvST3BetPz9YaImAycDnwhM++OiAE8TUnqHdYaa00vsSkxcLsDqwDn1D+fDRxN1VE8qmW944DlgVdl5l9alp8NkJm3RcQTwKTMvK7bZDLz/Yu/j4iJwKVUv4C/Hzis23G7HP/WzDyg/v7SiHgUODcidsrMmRHxOqoPvf0yc/Hxu6JlvS0y86ZSKlQd3EX9pLwa8HhmtndjH22JS9JYYL3p7XoDcCQwBfiPAawrSb3IWmOt6RlevjFw+1F1/S4EyMw7gV8B+0ZE63HcBfhR25t22EXEzhFxZUQ8AiwA5lNdE7ZxA+Of3/bzd6neaNvVP+8KzAO+V5/qNCkiJgE/qeM7lPLIzJ9l5qSWN7wkLeusNz1cbyJiA+AY4NDMnNPP05OkXmWtsdb0DJsSAxARL6K6Z8GPgSkRsUpUd0H9PtWpQDu1rP4CqtOCRjKfrahuSPMUcBDwauAfqG6AMuTrmLoY/8HWHzJzHvAY1bEBWIPqfg9PU30ALH48VMe7vdNvq8eAVWLp85oWnyHxKJLU46w3Y6LefAn4KXBdy7/PctXTiVUiYvlh2IckjRhrjbWm13j5xsDsQ3Wd1N70fVfa/YDL6+8f5rkX7GDNAYiI5eoX/2LtL+x3UHX43p6Z8xcvjIhVgce73PdQxp/W+kNELAesCtxXL3qE6rm9rrC/4ei83kp1etP6LHlfiU3rr7cNwz4kaaRZbzqP3wv1ZlPgZVT/QW33GHAKcPgw7EeSRoq1pvP41ppRZlNiYPYD7qGag7fdkcCeEbFSZj5JddrO2yNizcy8vzDeXGClPpbfU399BXAj/H1e2tcAT7astwLVtUh/v39CRLyB6qYzw3Ezx8GOvxdwZsvP76I6C+fa+udLqY7T8zNz5jDk15dLqTqU+1Bd+7bYvsAtmelNLiWNBdab3q8372Hpv6wdBWxd5zOif1GUpGFgrbHW9BSbEv2IiC2BVwIzMvOqPuJTqa4reifwDeBTwG7ANRHx71R/tV8L2DUz9603uw04OCLeDfwBeLK+jut/gL8BX4uIT1H95f//UZ1q1OpSqs7YWRHxDarroY7lue7dUA12/M3q9b5dr/tZ4KrFb9LMvCoivkV13dVJVHfwXUR1d9rdgCMz866+Bo6I1wMzgQM7XXuVmQ/VYx8dEU9SffC9G3gD8E+DefKS1ATrzZipN0vdyC0i9gfm9vXvJkm9xFpjrelJmemjw4NqSpiFwMsK8QnAn6leqIuXrQ98i+p0pzlUb86TWuIvorqu6Umqjl3rttsD11NNu3MX1V/6zwJmte33I1SdvWfr9XcGrmoba3o9/vR+nuNQx397Pcbj9XP6JrB6H8fpMKprt+ZQfUDdDHyeqssI1Rs5gf372Mf+nZ5Dve5E4F+purJzgd8B72z6NeTDhw8fA3lYb8ZOvSk8r9lNv4Z8+PDho7+HtcZa04uPqJ+gJEmSJEnSqHL2DUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEYMaUrQiNgVOIVq1oOvZ+bnOq2/XEzJqaw4lF1Ky7Q5PM28nBtN5yH1EmuNNLysNVLfBlNvrDVS/57ksYcz84X9rdd1UyIiJgKnAW8EZgPXR8TFmXlbaZuprMg/xk7d7lJa5v2qmv5YUs1aIw0/a420tMHWG2uN1L8r8nv3DGS9oVy+sS1wd2b+MTPnAd8Gdh/CeJIktbPWSJJGg/VGashQmhJrAfe2/Dy7XiZJ0nCx1kiSRoP1RmrIkO4pMRAR8UHggwBTWWGkdydJGoesNZKkkWatkUbGUM6UuA9Yu+Xnl9TLlpCZp2fmNpm5zWSmDGF3kqRxyFojSRoN/dYba400MobSlLge2DAi1o2I5YD3ABcPT1qSJAHWGknS6LDeSA3p+vKNzFwQEYcCl1FNm3NmZt46bJlJksY9a40kaTRYb6TmDOmeEpl5CXDJMOUiSdJSrDWSpNFgvZGaMZTLNyRJkiRJkrpmU0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1IhJTSeg50zY/OXF2MX/c17HbS94erVi7DP/uU8x9pKz7yjGFj7yaMd9SpIkSZI0FJ4pIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiOGNCVoRMwCngQWAgsyc5vhSGq8uueT5X+ORSzquO3uKz5cjn30lGLstAM3LsZmvmWzYmzBPfd2zEeShpP1ZmybuOF6xdjC//1j1+NOWvdlxdinfvq9Ymzr5SYWY7uttVXX+Uga26w1Ggue3X3bYmzXT/+sGDvngp2KsZd96poh5TRUQ2pK1HbMzPJvxJIkDQ/rjSRppFlrpFHm5RuSJEmSJKkRQ21KJPCTiPhNRHxwOBKSJKkP1htJ0kiz1kgNGOrlG9tn5n0RsQZweUTckZk/b12hfkN/EGAqKwxxd5KkcapjvbHWSJKGgbVGasCQzpTIzPvqrw8BFwBL3XUjM0/PzG0yc5vJTBnK7iRJ41R/9cZaI0kaKmuN1IyumxIRsWJErLT4e2AX4JbhSkySJLDeSJJGnrVGas5QLt+YBlwQEYvH+WZmXjosWS3DHv7gdsXYzdt9uRjrPCFo9w5Z9c5i7M8/WK0Yu33rkchGkvpkvekRE1ZcsRh74JtrF2PPzp1cjL1sr+7zWbjK84qxLZcr/91lEdn9TiUtq6w1Y9x9R76mGDv7wycXY5983Z4dx10w+76ucxoJnzrpjGLsvvmrFmPrnVjusS0cUkZD13VTIjP/CLxqGHORJGkp1htJ0kiz1kjNcUpQSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjRjKlKDqwlMv7W67r/9tvY7xW59eqxj74ouv7mqfe6xyYzF2O5t3NaYkaey66zOvLMbu2Oa0YmzmsysUY19kk67zWbDKlK63lSSNPfnaLYqxr3/o1GLsjnlrlsd89tkh5TTcnr608+99/zjlumLslZcfVIxt9MSvu85ppHmmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhoxqekExpuVt3ikq+2+evbbOsbXvK48v+4N37imGNtmysJibNspc4qxu76ybcd8Njq4d+fBlST1beImG3aMn7f7aV2Ne+gFBxZj63NtV2MC/Omflutqu93v6lRT/9JdMpKkAZkwdWrH+AMHbVWMfeaIM4uxl00q/z70f29/QzG26iP/2zGfrk2YWAzN+rfy71L/vfGXOg77zSfXK8Y2PqN8DLLjqM3yTAlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIa4ZSgI2DBG7Yuxr6/+SnF2LVzn1eMrXV8eVrP/hy3zwHF2GPHlKeNuX6r84uxu3b/asd9bjTh/xRjGx92czGWc+d2HFeSNHLmntr5M3jrKeXY8Y9sVoxt/KV7i7EFHfY36SVrdcxn7x1/2TFeMvtH6xRjazolqCSNqD//S3nKT4CbDjm1q3F3uW2fYmzVt4zQtJ8dPHJQedrPWw74cjE2gfJUogCfOHDXYmzi9Tf2n1gP8kwJSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGtHvlKARcSbwVuChzHxFvWw14DvAOsAsYK/MfGzk0hxbHjv8qWJs2sTyfGrf+tuGI5EOcW15Cs7V3lre7qRb1ivGDln1zo77vONtpxVjL190SDG28RFOFyqNV9ab0fHEe19djP1w4xM7bjuB5YuxH3xlx2Lshfde239ifXhmszU7xo974Q+LsZvmLSzG1jyx+2m2JY1t1prRMXHD8u8Rtx76lY7bLswoxnb4/TuLsVXeX/5dodP000MRW5anw/7VjPLvQ1B+jluedGjHfa555bJXwwZypsRZQPtkqEcBMzNzQ2Bm/bMkSUNxFtYbSdLIOgtrjdRT+m1KZObPgUfbFu8OnF1/fzawxzDnJUkaZ6w3kqSRZq2Rek+395SYlpn3198/AEwbpnwkSWplvZEkjTRrjdSgId/oMjMTyFI8Ij4YETdExA3z8Z4AkqTudKo31hpJ0nCw1kijr9umxIMRsSZA/fWh0oqZeXpmbpOZ20ymfJNHSZL6MKB6Y62RJA2BtUZqULdNiYuB/erv9wMuGp50JElagvVGkjTSrDVSgwYyJei3gOnA6hExG/gU8Dng/Ig4CLgH2Gskkxxr3r5OeVrLTs68aOdibB26m05tKC7f9kXF2DWXlaf5AThvvf8pxu7YvTw9ztuPf3sxtuCeezvuU9LYZr0ZHfv9a3kazedPmNpx221vfE8xtsYZvynGitd49uPPb+r835RFHUa+/tl1u9yrpGWZtWb4TNxkw2Jsjx/8shhbmIs6jvuJh7Yqxlb50PxibMHs+zqOOxIe3O75xVinGtXJeJy2ut+mRGbuXQjtNMy5SJLGMeuNJGmkWWuk3jPkG11KkiRJkiR1w6aEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRvQ7JajGr0XPPFOMPf7Jl3fc9sqvPa8Y23H5p4qxP56wSjH20nfd23GfkqTKH76wXTH2geefVoyd+vj6HceddmQ5tnD+vH7z6ktsuVkx9rN3ntDP1ssXI6eduXsx9mK6mwN+woordo6vUp6vfsF9f+lqn5LUpElrvbgYe/FZ5c+1A1bu9P/26LjPH1xWrmHr3XNtx21HxLavLIaOPvy8Ymxuzi/Gtjr3iGJsXRp4jg3zTAlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIa4ZSg6srEK2/sGD/xoPcWYyt944xi7KbXnFmMvXnnDxdjk6/4Tcd8JGlZM+et2xZjF73rpA5bTilGvn7Obh33udZt3U2l2ck9u5en0Zw2sTzlJ8CPnylvu/YZt5c3XP0FxdCfDt64GHvrHp2nafvpf25e3uXpTgkqqffE5OU6xjf94f3F2L9Pu2G40wHg0r2/UIzt/ILyVJqbfPyuYmzh438rxiZMndoxn+VPeKAY23PFR4uxK59dqRhb96jxN+1nJ54pIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiOcEnQETCA7xMp9oAUrlLcbayb87LfF2CEnH1qMXX/kqcXYPbtNLsY2uGJgeUnSWDGxw7SVAAeccGExtlGHKd5mPlueEvSlp3eYRhNY2DFaNuEVLy/GTtn3a12OCitNeLYYm33gJsXYVw/5cjH26imXF2M7HHFwx3xWP98p3iSNLX/89NYd4z+cVv68fGzRnGJs55M/XozN3fapjvu8ZftvFGN37fafxdgFr1+tGDv2u+8txua9oHN1u2v9rxZjf1pQPgafPeL/FGNT+XXHfY43nikhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI/qdEjQizgTeCjyUma+ol80APgD8tV7tE5l5yUglOdYsIjrEFhVjZ+x+ejH2H/+y+ZByGis6HZ9fvPOEYmzvmUd0HHfKj6/vOidJI89as7T79t24Y/x9K5WnrqRDHbrwsfL0b7O/8aJ+suov3rftXjyrGNtlhfnF2MIsPw+A6VPL2/72X8rT2HWy5UnlaavXPP+arsaU1DusN0uaP21ex/iEDvVk189+rBhb8z+7/7zcYe9DirH3f7I8Hfb+K/+lGHvH/qcVYxOj89/pb51XPkZ7nF8+Buv90GmiB2ogZ0qcBezax/IvZuYW9WNcvGklSSPmLKw1kqSRdxbWG6mn9NuUyMyfA4+OQi6SpHHKWiNJGg3WG6n3DOWeEodGxO8i4syIWHXYMpIk6TnWGknSaLDeSA3ptinxVWB9YAvgfuDE0ooR8cGIuCEibpjP3C53J0kah6w1kqTRMKB6Y62RRkZXTYnMfDAzF2bmIuBrwLYd1j09M7fJzG0mM6XbPCVJ44y1RpI0GgZab6w10sjoqikREWu2/LgncMvwpCNJUsVaI0kaDdYbqVkDmRL0W8B0YPWImA18CpgeEVsACcwCPjSCOWoZs9aPytP13H5EeUrQzZZbvhh7fL3JHfc5rf+0JDXIWrO0+ds/0TG+iOxq3FNfXJ6mbdGLf9nVmEPRadrP/p7jwwufLcY+8Zc3F2P3HbFeMbbmtU77KS3LrDdL2uTznWvNK/9QniZ53UtnF2MLus4IVv7WdcXY9360YTF22j57FmO/OrbDNNFZ/v0D4L8e3qEY2/D4O4uxhR1HVat+mxKZuXcfi88YgVwkSeOUtUaSNBqsN1LvGcrsG5IkSZIkSV2zKSFJkiRJkhphU0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkR/U4JqsE794I3FGMff//vi7FpE58qxiZs/vKO+1z0uzv6T6xHLPjjrGLs8UXLd9hyfjHyxMadZwKe1k9OktRrVrp4pY7xb2y2djH2xhXuKsZeOmmFrnMaCfctfKYY2+Ub/6/jtut95+FibOFt5WMQ3Nx/YpI0Diy8/X87xtf+TDm+YLiTGYj55d8H5r/pb10NefWczr8S3/3elxZjCx/5Q1f71JI8U0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRGOCXoCFj3hPK0nze8b2Ixtu2Uco/ond+5quM+v//O1xdjC2+9s+O2y4L9Xnd1x/g1LDdKmUjS8FjlnGs7xr9/zhrF2A+2KU9NPW+1qV3n9PS0ycXYKcd9uRjbekp5zH1vf18x9rJPXdMxn86TQUuSljULt9y4GLtx2zO7GvOjx3/gUk3uAAAgAElEQVSoY3z1uzrXYw2dZ0pIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCKcEHQGLnnyyGDvwm4cUY3ce+NVi7J9Xvq/jPr+y/WrF2AvvLs/FlnPndhx3tE2IRcXY5ChPp/rgvJX7GXlOlxlJ0tiTN9xSjJUn9ezfw198dTHWadrPTp5XLotO+SlJ49D8nbcuxuZ+7LGuxnzfrDcWY6uf7pSfTfNMCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhrR75SgEbE2cA4wDUjg9Mw8JSJWA74DrAPMAvbKzO7maBlH1vnR08XYL99bng5zmynlGMA1n/xSMfbyV5XnW9vkpL8WYwvv/lPHfXYrtt6sGJsa1xdjf1u0oBi78ZQtOu7z+VzXf2KSGmOt6Q0Tp63RMf7Vt51RjE0girENLv5wMbbR3b/uPzFJGgbWmrHhgX8szzH9+1d+rxj7/tOrFWN3n7VxMfYCnBK0aQM5U2IB8NHM3BR4NXBIRGwKHAXMzMwNgZn1z5IkdcNaI0kaadYaqQf125TIzPsz88b6+yeB24G1gN2Bs+vVzgb2GKkkJUnLNmuNJGmkWWuk3jSoe0pExDrAlsCvgGmZeX8deoDqNChJkobEWiNJGmnWGql3DLgpERHPA74PHJ6ZT7TGMjOprsvqa7sPRsQNEXHDfOYOKVlJ0rLNWiNJGmnWGqm3DKgpERGTqd6452XmD+rFD0bEmnV8TeChvrbNzNMzc5vM3GYy5ZuWSJLGN2uNJGmkWWuk3tNvUyIiAjgDuD0zT2oJXQzsV3+/H3DR8KcnSRoPrDWSpJFmrZF6U79TggKvBd4H/D4ibqqXfQL4HHB+RBwE3APsNTIpLmOu+10xNOOAg4qxj57xzY7D7rj8U8XYHbufVoxdsFN5+rdjL3pPMfa8P5enfgN42bv+UIx9fp3Ty9tNWq4Y2+TyQ4uxDc91yk9pjLPW9IA/fbnzZdQ7Lj+nGPvzgmeLsU1OfbwYW9h/WpI0XKw1PWDCFpt2jL/33T8txh5a+EwxduQvP1CMbfQ1p/3sZf02JTLzF1CcfHyn4U1HkjQeWWskSSPNWiP1pkHNviFJkiRJkjRcbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqRL9Tgmr0TPjZb4uxEz6wT+eNv3ZeMbTj8k8VY3s+76Fi7B37fLkYW8Sizvl0tFwx8qXHXl6MbXLsg8XYgiFkI0njycIdtyrGLv/HL/Wz9fLFyBuuOLwY2+i2G/pLS5I0xsSk8q+Ssz+6bTF20cGf7zjuSyeVa82nHnpNMbbRAb/pOK56l2dKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AinBB0jJl55Y8f4qdtPL8YO+8h6xdil+36hGHtJh+l4+nPBU2sUY0df+a5ibNP/uL8YW3DvvV3nI0mqPHTYs8XYtImdP/evnTuxGNvkxCeKsYX9pyVJGmMmTiv/f/+3//fUYmwCK3Qc96mcW4xd9tXXFmOrc23HcdW7PFNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRjgl6DJiwQMPFmPrHFOOffiY7UcinY424tfF2IJRzEOSxqMTXvG9rrf9yCkHF2Mvuu2arseVJI0fB99XntYT4PZ/f2UxtvqFTvu5LPJMCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhrR75SgEbE2cA4wDUjg9Mw8JSJmAB8A/lqv+onMvGSkEpUkLbusNaPnxA02K8f62fZFOO2npLHLWjO8Ftz3l2LsrWtt3WHLZzuOuzy/7jIjjVX9NiWABcBHM/PGiFgJ+E1EXF7HvpiZJ4xcepKkccJaI0kaadYaqQf125TIzPuB++vvn4yI24G1RjoxSdL4Ya2RJI00a43UmwZ1T4mIWAfYEvhVvejQiPhdRJwZEasOc26SpHHIWiNJGmnWGql3DLgpERHPA74PHJ6ZTwBfBdYHtqDqOPZ5KWpEfDAiboiIG+YzdxhSliQtq6w1kqSRZq2ResuAmhIRMZnqjXteZv4AIDMfzMyFmbkI+BqwbV/bZubpmblNZm4zmSnDlbckaRljrZEkjTRrjdR7+m1KREQAZwC3Z+ZJLcvXbFltT+CW4U9PkjQeWGskSSPNWiP1poHMvvFa4H3A7yPipnrZJ4C9I2ILqul0ZgEfGpEMJUnjgbVGkjTSrDVSDxrI7Bu/AKKPkHP3SpKGhbVGkjTSrDVSbxrU7BuSJEmSJEnDxaaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiMiM0dvZxF/Be5pWbQ68PCoJdBZL+UC5tOfZTWfl2XmC4dhHGnc6vFaA+bTH/Mps9ZIPcJaM2jm09myms+A6s2oNiWW2nnEDZm5TWMJtOilXMB8+mM+kgaq196f5tOZ+ZT1Ui6SltRr70/z6cx8OhvtfLx8Q5IkSZIkNcKmhCRJkiRJakTTTYnTG95/q17KBcynP+YjaaB67f1pPp2ZT1kv5SJpSb32/jSfzsyns1HNp9F7SkiSJEmSpPGr6TMlJEmSJEnSONVIUyIido2IOyPi7og4qokc2vKZFRG/j4ibIuKGBvZ/ZkQ8FBG3tCxbLSIuj4j/rb+u2nA+MyLivvoY3RQRu41iPmtHxJURcVtE3BoRh9XLGzlGHfJp7BhJWpq1Zqn9W2s652OtkTRo1po+c+iZemOt6TqfUT1Go375RkRMBO4C3gjMBq4H9s7M20Y1kSVzmgVsk5mNzA0bETsATwHnZOYr6mWfBx7NzM/VH3CrZuaRDeYzA3gqM08YjRza8lkTWDMzb4yIlYDfAHsA+9PAMeqQz140dIwkLcla0+f+rTWd87HWSBoUa00xh56pN9aarvMZ1VrTxJkS2wJ3Z+YfM3Me8G1g9wby6BmZ+XPg0bbFuwNn19+fTfXiaDKfxmTm/Zl5Y/39k8DtwFo0dIw65COpd1hr2lhrOrPWSOqCtaYPvVRvrDVd5zOqmmhKrAXc2/LzbJovsgn8JCJ+ExEfbDiXxaZl5v319w8A05pMpnZoRPyuPg1q1E7xbRUR6wBbAr+iB45RWz7QA8dIEmCtGajGP0f70PjnqLVG0gBZawau8c/SNo1/jlprnuONLivbZ+ZWwJuBQ+rTfHpGVtfYND1NyleB9YEtgPuBE0c7gYh4HvB94PDMfKI11sQx6iOfxo+RpJ5mrelf45+j1hpJY1xP1xroiXrT+OeotWZJTTQl7gPWbvn5JfWyxmTmffXXh4ALqE7FatqD9TU+i6/1eajJZDLzwcxcmJmLgK8xyscoIiZTvVHOy8wf1IsbO0Z95dP0MZK0BGvNwFhrWlhrJA2StWbgeqbeNP05aq1ZWhNNieuBDSNi3YhYDngPcHEDeQAQESvWN/UgIlYEdgFu6bzVqLgY2K/+fj/gogZzWfzmWGxPRvEYRUQAZwC3Z+ZJLaFGjlEpnyaPkaSlWGsGxlrz3L6tNZIGy1ozcD1Tb6w1/ecz2sdo1GffAIhqSpGTgYnAmZn52VFP4rlc1qPqIgJMAr452vlExLeA6cDqwIPAp4ALgfOBlwL3AHtl5qjcpKWQz3Sq03cSmAV8qOW6p5HOZ3vgauD3wKJ68Seornca9WPUIZ+9aegYSVqatWapHKw1nfOx1kgaNGtNn3n0TL2x1nSdz6jWmkaaEpIkSZIkSd7oUpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhphU0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhphU0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhphU0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1wqaEJEmSJElqhE0JSZIkSZLUCJsSkiRJkiSpETYlJEmSJElSI2xKSJIkSZKkRtiUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNCUmSJEmS1AibEpIkSZIkqRE2JSRJkiRJUiNsSkiSJEmSpEbYlJAkSZIkSY2wKSFJkiRJkhphU0KSJEmSJDXCpoQkSZIkSWqETQlJkiRJktQImxKSJEmSJKkRNiUkSZIkSVIjbEpIkiRJkqRG2JSQJEmSJEmNsCkhSZIkSZIaYVNCkiRJkiQ1Ytw0JSJi/4jIlseTEXFzRBwaEZNGYf8zIiLblmVEzBjkOIdHxNuHNblq3FkRcVY/60yvc57e5fjndptfH+NdFRFXdbHdxIj4WET8NCIerF8HN0bEQRExbt4PkkaGtabfccdFram3ndH2Wlj8uHC48pM0Pllr+h133NSaevvtI+KaiHg2Ih6IiJMiYvnhym80jPiLtge9C5gNrFx/fyqwBvDJBnLZrs5lMA4HfgH8YPjTGReWB/4VOAc4BXgK2A34GvBy4OPNpSZpGWKt0WLbAwtbfn60qUQkLXOsNeNcRGwOXA5cBrwVWBf4ArAW8O4GUxuU8diUuCkz766//0lEbAAcRuHNGxEBTM7MecOdSGZeN9xjql/PAutlZut/CmdGxKrARyLik5n5bEO5SVp2WGu02K8yc0HTSUhaJllrdBxVM+hdmTkfICLmAWdHxPGZeWOj2Q2Qp6vD9cDKEbEGPHc6TkQcGBF3APOAt9SxFSLi+Ij4U0TMq78e037af0RsGRFXR8SciLgvIo4Fon3HfZ3mFBGviogLIuKR+hScOyPi6MW5AS8D9mk5Xeustm0vjojH6m1/GRGv62O/h9XPc05E3NDXOgMVEbtExCURcX9EPBMRt0TERyNiYmH9D0TE3fW+b4yIHftY5/URMbM+Fe3piLgsIl7RbY6tMnNhW0NiseuBKcDqw7EfSWpjrRlHtUaSGmKtGUe1JiImA7sC5y9uSNTOp/q33n049jMaxuOZEu3WpTqt8qmWZTsCW1B1nh4CZkV1fdZlwKbAp4HfA68GjgVWAz4KEBGrAz8FHgD2A+ZSXRLw0v4SiYhtgauAu4EjqLpeGwKb16vsCVwC3AzMqJf9td52K+Bq4LfAB4BngA8DV0TEazLzN/V6BwEnA2cB3wE2AL4FrNRffgXrATOpThebA2xT5/ZC4Ki2dacDWwPHUB2XI4H/iYhXZeaddX5vAS4CfgzsW293JHB1RGyemfeWEqk/3GZl5vQunsfrgceB+7vYVpL6Y60Zv7Xm3voXhNnAt4EZnpEnaYRYa8ZXrVkfmArc0rowM+dExB+o/n3HhswcFw9gfyCBjamaMasCH6J6417Yst4sqhf+i9q2f1+9/Q5ty4+h6kStUf/82frntVvWWRF4uDrcS2ybVP85Wfzzz4F7gRU6PI9ZwLl9LJ8J3A4s17JsYr3swvrnCfX4l7Zt++46l7P6OYbT6/WmF+JRH9tjgMeACW15tx+Xlaiurf3vlmV3AzPbxl25Pn4ntyy7Criqbb2lth3ga+NNwCLgmKZfpz58+BjbD2uNtaZlvX2p/vO5C/BGqmt85wGXN/069eHDx9h+WGusNfU6r6mfw659xH4xkFrVK4/xePnGHcB8qhfNV4DzgAPb1rkuMx9oW7YrcA9wTURMWvwAfgJMpuouQnWTl+uypfOVmU8DP+yUVESsALwWOC8znxnME4rq7qqvB74LLGrJLYArgB3qVV9SP85vG+L7QFfXu0bEmhHxXxFxD9Wbcz7wGWAVqhvttGo/Lk9SdQ63q8fakKrjd17bMX4GuLblefQpMzfIzJ0Gmf+mVB3VK4HjB7OtJHVgrRnntSYzz83M4zPzJ5l5eWZ+nOovjDtHxM4DfOqS1Im1ZpzXmmXFeLx8Y0+q04eeBO7JzDl9rNPXKfxrUF33NL+PGMAL6q9r0nYKTe3BfvJalarjN9i71kJ1mtVEqlOuju1rhfr6sDX7yiUzF0TEI4PdaT3mxcCLqU5tuoPqRpJ7UHUVp7Zt0tcxeJDq7rDw3Jv9jPrR7s+DzbGTiFiP6m61fwL2TG9EJmn4WGusNX35FtWpxv9A9Z9rSRoKa834rjWP1V9X7SO2GnDrMOxjVIzHpsQt+dxdakuyj2WPUP3yuldhm1n11/uBaX3E+1rW6jGqSwjW6me9vjxeb3sa1VSXS8nMRRGx+ENpiVzqrt0Llt6qX+tTXWv1vsz8+1y9EfG2wvql43Jf/f3iD5Cj6fs/a8N2p+CIeAnVqWFPAG/KzCeGa2xJwlqzVC7jsdZ00Ne/vSQNlrVmfNeaP1Ddz2Kz1oURMZXq/hjfHYZ9jIrx2JTo1qXAO4CnMvOODutdC3w8ItZefEpPRKwIlF7QAGTmMxHxC2DfiPi3LN8Eay6wfNu2T0fE1cCrgBszc1Fh29lU117tBZzZsvwddPdaWKH++vcua30X2H0K67+67bisRHUH4B/X8TupPgQ3y8zPdZHPgETEC3nuw+GNmfnwSO1LkgbJWrO0MVlrChbn/OtR3q8ktbLWLG3M1ZrMnBcRlwJ7RcSMlrO+30k1q+DFI7HfkWBTYuDOAw4AZkbEiVR3il2Oqqv2T8Ae9TVTXwQOpporeAbP3aV2IHfa/hjwM+Daeh+zqbpcW2TmR+p1bgNeFxFvpboT7sOZOQv4F6obylwWEWdQdTZXB7YCJmbmUXVX8Tjg6xHxDaq7gG9AdTfZbs4UuJ3qerTPRsRCqjfxER3Wf5Alj8uRVDfL+TRUd8uJiEOAiyJiOaprxB6m6jq+BvhzZp5UGjwi7qY6da14/VV9ndplwDpU19y9pD5rYrHbPGtCUoOsNUsbc7WmXu+3VH/lu5PqL5VvBD5CdVO2n/b3pCVpBFlrljYmaw3VpSbXAedHxGlUv+N8Afhe1rOUjAmjcTfNXnjw3F1qN+hnvVn0cRfYOjaV564xmkt1U5nr62WTWtZbPI3NHKpTeI6lmoYn28Zb4i619bItqW4e8zjVG/4O4MiW+MvrsZ+h7c6ywCZUb8iH6vxmU3XIdmvbx2FUb7o5wA3A9vXzPqufYzOdtrvUUk0x9Is6n9nAvwHvr9dbp/241rHFpxr9FnhDH/vZDvgR1alfc+ptvw1s17LOVSx9l9pZ7cv6GHudOrfSY3qn7X348OGj08Nas8Q+xm2tqdf7dp3DM/X4t9X/RlOafp368OFjbD+sNUvsY1zXmnrdHajOaplD1Sw5mQ6znvTiI+onIkmSJEmSNKrG45SgkiRJkiSpB9iUkCRJkiRJjbApIUmSJEmSGmFTQpIkSZIkNcKmhCRJkiRJaoRNiUGKiGMiIiPigiGMsX9EHDiceRX2M73OdXo/650VEbOGMP7O3ebYNt469Xj7d7HtRhFxSkT8LiKeioj7I+LiiHjVcOQmSaPNetPn+I3Xm3r7WfX27Y89hiM/SRot1po+x++VWjMhIo6ua86ciLg5It4xHLn1GpsSg/fP9dfdIuIFXY6xPzDib9xxZhdgR+Bs4G3AwcALgesiYusmE5OkLllvettlVPPPtz5+1mhGkjR41pre9WlgBvBl4M3AdcB3I2K3JpMaCTYlBiEitgM2Ai4BlgP2bjYjtfg28KrMPDEzr8zMC4BdgTnAYc2mJkmDY70ZEx7OzOvaHo81nZQkDZS1pndFxBrAx4DPZeYJ9e83HwKuBD7XbHbDz6bE4OwHLAQ+ANxb/7yUiFg3Iv47Ih6IiLkR8ceIOKWOXQW8Hnhty+meV9WxGRGRfYy31ClIEXFcRNwYEU9ExMMR8dOIePVwPdFBjv/8OsfH6vXPa++0RsSk+vSjO+pj8peIODEipg5Hvpn5cGZm27K/AXcBaw3HPiRpFFlv+tZ4vZGkZYi1pm+9UGveRNUoOrdt+bnAKyNi3WHaT0+wKTFAETEFeDdweWb+heoFsU1EbNK23rrAr4EdgE9S/bX+OGD1epWDgd8Cv+O50z0P7iKltYAvArtTnTL1EPDziHhlF2MNdfyTgaTqrh4D/BPwvbZ1zgX+Ffgm8BbgP4CDgPM6JRHPXdu1/2CfQESsBrwCuH2w20pSU6w3Y6bevC0inqn/M3pdeD8JSWOItabna81mwFzg7rblt9ZfN+1n+zFlUtMJjCG7A6sA59Q/nw0cTdVRPKplveOA5akuJfhLy/KzATLztoh4ApiUmdd1m0xmvn/x9xExEbiU6kX6fobhcoVBjn9rZh5Qf39pRDwKnBsRO2XmzIh4HdWH3n6Zufj4XdGy3haZeVMpFaoO7qIunsapQFB9sEjSWGG96f1680PgeuBPwDTgUOCCiHhfZrb/VUuSepG1prdrzWrA4+1nggOPtsSXGZ4pMXD7AU8AFwJk5p3Ar4B9I6L1OO4C/KjtTTvsImLniLgyIh4BFgDzqa4J27iB8c9v+/m7VG+07eqfdwXmAd+rT3WaFBGTgJ/U8R1KeWTmzzJzUssbfqD5Hw28Fzg0M9s7jJLUy6w3PV5vMvMjmXlOZl6dmd8DdgJuoPpLmSSNBf+/vTuPsqws7wX8+2i6m3lWQARRBhHMFbQFoyZiiAqGiAQFiRocccI4JPcavd6rWSuDK3FC4yVhEnKjmEQ0sAzBAWPUQBBQQAVF1EZA6JZBGaShh+/+cQ7XoulvV3UNZ5/uep61zqLq/M7e+61D1Xm73tr7fHrNmPea+cRQYgpKKbtkcF3PvyZZXErZrpSyXZJzMzgV6LAJD98xyU1zXM+TM3hDmnsyOE3oaUmemuSqJDO+jmka+1828ZNa6wNJ7syv3svhkRlcE3VvBi8AD96WD/PpvtNvq/7XJ/mLJO+utZ45m/sGmEv6zYbVbybUsTqDf7Q+upSy61wcA2C26DUbRK+5M8l2pZSy1v0PniFxRzYiLt+YmpcmWZDBdUXrelfaE5J8cfjxbZn+GyuuSJJSyqLhN/+D1v7GPiaDCd/v1VpXPnhnKWX7JD+f5rFnsv+dJ35SSlmUZPskNw/vuj2Dr+03GsebtclrKeXlSf5Pkg/UWv98tvYLMCL6Tff+x6bfdHjYm7oBjBm9pnv/49BrvptkcZK98tD3lXjwvSSumYVjjA1nSkzNCUluSPLsddwuTHJ0KWXr4WO/kOTISf5Scn8G12at7Ybhf5/44B3DqeXT13rcFhlci1QnPO63kuwxxa9nMuu7/2PX+vzFGXxvXTL8/MIMppDb1lovX8dtVv6RWEo5OsnHk5xea/3j2dgnwIjpNxtAv1nb8LTd45L8pNZ661wcA2AW6TXj32suzODsi5eudf/Lknyn1vrjWTjG2DCUmEQp5aAkv5bk47XWr6x9S3JyBt/oLxpu8p4MJmcXl1JeW0p5dinlZaWUiW98dU2SJ5ZSjiulLCmlPHgt078l+UWS00opR5ZSjkny+QxONZrowiRbJTmrlHJYKeUNGbwD7M2ZHeu7/wNKKR8vpTyvlPLmJKck+Uqt9aIkGT5P52Rw3dX/Gj7uOcPn57OllH1bhZRSnlVKWVVK+YOugkspvzk8xlXDup824XbQen79ACOn32ww/eb4UsqnSil/MHzOX5LBuvFPTvKO9fz6AUZKr9kwek2tdXmSDyZ5Zynl7WWwascpSX4rgzck3bjUWt06bhms3LA6yWMa+SZJfpLBN+qD9+2VwTfqbRn8EP8wyQcn5LtkcF3T3RlM7CZu+8wM3tH7l0muy2AadlaSpWsd980ZvOv3fcPH/3aSr6y1r0OH+z90kq9xpvv/veE+fj78mj6ZZKd1PE9vyWBosCKDF6irkvxVBlPGJNlzuL9XrOMYr5jka3jv8HHrui3t2tbNzc1tHG76zQbTb56W5MsZXHO8cljLl5I8r+/vITc3N7fJbnrNhtFrho9dkMGyozdkcDbK1Ule1Pf30FzcyvALBgAAABgpl28AAAAAvTCUAAAAAHphKAEAAAD0wlACAAAA6MWmM9m4lHJ4BsvGLEhyeq31fV2PX1QW182y5UwOCRu1Fbk3D9T7S991wDjRa2B26TWwbuvTb/QamNzdufO2WusjJnvctIcSpZQFST6W5DlJbkpyWSnl/FrrNa1tNsuWOaQcNt1Dwkbv0sHyx8CQXgOzT6+Bh1vffqPXwOS+VD99w1QeN5PLNw5Ocn2t9Ue11hZ2NsgAABq1SURBVAeSfCrJUTPYHwCsTa8BYBT0G+jJTIYSuyW5ccLnNw3vA4DZotcAMAr6DfRkRu8pMRWllBOTnJgkm2WLuT4cAPOQXgPAXNNrYG7M5EyJm5PsPuHzRw/ve4ha66m11iW11iULs3gGhwNgHtJrABiFSfuNXgNzYyZDicuS7FNKeWwpZVGSlyQ5f3bKAoAkeg0Ao6HfQE+mfflGrXVVKeWkJJ/PYNmcM2ut3521ygCY9/QaAEZBv4H+zOg9JWqtFyS5YJZqAYCH0WsAGAX9Bvoxk8s3AAAAAKbNUAIAAADohaEEAAAA0AtDCQAAAKAXhhIAAABALwwlAAAAgF4YSgAAAAC9MJQAAAAAemEoAQAAAPTCUAIAAADohaEEAAAA0AtDCQAAAKAXhhIAAABALwwlAAAAgF4YSgAAAAC9MJQAAAAAemEoAQAAAPTCUAIAAADohaEEAAAA0AtDCQAAAKAXm/ZdAACwYdh090c3swce94hZP96d+27Wmf/80BXNbP9H39LM3rPH+c3sXY89ePLCAIBZ40wJAAAAoBeGEgAAAEAvDCUAAACAXhhKAAAAAL0wlAAAAAB6YSgBAAAA9GJGS4KWUpYmuTvJ6iSraq1LZqMoAJhIv5k9P/joIc3syF//Zue2z9zma83smC3vnHZNo7Z01QPNbMH++zaz1ddcNxflAGNCrxmN8pQDOvOlL9i2mS28t73do/7q4ma26ree0sw+cubfdNbz3Qd2aWZnHn9kM6tXfLdzv/zKjIYSQ8+utd42C/sBgC76DQBzTa+BEXP5BgAAANCLmQ4lapIvlFKuKKWcOBsFAcA66DcAzDW9Bnow08s3nllrvbmU8sgkXyylfK/W+tWJDxj+QJ+YJJtlixkeDoB5qrPf6DUAzAK9BnowozMlaq03D/+7PMlnkxy8jsecWmtdUmtdsjCLZ3I4AOapyfqNXgPATOk10I9pDyVKKVuWUrZ+8OMkz03yndkqDAAS/QaAuafXQH9mcvnGzkk+W0p5cD+frLVeOCtVAcCv6DfracF27eXUTnv+6c3s0M1WzkU5Y+f2Ne2/cK75wdLRFQKME71mFnUt+/n7n/h857bHbX1LM/vcvTs2s7+99Jhm1rXs594Lu38l3nthezGWX37ii83sUy99TjOzXOhDTXsoUWv9UZInzWItAPAw+g0Ac02vgf5YEhQAAADohaEEAAAA0AtDCQAAAKAXhhIAAABALwwlAAAAgF7MZElQpmHB3o9tZss+tLCZ3Xtfe/myJNnuvC2b2fbnXtnM1qxY0blfADY8d/zOE5rZoZv9+wgrmVu/fc3RzWzFqvY/ce64/JHNbM+Vl8yoJoD54ifveXoz+9OXfqKZHbVle4nNyXzujvYCKde/tP26v+/CRc1sTdZMu57jt765mW32yQua2TsvenEz2+/tV3cec2P8/c2ZEgAAAEAvDCUAAACAXhhKAAAAAL0wlAAAAAB6YSgBAAAA9MJQAgAAAOiFoQQAAADQi/Zirkzbsje31+w97BX/1czO2+Wy6R/0Ge3o5W98TjO7448f38zKJVdNvx4AerPpitrMlq76ZTPbc9MtOvf7/jvaPeP/fqLdax7zDzd07ne6Fv30pna2ZnUz2yY/nItyADY6mzzpCc3sLced18yO3vKOZrZmkmOe/ovHNbNlr9q1mW367pWT7Hm0jt5qeTM75qhTmtnjV76pc7+Pf+fVzWzNL9s9fpw5UwIAAADohaEEAAAA0AtDCQAAAKAXhhIAAABALwwlAAAAgF4YSgAAAAC9sCToHOha9vPqn+/WzH739Qc0s1VbLew85o9e2c6+f9hpzeykk5/ZzG48YZ9mtvraH3TWA0B/tjz30mb2Z//9iGZ2+u7/0bnfC2/dv5nt9r6Lm9mqzr0C0JcFB7SXek6SF5zz1Wb2ym2Xdmw5/b99X33Po9vhstua0VMf014Oe0Ny7Ys+2pk/YcFJzWyfk9r9f5w5UwIAAADohaEEAAAA0AtDCQAAAKAXhhIAAABALwwlAAAAgF4YSgAAAAC9mHRJ0FLKmUmOTLK81vrE4X07JPnHJHsmWZrk2FrrnXNX5obl2t/etplt8oufNrO65sZmtmCSY+779UXN7Kmvf3Mzu/gdH25mrzn9ec3s50ds01nP6rvu6swB1qbfjMZRO35r2tsuvWmnZrZvbpj2fgFGRa95qF/u0f1v+ldv+5OOtP337YWl/dvLyklW7rzufz+xmS26/bJmdvsz2vs87IVvaGYfP/mDnfXssenmnfl0zOT5OfyQq5rZD6dbUM+mcqbEWUkOX+u+P0lyUa11nyQXDT8HgJk4K/oNAHPrrOg1MFYmHUrUWr+a5I617j4qydnDj89O8sJZrguAeUa/AWCu6TUwfqb7nhI711pvGX58a5KdZ6keAJhIvwFgruk10KMZv9FlrbUmaV75Uko5sZRyeSnl8pW5f6aHA2Ce6uo3eg0As0GvgdGb7lBiWSll1yQZ/nd564G11lNrrUtqrUsWZvE0DwfAPDWlfqPXADADeg30aLpDifOTnDD8+IQk581OOQDwEPoNAHNNr4EeTWVJ0HOSHJpkp1LKTUnek+R9Sf6plPLqJDckOXYui9zQrL5z9CsI1ZUPNLOdP3pxM/u1x/1hM/vesR9rZk9+XXuZ0SR51F+3jwmwLvrN+HvHIRc2s0++8HdGWMnAgvvWNLNFn798hJUAGwq95qG2esdNnfmatF9nu3Qta7nfP7+pc9t9v3JlRz3Ts/m/fKOZvWzzP+7c9kN/3v6d6KDFs//8TPacH7Dlzc1s6b4HN7PV143vgqGTDiVqrcc3osNmuRYA5jH9BoC5ptfA+JnxG10CAAAATIehBAAAANALQwkAAACgF4YSAAAAQC8MJQAAAIBeGEoAAAAAvZh0SVA2botvN5cCYGpeu+2N7exjfzvCSgbuqfc3s5de/3vNbM1xq5vZ6mXLZ1QTwLi55Y+e3sw+/di/nmTrRbNbTJLHf6z7dXb1ihWzfswu25zzX535G7c7qZld8u6PzHY5k3rNtj9qZh9/1pHNbMfrfjgX5cwKv5ECAAAAvTCUAAAAAHphKAEAAAD0wlACAAAA6IWhBAAAANALQwkAAACgF5YE3UjUX39SM/vhizdvZs/7jW9O63gHH3N1Z/61J7fr6bLq9s2a2U6XtWdoi+5Z07nfLc+9dFr1ADC+tiqLm9l5+/xrM/vepe2lRP/H017YzFbdumxqhQGMkT97/VnN7DGbzv6Sn0ny41XtZT3LqvayzONo1/NvaGZve9VvNLMPPeprc1HORsmZEgAAAEAvDCUAAACAXhhKAAAAAL0wlAAAAAB6YSgBAAAA9MJQAgAAAOiFJUFHbJMttmhmN/7hgc3sNS+/oHO/b97u49Ouqa00k3fs+vnOLc/YY8tmtrp2L9/ZdPT0NkuSK9+/qpm97brjmtnq0x/ZzLY+/8rOY9b720vOAdCf/Ra2lxK95dTtmtkjXmBJUICpeN7n39rM9v3xZSOsZOZW3fzTZnbJ2U9vb/jOuVkS9NP37NLMdrr8rmZW56KYWeJMCQAAAKAXhhIAAABALwwlAAAAgF4YSgAAAAC9MJQAAAAAemEoAQAAAPRi0iVBSylnJjkyyfJa6xOH9703yWuT/Gz4sHfVWrvXrJxHykEHNLODz2ovI/mX23yomZ3ys0M7j7n3xc9pZlce8ZFmtkVZ1Mz2Pe8NzWz/v2wvjZMk9z2hvVTNdN21+8JmdvvT2kt+JsmrD2kvyXPBAec0s8Ufbh/zg/97v85j/t2XD2tm+7zlvzq3hflGrxmdj7yhvQzy24/u/mfB2w69sJlddtdjmtnlFzyxmT32H25uZnWzdo9Kknv32r6Z/fT3H2hm33vWmc3s0qd8spk944STOuvZ/uxLOnOgf/Ox3ywoa5rZJnP0N+pHXDzpr5kbhV2/uLyZbfLO9nO7sCxoZisnWbvzP+/ap5nVb323e+MxNZXvwrOSHL6O+z9Uaz1weNtofmgB6MVZ0WsAmHtnRb+BsTLpUKLW+tUkd4ygFgDmKb0GgFHQb2D8zOR8nZNKKVeXUs4spbTPnwSA6dNrABgF/QZ6Mt2hxClJ9kpyYJJbknyg9cBSyomllMtLKZevzP3TPBwA85BeA8AoTKnf6DUwN6Y1lKi1Lqu1rq61rklyWpKDOx57aq11Sa11ycIsnm6dAMwzeg0AozDVfqPXwNyY1lCilLLrhE+PTvKd2SkHAAb0GgBGQb+Bfk1lSdBzkhyaZKdSyk1J3pPk0FLKgUlqkqVJXjeHNY6dBw5/amf+upM/3cx+tmqbZvY/D31RM6u/uKu7qPeVZtS17OdR1/1uM9vv7Vc3s1UrVnSWs/DGmzrz6dixKzuje9uvZbNmdvHjX9bM7nnCDs3sgRO73yNp80ff3V0U8P/pNaOz8EtXNLN9vtS97efSdZl1u0/tnoubWfeCzt02u7ad7XHfU5rZ7b9xXzPbcZPN29lVv+isp73oHjAu5mO/WV3bf4deM0evXNufZYnkrue2a9nPyf6fXHjt/s1sn3xz0rrG0aRDiVrr8eu4e5JfAQFg6vQaAEZBv4HxM5PVNwAAAACmzVACAAAA6IWhBAAAANALQwkAAACgF4YSAAAAQC8MJQAAAIBeTLokKA+3+Ttu7sz3W3RrMzvjNS9sZmW39pq0t568Vecxr3vqKc3sw3fu2z7mqxc1s9UrVnQec2Ox+vvXN7PNv9/ebvN/6d7vttOsB4CZ2/SiK5rZtx/YppkdutnKZnb98d2v7I+7cvK6ANh43LvPDrO+z3cvO7gz3/uU1bN+zL45UwIAAADohaEEAAAA0AtDCQAAAKAXhhIAAABALwwlAAAAgF4YSgAAAAC9sCToNLxk12905stXt5fvvP73Fzaz7/7u3zSzhWVB5zGfdMkJzWyP469rZnXl0s79AsCGaPkbn97MnrToPzu23LyZ7H3OLzqP2V7YG2B+WbDzI5vZ6mXLR1jJzJWnHNDM3vahT05rnz9etaKZXXPMHt31/PiqaR1znDlTAgAAAOiFoQQAAADQC0MJAAAAoBeGEgAAAEAvDCUAAACAXhhKAAAAAL2wJOg0/OU5x3bm573qr5vZZw//aDN7xdLnN7Mfn7Fv5zEfc863mtmalQ90bgvA+Nlkiy0687KovcT06p93L105XZtsvXUz66qnTPK1tPzk+O5l0d77mn9oZs/avL3s5/abtJf97HL7gdt25ttfOa3dAmx0Drzw1mZ25XN37tx29c9+NtvldFrzrIM689vefl8zO2KLO6d1zOd9/q3NbN8fXzatfW7InCkBAAAA9MJQAgAAAOiFoQQAAADQC0MJAAAAoBeGEgAAAEAvDCUAAACAXky6JGgpZfckf59k5yQ1yam11pNLKTsk+cckeyZZmuTYWuv01kTZwOzxpxd35m/9yJHNrO6xSzNbc9W1zWyHXNJ5zDWdKcB402se7odn7NOZv/G//Ucz++cbnzzb5SRJjnjUNc1s78XLmtmLt7p9LsqZxPSW/fz2Ayub2U7/2f4ak2T1tI4IjMp87TULSvs3hU3m6G/Uf7Hz1c1sr3e9vnPb/T66ZTO77Rm7NrNf7F2a2a5Pv7mZfWH/0zvr6dZ+/l55w2HNbN8T59+yn12m8l24Kskf1Vr3T/K0JG8qpeyf5E+SXFRr3SfJRcPPAWA69BoA5ppeA2No0qFErfWWWus3hx/fneTaJLslOSrJ2cOHnZ3khXNVJAAbN70GgLmm18B4Wq/zdUopeyY5KMmlSXautd4yjG7N4DQoAJgRvQaAuabXwPiY8lCilLJVknOTvLXWetfErNZaM7gua13bnVhKubyUcvnK3D+jYgHYuOk1AMw1vQbGy5SGEqWUhRn84H6i1vqZ4d3LSim7DvNdkyxf17a11lNrrUtqrUsWZvFs1AzARkivAWCu6TUwfiYdSpRSSpIzklxba/3ghOj8JCcMPz4hyXmzXx4A84FeA8Bc02tgPJXBGUodDyjlmUm+luTb+dXKk+/K4Pqrf0qyR5IbMlg6546ufW1TdqiHlPbSKDDfXVovyl31jvZ6RrCRmq+9ZtNd2pctv/gr3+rc9uVb3zrb5cwbS1f9spm96k1vb2abfe4bc1HOyOk1zFfztdesuWj3Zva5/T7TzGaia6nRNWkvUZokl96/sJntuek9zWznBe2zV2ZST5cXfO/oZrbgRfc2s9V3bjQrznb6Uv30FbXWJZM9btPJHlBr/XqSVuPaMH4SARhreg0Ac02vgfG0XqtvAAAAAMwWQwkAAACgF4YSAAAAQC8MJQAAAIBeGEoAAAAAvTCUAAAAAHox6ZKgAMDcuOEVezWzl2/9byOsZMN08p17N7OzTzu8mT3yyvua2Wb/8Y0Z1QQwbhb9wepm9vyzj+nc9oInnDvb5UzqkMUrO9LFs368dy87uDP/8t89rZnt8rkbmtmqO++cdk3zjTMlAAAAgF4YSgAAAAC9MJQAAAAAemEoAQAAAPTCUAIAAADohaEEAAAA0AtLggJAT3b78t3N7Jgjjujc9tCdvt/M3rr90ma2uq5pZsf96Lmdx9xx8b3N7JU7fb2ZvfTi1zSz+vNFzWyvT93fWc+iG29vZrvccHHntgDzxaqbf9oO/+Ipndv++6lbNbNnb37PdEuathV1VTM78IsnNbMdL273mkd86judx9zp7kuaWbsa1oczJQAAAIBeGEoAAAAAvTCUAAAAAHphKAEAAAD0wlACAAAA6IWhBAAAANCLUmsd2cG2KTvUQ8phIzsebGgurRflrnpH6bsO2JDpNdBNr4GZmy+9ZsGOOzSz7c5v/x559p5famZr0l6aOkk+fc8uzex9px3XzB71fktBj5sv1U9fUWtdMtnjnCkBAAAA9MJQAgAAAOiFoQQAAADQC0MJAAAAoBeGEgAAAEAvDCUAAACAXmw62QNKKbsn+fskOyepSU6ttZ5cSnlvktcm+dnwoe+qtV4wV4UCsPHSawCYa3rN+lt9+x3N7PZntLc7Mk+Zg2qSR8WynxujSYcSSVYl+aNa6zdLKVsnuaKU8sVh9qFa6/vnrjwA5gm9BoC5ptfAGJp0KFFrvSXJLcOP7y6lXJtkt7kuDID5Q68BYK7pNTCe1us9JUopeyY5KMmlw7tOKqVcXUo5s5Sy/SzXBsA8pNcAMNf0GhgfUx5KlFK2SnJukrfWWu9KckqSvZIcmMHE8QON7U4spVxeSrl8Ze6fhZIB2FjpNQDMNb0GxsuUhhKllIUZ/OB+otb6mSSptS6rta6uta5JclqSg9e1ba311FrrklrrkoVZPFt1A7CR0WsAmGt6DYyfSYcSpZSS5Iwk19ZaPzjh/l0nPOzoJN+Z/fIAmA/0GgDmml4D42kqq288I8nLk3y7lHLl8L53JTm+lHJgBsvpLE3yujmpEID5QK8BYK7pNTCGprL6xteTlHVE1u4FYFboNQDMNb0GxtN6rb4BAAAAMFsMJQAAAIBeGEoAAAAAvTCUAAAAAHphKAEAAAD0wlACAAAA6IWhBAAAANALQwkAAACgF4YSAAAAQC8MJQAAAIBeGEoAAAAAvTCUAAAAAHphKAEAAAD0otRaR3ewUn6W5IYJd+2U5LaRFdBtnGpJ1DOZjbWex9RaHzEL+4F5a8x7TaKeyainTa+BMaHXrDf1dNtY65lSvxnpUOJhBy/l8lrrkt4KmGCcaknUMxn1AFM1bj+f6ummnrZxqgV4qHH7+VRPN/V0G3U9Lt8AAAAAemEoAQAAAPSi76HEqT0ff6JxqiVRz2TUA0zVuP18qqebetrGqRbgocbt51M93dTTbaT19PqeEgAAAMD81feZEgAAAMA81ctQopRyeCnl+6WU60spf9JHDWvVs7SU8u1SypWllMt7OP6ZpZTlpZTvTLhvh1LKF0spPxj+d/ue63lvKeXm4XN0ZSnl+SOsZ/dSyr+XUq4ppXy3lPKW4f29PEcd9fT2HAEPp9c87Ph6TXc9eg2w3vSaddYwNv1Gr5l2PSN9jkZ++UYpZUGS65I8J8lNSS5Lcnyt9ZqRFvLQmpYmWVJr7WVt2FLKbya5J8nf11qfOLzvr5LcUWt93/AFbvta6zt6rOe9Se6ptb5/FDWsVc+uSXattX6zlLJ1kiuSvDDJK9LDc9RRz7Hp6TkCHkqvWefx9ZruevQaYL3oNc0axqbf6DXTrmekvaaPMyUOTnJ9rfVHtdYHknwqyVE91DE2aq1fTXLHWncfleTs4cdnZ/DN0Wc9vam13lJr/ebw47uTXJtkt/T0HHXUA4wPvWYtek03vQaYBr1mHcap3+g1065npPoYSuyW5MYJn9+U/ptsTfKFUsoVpZQTe67lQTvXWm8Zfnxrkp37LGbopFLK1cPToEZ2iu9EpZQ9kxyU5NKMwXO0Vj3JGDxHQBK9Zqp6fx1dh95fR/UaYIr0mqnr/bV0Lb2/juo1v+KNLgeeWWt9cpIjkrxpeJrP2KiDa2z6XibllCR7JTkwyS1JPjDqAkopWyU5N8lba613Tcz6eI7WUU/vzxEw1vSayfX+OqrXABu4se41yVj0m95fR/Wah+pjKHFzkt0nfP7o4X29qbXePPzv8iSfzeBUrL4tG17j8+C1Psv7LKbWuqzWurrWuibJaRnxc1RKWZjBD8onaq2fGd7d23O0rnr6fo6Ah9BrpkavmUCvAdaTXjN1Y9Nv+n4d1Wsero+hxGVJ9imlPLaUsijJS5Kc30MdSZJSypbDN/VIKWXLJM9N8p3urUbi/CQnDD8+Icl5Pdby4A/Hg47OCJ+jUkpJckaSa2utH5wQ9fIcterp8zkCHkavmRq95lfH1muA9aXXTN3Y9Bu9ZvJ6Rv0cjXz1jSQpgyVFPpxkQZIza61/PvIiflXL4zKYIibJpkk+Oep6SinnJDk0yU5JliV5T5J/SfJPSfZIckOSY2utI3mTlkY9h2Zw+k5NsjTJ6yZc9zTX9TwzydeSfDvJmuHd78rgeqeRP0cd9Ryfnp4j4OH0mofVoNd016PXAOtNr1lnHWPTb/Saadcz0l7Ty1ACAAAAwBtdAgAAAL0wlAAAAAB6YSgBAAAA9MJQAgAAAOiFoQQAAADQC0MJAAAAoBeGEgAAAEAvDCUAAACAXvw/2AoXQm8seKsAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Test the model over the test data\ntest_df = pd.read_csv(os.path.join('../input', 'test.csv'))\n\ntest_inputs = test_df.values\n\ntest_inputs = np.array([i.reshape((28,28,1)) for i in test_inputs])\ntest_inputs.shape","execution_count":35,"outputs":[{"output_type":"execute_result","execution_count":35,"data":{"text/plain":"(28000, 28, 28, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_proba = model.predict(test_inputs)\npred_labels = np.argmax(pred_proba, axis=1)\nprint(pred_labels[:10])","execution_count":36,"outputs":[{"output_type":"stream","text":"[2 0 9 0 3 7 0 3 0 3]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# To store the values in the prescribed format\nsubmission_df = pd.read_csv(os.path.abspath('sample_submission.csv'))\npred_label_series = pd.Series(pred_labels, dtype='int32')\nsubmission_df['Label'] = pred_label_series\n\nsubmission_df = submission_df.set_index(['ImageId'])\n\nprint(submission_df.head(10))","execution_count":37,"outputs":[{"output_type":"stream","text":"         Label\nImageId       \n1            2\n2            0\n3            9\n4            0\n5            3\n6            7\n7            0\n8            3\n9            0\n10           3\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plot figures and find out \nplt.subplots(2, 3, figsize=(20, 10))\nfor i in range(6):\n    # Choose any example\n    a = np.random.randint(0, 28000, 1)[0]\n    pred = model.predict(np.array([test_inputs[a]]))\n    plt.subplot(2,3, i+1)\n    plt.imshow(test_inputs[a].reshape((28,28)))\n    plt.title(\"Predicted label: {}\".format(np.argmax(pred, axis=1)[0]), fontsize=16)\nplt.subplots_adjust(hspace=0.7, wspace=0.4)","execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x720 with 6 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Writing into the submission file\nsubmission_df.to_csv(os.path.abspath('sample_submission.csv'))","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Analysing the training and validation accuracies \n\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.plot(range(20), his.history['acc'], label='Train accuracy')\nplt.plot(range(20), his.history['val_acc'], label='Validation accuracy')\nplt.legend(loc='best')\nplt.grid()\nplt.show()\n\n","execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Analysing the training and validation losses\n\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.plot(range(20), his.history['loss'], label='Train loss')\nplt.plot(range(20), his.history['val_loss'], label='Validation loss')\nplt.legend(loc='best')\nplt.grid()\nplt.show()","execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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