{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Importing required libraries and import dataset and describing it","metadata":{"id":"NaPHutptpfq1"}},{"cell_type":"code","source":"import tensorflow as tf\nimport seaborn as sns \nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split","metadata":{"id":"__CK1rbwlDV7","execution":{"iopub.status.busy":"2021-08-06T15:53:05.186938Z","iopub.execute_input":"2021-08-06T15:53:05.187316Z","iopub.status.idle":"2021-08-06T15:53:05.192429Z","shell.execute_reply.started":"2021-08-06T15:53:05.187283Z","shell.execute_reply":"2021-08-06T15:53:05.191044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#import numpy as np # linear algebra\n#import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"id":"b5BfN8uIKOsK","execution":{"iopub.status.busy":"2021-08-06T15:53:05.195098Z","iopub.execute_input":"2021-08-06T15:53:05.195713Z","iopub.status.idle":"2021-08-06T15:53:05.215005Z","shell.execute_reply.started":"2021-08-06T15:53:05.195674Z","shell.execute_reply":"2021-08-06T15:53:05.214224Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset1 = pd.read_csv('../input/digit-recognizer/train.csv')\ndataset = dataset1","metadata":{"id":"iHX-JQ7YlFgM","execution":{"iopub.status.busy":"2021-08-06T15:53:05.216105Z","iopub.execute_input":"2021-08-06T15:53:05.216486Z","iopub.status.idle":"2021-08-06T15:53:08.010611Z","shell.execute_reply.started":"2021-08-06T15:53:05.216456Z","shell.execute_reply":"2021-08-06T15:53:08.009298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset.head()","metadata":{"id":"qPhF5StMlIOA","outputId":"795c3c10-da3d-42d7-cbbd-812aea75246b","execution":{"iopub.status.busy":"2021-08-06T15:53:08.012875Z","iopub.execute_input":"2021-08-06T15:53:08.013207Z","iopub.status.idle":"2021-08-06T15:53:08.032697Z","shell.execute_reply.started":"2021-08-06T15:53:08.013174Z","shell.execute_reply":"2021-08-06T15:53:08.031541Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset.info()","metadata":{"id":"xdVslzoflKvV","outputId":"99cb3613-fc5b-4cdb-882d-eab48e94e061","execution":{"iopub.status.busy":"2021-08-06T15:53:08.034446Z","iopub.execute_input":"2021-08-06T15:53:08.034805Z","iopub.status.idle":"2021-08-06T15:53:08.085620Z","shell.execute_reply.started":"2021-08-06T15:53:08.034772Z","shell.execute_reply":"2021-08-06T15:53:08.084630Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset.describe()","metadata":{"id":"b_birj3plMF2","outputId":"67d06011-52c9-4f13-ae8a-2d35bffd1f86","execution":{"iopub.status.busy":"2021-08-06T15:53:08.086959Z","iopub.execute_input":"2021-08-06T15:53:08.087260Z","iopub.status.idle":"2021-08-06T15:53:10.593727Z","shell.execute_reply.started":"2021-08-06T15:53:08.087231Z","shell.execute_reply":"2021-08-06T15:53:10.592696Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.catplot('label',data=dataset,kind='count')","metadata":{"id":"9gSo4L_ClNeZ","outputId":"5f9c755f-ef10-4c3d-8b4a-e22bac2d5029","execution":{"iopub.status.busy":"2021-08-06T15:53:10.594898Z","iopub.execute_input":"2021-08-06T15:53:10.595183Z","iopub.status.idle":"2021-08-06T15:53:11.082464Z","shell.execute_reply.started":"2021-08-06T15:53:10.595155Z","shell.execute_reply":"2021-08-06T15:53:11.081354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Extracting data and labels from dataset**","metadata":{"id":"YxfoIdk7qAm1"}},{"cell_type":"code","source":"labels = dataset['label']\ndata = dataset.drop('label',axis=1)","metadata":{"id":"368uwtqolQFt","execution":{"iopub.status.busy":"2021-08-06T15:53:11.083570Z","iopub.execute_input":"2021-08-06T15:53:11.083903Z","iopub.status.idle":"2021-08-06T15:53:11.167668Z","shell.execute_reply.started":"2021-08-06T15:53:11.083861Z","shell.execute_reply":"2021-08-06T15:53:11.166472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.shape","metadata":{"id":"5v7Moyj3qq9C","outputId":"828c0705-05b4-45c4-a04a-f1f2a0fc2dcf","execution":{"iopub.status.busy":"2021-08-06T15:53:11.171248Z","iopub.execute_input":"2021-08-06T15:53:11.171633Z","iopub.status.idle":"2021-08-06T15:53:11.181940Z","shell.execute_reply.started":"2021-08-06T15:53:11.171602Z","shell.execute_reply":"2021-08-06T15:53:11.180915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Reshaping data from 784 to 28 x 28 image","metadata":{"id":"KaFkTJbkqgJJ"}},{"cell_type":"code","source":"data = np.array(data.values).reshape((-1,28,28))","metadata":{"id":"0d3p3vjHlWp_","execution":{"iopub.status.busy":"2021-08-06T15:53:11.183998Z","iopub.execute_input":"2021-08-06T15:53:11.184296Z","iopub.status.idle":"2021-08-06T15:53:11.318956Z","shell.execute_reply.started":"2021-08-06T15:53:11.184268Z","shell.execute_reply":"2021-08-06T15:53:11.317939Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Visualizing an image and its label","metadata":{"id":"u56vGrPTq5VF"}},{"cell_type":"code","source":"plot_image = np.random.randint(dataset.shape[0])\n\nplt.imshow(data[plot_image])\nplt.title(labels.iloc[plot_image])\n","metadata":{"id":"afp_ncHzldYi","outputId":"64c42269-0bf5-4bc3-fe47-d2d7529d7c3d","execution":{"iopub.status.busy":"2021-08-06T15:53:11.322627Z","iopub.execute_input":"2021-08-06T15:53:11.322950Z","iopub.status.idle":"2021-08-06T15:53:11.498407Z","shell.execute_reply.started":"2021-08-06T15:53:11.322920Z","shell.execute_reply":"2021-08-06T15:53:11.497335Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Pixel Values of an image**","metadata":{"id":"qwZ4gTbTrc5V"}},{"cell_type":"code","source":"data[plot_image]","metadata":{"id":"YLP77dGbrO2B","outputId":"b4d4ec63-94d8-456b-8bad-007962e095ab","execution":{"iopub.status.busy":"2021-08-06T15:53:11.500006Z","iopub.execute_input":"2021-08-06T15:53:11.500426Z","iopub.status.idle":"2021-08-06T15:53:11.510596Z","shell.execute_reply.started":"2021-08-06T15:53:11.500383Z","shell.execute_reply":"2021-08-06T15:53:11.509602Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Converting pixel values to  [0,1]**","metadata":{"id":"xNSvRLjmrkqn"}},{"cell_type":"code","source":"\ndata = data/255.\nlabels = labels.to_numpy()","metadata":{"id":"4Cfl_YdClh4m","execution":{"iopub.status.busy":"2021-08-06T15:53:11.512279Z","iopub.execute_input":"2021-08-06T15:53:11.512698Z","iopub.status.idle":"2021-08-06T15:53:11.624926Z","shell.execute_reply.started":"2021-08-06T15:53:11.512653Z","shell.execute_reply":"2021-08-06T15:53:11.623901Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = data.reshape((-1,28,28,1))\nlabels = labels.reshape((-1,1))","metadata":{"id":"b472hpq2lvrM","execution":{"iopub.status.busy":"2021-08-06T15:53:11.626652Z","iopub.execute_input":"2021-08-06T15:53:11.627070Z","iopub.status.idle":"2021-08-06T15:53:11.632814Z","shell.execute_reply.started":"2021-08-06T15:53:11.627025Z","shell.execute_reply":"2021-08-06T15:53:11.631673Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset.shape,labels.shape","metadata":{"id":"87gb5JuvlyL1","outputId":"80b45a63-f774-4dd1-ab8e-de2dbf77bbd0","execution":{"iopub.status.busy":"2021-08-06T15:53:11.634369Z","iopub.execute_input":"2021-08-06T15:53:11.634778Z","iopub.status.idle":"2021-08-06T15:53:11.646900Z","shell.execute_reply.started":"2021-08-06T15:53:11.634735Z","shell.execute_reply":"2021-08-06T15:53:11.646149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Converting into training and test sets**","metadata":{"id":"4b2tPTEPrwlZ"}},{"cell_type":"code","source":"X_train, X_test, y_train,y_test = train_test_split(data,labels,test_size=0.3,random_state=42)","metadata":{"id":"GfEUHe-mlz7M","execution":{"iopub.status.busy":"2021-08-06T15:53:11.648213Z","iopub.execute_input":"2021-08-06T15:53:11.648487Z","iopub.status.idle":"2021-08-06T15:53:12.085310Z","shell.execute_reply.started":"2021-08-06T15:53:11.648461Z","shell.execute_reply":"2021-08-06T15:53:12.084304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**To Make confusion Matrix**","metadata":{"id":"TQkSelkc7UdJ"}},{"cell_type":"code","source":"import itertools \nfrom sklearn.metrics import confusion_matrix\nfigsize = (10,10)\n\n# create the confusion matrix\ndef make_confusion_matrix(y_true,y_pred,classes=None,figsize=(10,10),text_size=15):\n\n\n\n  cm = confusion_matrix(y_true,y_pred)\n  cm_norm = cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]\n  n_classes = cm.shape[0]\n\n  fig,ax = plt.subplots(figsize=figsize)\n  cax = ax.matshow(cm,cmap=plt.cm.Blues)\n  fig.colorbar(cax)\n\n\n\n  if classes:\n    labels = classes\n  else:\n    labels = np.arange(cm.shape[0])\n  ax.set(title=\"confusion matrix\",xlabel='predicted label',ylabel='True label',xticks = np.arange(n_classes),yticks=np.arange(n_classes),xticklabels=labels,yticklabels=labels)\n  ax.xaxis.set_label_position('bottom')\n  ax.xaxis.tick_bottom()\n\n  ax.yaxis.label.set_size(text_size)\n  ax.xaxis.label.set_size(text_size)\n  ax.title.set_size(20)\n\n  threshold = (cm.max()+cm.min())/2.\n\n  #plot text on each cell\n  for i, j in itertools.product(range(cm.shape[0]),range(cm.shape[1])):\n    plt.text(j,i,f\"{cm[i,j]} ({cm_norm[i,j]*100:0.1f}%)\",\n            horizontalalignment='center',\n            color=\"white\" if cm[i,j]>threshold else \"black\",\n            size=15)","metadata":{"id":"5gAsMAxSmOiW","execution":{"iopub.status.busy":"2021-08-06T15:53:12.086650Z","iopub.execute_input":"2021-08-06T15:53:12.086995Z","iopub.status.idle":"2021-08-06T15:53:12.099461Z","shell.execute_reply.started":"2021-08-06T15:53:12.086962Z","shell.execute_reply":"2021-08-06T15:53:12.098236Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Kaggle Score for the model 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This is the best performing model out of 12\n","metadata":{"id":"XRteviX4QW2e"}},{"cell_type":"markdown","source":"","metadata":{"id":"hf_d6TOoQdGr"}},{"cell_type":"code","source":"# setting seed for reproducabiliity\ntf.random.set_seed(42)\n#creating a callback to cancel training when accuracy reaches 99.15%\nclass myCallback(tf.keras.callbacks.Callback):\n  def on_epoch_end(self, epoch, logs={}):\n    \n    if(logs.get('val_accuracy')>0.9915):\n      print(\"\\nReached 99.15% accuracy so cancelling training!\")\n      self.model.stop_training = True\n#Creating a learning rate scheduler which reduces learning after some epochs to prevent gradient explosion but is susceptible to gradient vanishing\ndef lr_sched(epoch,lr):\n  if epoch>10 and epoch<15:\n    return lr*0.9\n  elif epoch>20 and epoch<25:\n    return lr*0.8\n  return lr\n\n\n# creating a model using sequential api \nmodel_12 = tf.keras.Sequential([\n                               \n\n                               tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)),\n                               tf.keras.layers.MaxPool2D((2,2)),\n                               tf.keras.layers.Conv2D(16,(3,3),activation='relu'),\n                               tf.keras.layers.MaxPool2D((2,2)),\n                               tf.keras.layers.Flatten(),\n                               \n                               tf.keras.layers.Dense(128,'relu'),\n                               \n                               tf.keras.layers.Dropout(0.5),\n                               tf.keras.layers.BatchNormalization(),\n                               tf.keras.layers.Dense(64,'relu'),\n                               tf.keras.layers.Dropout(0.25),\n                               tf.keras.layers.BatchNormalization(),\n                               tf.keras.layers.Dense(32,'relu'),\n                               tf.keras.layers.Dropout(0.125),\n                               tf.keras.layers.BatchNormalization(),\n                               tf.keras.layers.Dense(10,'softmax')\n])\nstop_training=myCallback()\nlr_scheduler = tf.keras.callbacks.LearningRateScheduler(lr_sched)\nmodel_12.compile(optimizer=tf.keras.optimizers.Adam(),loss=tf.keras.losses.SparseCategoricalCrossentropy(),metrics=['accuracy'])\n\nmodel_12.fit(X_train,y_train,epochs=30,callbacks=[lr_scheduler,stop_training],validation_data=(X_test,y_test))","metadata":{"id":"WnYM4gs6mYgV","outputId":"1776af4f-1df4-4fff-87b8-b7f65c0ba838","execution":{"iopub.status.busy":"2021-08-06T16:05:49.102585Z","iopub.execute_input":"2021-08-06T16:05:49.103095Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Confusion matrix for the above model","metadata":{"id":"4QU9IFLOm5H_"}},{"cell_type":"code","source":"make_confusion_matrix(y_test,tf.argmax(tf.round(model_12.predict(X_test)),axis=-1), classes = ['0','1','2','3','4','5','6','7','8','9'],figsize=(15,15))","metadata":{"id":"O7yU01gDmZcA","outputId":"b2958bdc-139c-46ec-de1c-3b5ca16195b6","execution":{"iopub.status.busy":"2021-08-06T16:00:59.605712Z","iopub.execute_input":"2021-08-06T16:00:59.606107Z","iopub.status.idle":"2021-08-06T16:01:02.752101Z","shell.execute_reply.started":"2021-08-06T16:00:59.606072Z","shell.execute_reply":"2021-08-06T16:01:02.749088Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Prediction for kaggle's test set","metadata":{"id":"R4gXBh6GRcs5"}},{"cell_type":"code","source":"test1 = pd.read_csv('../input/digit-recognizer/test.csv')\ntest1.describe()","metadata":{"id":"KC3-bcbMncQC","outputId":"7831f471-d408-48cd-864e-a90ef0eb88be","execution":{"iopub.status.busy":"2021-08-06T16:01:02.753649Z","iopub.execute_input":"2021-08-06T16:01:02.754133Z","iopub.status.idle":"2021-08-06T16:01:07.244124Z","shell.execute_reply.started":"2021-08-06T16:01:02.754083Z","shell.execute_reply":"2021-08-06T16:01:07.242893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test1.info()","metadata":{"id":"ehqk3oiPnfLW","outputId":"a3a28ec5-46b9-44bc-a366-4f95e8b0e6cc","execution":{"iopub.status.busy":"2021-08-06T16:01:07.245585Z","iopub.execute_input":"2021-08-06T16:01:07.246128Z","iopub.status.idle":"2021-08-06T16:01:07.295068Z","shell.execute_reply.started":"2021-08-06T16:01:07.246087Z","shell.execute_reply":"2021-08-06T16:01:07.293765Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test1.head()","metadata":{"id":"JwQ428HNngkf","outputId":"b93d96f6-b6d9-40e6-8f17-2910875d64ba","execution":{"iopub.status.busy":"2021-08-06T16:01:07.296355Z","iopub.execute_input":"2021-08-06T16:01:07.296685Z","iopub.status.idle":"2021-08-06T16:01:07.318582Z","shell.execute_reply.started":"2021-08-06T16:01:07.296648Z","shell.execute_reply":"2021-08-06T16:01:07.317439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_on = test1.to_numpy()\npred_on = pred_on/255.\npred_on = pred_on.reshape((-1,28,28,1))\npredictions = model_12.predict(pred_on)\npreds = tf.argmax(predictions,axis=1)\npred = {'Imageid': np.arange(1,28001),'Label': tf.argmax(predictions,axis=1)}\npred = pd.DataFrame(pred)\npred\npred.to_csv('Digit Recognition.csv',index=False,header=1)","metadata":{"id":"PcBusMY-njm_","outputId":"1ca3e00d-2573-49a0-c822-1c24f6085dc5","execution":{"iopub.status.busy":"2021-08-06T16:01:30.358050Z","iopub.execute_input":"2021-08-06T16:01:30.358410Z","iopub.status.idle":"2021-08-06T16:01:35.011820Z","shell.execute_reply.started":"2021-08-06T16:01:30.358380Z","shell.execute_reply":"2021-08-06T16:01:35.010815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Capture.PNG](data:image/png;base64,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5r1Nft67NfvDaO/DqLubMvfymhwpqedPXZHuu/lA47a0/OPz6w/8fXVVPHf8yAYZ4y0DqkhW/PPz9cyJS9JuLHzZA/evy7zf1tY7/j75TMYlAdBofcPvl2vS9/ftmlvXZfF4/rW4LN1bn/kJAAAAAABPbiT99rIszM3JXFwLKxJE9ZOZYdiW5fnU8+cWZCWIpHQO40iC1qLMZ5a3LO2wvFlE3XlH3fQ0ZdU1V/xZ4yiQ1uJ8ZrqF5bZUrAq2YXbCqBOXpHXmmq/LMn/8ohz14cL8a/f9xOqxjPpvSevcdRlsO/spf+7wwht2OSub/o5KPow69oYsJet6TRZPXZRn7LpeNPP42E970Hwsg/U3pfVqlGoZ5ffHyU0Z+nusR3eke+6atPvbDQu36e51ecnu93WZPzeQXv+mWe6GPG/v25AgfdgAAAAAALZpLP1VF8YsrHQl7A+k34vDnyXp5tObElGwZJ8/v9ySIBzIoB9Kd2XB3bfaz4VGkQRLOu95We6E0h+Y6cNAWv6+1X5u6m3Me9QPJAhKqtuSZTP93HwujIoCWbL3L0sn7MvArEsYtPx9q5JbFWzT7IRRnZKj+NZ1WbCtXi7Kyju71/1q22FUPnwxxptXXAhy/K3sAXygVe+PJvS/fdG+botXswFg3DLrhVf3Yq0AAAAA4ICIAlnU8GUlzA7XEnVdKLPcmz6MyziUlk632MldJ4+l39aQa1GC9ANmvtoiar498Hd4o54LjNLrsd15Vxm07TKXchNHXZ3HvBRXZdmGXSs0j3oqsx1GqXeuyXP6+KnryUHXP7cuLxy/lGv58kgGFy7L3DEzrZn++VPXJLz/uDBt9vZtWT1ubvvnPHNMH1uX1aQbWt608GUobRucvSHd9Ho9uCPdzkayDO162HmnpMXQ5x9I79ylZP2fPb4hK+t3im/sutPJZzLsX0tamD174rJ03vtYhuuXstv4zpv29uL6BzLKTJ/vdviBBG3dP2/arnduPpMWYTqPF8y71O6X+wNZ1NvnbuutiTrrnnru+L1NWTzhlvHMMTPt5ff9RMq8tt/W9ckfB8ZWx1TisYzevS4rpy7Ks3475tpvSu9urotfbr2fOfaGLF5IrffmNXlR1/nbt/wdE6PLl+2+ybbuAwAAAIDZFwcynVwgo4FP2Jozjy1Lr3gxOhF1bXCzXDZRHDylm1f56YtBTyRdc//cYjAJnrY771Ij6S2b+Za0dHLd+laKXfL8chdrJV2oMvthVBLybEjgM5ywo7ffkM5dd1u7kIUd10pGA4X5Vy7LwomLcvT4FVlpZ6fNPncnw6j70jnh5p2EUXcm4yy92L4src4ledHffulCKpx4ZObrxz3S0GVRp/Pr9LzZL8l7Ij3diUuydOayvBSv+6nrqTT4sUQX3vDdHC/aZbuuhG/ISifXCmzzir09d0q7t/lQKekieVFa7z72E34gnZN63xUJza2pYVTZa1p33ePnmvVZMP993q+PW86aLKw/9BNWG1++ZKedu/CBv6eM2UevbSTrP9lHetscH3fi7dZjxC37hVPmNdRumXY/mHU7c8u/Nv648Ptm4qF0T+n969Iu5lQAAAAAMMNGEq5oIFPeJW0UrthQJt91LmPQsdOUtiKa1tqp0BKrY1toLabDpe3Ou0xFqyg1Dlul8486i+b+xVpdFFFtH4RRcYCkwUj69iRg0i5ytvXUybdSv5KXDmSqwihnd7rpPZTABhG5caQ+f1/aNszY8KGVtvBxQVomeNJwy04Xb/dkurlX0y2EHknPB3FJUBPv02OXpfcgDlVS62gqH0Zlp3X7Tu8/mrwu2TDKqdgfhdf0CdbdvEbt1CDk4/5l/xpv0SUwCb22GDPq/sCGXdngKbWPTg3cSeudK/YYeu7b6VZe5rV9ZV3m2psysMfbZPtWNifzSgZ9P3GYum4CAAAAOBh8a6SSgb3VuL9aHQbFpoRC8XhP+cdGfTcm1cLyqnR0TKd2SxbN7fnlVKso9QTzzvLjU1WO/+QGbp+fW5Dl1Y4dX6ptB1afl2VaRT21fRRGTQKUfKDUP+MeX7qaH1fqtrRs65sdDqMyA5i7VlhxC5skeHrgw45XbqQCJmd81bXccQFMvI6XpZebcHznlh2Uu39Ht6t6Orn1lmtxdfK6DWqGr7nteTET/Kg4ICuGUdmwxYhflyT8eZowqv66J8+Nw6CEX1ah9VHaJMDbaiD5eFypsn00MPu81x+65fsw6qjZxknQWcJ3J52Ed5NlzNdozQUAAAAAs2V6GFXdpS5tMgD60moo0Wgs4/FIBsGKLMzP21ZJhcBoOBmEfFJL0gryv5D3BPNO0TCtqlVUbBh2ZWUhvR6mllrb+iVBlNsHYdRjHyBNujplA6U4JMkGTM4n0nsl+9iOhFFldfySdN9NjbHkQwwNRlzXt1T57mm2G9l9/4twW7X4KYRDadmgxm1j+fZEr65nH/NhVKFL206GUdtY9+rjYaswKg6icl0gK8QB5tav+WS+Ws9rF8NXBz4gTPOB27FrdkytSfAXt4ADAAAAgP1kB1pGWZEE+XBpoSW9Yd/NPx0Yxb9gt7QqYRL4jGTQ1RZKczLfyodL25h3xlatonRVXOsqG3QlqzKQ7rIbR6u15XZjmn00ZtSlpFVN/TAqDrJ2OIxKhyqPbsiiDSpyy4+7v5245FtQFau9+XCLoCZlG4HO1DAqv60HIoy6L0HbdZN76dU7hZZoZabto6JHEl1+SxZPrSfdHLVeOJNN5l3Qd1Fa7zyetIwrtPACAAAAgP1gizGj/K/KTR0zKmU8jGQwGMggGrrrqJEfHyq59tOWTrq88l/Bm/bY1vPO2rJV1Lgvq/r89IDpiWmPoa6ZD6OSsYJSF/X5QGnSyiU1Xo81+YW7XQujjPj5R9Nd8u5M366JSJZ0uqRFTcrnH8vowUMZjXW7pkwXBx8nNu2bIe6mVxzAO24p1nAYtY11334Y9cBskwui8uHQNHEoV+xC91jGj8w+f1DVzc88fueGLNnjKjcwue9yqF0e4y56i4WuowAAAACwPzz1r+mNRxJpSDQsXqnFA4RPwqw4/Cr5BTtj6FsqtUI//bbmnbZ1qygNs1Y0cCptWTV0z59rSbwq2L4ZDqM+k5H+ZL4+Zi/6J0FTPlAaX73susQ95QDmWwcHFeGLdtGy4UT61+fel7b9dT3tppUOyfRX3K7I4pk3JbADdE8CosWr6QAkPyh29XRx17tk/KN4HKZtDGD+VGFUMmi7V3hNt7Hu2wqjHpnX0+2j7ODvJR68L2H/zuREEu+j/ODi8aDjfrujy9dkqb0hq8nrquIWd2Y/Zn550e+jY5dk0XbRKxkjCwAAAAD2iyiwv2JXCGWirutOl7t/FA0kykw4kM68Bj9t86+0SLo2EOqk7o9bRs1LuxB+jaS3rI+lW0ZtZ94TdcaKSlpGFeZtxAOn0zLqqcxOGJUfW8mPq6RB1NLVB35ipxgofZyEEsl89PnHr8hKOzttWRiVhFnHN2TpzJvSSz2WVRVGmeNxfcMHYpPHJuHPG7Jy4YYdjLx75g032LlZtySFvbPpxo0yNdfZlKA/kNVT625+6bAkme6izJ8bmPndkE5nMr8wGbIqHcTF+/WiuW3Ww0xvQ5QdCaPiwG1NXmxfk9Zrt10gVBYo1V33bYRRg3Pxa74uCyXdIFuv3vInxg+ka9c9PVD75Jg5al731fWbEly44o/FVGj27jX3Gvpp7Gt4bsPfV2zpFbeIsvMtbAMAAAAA7C9R17VIml9uSRCG0uu07K/d6aDimTwn6rpBw3Pd+kZhy92/sCJt/XW8zqos20HBF4otl7R7nZ33grmW70k4GEg/DKRlx2mak6Vu9hprW/O2IgkWzePTWkV5mXn3QhkM+hIGk23PrQq2aYbCqGw9c+wNWThzXfoPJj/vHysLlLSVzGD9miyccGHW3CubMnhQb8woDSb653wwYsr9yl2Z6jBKm+rFwcyk9c9jGb3zlszbVlOTevbkNQkzraWMW9dl0T8/me7UWzJIjYlulU138k2zn3Lzk89k2HfjHNkw6tQ16d019/lWYDsTRhl3BrIYb1/cDa8qUKqz7tsIo9xrOaWSdf9E+mdc8JR9bR/Z1/3ZzPPWZSUTfprXsH9NXkrCUVelr6GKux2+7MeOAgAAAIB9bSyR/kKdDZpczS/qL8r5h2NxkLRUbDE0DNs+JEo/vyINGg0kaC36YMvXwrK0Qz8eVM525h1335vaKiplNAikteiCsLgWltsSlnQNxPbsfRi1q+JfNMuHT1U+kdGjXRrjx45DFI//NMVOT5cz6frn79gpZn3GSRfJLTzhuj+dxzIeF4NNx7zuuj6V40SpeCypptcbAAAAAGbDeDw25W88iW0+f1vL2+a8t0fnbcrfwtM7GGHU3Zuy2l6XFzu5cYPirmFlA2cfZA/uSLfzhjx/yg8KHnsUyYptxbQhQbbnIwAAAAAAQCMOSMuo+74Lme/e17kmi6cu+m53F2XxalW3u4MqNR7SsYsy/4oOwr2edEd78dViJ0MAAAAAAIAmHKBueg+kf+GKzNvBul3Nv/KWBLfygy4dFh9LdPnNZAwtO45W+5p0NnNjQwEAAAAAADTogI8ZBQAAAAAAgFlCGAUAAAAAAIDGEEYBAAAAAACgMYRRAAAAAAAAaAxhFAAAAAAAABpDGAUAAAAAAIDGEEYBAAAAAACgMYRRAAAAAAAAaAxhFAAAAAAAABpDGAUAAAAAAIDGzEgYNZIoDKTTbkvbVCcIJRr5h0qNJeq1pT91GsRG/bb0orG/1ZQPpHNyTVY2/c09F8nKy9PWZwfWd/OKHDm5KUN/EwAAAAAAFM1AGDWSQbct3X4kI5uXjGUU9aTTCadc1BNGbQdhlCKMAgAAAABgFux9GDUaSBAMJJsrjSUK2hJWXtXvURg1HvnAbH/Z1TBq/EhGDx6a+tjfEUuHO59UTPOZjB88Mq+mEc9n/Ng+IvLY3KXPeSjjz/1dGfpc9/jkOTnxPB99Zm48SRjll2GfH5uy3Kowym9Hdj5pk20tzPPzj83zPjH/iKfx+8uq2q8AAAAAAMyuGR0zSltLdaaETT6MioYSmum6QSDdjnbv6xdCraF2/+t0JQi60ml3JAiHqYv5kfTbPcnmNLmga9SXdm8gUb9ruxC24wdGA+npMruBm3cnP5+89Lr49TXzzazv2GxP0JF2vL66PUNdfpRZ50EvNY3Zpt6gckdZLowame3quPU1+6yt6xs/bRxJoPvB34wNw7Z0K+f9sfTPvSHPvLwmzx9flxeOrcnR45ckuOsfjsOdCzdk4bhOc1GeNdMePX5Fwkd+EhsQvSGrF66Y51+08zjy8rqs9O+Y9bkozxxbl+fNc468vCHdZL7GnYEsmnke1eeY+eo6zJ27ndpHH8vg1Q17/7Nm3XQe869typL5b/0w6jOJLryRXd+tllsIo+6b7ViXoy9Ppn+hcyP7mj+6LatmuUf8NEd1XV+9k5vnm9I5sy7PmuUm+/DdQbJfdb7PnLpOiywAAAAAwL4we2HUeGSDn04mgMlzgVG7mw6fRtLvtiVIJUKjwnw0yEkHLDXDKA18MhOZaYJsa6Nx1JMgrF5nfbyTaQGWn4e73U0ncONIep1OKozSdetkpxEXyFW3ItNNaEunE2TCPbs+yf6L18Xe8Mx820FlwDbevCbPH9+Q7p24Jc/H0ntlTY524pm4cOeIBicP/DSPIlk5viYvvvq+u+1bKz3XuenX4zMZnFu3wcxSP06A7tv5PPft28nt7ikzD3M7WbVbb8mLL78hHR9YjTevyHN6+1bcEumRhJ2LZr51w6jHkyAqXvcay82GUY+l/+2LctTcjuKWXQ/c9s9d+MDf8VACM8/nzT5LXppbm/KS2f6VTW0NZeg80/vj8zuyesLs12OXpRev251NmTPTtN51NwEAAAAAmGUzFEZpMOQGMG936w1gnu96No6CScslG6Z0pdCwZzSQbicOYWqGUd18N0I3ztW0AKgODYmS9bWtk4rjZI0G3UkYVdGCyaxwrvVUll1OYWWz26DhlM4jobeD6nmW0QBnEsa4cOel1+LgxYleXZcjSWClYVQuRLHhyxUJ/U011PkmzykTpVo9fWJDsRdezW3v/esyXyuMKguiqqSXa6TDqPENWXx5Xdq37COJ8dVLZhrfiunWW/LCy5ell9vJuo+OnvFP1HkeuyZ9d8sKO2u5/ZFv1QUAAAAAwOyazW56I9ciqJ/LEyZygZFnA5VUuNNr57vtGfb+OICq202vJJQx9we2m15Pwn4kw8KCSoyGEg1C6QW+q1ym21/5cuKQyK1u9bp0yrbV0zAq3WIspt3wJoFeer+YWzbsc/8u91jGd25J59xlWTixnnQxy4dR+YAkGyy5bnpJyyJVJ4z6/KEMLr8lrVc2zHLjrnzxstxyl/r5IMm1wtoqjHrxpOt6uHg16Us4MXW5RjqMuqutlXwXxnTZroh+++y2ave8kmni+aTn6RFGAQAAAAD2sxkdM0pDmKA8eLHqhlElv8i3U2GUNxpFEvVDG0xlu89ljQY6XlTPjnM19KOgZ1pG6XJKWiIVwqiy1ko1wqiysZ+yYVT6tt9H7u5So8uX5JmX12X+3EB6m2abHnxc2jJq58Oo27J6fE2eOXlFOpdvSv+ODvqdDprcchev+m5uiXph1NGTb0nvwoYc1fVKuiCqrZZrFMKoDVm9elN6/XwN3Wul0x+/LJ3C46beue9eZ8IoAAAAAMABs+dh1HgYlo61lAlhCmqEURo0dUq66Q370km63WkYlR8oXZ9XP4xK6K8CloVfllvffE+5fDe9XskYTcMwNWZUHKTZRyZscDelz6BdTmEbdDtz225eCw27hroft+iDqIFIvitcI2GUDXku5bq2FbvpTcaY8uz4TluHUe7xj90YU+nBy7dcrpEOjsY3zWPrsvperoXW+OPJ62DX6ZIE+UZYj1K/jkcYBQAAAAA4YPa+ZdRYx0HqSpgeJMp306vOQ+qEUXo7sIOcJ7MZD6XfzQ5Grq2BdJBz21hJB08Pe9LtbhVGDSXsdDPdCO2yC2NLTdhfpkuvsA2vUmGUoQOuu/GyzNLMugz7PQm6qTGjDA2nMoOy231l1qVqwYaGUforfpnttsvKr6/ZP2a/d6Z2kXSyg3M/lvGt67KQ7l62W2HUg4Es6ADfyYDeDyX8tg56nlrWu9fk+cw070vnlPvVuerAJr++buD0o/H4TnWWmwmO/ADmJ65J+MAPpP7gjrTNPJ8z2+JeCT+Aefu6RGMfWt29IUvpQc4JowAAAAAAB8xsdNPTQCXwYyhpdQIJ802EMuqFUWo06EknmW9XevmmUuOhhPGy9RfnhqN63fRGA+l1/HxNdYKw0KopI70cnd7MM0q3jPJGUd+NKRX0bEA30m3KLN9sexiktsnsq+G0BesmaPe7kQxTz+sEqZAuZdQ365gM8D7Fo9uyenLNhjFaOuB351wDLaP0l+7WL8kLfrla869tZlsoFaZZN4/d2SKwKVlf/+t/L13QX/+rsdxCcPRAwjNuDKr4OS9o8BT/up56ZNarve7G2/I1d+7WZP8TRgEAAAAADpiZHTMKzvTuijtPf72vbHypSo8eyuhRfnymJnwiowcPZZwOdgrqTLNdTzLPGs/5/GMzzaPGXmcAAAAAAPYKYdTM0NZe2a50IiMZ9MoHH98VtstfcdwqAAAAAACAnUIYNUtsGKRd77oSdLVLX0eCcNhAaxnX7dF2YySJAgAAAAAAu4gwCgAAAAAAAI0hjAIAAAAAAEBjCKMAAAAAAADQGMIoAAAAAAAANIYwCgAAAAAAAI0hjAIAAAAAAEBjCKMAAAAAAADQGMIoAAAAAAAANIYwCgAAAAAAAI0hjAIAAAAAAEBjCKMAAAAAAADQGMIoAAAAAAAANIYwCgAAAAAAAI0hjAIAAAAAAEBjCKMAAAAAAADQGMIoAAAAAAAANIYwCgAAAAAAAI0hjAIAAAAAAEBjCKMOjLEMh0Pz/ztvZOY78v8+9EZDGbIzAAAAAAB4YgcgjBpJv92Wdr9OQrCdaXfGOOpJu92TaDdSopRxFJjltKW30wsahna+Te6zrYz6Zn3a/T0IyIYS6r5Ilj2WqDdb+wYAAAAAgFlHGLXLmgqjdNuiKNqVgGa0S/N9Uk8TRrnX4ymCrFEkUfJkwigAAAAAALaLMGqXNRdGHR57GkZlEEYBAAAAALBdMxJGjWU4CKXX7Ui73ZFu0JP+pPlJykiifk+6nba0O10JQm2xUxUwbWfaovFwIGGvKx0zfacbSG+Qfc5oEJh5FsdoGg9DCYKBWZK/nQqjRoP0+kymiU3mOZKBXbbuizBpiTOK/D7S5/dyzx8NzHJDGWZWyO2DIN6vvcm8Jor7PrOp46GEQZC9z6ox79Q66bbbaSu2vcoo6qfWzS2jKozKvmY9CQfp18fsU7Mdyfrqv/P7axRJvxckr1HPrGd2f5p9FZrnJTujIowqzGe2WpYBAAAAALCXZiCMGkm/qxftgYRRZAfhjjS0abelm0lA4uk0qBqa6SKJwkA6vZ70CgHTdqYt0gBJg6DewK3PMOpL0GlLJxz6KcwSNBDpRamww8m3vHG3A+lpSNLrS2TnN5BeoMFMtsWUm2dow594vUM7XSgD3Se9gX9+aNenHaSWP+qb5aTn5/ZBRwMcfY7u17Br12UyjYYpbj0Gdnlmmr7ZT2bbk00dR3afZXdZPO/4eZEMeu55mTGr/DqFGuAk6+72Zbu7deukUV/X18yz748Lu93mNdQAKBdGJa+Zn1aX09PXLPUa6UDsun26TgOdZjhKHnPPN8dcvJ5mm/p+309e9Xz4VBJGmW3umvXQ8NOuh76GeiymXysAAAAAAA6xvQ+jxkMZhGGhG5u2Emp30qGO3i52d4sH7k4HAtuZtmhKa5do0tJme2FUNshyfKCTD7ja3VwrJD9odj7MsAOLp7YxH0YVwimlraAGkxZMpUGTeWqkoZl/Ysk0w36nNEwaDbrZ8MauQ1u6hQVoYFNcboZZbpAPt5S9X/dTavlTptV1Ny9TIv/6xHSbw0LzL7fvJ/PdOowqPS70GB/QOgoAAAAAADW7Y0ZlwhZz0R+0pVOaXviwJh0Q1J62jA8YSoKmtO2FUfmAyXGPTcIbO89CC5pRedfCfEhUEkZ1zHL7+QwsLQlrpmxpfjmFgCbNPZbka3adyrbdbdO05bpAsRgaqWFo9kdmH5tpK1oe2WlTgV9VGFVOu/al932dMMoFddN2OwAAAAAAh9nMhFEj/ZWygY75o2P5uHF/JuHKtPDChU+TQGA701YY+a5kdnyjvkTRpDtXbHthVLqrV0ouQCqf5xOGUbqt2gXPTOPGT4pkWLLZI9stT6cJJOzrL8Xltii/nEI4lebCmSQILKxTbOswqmr/KtfCbbKP7bT6WtlxoLJlx21KzWdqGDUe2S6Ug34ovfi5Zj2rw6f8bUP3j32eG0dLuzGWLgsAAAAAgENqBrrp+Yt3H/xod6ZIx/IZpoOMLQKmTCCwnWmn0WCib8MxF0p0MoOY71wYNRnDaWfDKGc8imQQxgONt+0YUtlBuY2RjqWkAYwPAXUMqckGbDuMStZ118Ko7D6203Z70h8MzPFTUqmR1avCqEkop8GdPmcoo9Foi/Cp6ngayyhywWp87JQNdg8AAAAAwGG052FU3K0pfzmfDTJG0jcX9dkBzWP5rnfbmbY+/TW4rYOjYtjhbqcHDU/Jjfu0G2FUhv4yno5TNbXvnv6Sn1le3O2tED7luuJl5EKmpwqj9Lgo/9W9fDc9O1ZVzQHCS8Mov43F9dkqfKoKo7L0FxZ1UPOpux0AAAAAgENiBsIoczFfEuq4X1KbBBlVg2ZroGNb86QCge1MWzS2LaJSjWmcsRs4O36qCzXM+rmbng8nCmFUWTjmp02FKDsaRmmLqEEx/bBBTryMsbaIKg6snekGVwijxK13yWvm9m8qeHuKMMq8iGZe3cxynaGEtrVR6vUdDaRbMS7XKNKuh/6G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