Kaggle_digit_recognizer_improved.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Kaggle_digit_recognizer_improved.ipynb","provenance":[{"file_id":"1dUMEdPMC8ibOqVnWbsYQr69dPnkPfYxS","timestamp":1639615352542}],"collapsed_sections":[],"authorship_tag":"ABX9TyM2FsWlcGdoLKgkjasY/5iO"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# IAI Assignment 6 - Kaggle digit recognizer"],"metadata":{"id":"WMpsH9pyRxem"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"psePrOVXQgcy"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.neural_network import MLPClassifier\n","from sklearn import datasets\n","from google.colab import drive\n"]},{"cell_type":"markdown","source":["# import training dataset"],"metadata":{"id":"_j-trINxaq1E"}},{"cell_type":"code","source":["drive.mount('/content/drive')"],"metadata":{"id":"oROJWEMbak9v","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1639645007838,"user_tz":-60,"elapsed":20682,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"4467706c-7510-4eda-be1f-bdf27e76c95c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["train_path = \"/content/drive/MyDrive/Colab Notebooks/IAI/digit-recognizer/train.csv\"\n","df_train = pd.read_csv(train_path)"],"metadata":{"id":"n1QT9sVqh69w"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["some data display to see what's in the file"],"metadata":{"id":"ua5qmYvZgVyU"}},{"cell_type":"code","source":["df_train.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"N-D5WofIhJoA","executionInfo":{"status":"ok","timestamp":1639645012901,"user_tz":-60,"elapsed":16,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"c9a76e32-4ea6-47e7-81d6-090596c7d682"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      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Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"e704d6f7-2476-4ba4-f16c-ac32059d23b3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(42000, 785)"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["train_label = df_train['label'] # train set with label\n","train_label"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HGqtGQeih5BG","executionInfo":{"status":"ok","timestamp":1639645012903,"user_tz":-60,"elapsed":15,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"59a860b3-f208-442d-c7e5-b474ecd69bef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0        1\n","1        0\n","2        1\n","3        4\n","4        0\n","        ..\n","41995    0\n","41996    1\n","41997    7\n","41998    6\n","41999    9\n","Name: label, Length: 42000, dtype: int64"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["train_label.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wXUQ_PBJ4V_Y","executionInfo":{"status":"ok","timestamp":1639645012903,"user_tz":-60,"elapsed":14,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"07cc7b24-dcac-49e6-96a9-fb7280ccab27"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(42000,)"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["train_noLabel = df_train.drop(['label'],axis=1) # train set without label\n","train_noLabel.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"GPG_5qeL3iqr","executionInfo":{"status":"ok","timestamp":1639645013614,"user_tz":-60,"elapsed":7,"user":{"displayName":"Xiaomin 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data"],"metadata":{"id":"rk9GOFYTvayU"}},{"cell_type":"code","source":["df_test.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"yF5lmvKUvYAt","executionInfo":{"status":"ok","timestamp":1639645016442,"user_tz":-60,"elapsed":29,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"b9014d4c-4efe-472b-f21f-aab6c080079b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>pixel0</th>\n","      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<tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","   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<td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 784 columns</p>\n","</div>"],"text/plain":["   pixel0  pixel1  pixel2  pixel3  ...  pixel780  pixel781  pixel782  pixel783\n","0       0       0       0       0  ...         0         0         0         0\n","1       0       0       0       0  ...         0         0         0         0\n","2       0       0       0       0  ...         0         0         0         0\n","3       0       0       0       0  ...         0         0         0         0\n","4       0       0       0       0  ...         0         0         0         0\n","\n","[5 rows x 784 columns]"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["df_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_1BF0EOovkF6","executionInfo":{"status":"ok","timestamp":1639645016442,"user_tz":-60,"elapsed":26,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"03218753-e6f1-4bb9-e1a9-8303e07ae9c2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(28000, 784)"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["# Prepare data for training"],"metadata":{"id":"4Bku03yIgAwu"}},{"cell_type":"markdown","source":["Transform features by scaling each feature to a given range.\n","\n"],"metadata":{"id":"xrxwGXnz1xZY"}},{"cell_type":"code","source":["from sklearn.preprocessing import MinMaxScaler\n","mm_scaler = MinMaxScaler()\n","train_noLabel = mm_scaler.fit_transform(train_noLabel)\n","train_noLabel"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ytddlBVKwzwT","executionInfo":{"status":"ok","timestamp":1639645016925,"user_tz":-60,"elapsed":188,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"f056c68e-d3fb-4b61-a608-be464e3d4516"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       ...,\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.]])"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["reshape data for train_noLabel"],"metadata":{"id":"UC2PWiuL5wuM"}},{"cell_type":"code","source":["train_noLabel = train_noLabel.reshape((42000,28,28,1))\n","train_noLabel.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dr72scDS1N-C","executionInfo":{"status":"ok","timestamp":1639645016925,"user_tz":-60,"elapsed":8,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"5ab47586-5739-44f6-eea2-1172f83b0931"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(42000, 28, 28, 1)"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["transform and reshape for test_noLabel"],"metadata":{"id":"Xx5-p1TL56MW"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"osiSz_3x550v"}},{"cell_type":"code","source":["test_noLabel = np.array(df_test)\n","test_noLabel = mm_scaler.fit_transform(test_noLabel)\n","test_noLabel = test_noLabel.reshape((28000,28,28,1))\n","test_noLabel.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ars-DqNx2ZLO","executionInfo":{"status":"ok","timestamp":1639645017351,"user_tz":-60,"elapsed":432,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"f74c4df2-be63-4da1-cdb0-5ddfbc012335"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(28000, 28, 28, 1)"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["now change train_label into a numpy array"],"metadata":{"id":"cf1jOxs06BJP"}},{"cell_type":"code","source":["train_label = np.array(train_label)\n","\n","from sklearn.preprocessing import OneHotEncoder\n","\n","enc = OneHotEncoder(sparse=False)\n","train_label = train_label.reshape((-1,1))\n","train_label = enc.fit_transform(train_label)\n","\n","train_label.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"diQ52okI7-mi","executionInfo":{"status":"ok","timestamp":1639645017351,"user_tz":-60,"elapsed":3,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"30c4236c-fecb-40eb-faea-12ea416beb4a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(42000, 10)"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["# Now the fun part :D"],"metadata":{"id":"cbzNnUjx8itz"}},{"cell_type":"markdown","source":["train, test, split."],"metadata":{"id":"IafQ2y1l8nxO"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","x_train,x_test,y_train,y_test = train_test_split(train_noLabel, train_label, test_size = 0.1, random_state=42)\n","\n","print(x_train.shape)\n","print(y_train.shape)\n","print(x_test.shape)\n","print(y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5cNvbvfc8dLn","executionInfo":{"status":"ok","timestamp":1639645017658,"user_tz":-60,"elapsed":309,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"ebdbfede-347e-4e07-fcd4-12326a9b2ed3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(37800, 28, 28, 1)\n","(37800, 10)\n","(4200, 28, 28, 1)\n","(4200, 10)\n"]}]},{"cell_type":"markdown","source":["here comes the prediction model! yeah!!"],"metadata":{"id":"3QMzgGYS9kA5"}},{"cell_type":"code","source":["# import necessary tensorflow and keras libs\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","import matplotlib.pyplot as plt \n","from keras.models import Sequential\n","from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n","from keras.preprocessing.image import ImageDataGenerator\n","from keras.callbacks import ReduceLROnPlateau\n","from tensorflow.keras.optimizers import RMSprop"],"metadata":{"id":"jb-PJigl9eyA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model = keras.Sequential()\n","\n","model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n","                 activation ='relu', input_shape = (28,28,1)))\n","model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n","                 activation ='relu'))\n","model.add(MaxPool2D(pool_size=(2,2)))\n","model.add(Dropout(0.25))\n","\n","\n","model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n","                 activation ='relu'))\n","model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n","                 activation ='relu'))\n","model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n","model.add(Dropout(0.25))\n","\n","model.add(Flatten())\n","model.add(Dense(256, activation = \"relu\"))\n","model.add(Dropout(0.5))\n","model.add(Dense(256, activation = \"relu\"))\n","model.add(Dropout(0.5))\n","model.add(Dense(10, activation = \"softmax\"))\n","\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YXHpI1vN_3ph","executionInfo":{"status":"ok","timestamp":1639645020486,"user_tz":-60,"elapsed":290,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"5d9c59a1-36d8-44b3-e9aa-47fd83e077a9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 28, 28, 32)        832       \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 28, 28, 32)        25632     \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n"," )                                                               \n","                                                                 \n"," dropout (Dropout)           (None, 14, 14, 32)        0         \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 14, 14, 64)        18496     \n","                                                                 \n"," conv2d_3 (Conv2D)           (None, 14, 14, 64)        36928     \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 7, 7, 64)         0         \n"," 2D)                                                             \n","                                                                 \n"," dropout_1 (Dropout)         (None, 7, 7, 64)          0         \n","                                                                 \n"," flatten (Flatten)           (None, 3136)              0         \n","                                                                 \n"," dense (Dense)               (None, 256)               803072    \n","                                                                 \n"," dropout_2 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 256)               65792     \n","                                                                 \n"," dropout_3 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 10)                2570      \n","                                                                 \n","=================================================================\n","Total params: 953,322\n","Trainable params: 953,322\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["srop training if no improvements possible anymore"],"metadata":{"id":"Szr285sDATMC"}},{"cell_type":"code","source":["model.compile(optimizer='adam', loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n","\n","history = model.fit(train_noLabel,train_label,batch_size=64,epochs=20,validation_data=(x_test,y_test))\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","\n","plt.figure()\n","\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"WybVjUhMAbh4","executionInfo":{"status":"ok","timestamp":1639650684059,"user_tz":-60,"elapsed":5663575,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"9f01bedc-25d4-4879-caff-1891f0332ecd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","657/657 [==============================] - 291s 441ms/step - loss: 0.3184 - accuracy: 0.8978 - val_loss: 0.0518 - val_accuracy: 0.9862\n","Epoch 2/20\n","657/657 [==============================] - 286s 436ms/step - loss: 0.1001 - accuracy: 0.9727 - val_loss: 0.0370 - val_accuracy: 0.9876\n","Epoch 3/20\n","657/657 [==============================] - 287s 437ms/step - loss: 0.0733 - accuracy: 0.9801 - val_loss: 0.0214 - val_accuracy: 0.9938\n","Epoch 4/20\n","657/657 [==============================] - 284s 432ms/step - loss: 0.0573 - accuracy: 0.9844 - val_loss: 0.0295 - val_accuracy: 0.9912\n","Epoch 5/20\n","657/657 [==============================] - 281s 427ms/step - loss: 0.0516 - accuracy: 0.9862 - val_loss: 0.0130 - val_accuracy: 0.9948\n","Epoch 6/20\n","657/657 [==============================] - 280s 426ms/step - loss: 0.0470 - accuracy: 0.9865 - val_loss: 0.0138 - val_accuracy: 0.9945\n","Epoch 7/20\n","657/657 [==============================] - 278s 424ms/step - loss: 0.0426 - accuracy: 0.9880 - val_loss: 0.0110 - val_accuracy: 0.9962\n","Epoch 8/20\n","657/657 [==============================] - 278s 423ms/step - loss: 0.0367 - accuracy: 0.9900 - val_loss: 0.0101 - val_accuracy: 0.9962\n","Epoch 9/20\n","657/657 [==============================] - 277s 422ms/step - loss: 0.0359 - accuracy: 0.9894 - val_loss: 0.0125 - val_accuracy: 0.9967\n","Epoch 10/20\n","657/657 [==============================] - 277s 422ms/step - loss: 0.0359 - accuracy: 0.9900 - val_loss: 0.0109 - val_accuracy: 0.9960\n","Epoch 11/20\n","657/657 [==============================] - 277s 421ms/step - loss: 0.0297 - accuracy: 0.9916 - val_loss: 0.0057 - val_accuracy: 0.9983\n","Epoch 12/20\n","657/657 [==============================] - 276s 421ms/step - loss: 0.0294 - accuracy: 0.9921 - val_loss: 0.0085 - val_accuracy: 0.9962\n","Epoch 13/20\n","657/657 [==============================] - 276s 421ms/step - loss: 0.0278 - accuracy: 0.9918 - val_loss: 0.0056 - val_accuracy: 0.9979\n","Epoch 14/20\n","657/657 [==============================] - 277s 421ms/step - loss: 0.0265 - accuracy: 0.9925 - val_loss: 0.0039 - val_accuracy: 0.9990\n","Epoch 15/20\n","657/657 [==============================] - 277s 422ms/step - loss: 0.0266 - accuracy: 0.9923 - val_loss: 0.0062 - val_accuracy: 0.9981\n","Epoch 16/20\n","657/657 [==============================] - 277s 422ms/step - loss: 0.0248 - accuracy: 0.9932 - val_loss: 0.0023 - val_accuracy: 0.9995\n","Epoch 17/20\n","657/657 [==============================] - 277s 422ms/step - loss: 0.0245 - accuracy: 0.9933 - val_loss: 0.0067 - val_accuracy: 0.9986\n","Epoch 18/20\n","657/657 [==============================] - 277s 421ms/step - loss: 0.0275 - accuracy: 0.9924 - val_loss: 0.0051 - val_accuracy: 0.9990\n","Epoch 19/20\n","657/657 [==============================] - 297s 452ms/step - loss: 0.0218 - accuracy: 0.9941 - val_loss: 0.0019 - val_accuracy: 0.9995\n","Epoch 20/20\n","657/657 [==============================] - 278s 423ms/step - loss: 0.0198 - accuracy: 0.9946 - val_loss: 0.0032 - val_accuracy: 0.9986\n"]},{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f89742b6a50>]"]},"metadata":{},"execution_count":21},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXhcd33v8fdX62gf2ZYXyWsSJ7FJQuwoDksIXLLgQEjoLYUApaYN5OEBXyi0F0KhAdInXJaW0pa0ECC3kIaGpVAMGEICCaS3JLFiO4vjOHYcb3LiTYsXWdvoe//4HUkjWbLGlkYjz/m8nuc8Z5/5aTz+nDO/8zvnZ+6OiIjkr4JcF0BERLJLQS8ikucU9CIieU5BLyKS5xT0IiJ5rijXBRhuxowZvnDhwlwXQ0TkjPL4448fdPe6kdZNuaBfuHAhTU1NuS6GiMgZxcx2jrZOVTciInlOQS8ikucU9CIieU5BLyKS5xT0IiJ5TkEvIpLnFPQiInkub4K+/XgPX3ngOZ7Y3ZbrooiITCl5E/Rm8JUHtvLYCy25LoqIyJSSN0FfnSimqrSI5rbjuS6KiMiUkjdBD1CfLGOvgl5EZIg8C/qEzuhFRIbJs6DXGb2IyHB5F/StHT10dPfmuigiIlNGXgV9Q7IMgL1tnTkuiYjI1JFfQV/bH/SqvhER6ZdXQV+fVNCLiAyXUdCb2Uoz22Jm28zslhHWv9/MnjKzjWb2X2a2NG3dJ6L9tpjZGyay8MPNqiqlwBT0IiLpxgx6MysE7gCuBZYC70gP8sh33f1Cd78Y+CLw5WjfpcCNwMuAlcA/R6+XFUWFBcyuTtCsOnoRkQGZnNGvALa5+3Z37wbuBW5I38DdD6fNVgAeTd8A3OvuXe7+ArAter2sURNLEZGhMgn6BmB32vyeaNkQZvZBM3uecEb/oVPc92YzazKzpgMHDmRa9hHVJ8vY266gFxHpN2EXY939Dnc/G/g48KlT3PdOd29098a6urpxlaM+WcaLbZ309fnYG4uIxEAmQd8MzEubnxstG829wFtOc99xa0gm6E71cfBYVzbfRkTkjJFJ0K8DFpvZIjMrIVxcXZO+gZktTpt9E7A1ml4D3GhmpWa2CFgMPDb+Yo+uv4llc6uqb0REAIrG2sDde81sNXAfUAjc5e6bzOw2oMnd1wCrzewqoAdoBVZF+24ys+8DzwC9wAfdPZWlvwVIb0vfybL52XwnEZEzw5hBD+Dua4G1w5bdmjb94ZPseztw++kW8FTppikRkaHy6s5YgOpEEZXqgEREZEDeBb2ZUZ9M6IxeRCSSd0EP4SmWaksvIhLkZdCHu2P1GAQREcjjoG851s3x7qw28BEROSPkZdAPdECi6hsRkfwMejWxFBEZlKdBnwAU9CIikKdBP6s6QYGh59KLiJCnQV9cWMCs6oSedyMiQp4GPagDEhGRfvkd9Gp1IyKSz0GfUAckIiLkcdA3JMvUAYmICHkc9PU1g8+lFxGJs7wN+oZa3TQlIgJ5HPS6O1ZEJMjboFcHJCIiQd4GvTogEREJ8jboQc+lFxGBWAS9zuhFJN7yOugbkmUcUgckIhJzGQW9ma00sy1mts3Mbhlh/UfN7Bkze9LMfm1mC9LWpcxsYzSsmcjCj2XgccV6FIKIxNiYQW9mhcAdwLXAUuAdZrZ02GYbgEZ3vwj4IfDFtHXH3f3iaLh+gsqdkcGbphT0IhJfmZzRrwC2uft2d+8G7gVuSN/A3R90945o9hFg7sQW8/SoLb2ISGZB3wDsTpvfEy0bzU3AL9LmE2bWZGaPmNlbRtrBzG6Otmk6cOBABkXKzOyaBKYOSEQk5oom8sXM7I+BRuC1aYsXuHuzmZ0F/MbMnnL359P3c/c7gTsBGhsbJ+xxk8WFBcyqUlt6EYm3TM7om4F5afNzo2VDmNlVwCeB69194JGR7t4cjbcDDwHLxlHeU9ZQqyaWIhJvmQT9OmCxmS0ysxLgRmBI6xkzWwZ8nRDy+9OW15pZaTQ9A3g18MxEFT4TaksvInE3ZtC7ey+wGrgP2Ax83903mdltZtbfiuZLQCXwg2HNKJcATWb2BPAg8Hl3n+SgT7C3XR2QiEh8ZVRH7+5rgbXDlt2aNn3VKPv9N3DheAo4Xg3JMrp7+zh0rJu6qtJcFkVEJCfy+s5YUFt6EZH8D3q1pReRmMv7oG+Igl7PpReRuMr7oK8uK6KipFBBLyKxlfdBHzogURNLEYmvvA96UAckIhJvMQp6ndGLSDzFIugbkgkOHeums0cdkIhI/MQi6NXEUkTiLBZB3zAQ9KqnF5H4iUXQ64xeROIsFkE/2AGJgl5E4icWQa8OSEQkzmIR9ND/uGIFvYjET4yCvozmVgW9iMRPbIK+IVmmDkhEJJZiE/T1aR2QiIjESayCHtTEUkTiJ0ZBnwAU9CISP7EJenVAIiJxFZugrykrprykUI9BEJHYiU3Qm1loeaMzehGJmYyC3sxWmtkWM9tmZreMsP6jZvaMmT1pZr82swVp61aZ2dZoWDWRhT9V9cky3TQlIrEzZtCbWSFwB3AtsBR4h5ktHbbZBqDR3S8Cfgh8Mdp3GvBp4DJgBfBpM6uduOKfGnVAIiJxlMkZ/Qpgm7tvd/du4F7ghvQN3P1Bd++IZh8B5kbTbwDud/cWd28F7gdWTkzRT11DMsHBo+qARETiJZOgbwB2p83viZaN5ibgF6eyr5ndbGZNZtZ04MCBDIp0evrb0r/YrguyIhIfE3ox1sz+GGgEvnQq+7n7ne7e6O6NdXV1E1mkIfqDXs+8EZE4ySTom4F5afNzo2VDmNlVwCeB692961T2nSwNujtWRGIok6BfByw2s0VmVgLcCKxJ38DMlgFfJ4T8/rRV9wHXmFltdBH2mmhZTsyqVgckIhI/RWNt4O69ZraaENCFwF3uvsnMbgOa3H0NoaqmEviBmQHscvfr3b3FzP6GcLAAuM3dW7Lyl2SgpKiAmVWlOqMXkVgZM+gB3H0tsHbYslvTpq86yb53AXedbgEnmtrSi0jcxObO2H6hLb1a3YhIfMQu6BuSZTS3HcddHZCISDzELujraxLqgEREYiV2Qd9QWw6oiaWIxEfsgl4dkIhI3MQu6Ac7INEFWRGJh9gF/WAHJDqjF5F4iF3Qm5keVywisRK7oIfQll6PQRCRuIhl0DckEzqjF5HYiGXQ19eUqQMSEYmNeAa9OiARkRiJddCr+kZE4iCWQT/Yll5BLyL5L5ZBP7smdECiM3oRiYNYBr06IBGROIll0IOeSy8i8RHzoNcZvYjkv9gGvTogEZG4iG3Q19ck6Orto0UdkIhInotv0KuJpYjEROyDXvX0IpLvMgp6M1tpZlvMbJuZ3TLC+ivMbL2Z9ZrZW4etS5nZxmhYM1EFHy91QCIicVE01gZmVgjcAVwN7AHWmdkad38mbbNdwHuAvxzhJY67+8UTUNYJlSwvpqxYHZCISP4bM+iBFcA2d98OYGb3AjcAA0Hv7juidX1ZKGNWhA5I9LhiEcl/mVTdNAC70+b3RMsylTCzJjN7xMzeMtIGZnZztE3TgQMHTuGlx0dt6UUkDibjYuwCd28E3gl8xczOHr6Bu9/p7o3u3lhXVzcJRQpCW3rV0YtIfssk6JuBeWnzc6NlGXH35mi8HXgIWHYK5cuq+mQZB492qQMSEclrmQT9OmCxmS0ysxLgRiCj1jNmVmtmpdH0DODVpNXt51p/y5uX1AGJiOSxMYPe3XuB1cB9wGbg++6+ycxuM7PrAczsUjPbA/wR8HUz2xTtvgRoMrMngAeBzw9rrZNTaksvInGQSasb3H0tsHbYslvTptcRqnSG7/ffwIXjLGPWqAMSEYmD2N4ZCzCrpjTqgERVNyKSv2Id9KVFhdRVltLc1pHrooiIZE2sgx7UAYmI5L/YB32DbpoSkTwX+6CvTybUAYmI5DUFfbJMHZCISF5T0A+0pVc9vYjkp9gHvdrSi0i+i33Q6+5YEcl3sQ/62vJiEsUFCnoRyVuxD3ozC00s2xX0IpKfYh/0EKpv9Fx6EclXCnp005SI5DcFPeGM/sARdUAiIvlJQc9gyxt1QCIi+UhBT3gMAqiJpYjkJwU9umlKRPKbgh6YXdN/Rq+qGxHJPwp6og5IqkpVdSMieUlBH6nXTVMikqcU9JGG6Ln0IiL5RkEfqa8JN02pAxIRyTcZBb2ZrTSzLWa2zcxuGWH9FWa23sx6zeytw9atMrOt0bBqogo+0Rpqy+js6aO1oyfXRRERmVBjBr2ZFQJ3ANcCS4F3mNnSYZvtAt4DfHfYvtOATwOXASuAT5tZ7fiLPfH0uGIRyVeZnNGvALa5+3Z37wbuBW5I38Ddd7j7k0DfsH3fANzv7i3u3grcD6ycgHJPOLWlF5F8lUnQNwC70+b3RMsyMZ59J9Xc2jLM4D83NJPqUz29iOSPKXEx1sxuNrMmM2s6cOBATsqQLC/h4yvP5xdPv8Rf/egp+hT2IpInMgn6ZmBe2vzcaFkmMtrX3e9090Z3b6yrq8vwpSfe+197Nh96/Tl8r2k3n/3pJrXAEZG8UJTBNuuAxWa2iBDSNwLvzPD17wM+l3YB9hrgE6dcykn0kavP5XhPim88/AJlJUV8fOV5mFmuiyUictrGDHp37zWz1YTQLgTucvdNZnYb0OTua8zsUuDHQC3wZjP7rLu/zN1bzOxvCAcLgNvcvSVLf8uEMDP+6o1LON6T4mu/fZ7ykkI+dOXiXBdLROS0ZXJGj7uvBdYOW3Zr2vQ6QrXMSPveBdw1jjJOOjPjtusv4Hh3H1++/znKigt53xVn5bpYIiKnJaOgj6OCAuMLf3ghnb0pbl+7mURxAe9+5cJcF0tE5JQp6E+iqLCAr7z9Yrp6Uvz1TzaRKC7kjxrnjb2jiMgUMiWaV05lxYUFfPWdy3nN4hl8/D+e5KdP7M11kURETomCPgOJ4kK+/u5LaFwwjY98byP3P7Mv10USEcmYgj5D5SVFfOs9jbysoYYP3rOe3z2Xmxu7REROlYL+FFQlivn2n17KWXUV3Hx3E49uP5TrIomIjElBf4qS5SX823svoyFZxp/96zo27GrNdZFERE5KQX8aZlSWcs97X8H0ylJW3fUYm/a257pIIiKjUtCfptk1Ce5572VUlhbx7m89xtZ9R3JdJBGRESnox2HetHLued8rKCww3vXNR9lx8FiuiyQicgIF/TgtmlHBPe+9jJ5UH+/65qO8oLAXkSlGQT8Bzp1Vxd03XcbRrl5WfuV33PHgNrp7h3e2JSKSGwr6CXJBQw2/+sgVXLlkJl+6bwtv+seHWbdjSj+oU0RiQkE/gWZVJ/jnd13Ct1Y10tGd4o++9ns+8aOnaO/oyXXRRCTGFPRZcOWSWfzqI1fwvtcs4vtNu7nyyw/xk43N6rFKRHJCQZ8lFaVFfPJNS/nJB19NQ7KMD9+7kVX/dx27DnXkumgiEjMK+iy7oKGGH33g1XzmzUtZv7OVq//+t/zzQ9voSelirYhMDgX9JCgsMN7z6kXc/9Er+B/nzeSLv9zCdf/4Xzy+UxdrRST7FPSTaE5NGV979yV8408aOdLZwx/+y+/55I+fov24LtaKSPYo6HPg6qWzuP+jr+Wmyxfx74/t4qov/5afPblXF2tFJCsU9DlSUVrEX1+3lDWrL2d2dYLV393An/7rOh7csp+2ju5cF09E8ohNtbPIxsZGb2pqynUxJlWqz/nO73fwt/dt4Vh3CoCzZlSwbH4ty+YnWTY/yXmzqigq1HFZREZmZo+7e+OI6xT0U8exrl6e3NPOht2trN/ZxsbdrRw8Gs7uy0sKuWhuTQj/eUmWza+lrqo0xyUWkaniZEFflOELrAT+ASgEvununx+2vhT4DnAJcAh4u7vvMLOFwGZgS7TpI+7+/tP5IzLSthuS87L28tlWUVrEK8+ezivPng6Au7On9Tjrd7WyYVcbG3a18o3fbae3Lxyc500rY9m8/rP+WpbOqaakSGf9IjLUmEFvZoXAHcDVwB5gnZmtcfdn0ja7CWh193PM7EbgC8Dbo3XPu/vFE1zuE7Xthq9eCouvgqv/BqYtyvpbZpuZMW9aOfOmlXPDxQ0AdPakeLq5PQT/7lYee6GFNU/sBaCkqIDzZ1dFQzVL5lSzZE4VyfKSXP4ZIpJjmZzRrwC2uft2ADO7F7gBSA/6G4DPRNM/BL5qZjaB5RxbRR1c8Zfw8JfhuV/Bq1bD5R+F0spJLUa2JYoLaVw4jcaF0waWvdh+fOCM/5kXD/PA5v18v2nPwPo5NQnOn13FkjnVnD+nmqVzqlg4vUJ1/iIxkUnQNwC70+b3AJeNto2795pZOzA9WrfIzDYAh4FPufvDw9/AzG4GbgaYP3/+Kf0BA4oTIegvfic88Fl4+O9gwz1w9WfhwrdBQf6G2pyaMuZcWMYbL5wDhCqfA0e62PzSEZ598TCbXzzMsy8d4eGtBweqfUqKCjh3ViVLZofwXzKniiWzq6mt0Nm/SL4Z82Ksmb0VWOnu743m3w1c5u6r07Z5OtpmTzT/POFgcASodPdDZnYJ8J/Ay9z98GjvN2EXY3evg19+HJofh7mXwsovwNxLxv+6Z7Du3j627T/Ksy8Nhv/mFw8PXPAFmFlVyrmzqqKhknNnV7F4ZiVVieIcllxExjLei7HNQPoVzrnRspG22WNmRUANcMjDUaQLwN0fjw4A5wLZb1Yz71K46QF48l544DPwzdfDy98JV30aqmZn/e2nopKiApbWV7O0vnrI8gNHugbCf8tLR9m6/wjffWwnnT2Dz+NpSJaxeFYl582qYvGsKs6bVcU5MyspKymc7D9DRE5RJkG/DlhsZosIgX4j8M5h26wBVgG/B94K/Mbd3czqgBZ3T5nZWcBiYPuElX4sBQWhKmfJm0NVzu/vgM1r4DV/Aa/4QKjuEeqqSqmrquM1i+sGlvX1hRY/W/Yd4bmB4Sj/ve0Q3dED2cxg/rTywbP/WVVcNDfJwunlTPYlGhEZXUbt6M3sjcBXCM0r73L3283sNqDJ3deYWQK4G1gGtAA3uvt2M/tD4DagB+gDPu3uPz3Ze2W1HX3LdrjvU7Dl51C7EN7wOTjvjSGxJCO9qT52HOpg674jbNl3hK37jrJl3xFeOHiMVFT/P72ihOULarlkQS2NC2q5oKGGRLHO/EWySTdMDff8b+CXn4ADz8JZr4M3/B+YtTS775nnunpTbD9wjI2722ja0cr6Xa0DHaWXFBZwQUM1jQunsXx+OADoZi+RiaWgH0mqF5ruggdvh64jcOlN8LpPQPm0sfeVjBw82sX6na08Hg1P7mkfqPZZML2cSwbO+qexeGYlBQX6ZSVyuhT0J9PRAg9+Dpq+BYkauPJWWL4KClTVMNG6elM83XyYx3e2DIR/f4ufqkQRy+fXMqcmQaK4kNLiAkqLCkkUF5AoCvOJosKwrqiARHFYN7BNtDz9YJH+1XaGzIw0CYRqJ91fIGciBX0m9m2CX3wcdjwMDZfAm/4O6pdNfjlixN3Z1dJB045WHt/VyvqdrbQc66azJ0VXbx9dvZPfC1d5SSGXLKhlxcJprFg0jZfPS+r6gpwRFPSZcoenfgj3/RV0HITGm+D1n4KyZG7KE3N9fU53qo+unj46e1MDB4DOnhSdPX109YZx+vIh3+dhF9nT59JXWbSmz53n9h3hsRdaePalI0C4vnDxvCQrFoXgX76glsrSjB4RJTKpFPSnqrMdfnM7rPsGlE+Ha26Hi96m1jkx0tbRzbodrazb0cKjL7TwdHM7qT6nsMB4WX31wBn/pQun6W5imRIU9Kdr70b4+UfD3bULLg/VOTPPz9779aV0bWCKOtbVy/pd4SFyj77QwsbdbXRHVUvnzaoKob9oGg3JBAVmFBbYwHhg6F8eTRcUQKEZRQUFYbrASBQV6qK0nBYF/Xj09cH6b4e7a7uPwitXw2s/BiUVE/P6h/fCsz+HZ38GO/4LFl0B1/19aOcvU1ZXb4on97QPBP/jO1oGOo0ZDzOoLC2iOlFMTVkx1WVhurosmk+MsCxtvqKkUDerxZSCfiIcOwgPfBo2/BtUz4VrPw/nX3d61TkHt8Lmn4Zwb348LJu+GBa8Cp7+D/C+cG3gsvfrDP8M0Zvq49mXjtByrJuUO319Tm9fGKfcSfU5fe6k+iDV1xfG0Xap/sGdju4Uh4/3cLizJ4yP9w5Od/ZytKv3pOUwg4qSIipKC6koLRqYriwtoqK0iPKSIiqjdZXRfPr6ogLDCZer3H1wGgeHvmg6LBvchmh5oqiQmvJikuUlJMuKKZ9iB57eVB/7j3Sxt+04zdGwt+04e9s62dt2nERxuBjff7/H7Joz5+55Bf1E2vUI/OyjsH8TLL4Grv3i2M++d4e9G0Kwb/4ZHIz6YalfFg4WS94MdeeFZe174Od/Ac/9EuqXw/X/BLMvyO7fJGeM3lQfRzr7w3/wINAeDce6ejnaleJYVy/HunvDuCvF0YH5sO54z/h/fWSiuNCoKSshWV5MsqyYZHnxifPRQSFZXkxZcTgwFKZXbw1Mp42HVX8VFhhmxpHOnoHQ7g/x9DB/6XDnwB3c/ZLlxTQky5hTU8bh4z08sadtoMVXQ7KM5QtqWT4/ySULalkyp5riKdr8VkE/0VK98NjXQ/v7vt7w7JxXfWjos3NSvbDz/4Vwf/bncLgZrDCctS95M5z/JqiZO/Lru8OmH8Haj0FnG7z6w3DFx/RsHpkwqT7nWHcvHf0HgWjo7XPMQkukMAai+QILneH0L7do5eC80dmToq2jh/bj3bR19NB2vGfIfPvAfM+Yv05OldnQeycAigqMOckE9TVlNCTLqE+W0VAbjZMJ5tSUUTGsFVV3bx+bXzwc7vXY1cqGna3sbe8EIFFcwEVzQ+hfMr+W5QtqmTZFLsYr6LPl8N7QFHPTj2HaWbDy8yH4N/8MnvsFHG+FogScfSUsuQ7OXXlqd952tMCvPgUb74Hp58Cb/wEWXp69v0dkEvWk+tKCv5vWYz109qaGVHOdWPU1tBps+LKK0qLBQE+WUVdVSuEEXNze2xa69Fy/s43Hd7Wyqbl9oG+HRTMqWD6/luULkpw7q4ppFSVMKy+huqx4Qt47Uwr6bNv2a1j7v6Hl+TCfqAmhfv51cM6V479w+/yD8NMPQ9tOuOQ9cNVn1bZfJIc6e1I81dw+cIf3hl2tQ/p1ACgwqCkrpjYK/iHjinAdY3A+TFclik671ZWCfjL0doWbrarnwMLXQOEEd9TR3QEPfS48arliJrzpb0MVkIjkXP9d3rtaOmg51k3rsW5aOnqicTR/rJvWjvDLpf+ZT8NdNLeGNatP71e7gj6f7N0Aa/4XvPRUCPprvxQOLiJyRnB3jnWnaI2Cv2Vg3ENVooi3Nc4b+0VGMN4epmQqqV8G73sQfv9VeOjzsP0yuOa28CC2KdSMTURGZmZURs1b500rn5T3VNCfiQqL4fKPwJLrQ939Tz8MT/4gXKydcc6J27tDqgd6O0MV04jjTkh1QyIJ1fVhmOjqJxHJCQX9mWz62bDqp7Dh7tBz1r+8CmoXjBDkXZz4QN6xGFTOhOqGEPo1c4dN10PVnNwdDNxDq6ZjB+Dofji6L9zUZgWQqIbS6jBO1AxOl1brBjSJJQX9mc4Mlv9JuHnrd1+CjkOhSWdR6YnjwtK0+RG2KSyG4y2h2Wh7c2j7f7gZDm2D7b+F7iPD3xwqZ0FNdAConhsCtagUisoGX7c4kfaeI8z3Lyssha7Dg+F9bH8U4sOno/V9Paf+eZVUnfxAMO0saFgOdefrF43kDQV9vqiaHR66lk2dhwfDv705HBAO7wnjA8/B8w+NcDCYAAVFoaVRZV0Yz7pgcLoyGipmQkUd4OHpo12Hw7jzcDR9OG35YeiK1h3dHw5k/ev7Dx5FCZh9UQj9+mXhLuXp54QO50XOMAp6yVwiOvOduWT0bdyHVRsdD+Oe40OvB/R2Qk/n0PneLiipjIK7LvxaqJwZrhucSsBWzDi9v6+vD1pfgOb1oXXT3vWw/jvw6NfC+tJqmPPyEPwNy0P4J+dndhG8v6pp4CDZP+wNj73ony4sjarHGkJV2QlVZg1QMjkX8CR/qHmlyMmkeuHgcyH0+w8A+54OF64h9FfQf8Zfvyz8+uj/ldPePDh9eC/0dAx9bSscvPDdH+ap7qH7HTtwYpnKakM12UCVWdoBoXJmWJ9IQlGWbs13D7+Mjh6Iro2kVauVlIcH9E0/J1SDZeuxHe7hs9m/OQwHNsP+Z6HnWPjbR6uaS9RE0zVD1+fB40XUjl5kIvV2ha4n+8/6mzeEoPG0m2CsIFysPtnF7MpZY18c7umEI2nXTNr7DxzNgweE460j71tcEUK/rDbcST1knDYk0paluk+8JnJ0X3RdZN9guKe6Tnw/KzjxM6iZBzMWh/Cfcc7gQaC6PvPmwB0tQ8O8f7rj0OA2iSTMXAqlVWnVc2njsRojFJaE0K+YGX6l1S4I4+SCaHpBODBMYeMOejNbCfwDUAh8090/P2x9KfAd4BLgEPB2d98RrfsEcBOQAj7k7ved7L0U9HJG6u4IN7GZRWfWs6BwkmpGu48NVgF1HArBf7wtGreGB+P1T/cPqe6xXxdCWJfPGHotpDKqVhs+XT4t/Go5tA0OPR8ex31oazR+Ppxt9yuuCK3GBg4C0QEg1T3sLH1zOLD0K6kKnf/MXAJ1S8J45pJQhtEOHH194drRCddp+q/jpF3TObIP2naFx410Hx36OonkYOgn54c+I/qnk/NPrFJL9Yb37ToaXqvr6NjzNXPh9Z/M7N9m+D/VeILezAqB54CrgT3AOuAd7v5M2jYfAC5y9/eb2Y3AH7j7281sKfDvwAqgHngAONfdR31GqoJeJMvcwzWT9ODvbAtnzkWlaYE+M1RNTUSTVPdwMDq0NRwIDm4bPAi07eKEM+7i8vDo7vQwn7kkHEQn48ZA9/B5tO0MQ+vOwQNA264w9HYO3adiZgj7/uAevn40VgilleEg1rAc3n73aRV5vHfGrgC2ufv26MXuBW4Anknb5gbgM9H0D4GvWuht4AbgXnfvAl4ws23R6/3+dP4QEZkAZiGQSspDPf9kvWdNQxjOen8pAIcAAAYvSURBVN3QdT2d0LI9BH9BcQj05ILctnAyg4rpYWhYfuL6vr5QvdW2KzoI7Ajj3q4otCtDNVJJZTRfEYJ8YF3l4HxRIusHr0yCvgHYnTa/B7hstG3cvdfM2oHp0fJHhu17wjfLzG4GbgaYP39+pmUXkXxQnIBZS8NwpigoCE2aq2bDvBW5Ls2YpkSjYHe/090b3b2xrq4u18UREckrmQR9M5D+OLW50bIRtzGzIqCGcFE2k31FRCSLMgn6dcBiM1tkZiXAjcCaYdusAVZF028FfuPhKu8a4EYzKzWzRcBi4LGJKbqIiGRizDr6qM59NXAfoXnlXe6+ycxuA5rcfQ3wLeDu6GJrC+FgQLTd9wkXbnuBD56sxY2IiEw83TAlIpIHTta8ckpcjBURkexR0IuI5DkFvYhInptydfRmdgDYOY6XmAEcnKDiZIPKNz4q3/iofOMzlcu3wN1HvBFpygX9eJlZ02gXJKYClW98VL7xUfnGZ6qXbzSquhERyXMKehGRPJePQX9nrgswBpVvfFS+8VH5xmeql29EeVdHLyIiQ+XjGb2IiKRR0IuI5LkzMujNbKWZbTGzbWZ2ywjrS83se9H6R81s4SSWbZ6ZPWhmz5jZJjP78AjbvM7M2s1sYzTcOlnlSyvDDjN7Knr/Ex4uZME/Rp/hk2Y2Qjc7WSvbeWmfzUYzO2xmfz5sm0n9DM3sLjPbb2ZPpy2bZmb3m9nWaFw7yr6rom22mtmqkbbJUvm+ZGbPRv9+Pzaz5Cj7nvS7kMXyfcbMmtP+Dd84yr4n/f+exfJ9L61sO8xs4yj7Zv3zGzd3P6MGwhM0nwfOAkqAJ4Clw7b5APC1aPpG4HuTWL45wPJouorQ3+7w8r0O+FmOP8cdwIyTrH8j8AvAgFcAj+bw3/slws0gOfsMgSuA5cDTacu+CNwSTd8CfGGE/aYB26NxbTRdO0nluwYoiqa/MFL5MvkuZLF8nwH+MoN//5P+f89W+Yat/zvg1lx9fuMdzsQz+oE+bN29G+jvwzbdDcC3o+kfAldGfdhmnbu/6O7ro+kjwGZG6D7xDHAD8B0PHgGSZjYnB+W4Enje3cdzt/S4ufvvCI/gTpf+Pfs28JYRdn0DcL+7t7h7K3A/sHIyyufuv3L33mj2EULHPzkxyueXiUz+v4/bycoXZcfbgH+f6PedLGdi0I/Uh+3wIB3Shy3Q34ftpIqqjJYBj46w+pVm9oSZ/cLMXjapBQsc+JWZPR712TtcJp/zZLiR0f+D5foznOXuL0bTLwGzRthmqnyOf0b4hTaSsb4L2bQ6qlq6a5Sqr6nw+b0G2OfuW0dZn8vPLyNnYtCfEcysEvgP4M/d/fCw1esJVREvB/4J+M/JLh9wubsvB64FPmhmV+SgDCdloUez64EfjLB6KnyGAzz8hp+SbZXN7JOEjn/uGWWTXH0X/gU4G7gYeJFQPTIVvYOTn81P+f9LZ2LQj6cP20lhZsWEkL/H3X80fL27H3b3o9H0WqDYzGZMVvmi922OxvuBHxN+IqebCv39Xgusd/d9w1dMhc8Q2NdfnRWN94+wTU4/RzN7D3Ad8K7oYHSCDL4LWeHu+9w95e59wDdGed9cf35FwP8EvjfaNrn6/E7FmRj04+nDNuui+rxvAZvd/cujbDO7/5qBma0g/DtM5oGowsyq+qcJF+2eHrbZGuBPotY3rwDa06opJsuoZ1K5/gwj6d+zVcBPRtjmPuAaM6uNqiauiZZlnZmtBD4GXO/uHaNsk8l3IVvlS7/m8wejvG8m/9+z6SrgWXffM9LKXH5+pyTXV4NPZyC0CHmOcDX+k9Gy2whfaIAE4ef+NkJn5GdNYtkuJ/yEfxLYGA1vBN4PvD/aZjWwidCC4BHgVZP8+Z0VvfcTUTn6P8P0MhpwR/QZPwU0TnIZKwjBXZO2LGefIeGA8yLQQ6gnvolw3efXwFbgAWBatG0j8M20ff8s+i5uA/50Esu3jVC/3f897G+JVg+sPdl3YZLKd3f03XqSEN5zhpcvmj/h//tklC9a/q/937m0bSf98xvvoEcgiIjkuTOx6kZERE6Bgl5EJM8p6EVE8pyCXkQkzynoRUTynIJeRCTPKehFRPLc/wd+0n6dzvYLVgAAAABJRU5ErkJggg==\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["predictions = model.predict(test_noLabel)\n","pre = predictions.argmax(axis=-1)"],"metadata":{"id":"rYCzqUmgxSE7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["submission = pd.Series(pre,name=\"Label\")\n","submission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),submission],axis = 1)\n","submission.to_csv(\"/content/drive/MyDrive/Colab Notebooks/IAI/digit-recognizer/final_submission.csv\",index=False)\n","submission.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"QfV99zHyxX3o","executionInfo":{"status":"ok","timestamp":1639652029699,"user_tz":-60,"elapsed":185,"user":{"displayName":"Xiaomin Jin","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"12555100211776147364"}},"outputId":"1e815915-b100-49d4-ba7a-23d278014631"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ImageId</th>\n","      <th>Label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   ImageId  Label\n","0        1      2\n","1        2      0\n","2        3      9\n","3        4      9\n","4        5      3"]},"metadata":{},"execution_count":23}]}]}