DeepAE_Rec.ipynb

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      "name": "DeepAE_Rec.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [
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    "accelerator": "GPU"
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  "cells": [
    {
      "metadata": {
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        "colab_type": "code",
        "outputId": "4b1f95d4-63ca-4473-85f0-3c7d2e3f941d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.python.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.python.keras.layers import Input, Dense, Embedding, Flatten, Dropout, merge, Activation, BatchNormalization, LeakyReLU\n",
        "from tensorflow.python.keras.models import Model\n",
        "from tensorflow.python.keras.regularizers import l2\n",
        "from tensorflow.python.keras import backend as K\n",
        "from tensorflow.python.keras import regularizers\n",
        "from tensorflow.python.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.python.keras import initializers\n",
        "from tensorflow.python.keras.layers import add, concatenate\n",
        "%matplotlib inline  \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from scipy.sparse import csr_matrix\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.keras.models import model_from_json\n",
        "from sklearn import preprocessing\n",
        "from keras.utils import plot_model"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "V7VSmc5O0ew1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "metadata": {
        "id": "z5dwS7kk0ew2",
        "colab_type": "code",
        "outputId": "90557ac1-ef5e-4e9b-de40-3975b8f3e8ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('ml1m_ratings.csv',sep='\\t', encoding='latin-1', \n",
        "                      usecols=['user_emb_id', 'movie_emb_id', 'rating', 'timestamp'])\n",
        "\n",
        "#+1 is the real size, as they are zero based\n",
        "num_users = df['user_emb_id'].unique().max() + 1\n",
        "num_movies = df['movie_emb_id'].unique().max() + 1\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_emb_id</th>\n",
              "      <th>movie_emb_id</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>5</td>\n",
              "      <td>978824291</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   user_emb_id  movie_emb_id  rating  timestamp\n",
              "0            0          1192       5  978300760\n",
              "1            0           660       3  978302109\n",
              "2            0           913       3  978301968\n",
              "3            0          3407       4  978300275\n",
              "4            0          2354       5  978824291"
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          "metadata": {
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          "execution_count": 2
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    },
    {
      "metadata": {
        "id": "3zlf0zbG0ew_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df, test_df = train_test_split(df,\n",
        "                                     stratify=df['user_emb_id'],\n",
        "                                     test_size=0.1,\n",
        "                                     random_state=999613182)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zPvDimmG0exC",
        "colab_type": "code",
        "outputId": "adab5285-807a-4b7d-d159-f7e5efe4252f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train_df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_emb_id</th>\n",
              "      <th>movie_emb_id</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>339280</th>\n",
              "      <td>1997</td>\n",
              "      <td>1018</td>\n",
              "      <td>2</td>\n",
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              "      <td>983</td>\n",
              "      <td>3860</td>\n",
              "      <td>3</td>\n",
              "      <td>975115869</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "        user_emb_id  movie_emb_id  rating  timestamp\n",
              "339280         1997          1018       2  974679219\n",
              "881301         5322          1293       4  960848231\n",
              "926190         5597          2293       4  959214335\n",
              "726548         4344            26       2  966273825\n",
              "153039          983          3860       3  975115869"
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    {
      "metadata": {
        "id": "7X-rfZ2V0exF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df, validate_df = train_test_split(train_df,\n",
        "                                 stratify=train_df['user_emb_id'],\n",
        "                                 test_size=0.1,\n",
        "                                 random_state=999613182)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rywZzzt-0exI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dataPreprocessor(rating_df, num_users, num_items, init_value=0, average=False):\n",
        "    \"\"\"\n",
        "        INPUT: \n",
        "            data: pandas DataFrame. columns=['userID', 'itemID', 'rating' ...]\n",
        "            num_row: int. number of users\n",
        "            num_col: int. number of items\n",
        "            \n",
        "        OUTPUT:\n",
        "            matrix: 2D numpy array. \n",
        "    \"\"\"\n",
        "    if average:\n",
        "      matrix = np.full((num_users, num_items), 0.0)\n",
        "      for (_, userID, itemID, rating, timestamp) in rating_df.itertuples():\n",
        "        matrix[userID, itemID] = rating\n",
        "      avergae = np.true_divide(matrix.sum(1), np.maximum((matrix!=0).sum(1), 1))\n",
        "      inds = np.where(matrix == 0)\n",
        "      matrix[inds] = np.take(avergae, inds[0])\n",
        "      \n",
        "    else:\n",
        "      matrix = np.full((num_users, num_items), init_value)\n",
        "      for (_, userID, itemID, rating, timestamp) in rating_df.itertuples():\n",
        "        matrix[userID, itemID] = rating\n",
        "\n",
        "    return matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VAd1YzDK0exL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Creating a sparse pivot table with users in rows and items in columns\n",
        "users_items_matrix_train_zero = dataPreprocessor(train_df, num_users, num_movies, 0)\n",
        "users_items_matrix_train_one = dataPreprocessor(train_df, num_users, num_movies, 1)\n",
        "users_items_matrix_train_two = dataPreprocessor(train_df, num_users, num_movies, 2)\n",
        "users_items_matrix_train_four = dataPreprocessor(train_df, num_users, num_movies, 4)\n",
        "users_items_matrix_train_three = dataPreprocessor(train_df, num_users, num_movies, 3)\n",
        "users_items_matrix_train_five = dataPreprocessor(train_df, num_users, num_movies, 5)\n",
        "users_items_matrix_validate = dataPreprocessor(validate_df, num_users, num_movies, 0)\n",
        "users_items_matrix_test = dataPreprocessor(test_df, num_users, num_movies, 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1meu5_xX_L1I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "users_items_matrix_train_average = dataPreprocessor(train_df, num_users, num_movies, average=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FMNsOU7xbZxG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Utility Function"
      ]
    },
    {
      "metadata": {
        "id": "U35ysyVB0exT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def show_error(history, skip):\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    plt.plot(np.arange(skip, len(loss), 1), loss[skip:])\n",
        "    plt.plot(np.arange(skip, len(loss), 1), val_loss[skip:])\n",
        "    plt.title('model train vs validation loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'validation'], loc='best')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9iw-lR3WPzip",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def show_rmse(history, skip):\n",
        "    rmse = history.history['masked_rmse_clip']\n",
        "    val_rmse = history.history['val_masked_rmse_clip']\n",
        "    plt.plot(np.arange(skip, len(rmse), 1), rmse[skip:])\n",
        "    plt.plot(np.arange(skip, len(val_rmse), 1), val_rmse[skip:])\n",
        "    plt.title('model train vs validation masked_rmse')\n",
        "    plt.ylabel('rmse')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'validation'], loc='best')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hbkAxwJIcPu7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_model(name):\n",
        "  # load json and create model\n",
        "  model_file = open('{}.json'.format(name), 'r')\n",
        "  loaded_model_json = model_file.read()\n",
        "  model_file.close()\n",
        "  loaded_model = model_from_json(loaded_model_json)\n",
        "  # load weights into new model\n",
        "  loaded_model.load_weights(\"{}.h5\".format(name))\n",
        "  print(\"Loaded model from disk\")\n",
        "  return loaded_model\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "niWEmbtbbnO4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def save_model(name, model):\n",
        "  # # serialize model to JSON\n",
        "  model_json = model.to_json()\n",
        "  with open(\"{}.json\".format(name), \"w\") as json_file:\n",
        "      json_file.write(model_json)\n",
        "  # serialize weights to HDF5\n",
        "  model.save_weights(\"{}.h5\".format(name))\n",
        "  print(\"Saved model to disk\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TD0nbU8NFPZn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def masked_se(y_true, y_pred):\n",
        "        # masked function\n",
        "        mask_true = K.cast(K.not_equal(y_true, 0), K.floatx())\n",
        "        # masked squared error\n",
        "        masked_squared_error = K.square(mask_true * (y_true - y_pred))\n",
        "        masked_mse = K.sum(masked_squared_error, axis=-1)\n",
        "        return masked_mse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KctLtyJbTzMQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def masked_mse(y_true, y_pred):\n",
        "        # masked function\n",
        "        mask_true = K.cast(K.not_equal(y_true, 0), K.floatx())\n",
        "        # masked squared error\n",
        "        masked_squared_error = K.square(mask_true * (y_true - y_pred))\n",
        "        masked_mse = K.sum(masked_squared_error, axis=-1) / K.maximum(K.sum(mask_true, axis=-1), 1)\n",
        "        return masked_mse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w14TGHrbLI92",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def masked_rmse(y_true, y_pred):\n",
        "        # masked function\n",
        "        mask_true = K.cast(K.not_equal(y_true, 0), K.floatx())\n",
        "        # masked squared error\n",
        "        masked_squared_error = K.square(mask_true * (y_true - y_pred))\n",
        "        masked_mse = K.sqrt(K.sum(masked_squared_error, axis=-1) / K.maximum(K.sum(mask_true, axis=-1), 1))\n",
        "        return masked_mse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1CruAG7-pM1K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def masked_rmse_clip(y_true, y_pred):\n",
        "        # masked function\n",
        "        mask_true = K.cast(K.not_equal(y_true, 0), K.floatx())\n",
        "        y_pred = K.clip(y_pred, 1, 5)\n",
        "        # masked squared error\n",
        "        masked_squared_error = K.square(mask_true * (y_true - y_pred))\n",
        "        masked_mse = K.sqrt(K.sum(masked_squared_error, axis=-1) / K.maximum(K.sum(mask_true, axis=-1), 1))\n",
        "        return masked_mse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DihlpqLv0exi",
        "colab_type": "code",
        "outputId": "ae948467-5d0e-4700-e3c9-7385812793b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Test custom cost function\n",
        "y_pred = K.constant([[ 1, 1, 1, 1], \n",
        "                     [ 1, 1, 1, 10],\n",
        "                     [ 1, 1, 1, 3],\n",
        "                     [ 1, 1, 1, 3],\n",
        "                     [ 1, 1, 1, 3],\n",
        "                     [ 1, 1, 1, 3]])\n",
        "y_pred = K.constant([[ 1, 1, 1, 3]])\n",
        "y_true = K.constant([[ 1, 1, 1, 1],\n",
        "                     [ 1, 1, 1, 1],\n",
        "                     [ 0, 1, 1, 1],\n",
        "                     [ 0, 0, 1, 1],\n",
        "                     [ 0, 0, 0, 1],\n",
        "                     [ 0, 0, 0, 0]])\n",
        "y_true = K.constant([[ 0, 0, 1, 1]])\n",
        "true = K.eval(y_true)\n",
        "pred = K.eval(y_pred)\n",
        "loss = K.eval(masked_se(y_true, y_pred))\n",
        "rmse = K.eval(masked_rmse(y_true, y_pred))\n",
        "\n",
        "for i in range(true.shape[0]):\n",
        "    print(true[i], pred[i], loss[i], rmse[i], sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 1. 1.]\t[1. 1. 1. 3.]\t4.0\t1.4142135\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-2BL8bsxiI4P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## AutoRec"
      ]
    },
    {
      "metadata": {
        "id": "K4sK_GQC0exX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def AutoRec(X, reg, first_activation, last_activation):\n",
        "    '''\n",
        "    AutoRec\n",
        "        INPUT: \n",
        "          X: #_user X #_item matrix\n",
        "          reg: L2 regularization parameter\n",
        "          first_activation: activation function for first dense layer\n",
        "          last_activation: activation function for second dense layer\n",
        "        \n",
        "        OUTPUT:\n",
        "          Keras model\n",
        "    \n",
        "    '''\n",
        "    input_layer = x = Input(shape=(X.shape[1],), name='UserRating')\n",
        "    x = Dense(500, activation=first_activation, name='LatentSpace', kernel_regularizer=regularizers.l2(reg))(x)\n",
        "    output_layer = Dense(X.shape[1], activation=last_activation, name='UserScorePred', kernel_regularizer=regularizers.l2(reg))(x)\n",
        "    model = Model(input_layer, output_layer)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KRUVywEjjSYO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def AutoRec_LReLU(X, reg):\n",
        "    '''\n",
        "    AutoRec\n",
        "    LReLu is not implemented in to Dense \n",
        "    Need to have seperate LeakyRelu layer \n",
        "    '''\n",
        "    input_layer = x = Input(shape=(X.shape[1],), name='UserRating')\n",
        "    x = Dense(500, name='LatentSpace', kernel_regularizer=regularizers.l2(reg))(x)\n",
        "    x = LeakyReLU()(x)\n",
        "    output_layer = Dense(X.shape[1], activation='linear', name='UserScorePred',kernel_regularizer=regularizers.l2(reg))(x)\n",
        "    model = Model(input_layer, output_layer)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LOoHWlL30exs",
        "colab_type": "code",
        "outputId": "2b654fee-233d-4bd2-83f7-6cf3fa94ee7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "# Build model\n",
        "\n",
        "AutoRec = AutoRec(users_items_matrix_train_zero, 0.0005, 'elu', 'elu')\n",
        "\n",
        "AutoRec.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip])\n",
        " \n",
        "AutoRec.summary()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "UserRating (InputLayer)      (None, 3952)              0         \n",
            "_________________________________________________________________\n",
            "LatentSpace (Dense)          (None, 500)               1976500   \n",
            "_________________________________________________________________\n",
            "UserScorePred (Dense)        (None, 3952)              1979952   \n",
            "=================================================================\n",
            "Total params: 3,956,452\n",
            "Trainable params: 3,956,452\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "5HGewpPp0exw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "hist_Autorec = AutoRec.fit(x=users_items_matrix_train_average, y=users_items_matrix_train_zero,\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  verbose = 2, \n",
        "                  validation_data=[users_items_matrix_train_average, users_items_matrix_validate])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c6mvsxq-7M9n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plot_model(AutoRec, to_file='AutoRec.png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lU8Q5PRN0exz",
        "colab_type": "code",
        "outputId": "cea066a5-9a3e-4b8e-cb80-e7e8113ffaf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Autorec, 30)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O2YNnWSBgJbkgikO/F+PuWUvvbsFEBgYes32rzUp5bQX+Hn4uvhOFQqFoP5SyaER8UCxz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vYYUBiTmMYzA",
        "colab_type": "code",
        "outputId": "4617867a-c587-4318-b3dd-d5ab098bb1cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Autorec, 50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UAOcB2c3Mfxj4xon777TOGRfNtOG9Sc8t57nPtlFvcf74AW0R7h3KM1Mf46nJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "q7-5s0F6q2Sw",
        "colab_type": "code",
        "outputId": "ce9d94c9-6359-4516-e1d3-6f357a3b347f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_result = AutoRec.evaluate(users_items_matrix_train_average, users_items_matrix_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 1s 135us/sample - loss: 1.0394 - masked_rmse_clip: 0.8810\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pQ7E3hlU7Icn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predict_autorec = AutoRec.predict(users_items_matrix_train_zero)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HVjQxEL86y0f",
        "colab_type": "code",
        "outputId": "609689ba-56c2-4ce3-b35b-bf5672017cb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "loss = K.eval(masked_rmse_clip( \n",
        "    K.constant((users_items_matrix_train_zero)), \n",
        "    K.constant(predict_autorec)))\n",
        "np.mean(loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4829845"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "metadata": {
        "id": "tqkzWtjhcz8Q",
        "colab_type": "code",
        "outputId": "6dd3cf4f-c83c-42a6-e440-76db29263367",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# model = load_model('AutRec')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "J26Rlr_VdCYf",
        "colab_type": "code",
        "outputId": "840670d5-a5e4-442b-cfff-1be53fea2ae9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# model.compile(optimizer = Adam(lr=0.001), loss=masked_se, metrics=[masked_rmse_clip])\n",
        "# score = model.evaluate(users_items_matrix_train_zero, users_items_matrix_test)\n",
        "# score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 2s 253us/sample - loss: 31.5291 - masked_rmse_clip: 0.9159\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[31.529148058859718, 0.91590214]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 388
        }
      ]
    },
    {
      "metadata": {
        "id": "nEN3Z1FmiR3s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Deep AE CF"
      ]
    },
    {
      "metadata": {
        "id": "UmMbXG3N0exc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "  def Deep_AE_model(X, layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode, side_infor_size=0):\n",
        "    '''\n",
        "    Build Deep AE for CF\n",
        "        INPUT: \n",
        "            X: #_user X #_item matrix\n",
        "            layers: List, each element is the number of neuron for a layer\n",
        "            reg: L2 regularization parameter\n",
        "            activation: activation function for all dense layer except the last\n",
        "            last_activation: activation function for the last dense layer\n",
        "            dropout: dropout rate\n",
        "            regularizer_encode: regularizer for encoder\n",
        "            regularizer_decode: regularizer for decoder\n",
        "            side_infor_size: size of the one hot encoding side information\n",
        "        OUTPUT:\n",
        "            Keras model\n",
        "    '''\n",
        "\n",
        "    # Input\n",
        "    input_layer = x = Input(shape=(X.shape[1],), name='UserRating')\n",
        "    \n",
        "    # Encoder\n",
        "    # -----------------------------\n",
        "    k = int(len(layers)/2)\n",
        "    i = 0\n",
        "    for l in layers[:k]:\n",
        "      x = Dense(l, activation=activation,\n",
        "                      name='EncLayer{}'.format(i), kernel_regularizer=regularizers.l2(regularizer_encode))(x)\n",
        "      i = i+1\n",
        "      \n",
        "      \n",
        "    # Latent Space\n",
        "    # -----------------------------\n",
        "    x = Dense(layers[k], activation=activation, \n",
        "                                name='LatentSpace', kernel_regularizer=regularizers.l2(regularizer_encode))(x)\n",
        "    \n",
        "    # Dropout\n",
        "    x = Dropout(rate = dropout)(x)\n",
        "    \n",
        "    # Decoder\n",
        "    # -----------------------------\n",
        "    for l in layers[k+1:]:\n",
        "      i = i-1\n",
        "      x = Dense(l, activation=activation, \n",
        "                      name='DecLayer{}'.format(i), kernel_regularizer=regularizers.l2(regularizer_decode))(x)\n",
        "      \n",
        "    # Output\n",
        "\n",
        "    output_layer = Dense(X.shape[1]-side_infor_size, activation=last_activation, name='UserScorePred', kernel_regularizer=regularizers.l2(regularizer_decode))(x)\n",
        "\n",
        "    # this model maps an input to its reconstruction\n",
        "    model = Model(input_layer, output_layer)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "W9zEkXbN0exn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layers = [256, 512, 256]\n",
        "#layers = [512, 256, 128, 256, 512]\n",
        "#layers = [512, 256, 512]\n",
        "#layers = [128, 256, 512, 256, 128]\n",
        "#layers = [512, 512, 512]\n",
        "dropout = 0.8\n",
        "# activation = 'sigmoid'\n",
        "# last_activation = 'linear'\n",
        "activation = 'selu'\n",
        "last_activation = 'selu'\n",
        "regularizer_encode = 0.001\n",
        "regularizer_decode = 0.001"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "id": "1A6MQupQ0eyB",
        "colab_type": "code",
        "outputId": "756d99f7-7ce0-49e5-d634-de5f56da8004",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# Build model\n",
        "Deep_AE = Deep_AE_model(users_items_matrix_train_zero, layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode)\n",
        "Deep_AE.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip]) \n",
        "Deep_AE.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "UserRating (InputLayer)      (None, 3952)              0         \n",
            "_________________________________________________________________\n",
            "EncLayer0 (Dense)            (None, 256)               1011968   \n",
            "_________________________________________________________________\n",
            "LatentSpace (Dense)          (None, 512)               131584    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "DecLayer0 (Dense)            (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "UserScorePred (Dense)        (None, 3952)              1015664   \n",
            "=================================================================\n",
            "Total params: 2,290,544\n",
            "Trainable params: 2,290,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nANT_fJp0eyF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "hist_Deep_AE = Deep_AE.fit(x=users_items_matrix_train_zero, y=users_items_matrix_train_zero,\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  validation_data=[users_items_matrix_train_zero, users_items_matrix_validate], verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NsA-qtrB0eyJ",
        "colab_type": "code",
        "outputId": "2eab8568-6676-40d8-d3a8-dcb9562e321c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Deep_AE, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BcpwXK/wt/sQFxnCkroCa1lrC/cJ8HaIQ4iRfG3l5t1fr3jBiRCaVleWUlpZQX1/PmjUr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\njMc5sAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ULoDeQSZ1YPK",
        "colab_type": "code",
        "outputId": "306d18a4-ecdc-434d-c36e-06126e3f595e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Deep_AE, 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2vGksvykv0XhdUFNkNE67zdoT29IqM/g5ozFxvLH7X1TatHGZDpQdavUmuie3\nvmj0emqaFJyujj8Xoi019lre3P0+h8vTj3tbSnMutwsASzs3UZ7qahxaDUB5fdvPOekop8vZ7u+6\no4NZFtYU87cN/+CD/R8b0/KqC/gxfbXx3Tyy6Wke3PjkCflb6g5dlhSklBuAbUKIDcBLwBIhxPVC\niIuklPnA08AqIcR6YIeUcp2UchOQLoTYCPwLWNJV8fUEgwIHcPf424jUh9UAmBE1udkyfbxDmr3f\nU5REq0wufflQZOlB0isa86ks1Z7s9mHyZ3yd+j0OV+PjP4tqS8iqzDHe1znrOxS70+Xk3/s/ZmfB\nnhbz9hYnsbtoH1vzd3RoWz2Z0+Vkc+62oz5972RqeF54V5QUah11fHd4ZbPRf7uLSa9tdtPxe1Pd\nbjeZldm43C5K68r4x5bnOVB6iEc2P8O7+/5z1HUrm3S2ONrxZ1VpfyvbChqbOl/Y/gbLDn1r/G1W\n6wmtqFYbnuZweQZ51fmcKrq0TUFK+ecjJu1qMu9N4M0j5iOlfAh4qCvj6snCfPrw2Jn38sjqF7E5\nbSwecjbv7f/ImN/eoHveNQPBXILNZSfA059KWxXrszezp2g/2wt2AzCp73hj+bf3ftDseRG1jjr8\nPHxbbPdIWVU5bM7bxua8bbx6xlNU2qpYmvQJi4ecTVpFBgAlPeBEmlOVh8VsaZZ4O+Pdff9hZ+Fe\nxoaP5OZR153g6DqvQi8Bmmj1ZtTj8rFcxtb87VTYqrhCXHjCt98el9vFlqydDPIYjF2/cDlyxAW3\n201eTQHLDn7L1QmXNuu9l5i/k/f2f8TiIQspqSsjuyqXF/USd1FtMcW1JW1W6VTZGkc8Lqwp4seM\n1cQGxTB7wDQtNpeL/cWSxFYudCr10ZLzqvNJCB1qTM+tzifMpw/PbHsFgDvG3ERS6QHOi1mAl8WT\nlNJDbMnbzu6i/dw48mpjlObupu5o7oGGhg3hkWl/Zm9xMhMjx+Jt9eKr1O/xtfqQUpaKCRNPznoQ\nq8lKnbOOLw99x5Y8rffu4VQ3nvpvK9IUi8uawqHyw822v6tgr/E6szIbP6svEyLHsjZ7A7X6leiB\n0oOsSPuZRUMWMiQomiNlV+Uar6vs1XyVuoK9xcmU2yqNE1ZHr673FO2nrL6CWf2ndvxD6gCHy8Hj\nW54D4NUznur0+m63m33FEoD9xRK3293s5sLOSCk9xEZ9dF0Pi8cxbQOgUu+h1vA9nUg51dp3Wlhb\n1O6ySSUHSK/IZGb/qazN2sBZg+Y0u+O/I3YX7sPb6oXd5SAxfycBHv78lLmWMwbOMnri2V0O6hz1\neFu9+OzAcvYVJ1Nmq8DmtLE/Qz1zAAAgAElEQVQmawOLhyyktK6MEO9gdundvY8cvbjB1vydnK1X\n1YKWhEyYMJlMVDcpKSTm7zT+NSSFFQdX896uT5ttr9peg4/Vu9lnUm5rrKo9WH6YlLJU4/0ru7Sh\n9fcWJREdOND4mwXYnLfdSAo2p43Xdr1LoGcAVydchlcnP9fjpZJCDxXg6W80eI0KG86osOHUOmpZ\nfuh7JvUdh7+H1kfd2+rFRXHnsSVvOwM940mpabz5rb64D0F9Sql2ZDTb9pep3zV7Pyw03niGRJ2j\njnqnjff2fUS5rZLUHek8NuN+Y38NGorRAAdKDxk3wNldDgpqCo1l3t67lBtGXHXU6o43dr8HwISI\n0fh2oJTSFpvTjofZapy4ZWnjMymeSXyF64ZfYYyAC1oyCvcJo69fRIttgfZH3zAsic1lp6C2qMMl\nDpfbxZ78ZGorHcQERRttRAmhQ5nUd9wxHR80lhTqnHUcKD3E0JBYkktSSCk9xJyBMwjw8G+WuNIr\nMrE5bcSHxLbYVm51Pt4WL3w9fKl11BpX5U2/qw+TP6O4tpQ7xt6EyWRiV+E+QryCeGWndoLLqMhi\nV9E+/D38EKHxhHoFYzVb+TZtJfHBMRTWFFNuq+CcwWc1i6veaePNPe8DEOIV3Ky0ujF3K6F6tand\nZeeRTU/xwNQ/siprfbP4nS4nP6avZtmhb7ll1HXG2GRtya3Oo8Zew8s738bfw4/8mgJCvUO4Y+xN\nzaqPfsxY3SxOL4snm7NalhB2Fu4hyLPx7y2lLLVZEliVub7FOgD5NYXk638jfbxDKakr5WDZYfKq\n83ll5zvNPotI33DOG7KA8vpKfkj/mXNj5pNankaUX98ua8hWSeEU4mP1abVYH+gZwP/NfAiryYPb\n16/FVR2IqyqIzDwf7JVmPPppy/X370dFfaVR3G0wKmy40dicWp7G7qJ9xhWP3WVnU24iZw2aYyxf\nY68hoyLbeJ9ZmW2UCgpqCo0GN4AdBbt5tDKHCZFjCPYKYkbUFBwuBxW2SkK8g42uuQCHytMYFTYc\n0K6W9hQlMTIsAS+LJ5mV2ewvlgwNiWNp0idcN/wKogMbn3OdXpHJU4kvc/WwS5mut8vsbrLtwxUZ\nfJj8OXeNv1U/hlojGbVViiip1xr0zSYzLreLA6WH2kwKZfXleJo9jKS2PX8X/9Kr/c6Obrw6zdd7\nlDVwuBzkVuczwD+qRSmkrL6c9IpMBvj358tD3zI/ei51zsYSwos73uS5OY/x1p5/U+esZ0X6zyyK\nWcg5MWcCWknnqcSXAXh8xl8J9gqi1lGL1exBnaOexzY/i4fZSmxQDMmlKUbibzi5ut1u47G0BbVF\nBHoG8E/9RN6g4ep8a/4OPj6wDD8PXxZEz+Pbwz82W25QwABGhiUY7w82OXke+bjbWkdds5Joua2S\n/yR9duRHzqHyw6TqnRk25Gxptk5TZpN2c2fD1X9TxXWl/GvfR6SWp7W67neHV1JSV0pS4UH8rL5G\newHAh8mfG6+HBEVT77S1GUNbFkbPY3fRfvYWJ/F5ytfGZxHm04ei2mJ2F+0n3DeMNVkbSKvIYEfB\nbuNv865xvyU8fGyn9tcRlr///e8nfKMnU02N7e/Huq6fnxc1Nd3fqHakY4nL0+KJ1WIhyN+LfiRg\nqowkt7gGs18FliCtwevJmQ8yd+AM4wa6O8bcxFnRcxAhceRW57O3OAlZepDDepvA9cOvZF9xMjlV\n+cQGDyYqNJyN6Tt4cuuLlNWXE+kbTrW9htTydGNsp9YaBqsdNRwsO8ze4iS+TVvJivSfWZW1nrSK\nDH5IX2Usl5i/k/5+fenrF8knKV/y5aHv2Jq/gwkRY3lxx5vsKtrHhtwtVOnP0O7jHYKP1YfEgh2s\nTF1HUV0JqeVpVNqrKKgpYnfRfqMXFoC3xcuoDiioLWJd9iZA+8NsrSSTVpHBtoJdzOo/lfTKLFy4\nmNx3POkVmXyX9hMiJI7k0oO8ued9lh/6jj3FSczuPw2X28WOgt0c1KvtMquyja6kVpOVKf0mANrJ\n9x9bn+f79FXaVfYRnQoe3fwsv+RsYVXWenKr80kpS6X6iGeBB3oFGHfJA1TYq5gzYDoAxXUlrM5q\n7JUmQuJ4cMOTJObvoKSujAPFqbjcLorqSrR49FKRCRPzBs6kyl7Nyow1gNbBIcInzGiXOlKpflFh\nd9lJLklpMT+9IpMp/SZSUldKRX0lOwp2G21PAOMiRhuNsa21lzR0z/7N8CuJD4llf7E09gna99nQ\nSUKExDEzagoZlVk4XA5mRE2m3lnf7LMbFTYcq8mCp8WDwxXpRuPyzaOuw+V2MjQkjvTKTFLL08jV\n47pq2CVkV+US7hNGua2xV1SgZwC/Hf1rzh58JgU1heRU53H+kLM5UNry6Yljwkbox1qAzWXnkvjz\nceIkqeQAhbXFWE0WLhcXcqW4mD1F+8muzmVX4V7joq1pI/iW/B2cHTcXR/2xDRTt5+f1cGvTVUnh\nNDN3rDZy6qerD5KUXoqjKApLeCYzwuYBWnfYq4ddxvaCXcSHDMGq97VuWjfaQITGMSFyLJtyE3kq\n8WWGp8eTXNT4Q79q2KX8c8/7VNtr8DBbjcZBgKHBsRwoO/ojRZNKDrSY9sWhb4ny78sG/Qq1pK6U\nRzY93ewKucEHSZ+0mOZyu/gpYy3QsodOvbOeSlsVXxz8xhiRFrRqrsGBg1psq6GhPD4kltTydA6U\nHKTWUctz217D4XYyOHAQS5vEkFedz5NbX6TSVkWM3g4T6RvRrHSQXplptE3sL5ZGtdvz21/nsqEX\nMHfADGPZI+8faahyaOrLg98CcOvo61mXvYl9xckU15YS7BXI5ylfG8vtLtrPjKjJVNqrqLRXkSPz\nWmyr6X73FiUZPZ20z6KUt/Z+0Gy5I7/ztsyMmsL6nM38ce2Drc738/DluoQrMAFz46bg5wzk5Z1v\nEx8cS3//vsYFBcDYiFFYzVZ+yd5MTrV2DFP6TjBGKr4s/gLmDtQ+w9HhI9iQs4VzY+bz3PbXmu3z\nllHXYTaZcbqcLE9dYSS/0WHDGRs+krL6ctZmbwC0EvYfZt6Ml82fiZFjMWHijlV/AuCahMuZGDHG\naCe6LuEKzh18FpF+EUzqOw67005pfTnv7fuISnsVPlYfFg9ZyMLoeZTWlxPu24cRDOPzlK8AOG/I\nAmb11y5cQryDjWNsKiF0KIuHLKSothg/T19qqWqxzPE4ve+A6cXOnjyIy+fFcdG04dTvnkN5dh+c\nLq1aZ3rUJO4Ye5OREAAGBgwgwies2ckx0DOAuQNmGu/3F6YYVUP3T76buOAY4+prXMRo4oO15zx4\nWTy5ZfR1XBR3nrHus7MfJcI3zHjvZfHE29KYiK4adglT+k6gqLaYV3a+g8vt4tqEywnyDDASQtPt\ntaVpA2zTaiyAanstP6avZnPeNj5LWW5MTyo+gNvtJqn4AKszf6HCVklRbbGxTIhXMGPDR+JwO3l7\nz1Ic+lX/902GLGmQVZVDua2CpBKJ2WQm5ohkU+uoIzF/JzlVeWRUZjeb9+mBLzlcnsH67E1H7eN+\n/fArWRSzENCu7v2sviSEDjWq3j5M/oxtBbuMEkSETxhFtcVGoznAoKD+TIoc33LjaDfIvb77X0b1\n17jwUS2W6esXyXkxC1pM/8uku5q9vy7hChYPOZu44JgWy06IGMNto3/Dg1PuxdPiwY0jr2HqwPH0\n9Yvksen3c/2IXzE/ei6T9d5y4yNGG7/ZKH/tvtihwbGcH3u2sc2JkY3VKX39Irg4fhHeVq9mpYQb\nRlxtXDBYzBYuiD3HmNcwPdgriCuGXoSP1ZsbRlzFgKB+xnyTycSd437LmQNnMzlyXLOOAxazhUi9\njSrUO4RIvwiGhcYbz05xuLUk6mnxNKoiI33DifANw2qyMK1f4wAOi2IWMDxUMH/QXGPa/ZPv5vYx\nNxAdONB4CNiJph6yc5rdun4kt9vNfa9voLiinkBfDx6/ZSp+3m33frG7HNyz5m+MCRvBTaOuBbQG\n189TvjKuxqb3m8TV+vMklh38lh8zVnPfxN/xecpXHCpPY2b/qVwpLsbtdvOX9Y/SxyeUeyfeQa2j\nFrvLwY6CPUzrNxEPswfJJSn8nLWOG0dcQ35NgVEHHh04kHsn3MH7+z9ma77WS+OluU+wIXcL/f21\nP9DD5Rl4W7z4UGp1uwMD+ut3ajeKDYpp0fuqPZ4WT3ytPsaV+v/NfIgKW6XRk+lI0/pNwmwykVWV\nS0ZFllGFFu7Xh8kR4/lGr18fFNC/WSLwtfq0GFK9LYtiFvD14R8AeGjqvYR4BXP3mr/hxs3MqClc\nOewSnC4n/9zzPnuLkwnw8KfSXsW5MfPx9/DjkwPLjG3dOvp6zkiYQmFhJWuzNvLJgWUMDhxEdOAA\ngrwC+fJQ844If5iwBJvTxjt7l+Lr4csNI64iyq8vTreLD5I+ZkSfBP6T/CkWk4Xn5zyGLD2Iw+Ug\nPiS2WQn0w+TPSKvIZGbUFD5NWc49428nJqh50mztt6/1AksmPiTW6IlTYaskqzKHuOAheFo82JK3\nHafb1ebdyPuKJUuTPuGucb81TtpNldWX43K7WlThNZTqjvdvck/Rft7Y/R5/mvR7BgUMaDE/pyqP\nOmcdQ4IGt5hX56jjvf0fMTNqarN2Geiah+yopHCaJwWAV/+3h20HtKqHxdMHc1GTJ7flFlez+1Ax\nCyYNNK46bE4bFpOl2ZPhnC4ngSFeJGWlEe4TZvyxO1wOKm1VhHgHk1mZw7rsDVwUt8iYb3faMZlM\nzUolbXG73Ty86SkKa4u5e/xtxAXHsCpzvXHF3laD8PM7X+NgSRp/mXQXT+h3aTe4atglRoNgQ4Nx\nU2cNmsNPGWtx46aPdwjDQuONxtWYwGiuHHYx/f374Xa7+ceW58mtzufmUdeSV13A8tQVgJasGj6r\nfcXJvLbrXQBGRAxlXJ+xRhXTeTHzjQTRIMw7lAEBUc0a3Fvz7OxHeHDjk9Q66nhhzuNYzBaSS1Io\nqClkfOQYo5G4tK6Mv296yrhB8f9mabf8vLn7faMx9e9T/8Tw6MHGb8zmtBndSTMqsvi/xJcAmD9o\nLn4evpw5aLZR1WIymVptf3lq68t4Wb24c9wtRz2OBnanvdWuub3lb/JEUU9eU47JBTNjsDlc7Ekt\n5uuNaWw7UIiXh5mbFg3n4fe2YrO7GBThT8JgrYtba/3NLWYLvp4+La5yrGYrId7BAAwMiGrxPIbO\n9Mk3mUz8eviVFNYWGdUNw/sISIGZR7mH4cG5d5JbWIa/hx+3jr6enQV72ZSXyOiwEc2u/P44YQn7\niyVxwTFGF9FFQxZyYey5lNSV4mP1wcfqjdXsgdVsYXHMQiN+k8nE78bdjNPlJMQ7mBF9HGRV5RAb\nHNMseTbtnRTu24cQryBAq45o+tl5WTypd9qY3G8CCwbNJb0yi+e3v86wkHg8LZ7kVufR1y8CX6sv\n/p5+eFu9uTbhcqps1cb+hoXGMyw0vtlnEeIdzOiw4UaDcEOyuGvcb/n96r8A0Men+dVw0+97QECU\n8fr82LObJYC2Hh8LcPeE2zp1O93x3KuhdC2VFHqBARH+3H35GA5ll/PcJ7vIKdL6ZD+xdDs2u3bl\nnJ5fZSSF7hQTNKhZlUKkbzj/mPG3FvdJNOXt4W3Mb7inY370XMJ9+uDGzYSIMUzpN4HowIFGN9Z5\nA2dSUV9pDGrWtM/35UMvaHU/Te+etZqt3DjymhbLNE1CZwyZgbNGO1VG+TfvV/6HCUsoqStlZJ8E\nTCYTccEx/HXyPVrdchulqoY2g/YkhIoWvYQsZgv/mPE36pz1R71nxGwyc8fYm3C53Z0aSqOtweGU\nU4/6JnuR2P5BPHHLVPYeLqaq1sHHPzV2Hdx+oJADmWWcM3UQ8QOCuzHKloLaeBrd0TS9Ie2GkVe3\nmH9p/PnHFVNbzCYzvxIXYTVZGRYeS0FBBYtiFiBC4wnzDsXT7MG4iNH09+9ntI00aGg8PV7jIkax\nNusXpkdNaTY9yCuQoA6s33SoBqX3UW0Kp1k9YWckJhfw+ZpD5Jc2b+x88rdTiQhpeWdxb/+8Oqu1\nuBraNLp76OtT6TPrCU7HuNpqU1BdUnuxicMieOK301gwaWCz6a9/uY/KHnhT3+nAbDJ3e0JQlKNR\nv06FBZMGYjaZmDoikpmj+pGeV8nbX7cxRLeiKKc11aagEBrozbN3zMDH04LVaia3uJq9qcWUVNQR\nGtjyTmdFUU5fqqSgABDk54mnhwWzycSM0f1wAxv3tT0UgqIopyeVFJQWJg+LwGoxs2FvXouHnCiK\ncnpTSUFpwdfbg/FDw8gtruFwbmPPBpUgFOX0p5KC0qrpI7U+8y99tovDuRV8suogv3n0B0orO/YM\nZ0VRTk0qKSitGhETipenhYoaO099uIMVmzMoLq/jw5Uth7tWFOX0oZKC0iqL2cyN52ojMtbbG4dx\n3nGgiKpae3eFpShKF1NJQWnTxGERvHTnLEbEhDI6tg+LZw3B5Xbz+xfXkZxeSnZhFdV1KkEoyulE\n3aegHJW/jwd/uEJ7cInDZOarddqzdV9btpfqOjuBvp7ctHg4I3rAYHqKohw/VVJQOqxfmB/P3TGD\noQODqaq143ZDebWN5z/eRV5JTfsbUBSlx1NJQemUYH8vblqUgBgYzLj4MC6bF4vL7Wbf4ZLuDk1R\nlBNAJQWl08KCfPjT1eP53SWjmTBUe6jMysRMHn1/KwezynG53PyYmKm6ryrKKajDSUEIYRZCnJgB\n35XTRniwD30CvcgvreVwbiU/b89i1Y5sPlqZwuvLjv6ISUVRep4OJQUhxJnAIWC1/v55IcSiLoxL\nOUWYTCZuOX8E158zDH8fDzbtz+c/P2r3MhzMLu/m6BRF6ayOlhQeB6YCuU3e/61LIlJOOfEDgpk9\nJooB4c0fmWkxm3CpoTEU5ZTS0aRQJaXMb3gjpSwC1FNYlGauOCOecfFhxnuny80rn+9h9c5sVm3P\nwu5wdWN0iqJ0REfvU6gVQswBTEKIEOBXQF3XhaWciqL7BvC7S0ZTUlHHlqQCPll1kJ0Hi9h5sAgA\nk9nE3LH9uzlKRVGOpqMlhduBe4FJaG0LZwO3dFVQyqktNNCbhZMH8uiNk7lsXqwxPSmttBujUhSl\nIzpUUpBSZgJGw7IQwiylbLcuQAjxPFpbhBu4U0q5tcm8JcA1gBNIlFLe1WReJJAMXCSlXN2xQ1F6\nEpPJRP9wf/qH+zNnTH/++vYmkjNKcbncmM2tPi9cUZQeoKO9j64XQtwuhLAIIdYDh4UQt7Wzzhwg\nXko5DbgReKnJvEC0kscsKeVMYLgQYmqT1Z8GUjt5LEoP5ettZUxsGJU1dj5bfYjyatUcpSg9VUer\nj34LvANcBOwFYoAr2lnnTGAZgJQyCQjRkwFojdQ2wF8IYQV8gRIAIcQZQCWwp+OHofR0l82LpW+o\nLyu2ZHD3y+tZuyuH2npHd4elKMoROtzQLKWsF0KcCyyVUrqEEO31NewLbGvyvlCfViGlrBNCPIxW\nGqgF/iulPCCE8AQeAi4AXuhIYCEhvlitlg4eRkvh4QHHvG5XOt3iCgce+e10/vzqOkoq6nnvu2RW\nbMng1XvPwOF0YXe4CPL3OulxdbWeGhf03NhUXJ1zouPq8CipQohXgRnAzUKIaYB3J/dlVCTrJYb7\ngaFABfCzEGIMWjJ4S0pZJoTo0EZLS499ILbw8AAKCyvbX/AkO13jsgLP3D6DT1cd5LvNGeQV17D0\nm31s2p9PUXkdNy8azsRhESc9rq7SU+OCnhubiqtzjieutpJJR6uPrgZSgPOllE5gMHBrO+vkoJUM\nGkTRePNbApAqpSySUtqAdcAEYCFwhxBiE3Ae8JoQYkQHY1ROEZfOjeW5O2bg7Wnhi3WHyS2uwe5w\n8f6KZGrU8xkUpVt1KClIKXOBn4EIIcRsIBsIame1H4BLAYQQ44EcKWVDSksDEoQQPvr7iUCKlHKG\nlHKqlHIq8A1wu5RyX2cOSOn5TCYTwf5enD1lEAADwv24cFYM1XUOvtuc0c3RKUrv1qHqIyHEcmAk\nWjJo4AZmt7WOlHKDEGKbEGID4AKWCCGuB8qllF8IIZ4GVgkhHMAGKeW6Yz0I5dR07tRoIkJ8GD2k\nDxaLmdU7svlxayajhvRh2bpUzpkazaghfbo7TEXpVTraptBPSjmksxuXUv75iEm7msx7E3jzKOte\n39n9KacWq8XM1OGNNYyXzInlnW+SePI/2wEoLKvj6dunA1BQVktabgWTEyK7JVZF6S062qaQKIQY\n3JWBKMqMUf24aPYQAnw9ACiuqCOvpAa3281f3tzIG1/uI6eoupujVJTTW0dLCjuBA0KIPMCB1pPI\nfSylB0U5msXTB7N4+mC2JOXzxpf7uP+fm/D1stIw2Orh3AqiwvyOvhFFUY5ZR0sK9wHzgXn6/2fp\n/ytKl5g4LIK4AVpfhpomN7ml5lR0V0iK0it0tKSwW0q5pksjUZQmzCYT91w+hvIqGzX1DmrqHbzw\nyS62pxTi5WFh4eSBmM0mvtuUwWULBGo0JUU5MTqaFPKEEKuAjWjVRwBIKR/skqgUBfD2tOId2vgT\nHTWkDzsPFrFiSwalVfVkFlSRU1SN22ziirmxR9mSoigd1dHqo/PQHsVZjzaqacM/RTlpLpgZY7ze\nvD/faHROTivB5XbjVk95U5Tj1uHqI+BFKWVZVwajKEcT3TeAh2+YzDZZwPJf0ozp6XmV3P7cGiYP\ni+SG8xK6L0BFOQ10tKTgjTZc9iYhxNqGf10ZmKK0ZmCEP/PGNT697Rz9rmib3cX6PbnU2dTIq4py\nPDpaUni0S6NQlE4I8vfi3KnRVNXaWTh5ULOhMV7+fA/xA4IoKKtlzpgoxKCQboxUUU49HX3ymup5\npPQolzZpWD53+mDW78qhrt5BUnopSenaYz837cvnjT/MwdPj2IdWV5TepsNDZytKT3XbJWO4dPYQ\nSirqWLktixVNSg5bkgqYObpfN0anKKcWlRSU00ZooDeXz4sjNMCLOpuT/61NZe3uHCaIcHy81E9d\nUTqiow3NinLKOGviQBZNH0xkqC8Hs8p57uOd3R2SopwyVFJQTlsXzdLuaziUU8HP27NwOF3GPJfb\njdPlamtVRem1VFJQTluTEyKNxLD0hwMs/yWNeruTwrJaHns/kUfeS+zmCBWl51EVrcppTeuSehiA\nrzek8fWGtGbzD2WXE9MvELNZjZ6kKKBKCsppLm5AEBfOjCGmX6AxbUC4PyEBXgA8/sE2ViZmdld4\nitLjqJKCclozm0ycPzOG86ZHU1Vjx9fbA6vFxPYDhbz6xV4Avt+aybDoEAL9PAn29+rmiBWle6mS\ngtIrWMxmgvy98LCaMZlMDIzwN+aVVtbz939t5dH3E7E7mo/zWFpZT1lV/ckOV1G6jUoKSq8UHuzD\nvPH9m00rraxn1fbsZtP++tYm7nnlF9VTSek1VFJQeiWTycS1CwTnzxgMwJyxUXhYzfy8I9sYgru6\nzk6dTSs57Ewp6q5QFeWkUm0KSq92zpRohg8OJX5AEHaHiw1783j2452cNzUai6Xxmul/a1MZHRuG\nh1VdRymnN5UUlF7Ny9PC0IHBAFysj5+0P62U/WmlhAV5G8vkFtewZmc2Z00c2J3hKkqXU5c9iqIL\nDfTmvqvG88CvJxLXP4ii8joAbr9wJBazidU7c9TT3ZTTnkoKinKEmH6B3HfVOM6ZOoiE6BASokOY\nIMLJKaomNbeiu8NTlC6lkoKitMJqMXPZ3DjuvXIcVouZqSP6AvD4v7exakd2O2sryqlLJQVF6YCR\nMaE0DITxwfcSp8uF2+0mKb0Um9151HUV5VSikoKidIDVYua+q8YR7O8JwLebMvh8TSpPf7SDZesP\nd3N0inLiqKSgKB0kBoVw12Vj8LCa+WJtKt9uSgdg3a4cbHYnNXV21RCtnPJUl1RF6YRBkQE8ftMU\nXvhsN1U1NoYPDmXT/nze+no/2w8U4utlJSTAi79dN7G7Q1WUY6KSgqJ0UliwDw/fMAmHw02dzcG2\nA4Vsk4UAVNc5qK5zkJxRRv+o4G6OVFE6r0uTghDieWAq4AbulFJubTJvCXAN4AQSpZR3CSGswDtA\nrB7bH6WU67syRkU5FhazGYundmPbpXNj2ZKUz6Hsxu6qL3y6iwH9Agn19QDA7nBRWlVPRLBPd4Ws\nKB3SZW0KQog5QLyUchpwI/BSk3mBwL3ALCnlTGC4EGIqcC1QrU+7EXiuq+JTlBNl/sSB/PXaifj7\neDSb/qdX1hsliE9XH+TPb2zkUHZ5d4SoKB3WlQ3NZwLLAKSUSUCIngwAbPo/f7104AuUAEuBe/Rl\nCoE+XRifopxQDQ/ySYgO4ewpg/D0MPPm8n0czq1gZWIWAF+qnkpKD9eV1Ud9gW1N3hfq0yqklHVC\niIeBVKAW+K+U8oC+nF3//y7gw/Z2EhLii9VqOeYgw8MDjnndrqTi6pyeENeffj2JFRvTuHheHB5W\nC5NH9uORdzbz6PuNz4Len1aCf6APPl7d35zXEz6z1qi4OudEx3Uyf5nGQ3D1EsP9wFCgAvhZCDFG\nSrlLn78EGA8sbm+jpaU1xxxQeHgAhYWVx7x+V1FxdU5PiuuMsVGU6b/JScP7cvX8oXy3OZ0+gd6E\nBHixJamAXz/8PfdeOZbBfQPb2VrX6UmfWVMqrs45nrjaSiZdWX2Ug1YyaBAF5OqvE4BUKWWRlNIG\nrAMmAAghbkRLBhdKKe0oyinszAkDeOb2GfzlmglMEBEA1NY7eOajnTic6sE9Ss/TlUnhB+BSACHE\neCBHStmQ0tKABCFEQ1eMiUCKEGIIcCtwsZSyrgtjU5STLjaqsWRQU+/gj69t4KdtWd0YkaK01GXV\nR1LKDUKIbUKIDYALWCKEuB4ol1J+IYR4GlglhHAAG6SU64QQ/0BrXP5WCNGwqQV6aUJRTmmhgd7c\nvHg4wf5ebE3KZ82uHIpuBQ0AABPmSURBVD5ceYDovgFEBPtgd7gICfTCbDK1vzFF6SKmU/22/MLC\nymM+gNOxnrAr/X979x4dVXUvcPw7M0km7xd5kYQAIWSTBAUJEFAxURABUVrRWi8WtdRXa1uv1nW7\nfHTVVW916aqPW2urlaqtj7KwPhBUQARE3oRnIOwg5MUrCUkgxIRAkrl/nMMxYMJDmTmT5PdZi7Xm\nnNkz85sfM/PL3uecvSWu83O2uEoqD/PUWxtP2Td5TBo3FWR4O7RumzO79MS44uMjOv3rQ+Y+EsIm\nmf2imTjKWMktq38MUWFBLFpXydbd36wH3d3/aBPdj/3nxQnRi918VQZX5aaSEB1Coa7mpfeLeOHd\nrdw5NZvyqqOs2VHFH2blfevCOCG8RXoKQtjI4XBYU1/kqgTuvD4bjwde+WgHC9dVcqTxOLqinrU7\nqvjd7HU0fH1ceg/Cq6SnIIQfyctKZM32KrbtqeXkb/+uvUdYtL4SgPv//CUxEW6SYkO5Z1oOEaFB\nNkYreiIpCkL4EYfDwf03DQOg5Xgb9z673CoIJ9UfbaH+aAuFJTUUDE+xI0zRg8nwkRB+yh3k4rKL\nkrq8f1flYR9GI3oL6SkI4cdmXZvNpLz+vL24hJEqnpiIYAb2jeCx2etYvb2KnIGxXDq0r91hih5E\nioIQfi4lLoyHbrnklH35w5NZsLqc2fOL2VvzNUEBTq4YlkxsZLBNUYqeQoaPhOiGpucP4pGZuXiA\nT9dWMG9lGb95aRUrtuy3OzTRzUlREKKbGpQcxZ1Ts+kTGczIIcZkex+tKuPdZbt5/4s9NkcnuisZ\nPhKiGxs7NImxQ42D0bPn72Bl0UE+XlMOQF52IvHRIQQGyN9+4tzJp0WIHuKavLRTth99dS2/emEF\ntUdkwmFx7qSnIEQPkRofzlN3j2F/bROL1lWws+IwLSfamPP5Lqrrm3G5nMREuLnzumy7QxV+TIqC\nED1IQkwoCTGhZKZGsXzLfuYu3c0GXWPdX3oA/vZBEY/MGmNjlMKfSVEQogcKDQ5kcl5/osKCeHV+\nMQAup4MQdwBbdtcye14RfWNC2FhSQ7+EcH44Lh2nU9ZxEFIUhOjRLh3al/5JkcRGuAkKdNLa6uGX\nL3zBQvNgNMDW3bWkxIUxJqfrq6dF7yEHmoXo4VLiwghxB+ByOnEHuQgNNqbhdge5mDnJWOHwlY92\n8LcPi+wMU/gJKQpC9DK3jB9MWEggD9+ay7iLv5kiY11xNT996nP+s3w3q4sO0tbebmOUwi4yfCRE\nL5OXncjU/AxrGcd+CeFUVjda9y9YbQwtLd5QybTLB3LxoD44ZN3oXkOKghC93K9vvJiaw830iQpm\n7tLdrN9ZjcvpoOzgUV54dysAk/LS+NGV3l87WthPioIQvVxsZLA1kd7d03KYMTETd4CLT9aWM29l\nGWDMr9Q/MYK87ESq6pr4bMNerrtsAJFhsshPTyNFQQhhcTocRJqruf1gXDrpyZHsKKtn+Zb9vDxv\nO8XldWwvraO2oYVNX9Vw/43DSE0IZ+G6CgJcTsbnptr8DsT3JUVBCNGliwfFcfGgOEaqBP74ZiFf\nbDlg3VfX0MKTbxUyIbcfH60qA2B9cRW3T8kiKTbUpojF9yVnHwkhziojNYqZk5Q1ud71lw3gp1Oy\naG5pswoCQMneI8xd+pVNUYoLQXoKQohzUjA8hYLhKVTVNdEnKpgAl5MBfSN449Od7N7XYLUrLq+n\nruEYsZHBtHs87Ko8TEJMKDERbhujF+dKioIQ4rwkdhgaSo0P57czRvD1sVa2l9ZRtKeO1dsP8sir\na7n16kw27zpEYUkNCTEhPH7HaNxBLhsjF+dCioIQ4ntxOZ1EhgYxNieJvOxEYiPdxlKhC4qtNtX1\nzcxfXcaIzHiCg1xU1zfL9Q9+SoqCEOKCcToc/HBcOg6Hg/nmsYZfTr+Ify7ULFhdbl0YBzA9P50J\nuf2k9+BnpCgIIS4op9PBDVekM2VMGpXVjQxOjeZEazt//2gHbe0eq91/lu9hY0kNj84cSUWVcUV1\nfHyEXWELkxQFIYRXBAcFMDg1GoDRWYkMGxTHhytL+XRthdWm9MBRNu06xIvvbQNg1vU5fLyylP+Z\nMYKyA0cZnBpFiFt+pnxJsi2E8Al3kIsrhiWzcF0FPxiXTkpcGC++t80qCACz520H4Nl/b6aiupHY\nSDfjLk5mcGoU2QNi7Qq9V5GiIITwmaTYUF56IJ+gACcOh4PxI1JZsnHvt9pVmBP01TW08OGXpQDM\nvEaRPzzZOjhdVd9EoMtpTdEhLgyvFgWl1HPAGMAD/Fprvb7Dfb8AbgXagA1a6/uVUoHA60B/c/8d\nWus93oxRCOFb7sBvDizfMmEwOemxhAS5CA8JxBEYwNL1FSwp3EtmahQRYUEUmsuJ/nOhJijQydic\nJBavr2Tust1Ehwfx5N1jCXDJdbgXiteKglIqHxistR6rlMoC/gGMNe+LBB4CMrTWrUqpRUqpMYAC\nDmutZyilJgJPAjd7K0YhhL2cTgfDM+Ks7fj4CPpGuRmp4kmMDSU63E27x8OyTft4c1EJc5fuZmf5\nYb7cZky3UdvQwqZdhxg1JAGA7WV1LNu4j1lTswgOkoGQ78Kb5XU88AGA1roYiDGLAcBx81+4UioA\nCAXqzMe8b7b5DLjMi/EJIfyQw+FApcUQHW5cAe10OLhqRCqT89I48vVxqyD85sfDAZi79Ct2ltej\nK+r50783U1hSw7riato9Huoajtn2Prorb5bSJKCww3aNua9Ba31MKfU4sAdoBv6ttS5RSiWZ7dBa\ntyulPEqpIK318a5eJCYmlICA736es7+eAidxnR+J6/z5a2xdxfXzH11CTkY8z72zkRuuzCB/VH+W\nbNzHppIann5n0yltPyvcy4H6ZhauKef/HixgYHKU1+Ky24WOy5f9K+vSRbPH8DCQCTQAnyulhp3p\nMV2pr2/6zgHFx0dYq0/5E4nr/Ehc589fYztbXNn9onj+V5cT6g6gpuYok/PS2FRS8612e6sb2Wse\nrF6ytpxplw/0alx2+T5xdVVMvDl8tB+jZ3BSMnBy3t0sYI/W+pDZC1gB5HZ8jHnQ2XGmXoIQovcJ\nCw60zkDKSIniL/99hXXfkLRoZlydSUJMiLVvddFBFq+vZM7nu5i79CsOHWlmw05jeMnj8Xzr+Xs7\nb/YUFgGPAy8rpUYA+7XWJ0taGZCllArRWjcDI4GPgWPATcBC4DpgqRfjE0L0ACHuAGZcncmJ1nYm\n5aUBkD88mUJdw1uLS6g+3Mw7S3ZZ7T8xL567dWImn6ypYGDfCH42NZugQJluA7xYFLTWq5RShUqp\nVUA78Aul1O3AEa31+0qpZ4ClSqlWYJXWeoVSygVcrZT6EmgBbvdWfEKInuP0Fd8CXE7yshNxOR1s\n2nWIlPgw2ts97K1pZNOuQ5xobeeDFaU0Np+gtuEYR5u2MPXSAWT2i6b0QAOJMSFEhffOqb4d3b37\nVFNz9Du/gZ44TuhNEtf58de4wH9j80VczS2t/OK5Lzq9L2dgLNtL6wAYkBTBzEmKAUmRPTJf8fER\nnR6zlSs+hBC9Sse5lBKiQ7htkrK2TxYEgLKDR3nviz0sWl/Jyi37fRqjneTqDiFEr5OXncjaHVX8\nePxghg+OY0xOEn+asxmXw8HMSYq2dg/PztlM0R5j4SCAp+8Zyxuf7iQmIpiZkxQHa5uIjQwmNLhn\n/Yz2rHcjhBDn4L8mDGbiqH4M7GtcT+sOdPHwrbmntLllQiZ//aDI2n7q7Y3UNbQAcLy1jXXF1fTt\nE8oTP8vD44F2j6dHTLchRUEI0etEhAYRERp0xjYjVTy5Kp7SAw00t7RaBQFgXXE1AAdqm/jHgmJ2\n72+gqaWVn07JIiw4gPTkyG67qpwUBSGE6ITD4eDeHwwFYNnWA7z5yU6Cg1wcO94GwKTRaXy6roKV\nRQetxzw/dwsAMRFuJoxMZXJefwAam08QFhzQLQqFFAUhhOiC0/wRn37lYNLiwggLDuDRv69lWEYc\nN+Snc6K1ncjwIEYMjuP9FaVsNK+urj/awtylu6msbqS1zUPhzmrGDk1i1rVZbPmqlgCXg5yBsX5Z\nJKQoCCHEWQS4nGSkGPMnPTwzl5S4MAJcTmZMzLTaXHfpALaX1XHH5CGkxIfzxBsbWLO9yrp/VdFB\nwkMCWbS+EoAZV2cyPjcVj8djFYf2dg8H6ppIiQvz4bs7Vfc/KiKEED40KDmq02m5+ydF8NcH8hmd\nlUhKXBh3T8shPTmS+264yDqIfbIgAMxfXUahruHeZ5dbPYy/z9/BY6+upfRAg0/eS2ekpyCEEF4w\nPCPOWiui40XCWf1jSE+OZMHqcv7yvrEU6YvvbSOrfwzF5fUAPPHGBm6fMoRLBsdztOk4ffv4rucg\nRUEIIbzM4XBw81UZrNlexV3XGfMsrSo6SMPXx2lrNwpGcXk9IW4XzS1teIDXPt7Ja+wE4IGbhxHi\nDiC9r/fPapKiIIQQPnDN6DSuGZ1mbT86cyRt7e2EuAPYWX6YjNQoIkICufOZpZw++9Czc4yzmqLC\ng+gTGcz4Eakk9Qn1yhoPUhSEEMIGMRHfTLiXq+Kt24/dNpJDh4/xUocL50460nicI43H2bN/BwBv\n/yH+W22+LznQLIQQfmRAUiQjhyRwz7QcXE4Hoe4ARqp4a9bXxNhQAOKjgwkPCbzgry89BSGE8EOj\nsxIZOSTBulai5UQb0/PTaffAO4tLuPHKDK8cX5CiIIQQfsrZ4UffHejCHWWsKPfrmzpbvfgCvabX\nnlkIIUS3I0VBCCGERYqCEEIIixQFIYQQFikKQgghLFIUhBBCWKQoCCGEsEhREEIIYXF4Tp95SQgh\nRK8lPQUhhBAWKQpCCCEsUhSEEEJYpCgIIYSwSFEQQghhkaIghBDCIkVBCCGEpccvsqOUGgp8CDyn\ntX5RKdUP+BfgAg4AP9FatyilZgD3A+3AK1rr2T6O63UgF6g1mzyjtV5gQ1xPA+MwPhtPAuvxj3yd\nHtf12JwvpVQo8DqQCAQDfwC24B/56iy2G/GDz5gZXwhQZMa1BD/IWSdxFWD/Z6wAmAtsN3dtA57G\ni/nq0RevKaXCgPnALmCr+eP7GvCx1nquUuqPQCXwT2AjMBo4jvFDeIXWus6Hcb0OvKu1nn9aO1/G\ndSXwkNZ6ilKqD7AJ4wtrd746i+tz7M/XzUB/rfXTSqn+wGJgJTbn6wyxrcLmnHV43f8FJgJ/AfLx\ng5x1ElcB9n/GCoD7tNY3dtjn1d+wnj581AJMAfZ32FcAzDNvfwRMAPKA9VrrI1rrZowv9mU+jqsz\nvo7rC+Am8/ZhIAz/yFdncbk6aefTuLTWc7TWT5ub/YC9+Ee+uoqtMz6PTSk1BMgGFpi7CvCDnHUS\nV2d8HlcnCvBivnr08JHWuhVoVUp13B2mtW4xb1cDfYEkoKZDm5P7fRkXwH1KqQfM17/PhrjagK/N\nzVnAx8A1fpCvzuJqw+Z8naSUWgWkAlOBz+zO1xliewD/yNmfzNe+zdy2/TvZRVzgH/nKVkrNA2KB\nx/Fyvnp6T+FsHOe535v+BfxWa30VsBn4fSdtfBKXUmoaxo/vfef4+nbE5Tf50lpfinGM483TXtP2\nz9dpsdmeM6XUTGC11rq0iya25KyLuGzPF8YQ8+PANIxiNZtT/5i/4PnqjUWh0TyYBJCCMYSzH6PS\nctp+n9FaL9FabzY35wEX2RGXUuoa4BFgstb6CH6Sr9Pj8od8KaVyzRMXMGMJAI76Sb46i22b3TkD\nrgWmKaXWAD8DHsM/PmOdxeWwO19a633mUKBHa70bOAjEeDNfvbEofAZMN29PBz4F1gKjlFLRSqlw\njLG4Fb4MSin1H6VUurlZgHEGhE/jUkpFAc8AUzscoLI9X53F5Q/5Aq4AHjTjSQTC8YN8nSG2l+3O\nmdb6Zq31KK31GOBVjLN8bM9ZF3Hda3e+lFIzlFK/MW8nYZxN9hpezFdPP/soF2OccABwAtgHzMA4\nVS8YKAfu0FqfUErdCDwEeIA/a63f8nFcfwZ+CzQBjWZc1T6O6y6MLnJJh923YXxJ7MxXZ3G9hjGM\nZGe+QjC68/2AEIxu/gaMM0Fsy9cZYmvEOJ3RtpydFuPvgTJgIX6Qs07iKsfmfCmlIoC3gWggCOP/\ncRNezFePLgpCCCHOT28cPhJCCNEFKQpCCCEsUhSEEEJYpCgIIYSwSFEQQghhkaIghI2UUrcrpd60\nOw4hTpKiIIQQwiLXKQhxDpRSvwR+hDFdxE6Mi5rmA58Aw8xmP9Za71NKXQv8DuOipybgLnN/HvA8\nxtTGdcBMjCtSbwAaMGboLAdu0FrLF1PYQnoKQpyFUmo08EOM+enHYkzfPQFIB17TWo8DlgEPmovb\nvApM11pfiVE0njCf6k3gTq11PrAcY74dgBzgLowFXYYCI3zxvoToTI+eOluIC6QAyACWmtOdh2FM\nOFartS4026zEWPUqE6jSWp9cv2AZcI9SKg6I1loXAWitnwfjmALGPPhN5vY+jCkNhLCFFAUhzq4F\nmKe1tqYSV0oNwFjp6iQHxpwzpw/7dNzfVc+8tZPHCGELGT4S4uxWApPN2SdRSv0cYwGTGKXUJWab\ny4GtGJP2JSil0sz9E4A1Wuta4JBSapT5HA+azyOEX5GiIMRZaK03YKzZu0wp9SXGcNIRjNltb1dK\nfY4xVfFz5lKIs4A5SqllwHjgUfOpfgK8oJRajjG1tZyKKvyOnH0kxHdgDh99qbVOtTsWIS4k6SkI\nIYSwSE9BCCGERXoKQgghLFIUhBBCWKQoCCGEsEhREEIIYZGiIIQQwvL/HP61BujMNiQAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "So-8-DF_0eyL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predict_deep = Deep_AE.predict(users_items_matrix_train_zero)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pWASIXoK0eyO",
        "colab_type": "code",
        "outputId": "ae3b2198-3066-4883-b99d-b37a2c08716c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_result_deep = Deep_AE.evaluate(users_items_matrix_train_zero, users_items_matrix_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 1s 134us/sample - loss: 1.0004 - masked_rmse_clip: 0.8589\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EzdpYuii0eyS",
        "colab_type": "code",
        "outputId": "fff4b06c-019f-4ff5-aee3-443e0e611997",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "loss = K.eval(masked_rmse_clip( \n",
        "    K.constant((users_items_matrix_train_zero)), \n",
        "    K.constant(predict_deep)))\n",
        "np.mean(loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7632456"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "n8vTD6NQY-Hu",
        "colab_type": "code",
        "outputId": "648eb48a-fcb3-46fd-b5c5-15387eb04ed9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "for layer in Deep_AE.layers: \n",
        "  print(layer.get_config())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_input_shape': (None, 3952), 'dtype': 'float32', 'sparse': False, 'name': 'UserRating'}\n",
            "{'name': 'EncLayer0', 'trainable': True, 'dtype': 'float32', 'units': 256, 'activation': 'selu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 0.0010000000474974513}}, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'LatentSpace', 'trainable': True, 'dtype': 'float32', 'units': 512, 'activation': 'selu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 0.0010000000474974513}}, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dropout_1', 'trainable': True, 'dtype': 'float32', 'rate': 0.8, 'noise_shape': None, 'seed': None}\n",
            "{'name': 'DecLayer0', 'trainable': True, 'dtype': 'float32', 'units': 256, 'activation': 'selu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 0.0010000000474974513}}, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'UserScorePred', 'trainable': True, 'dtype': 'float32', 'units': 3952, 'activation': 'selu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 0.0010000000474974513}}, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Oi4q5aezTx_A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "first_layer_weights = Deep_AE.layers[1].get_weights()[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYrEB-sxVqtU",
        "colab_type": "code",
        "outputId": "994ba763-f846-4ff4-cea0-28bf72082374",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "first_layer_weights = Deep_AE.layers[1].get_weights()[0]\n",
        "plt.plot(np.arange(0, 256, 1), first_layer_weights[0])\n",
        "plt.plot(np.arange(0, 256, 1), first_layer_weights[1])\n",
        "plt.plot(np.arange(0, 256, 1), first_layer_weights[2])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAFoeDKoGEFIuepKzjN/D7Hjk2gvLR/DU3EEcnlUFtox+bCzmMSQkYp6BVL8JUEi55v0N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Qomi+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\nvoWFhcUVhP8fHXixYtitm/YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y1G5ukdeUENj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Tko0sfZKndDD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Add Denoise"
      ]
    },
    {
      "metadata": {
        "id": "SB2CBjZNAfZ_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Gaussian Noise"
      ]
    },
    {
      "metadata": {
        "id": "LM4rPgpJApXN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Gussian AutoRec"
      ]
    },
    {
      "metadata": {
        "id": "WNjpJ_gUngjW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## Adding Gaussin noise to input\n",
        "noise_factor = 0.4\n",
        "users_items_matrix_train_average_noisy = users_items_matrix_train_average + noise_factor * np.random.normal(size=users_items_matrix_train_zero.shape) \n",
        "users_items_matrix_train_zero_noisy = users_items_matrix_train_zero + noise_factor * np.random.normal(size=users_items_matrix_train_zero.shape) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jl8L1MzkhpSC",
        "colab_type": "code",
        "outputId": "56f0976f-62f7-47a4-846f-8a19457b51ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "users_items_matrix_train_average_noisy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.99900726, 4.30492207, 4.29922036, ..., 4.21125536, 4.19833128,\n",
              "        4.21537676],\n",
              "       [3.80915317, 3.6645938 , 3.49922715, ..., 3.88575614, 3.74631529,\n",
              "        3.63690338],\n",
              "       [3.79368874, 4.04708265, 3.90333259, ..., 3.72202725, 3.91570177,\n",
              "        3.81405065],\n",
              "       ...,\n",
              "       [3.92870269, 3.63513243, 3.80363898, ..., 3.77111358, 3.95865566,\n",
              "        4.05883521],\n",
              "       [3.99251769, 4.06808587, 4.0514266 , ..., 3.95321864, 4.02780201,\n",
              "        4.01934031],\n",
              "       [3.6423853 , 3.66484986, 3.57073631, ..., 3.47208484, 3.62669314,\n",
              "        3.55643493]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "metadata": {
        "id": "dovJDJEAVIjO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "AutoRec = AutoRec(users_items_matrix_train_average_noisy, 0.001, 'elu', 'elu')\n",
        "\n",
        "AutoRec.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip])\n",
        " \n",
        "AutoRec.summary()\n",
        "\n",
        "hist_Autorec = AutoRec.fit(x=users_items_matrix_train_average_noisy, y=users_items_matrix_train_zero,\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  verbose = 2, \n",
        "                  validation_data=[users_items_matrix_train_average_noisy, users_items_matrix_validate])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dhEUlnQeVIsL",
        "colab_type": "code",
        "outputId": "6e3e1d5f-501c-4d14-dc42-ed3ad1fefcfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Autorec, 20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o2gPA5VkXc7y5mIszLkTFZQ9JTP0J5b97CO38pQBIAQCCk7vL7eHN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\nCvU3XXZwnC43XPQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6cMHBgQFVIwK",
        "colab_type": "code",
        "outputId": "8e440913-f926-444b-e033-e4a359a3cd8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Autorec, 300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NZGvq8TIY8LPVrNOHYuoZ7LHKcv1DA8Nej1drjBOKhWapsb2/gxJfGfcd+gON\nPV6eq3rZ2i/A+lwe85VaQ6cBdjfux213cVnRRaTHpfLhOdeS6E5ge/0uHi01DoIqxRi1VNfdMOx3\nQUJXAD51ePSsZKNFlRqdjD84NOLlacJLQwNDA/j62q3XM/w5m8zO5seOPsvTlS+OWbp58cQW/MEh\nLivahNPmoKWvlebeVtKjU7HZbGTGZeDrb7NaI+NV1VFtDRJo6GmK2JyXiCUFrfU2YI9SahvGyKNb\nlVI3KaWu1lo3Aj8ENiulXgP2aa23At8FrlRKvQL8F/DFSMX3ZoVGIkU7oilMyD/t8aSoRC4rumjY\ncM3ROOwOVpr9EDOTp7E0ZwGzkqdT4iujrK2c/PgcEtzx2G12Pjb/Q3xr7Vf5wpJPsyB9LgUJeRQm\n5lPcqvH1t7Eic4nV1A3ntrusvo3ZKTOw2WxMNxOB0+4kLyF3xNhCZ/PxrjhSYpJYkD53xNFUMc4Y\nUqKSrX2PPyUphCYQlZqjPIpbNV0D3fQM9vJspXHwCH0JV2UtBYzOxMYeL92DPSzPXMJnFn8cT2wa\nmwovwB/wU9J2nGhHNMszzwNO1mmrOqutYbMfmnMtlxUaZZBZydNJizm9rwggwX0yaRYm5LEicylN\nvc1sr9912rLaV4avv42lGYuIc8XS0N3IFnPm7C2LbyLD7NPZkLeWjUWraezxsrn6NQAyYzNIjzGS\nQujAGDoADwwN0HnK3I7wOnpdd8Np/QFNPc38YNdP+WfFc2ypeZ37j/yJ12t3kBadykrzdWzubaW0\nrRy7zc55noUA7KozRpgtzVgEGAeZ3Y37recPXTfrru3/zVe33s1/7/oJX9pyJw8efcSMvYXdjfvJ\nictiVbbx2W0f6OCRkn8QCAZJj05F+8po6W21kt+abCN5V7RXWYki1mmcF15csNEa/bc2ZyXfWP0f\nFCTkMRgYNE4UzO9HXVfDsNco1J92vK2SeFccV067lIXpc8mNN0bbpcakmPtzemdzo5kU0s3+w1BH\ndq35mi8xX6vQYIlQy/fUCaxPV7zAXdu+T//QAJ0DXWyt3UFyVBJrclaQGp1CbVcDg4FB633PjPWY\nz99M/9AAm6tfG9d8iM3Vr1vxDgb8Z+wreTMi2qegtf7qKXcdCHvsXuDeU5avBy6PZEyTJXRmPSd1\n5rgO/GNZn7uGQy1HudgcE70+dw2lbeUEggHmmiOUQlKjU0gNGx21PneN1Rm3LncVI7HZbMQ5Y2kf\n6LAOvjOSi9jVuJfChDxco3SGJ7oTSI5KYlby9FH7WEJy47Pw9beRZI5YColzxdLS10ogGLCG1gaC\nAbbUvM7upv009TQzN3W21TFIgbIvAAAgAElEQVSYEeshPyGXUt9xq2MyfGLhupyVxLli8Qf85MXn\nGDFGJ9LWZ9T467rqrSu75iVkszZnBbNSZlgT+EaSEJbEMmMzOC9jAbsb9/Fk+XMsy1hMtNMo+QSD\nQZ6qeB6AC/LX0dzbSnl7Ja39beTEZVGQkMfVM6/ghRNb2JC3hrS0eHbVHqRzoAunzUFadAo2mw2n\n3Ym3t4WhwNCwy4g097aS6E4gEAwQDAatETmzkqdT2lbOoeZiNuSu4Q/FfyEj1kN9dwP+4BBXz7yC\n/U2HrTPJy6dtosP8wShvbzO1XfXkxmWRbbboWnp8JLoTrM9xY7cXX3+p+T5mU9tVT3l7JT3+Xpx2\nJ9WdddhtdmtAxAsnNhMkyKVFF5FsHsyNUUhNFCUWsDZnJQ8efZgd9butQRMqdSaxzhiqO2tJikrE\nho0Pz/0gexsPsOmUuQAOu4Ob5v8r39/5Y3LjsylMzDPf24Zh76Ovr53WPh++/jYWp8/nvdMuHrad\n0Pektc837DMEJ5PCYs8CXqp+1ZqweaKzhlhnjDUcvamnmfb+DqtVXdFxgkWe+dbnYVvdLnz9bdR2\n1XG0pYTBwCCXFF6Iy+4kNTrFammcnhSaqO6s5W+l/+CA9zCfO++Tox5LgsEgx1pLSHInsCZnBU+W\nP0dDd5O1zckkM5rPUk58Fjcv+Aj/MuuqSdleWkwKd676ktUUX+yZbx2o5o4x8WtZxmKS3AlMTyqi\nICFv1OVCI5BCSWFu6mxcdqf1nCNx2B3856ov8eE51465Dznm2VmyO9GK3YaNmUnT8Af8dAx0UtZW\nQYwzGhs2nq58kaaeZi7MP5/PLv74sC/EvFSFPzhkTUKaFvaFttvsLM1YxMqspeTEG91W01IKsNvs\nzEiaxmDAz37vYew2O9mxxgFkTuqsM06uSnCdrK9mxnlIjkpiU+EFdA508edjj9La5+Oxsqd4pPQf\nlLdXsTh9PgUJeWTHZRAkiD/gt0ZsLfLM50vLPmslq4/OuwEbNjJiPTjsDuw2O2nRqTT3thi/8EfQ\nuqRI6Kz6T0f/xje2f58yczTUpUUXYcPG3qYDFLdq9jQd4JnKF9nvPcy0xAIuzt/ATfNvINoRRVZs\nBisyl5Bulqm0r4zBwCDZ8VnDDiK58dl4YtOxYaO+u5Hy9koyYzOYkTQNwEow/zLzSu7Z+F8s9syn\na7Cb+u5GdjbsJT0mjaUZi6ySWVlbBYFggKzYDJZ4FuJ2uNlev5vGniZinDHEOWPJS8jF29tCbVcD\nKdHJnOdZwMcXfNhKuuEyYz38v5Vf5BMLb8QTk47T7qSuu35Y+aitv51ycw7N9OSi07YRGlpe4iuz\nRriFhIabLvYsAIwyYHGLprXPx4L0uaRFp2K32Wnq8Q4bah3eUqjvbrRKWLVdDZS1VWDDZrXSUsMm\n4YVaqVmxGQA0djfRYJYVS9vKeazsqdPiD3+ezsEuZqfMtIa1hkqSk+2tHH30jhNqekeC0+7k6hlX\ncKi52PqSjsbtcPG1VbfjtJ25xbIuZxX1PY3WhzM9JpXvrrtzxHJTuJgRvrAjyTOTQkp0stWBlxKd\nTIZ5ZqRby2gf6OA8z0J6/b1oXxmrs5bzLzOvOq0VMjd1Ns9VvYyvvw23w03OGc7yAT61/ENU1NdT\n0XGC4+0VdA/2kBOXhesMs8TDhZePQl/a9xRcwLHWEvY0HWC/97B1jSSAy6e9x1g2LK7Q2eOp5qbO\n5pZFNw1rjXhiUmnsabLGyRck5lHVUY23t4XWPh9vNOwhSJD93nacNgezk2egUmZyzFdqXWE3Pz6H\n+u5GrpllvH7pMWl8fdXt1lyaUFkkdI2qnLgs0sKGWufEZ+GyO0mPSTXmTRBkZnKRdSALtdIyYzNw\nOVwUJuazt+kgL57YwmDAzxLPQuw2uzW/J1SLz4rLINoZxbrslWyuMcpmBQm52Gw28hNyKPGV0evv\npWCUkmW48Fnfnpg0mnpa6Aqr6fv62zluHqRDrZ5woZbC63U7eb1uJ3es+Lx14tTU4yXOFUtRYj5O\nu5PiVm0daC/O34DD7sATk0ZDT5N1tg9Yl7px2B3DhhHXdTVQ3VVLRmy69Z0Jb9Gnm9+7zLhQS8FL\nvzmMNzU6hS2123hP4YUkRSUQDAY50HyE12p34A/4mZdqVAtUykyyzc9n6EKOk02SwhS2KnuZVa8d\nS3xYR/JoLshfd9p9oUt4TIbzPAu5QV3N8szz2GnOJUiLTrG+DDvMiX0zk6cxO2UGR5qPcVHB+hHL\nUtOTCol2RNE31E9hQt6YJbq02BQCic5hB+7cM0waPJXb4SbK4SYIVovC7XBxy6KPcc/eX9HS5+Pa\nGVcR744nyuEmL8HYdlac8QVNcieSf4aD3IL0ucP+Ds0lCXUAq5SZVHVU09zbwtbaHVbrIUiQzLgM\nHHYHa7KXc8xXSlVHNRmx6dyx4gv0+nuHXfphpINQ6Ew2Oy6T9JiTj+fGGUk8M9Zj1fhnJE2z+p5C\npavQQSw0ACE0mTJ06RKH3UG8K846gw+9Ju+dtomdDXvp9vdYLZT8+JOvkWeCl/lIi06lvrtx2Czj\ntr42Ogc6cdldI77+2XGZrMtZia+/neIWzb6mQxQk5NHa56O5r5WixHwcdgdXTb+Ux8qeomOgkzkp\ns6z3tzAxn50NezncbMynyIhJp6m3mbruRvITcqwfZrJh43DLUXr9fVYZFIa/Hx7zNUiOSsJld1lJ\nId4Vx6aCjTxc8ji7GvdyQd46/nzs0WETYUOtk9kpM0mOSsRhc1gjuCablI/EpHHYHazPXUOMM8Y6\nK06LTrVaJqHJUzOSi8iNz+aSogtHndjnsDtQ5kFnWtLIcw9GkhOXbZVi8sxLe4zXiswlrMleMWzo\nYpwrljuWf57vrvs6G/LWsjRj0bAvfX5CLjHOaFZnLz9tyOOZhA6SR8yDzYykIuw2O7Vd9Wyr20mc\nK5YrzNZIjnnwXuRZYJ2Brshcgs1mO+O1gGJdscQ4Tyb9nPgsYpwxVuduqNyXaZ55gpGwww9kUQ63\nVR7KT8i1hmm67S5mJJ9swYaX5kItrThXrDWkN3Rf+IHbM8F6eCjJVZkTG6MdUTT3tVLf3cis5Okj\nfpbsNjsfmnMtn1xwI267i/1NhwgGgzxe9jSBYIB1OUYf3KaCjbxv+mXEOKN577RN1vqhfojQJLsV\n5ui112q3U95eyfG2CvLjc0iLSbU6fsP3MfRa2m12ayCG3WYnM9ZDY08TLX0+MmI9LMtcjNPmYHvd\nLuvKCIUJ+Xxl+edIjkrCHxwiPSaNtJgUHHYHGbHpNHQ3RuTCepIURESEOtPyE3IpSswnPyGXvPgc\nNuSuPeNlP8KFRhUtSJs7xpInRTujrJLDeJ8n5F/n/AvXzX7/afe7HK5hB9dw8a44vrvuzlHnM4xm\nftocnDaHNdPbE5NGalQytV1GzXxD7houLtjIRfnruajgfMBouazNWYnL7rJq1mPxmAfSaEeUdVDK\njPXgsjutM/pQSyA5KskcxHCyDp4Zm2G15KIcbquTd1bKjGGDE0KJw213DbuY3fm5q/nUwo9yYf56\nwCgHuR1uM7YJthTMfQnNNs5LyLEGNMw7ZTDGqdwON/PT5tDU28wLVa+wp+kAhYn5w17HS4su4gfr\nv8nMsGQXGqE3FBzCZXeyLmcVie4EXqt7g//d80v8wSFWZS8nN+7klKzw1lAoKaREJQ9r7WbGehgM\n+AkEA2TGeoh3xbEwfR4NPU2U+MpYnD6f25beQmFiPtfMvNLYx7Ch5llxmfQN9dMRgZnNUj4SEZEb\nn81dq/+DtOhUHHYHX13xhQlvY2nGIuasv3vCJS6VMpP2/o4zlnMm05mubjsaT2wamwo28qw5lj8l\nOoWV2cs43HyUdTkrWZO9AofdcdpAhvdPfy+XFF44rnIhQFpMGic6a8mOy7IO7h+acy3OuCAum/H1\nD7UUZiZPw2azkeCOx2lz4A8ODWtFgFFCqu2qP20uTKI5XyUz1jOsxWS32Vkc1tdit9nJi8+mvL1q\nwi2FUH9IIBjAbrOTE5dljWYbaW7Oqc7LWMg+7yGeKH8Gu83OB2e9/7TW3al/Z8dlEu2Ipm+oj8x4\nD0lRidy56ss8X7WZlr5W1uWsQqXMpHuwmwPNxuzu8M9dclQiie4EihKHD1vPjDv5uoZOoNbnrmG/\n9zDLM8/jxrnXWUlkacYi4l1x5CecPMm5IG8dcc4Y4sdx1diJkqQgIibUwfxmnE2fxzUzr+Tyae+x\nRltNVZcWXcTupgM4bHbcDhdXTHuPVTIajcPuIN4+voQAJ8fgh08uzInPGjYLdnpSIVdNv9Qal2+3\n2UmJTsbb22IdsEKM+nyb1YoLSTZbCuEHu9FszF1LUlTSadsec1/C5pjEu+Kslk/ogoZjWZA2h5So\nZBLc8dygrh42W3o0dpudaUkFHG0tITPeaNnEumL4wMzhI+ezzZZCenTqsM+sw+7g6ytvP23AQ3i8\nodsqdSbfP/8bxLlih/Wz2Wy20y45PjN52rAWzWSSpCDecVwO17hHHZ1LboebO5Z/jqEI/hpbqJQW\n6pcYid1m57Ki08f3e3tbrNJSSGFiPrcuvvm0bSSafQpnmgsSsjxrCctHmVl+JuEjp+JdcSRHGxMr\n56WpMefQAEQ7o/nW2q9OqO8HjKRpJIXRE0+oY7og8fQh4fHu05N4eAssPEGMtOxbTZKCEOfQeH40\n5s1YnrmEXn+fdSmQ8cqI9aB9ZeTEZY29MMYl4KcnFVqtjUiIdkZZo5zi3fHMS1Ms9izgwvzzx72N\niSYEgIXp83mm8iXmZ4xeosqM9fDx+R8+rUw0+vLpVjyRmID2ZkhSEOIdzO1wWbPkJ+LyaZtYmD7P\n6oweS3pMGl9aduuEn2ei0mJS6RrsJsEVR7wrbtivGUZKfkIOP974HbIyk8944bllmYvHvU23w01B\nQi5Oc07JVCJJQQhxmkR3gvUDUFNJenQqVR3Vb3mZJRIH7i8s+TRhvygwZUhSEEK8bYSGpY539NVU\nNtKlPaYCmacghHjbCA1jDf/pXDG5pKUghHjbWJ65hK7BbpZnTnz0khgfSQpCiLcNt8PFJYVj/wiW\nOHtSPhJCCGGRpCCEEMIiSUEIIYRFkoIQQgiLJAUhhBAWSQpCCCEskhSEEEJYJCkIIYSwSFIQQghh\nkaQghBDCIklBCCGERZKCEEIIiyQFIYQQFkkKQgghLJIUhBBCWCL6ewpKqXuA1UAQ+ILWelfYY7cC\nHwGGgN1a69vM+79s3j8IfDZ8HSGEEJEVsZaCUmojMEtrvQa4Gfhp2GOJwH8A67XW5wPzlFKrlVLz\ngRuA5cCngSsjFZ8QQojTRbKlcDHwOIDW+qhSKkUplai17gAGzH/xSqkuIBZoBa4GHtZa+4G95j8h\nhBBvkUgmhSxgT9jfXvO+Dq11n1LqbqAc6AX+orUuUUoVAUNKqWcBF3C71vrAmZ4kJSUWp9Nx1kF6\nPFPzB8AlrombqrFJXBMzVeOCqRvbZMb1Vv5Gsy10wywffQ2YDXQALyulFpvLOID3AuuA3wArzrRR\nn6/nrAPyeBLwejvPev1IkbgmbqrGJnFNzFSNC6ZubGcb12iJJJJJoQ6jZRCSA9Sbt+cC5VrrZgCl\n1FZgGdAIHNNaB4HXzJaDEEKIt0gkh6Q+D1wLoJRaCtRprUPprBKYq5SKMf9eDpQCzwCXmuvMAaoj\nGJ8QQohTRKyloLXeppTao5TaBgSAW5VSNwHtWuvHlFI/BDYrpfzANq31VgCl1HuVUtvNzdwaqfiE\nEEKcLqJ9Clrrr55y14Gwx+4F7h1hnbuAuyIZlxBCiJHJjGYhhBAWSQpCCCEskhSEEEJYJCkIIYSw\nSFIQQghhkaQghBDCIklBCCGERZKCEEIIiyQFIYQQFkkKQgghLJIUhBBCWCQpCCGEsEhSEEIIYZGk\nIIQQwiJJQQghhEWSghBCCIskBSGEEBZJCkIIISySFIQQQlgkKQghhLBIUhBCCGGRpCCEEMIiSUEI\nIYRFkoIQQgiLJAUhhBAWSQpCCCEskhSEEEJYJCkIIYSwSFIQQghhcUZy40qpe4DVQBD4gtZ6V9hj\ntwIfAYaA3Vrr28IeywSOAVdrrV+JZIxCCCFOGndLQSllV0plTWD5jcAsrfUa4Gbgp2GPJQL/AazX\nWp8PzFNKrQ5b/YdA+XifSwghxOQYV1JQSl0MHAdeMf++Ryl15RirXQw8DqC1PgqkmMkAYMD8F6+U\ncgKxQKu57YuATuDQhPZECCHEmzbe8tF3MMpAfwn7+5/mv9FkAXvC/vaa93VorfuUUndjtAZ6gb9o\nrUuUUm7gLuD9wI/HE1hKSixOp2Ocu3E6jyfhrNeNJIlr4qZqbBLXxEzVuGDqxjaZcY03KXRprRuV\nUgBorZuVUgMTfC5b6IbZYvgaMBvoAF5WSi3GSAb3aa3bQs81Fp+vZ4JhnOTxJOD1dp71+pEicU3c\nVI1N4pqYqRoXTN3Yzjau0RLJeJNCr9lHYFNKpQA3AH1jrFOH0TIIyQHqzdtzgXKtdTOAUmorsAy4\nFHAopf4dmAGsVEp9UGt9ZJxxCiGEeBPG29H8WYyO4RUYfQuXAZ8aY53ngWsBlFJLgTqtdSidVQJz\nlVIx5t/LgVKt9Tqt9Wqt9WrgKeCzkhCEEOKtM66Wgta6GrA6lpVSdq11YIx1timl9iiltgEB4Fal\n1E1Au9b6MaXUD4HNSik/sE1rvfWs90IIIcSkGFdSMA/mscC9wBYgXyn1fa31r860ntb6q6fcdSDs\nsXvN7Y227k3jiU0IIcTkGW/56NPAb4GrgcPANOD6SAUlhBDi3BhvUujVWvcDlwMPm6WjYOTCEkII\ncS5MZEbzL4B1wBal1BogOmJRCSGEOCfGmxQ+DJQC79NaDwFFwC2RCkoIIcS5Ma6koLWuB14GMpRS\nG4BaICmSgQkhhHjrjXf00T+ABRjJICQIbIhEUEIIIc6N8c5oztZaT49oJEIIIc658fYp7FZKFUUy\nECGEEOfeeFsK+4ESpVQD4Me4uF1QWg9CCPHOMt6k8BXgPUBNBGMRQghxjo03KRzUWm+JaCRCCCHO\nufEmhQal1GZgO0b5CACt9TciEpUQQohzYrwdzVdg/BRnPzAU9k8IIcQ7yLjLR8BPtNZtkQxGCCHE\nuTXepBANVCilNGD9DKfWWiavCSHEO8h4k8K3IxqFEEKIKWG8v7wmI4+EEOJdYNyXzhZCCPHOJ0lB\nCCGERZKCEEIIiyQFIYQQFkkKQgghLJIUhBBCWCQpCCGEsEhSEEIIYZGkIIQQwiJJQQghhEWSghBC\nCMt4L4j3jvP7Z46RmhzDVasLrPtsNtuIyw76A7ickj+FEO98EU0KSql7gNVAEPiC1npX2GO3Ah/B\n+LGe3Vrr25RSTuC3wAwzti9rrV+LRGwt7b28eqCOxuYujtd1EBPl5HPXLCQ1MXrYchX1HXzvwb1c\nd+EMNi3Pj0QoQggxZUTs9FcptRGYpbVeA9wM/DTssUTgP4D1WuvzgXlKqdXAjUC3ed/NwI8iFd8n\n3zef7LQ4th9pxOvrpaqhk28/sJuqhs5hyz3xWgX+oQCPb62gq3cwUuEIIcSUEMmayMXA4wBa66NA\nipkMwPihngEg3mwdxAKtwIPA7eYyXiAtUsElxrr51qfXcNmqAr79iVVcf9FMOroG+N6f9rD1YB09\nfX4q6js4eLyFKJeDnn4/T2+vAqCrd5AmX0+kQhNCiHMmkuWjLGBP2N9e874OrXWfUupuoBzoBf6i\ntS4xlwudjt8G/DmC8ZGVFsd1F84EICc9Dk9yDP/3jyP87ulj/O7pY9Zyn/nAfB54TvPszhMUV7ZS\n19JDMBjkWzevJDstLpIhCiHEW+qt7Gi2enHNFsPXgNlAB/CyUmqx1vqA+fitwFLgqrE2mpISi9Pp\nOOugPJ4E6/alngTmz/KwZW8txRUt+IcCzMxP5qJVRRTmpvDA08XsK/GSmhhFa0c/z+6q4Ss3Lj/r\n5x5vXFPJVI0Lpm5sEtfETNW4YOrGNplxRTIp1GG0DEJygHrz9lygXGvdDKCU2gosAw4opW7GSAYf\n0FqPWcT3vYkyjseTgNc7vA8hygaXLMvlkmW51n3NzV0kRTv43DUL6e33E+Vy8O0/7Gbr/lreszSX\nvIz4MZ8rEAxiH2V003jimgqmalwwdWOTuCZmqsYFUze2s41rtEQSyT6F54FrAZRSS4E6rXUo8kpg\nrlIqxvx7OVCqlJoO3AJco7Xui2BsZy0myondbuPqDdMB+MnfDlBW2z5smZb2Ph58XvON3+7k768e\n55ePHeKzP9rCHt10LkIWQohxi1hLQWu9TSm1Rym1DQgAtyqlbgLatdaPKaV+CGxWSvmBbVrrrUqp\n72J0Lj+tlApt6hKt9UCk4jxbC6encvX6aTz+WgXff3Avl6zM56q1RVTUd/Czvx+if2AIG1Dj7bLW\nue/JYuw2GxkpMeSkx9E3MMSuY00sVx5io13nbmeEEMJkCwaD5zqGN8Xr7TzrHZiM5qA+4eO3Tx2l\nub0Ph90oD9ls8OH3zGbFnEwOlDUTF+MiEAjys0cPEgp2ek4iHd0DNLf3sXhGGp+/dhE2m439Zc10\n9PpZvyBz1Ml058pUbT7D1I1N4pqYqRoXTN3Y3kT5aMQDzLt2RvNkUQUpfPsTq3h+VzV7tZfWzj4+\n/b75zCtKBWDNgpPdKl+8bjFHq3zUt/Swv6wZG5CeFM2B4y28uKcGT3IMP3/0EIFgkFiXneVzMqx1\n/UMBWjr6yEyJfat3UQjxLiJJYRJEuRxctbaIq9YWnXG5BdPTWDDdmHpR3dRFIBAkIdbFnb/dyUMv\nlgLgctqxB+HhzWXMn5aK22XHbrPxq8cPs6+0mQ2Lc7j+opnERMlbJ4SYfHJkOUfyw0YsffmG89iy\nv45abxeXrymkurmHx7cc59Z7XiU+xsXKuRnsK23GYbfx6oE6mnw9fPlfl4x7NJMQQoyXJIUpYFp2\nItOyE62/1y2Jprq+g55+P8dr23l5by1ul527blrBwy+XGeWmXdVcsrLgDFsVQoiJk6QwBcXFuLj1\nmoUA1Hq7+OvmMtbOzyI7LY6PXT6XO3/7Bo+8cpxDFa28d1WB1X8hhBBvllwPeorL9cRz+3XnsXq+\n0WGdGOfmlvcvICs1liMVrfzs0UM0tA6fwOcfChB4m48qE0KcG5IU3obmFhojnj79vvn0Dw7x6ycO\n09vvB6C338/dv9vF1+97g8ZTkkVJdRs7jjRM6Lm6ege56/6dPLy5bNLiF0JMXZIU3sZWzctk/aJs\nTjR2cdf9O9l9rIkHny+htrmbxtYevvPHPdZs69aOPu55+AD/92Qxu4+Nb2Z1MBjk988co7qpi9cO\n1hMISOtDiHc66VN4m7vxUkVSvJuntlfxy8cPA1CYlcCGRdn86YVSfvjQPq6/aCaHjrfQP2jMsv79\nM8f4x+sV2Gw2vn7jMo5UtvLy3loCgSCbluexZJYHgK0H69lb4gWMFkNlfQcJbjmPEOKdTJLC25zT\nYeeaDTNYOTeTN4obqW7q4l83zSIzJZa0pGh+9fgRHnzeuCr57LwkVi/I4oFnNT1eo9z08t5ann2j\nio4e49qDpTVtfOVDSynMTOCJ1ypwO+1cta6IR7eUc7DMy7p5medsX4UQkSdJ4R0izxNP3sbhV2td\nNCOdb358BQdKm2n09XLZqgI8yTEUZiYQ7XZw1/27eOSVMoJBuGJNIaogmXsePsDPHz3IsjkZ+Dr7\nuWRFPmsXZPPolnIOlDZPKCkMBQI47NKyEOLtRL6x73CZKbFcsrKAGy9VeJKNi9JOy04kOy2O8xdm\nEQxCTJSDy1YVsGBaGjdeoujsHWTz3lqcDjuXrSogJSHKGO1U3kyTr4dBf2DM5y2rbefWH73K1oN1\nkd5FIcQkkpbCu9hlqwvZrb1cuaaQOPMqrRcsySUnPY4/v1DCirkZJMdHATC3KIXNe2v56r07SIh1\ncf6ibAKBIIP+APExLi5cmkdSnJuevkHcLgd/ePYYA/4AO482sX5RDmC0HPoHAsRGy8dOiKlKvp3v\nYhnJMfz0C+tPu392fjLf/PjKYfddurIAt9tJe0cfB4+38MyOE8Me33a4gek5iew82kRctJPuPqPP\noqS6jUH/EC6ngwefL2HL/jpm5CRyw6ZZzMhJitzOCSHOiiQFMS4ZyTH8+wfPw+vtpLffT0l1G/Gx\nLqKcDnYda+LJbZU0t/eRnRZLa0c/SfFuFhSl8vrhBkpr2snzxPP6oXqiXA7K6zr43dPH+PbNK6fc\n5cGFeLeTpCAmLCbKyeKZ6dbfeRnxFGQm0NM3yLqF2QwOBQgEgpTWtPP64QaOVLZSXteBfyjI9RfN\noLyune1HGjlU3sKiGelneCYhxFtNkoKYFMuUx7odZXcAoPKTcTps7DnmZcA/RJTbwdoFWczKS2L7\nkUaefeOEJAUhphgZfSQiJsrtQOUn09TWS1vXAOsXZhMT5aQgM4F5RSkcO9HG/rLmcx2mECKMJAUR\nUZ+4ch63Xr2QOz60hA9eONO6//qLZuF02Ln/qaOU1bTT2tEHQCAQpNHXQzAYZCgQoL6l+1yFLsS7\nkpSPREQlxUcNKy2F5GfEc92FM/jzi6V898E9AFy9fhqlte0cLm9l0Yw02rsGqGrs5IaLZnLJygKC\nwSD3/bOYnj4/n/+XRdjt0kktxGSTpCDOmYuX5eGw26hr6WFviZfHtlYAxuXBDx5vAcDtsvPIK8eZ\nkZeEr6OfHUcaAXh5bw2blucTCAbRJ9pISIo5Z/shxDuJJAVxzthsNi5cmgfA5asL+d3TR8lKi+W6\nC40L+MXFuPAPBfjfv+znfx7aj8tpx+mw4XY6+Pur5cwrSmXzvlpe2lPD0n21fPb98+UnSoV4kyQp\niCkhJSGK268/z/p7yeyTJadPXDmPv24uo6N7gCvWFOL5/+3deXhU1d3A8e/MZLKvZA+JgSycBAIh\n7BiBANYNtAqIW3GvteqEaYEAAB3KSURBVIWqaGu1fX21rUtrXXFBLYL7wguKFItSVllE1kACyYEk\nEMi+kIVkskySef+4kzEhCQhlMkM4n+fheTL3njvzu5eTnLnn3PM7/h68tzqbJxb9gAXQ6WBPdhnf\nhPlwzbjos/rcmvpmGptbCPb3UA2KoqAaBeUCMD4pjBGDgjlcWM3g6H7odODp5sLKrUdpbWvjV9cN\nYcHyDL78Lo9RIpitGSV8f6CEmZNiGZMY0uMEuSZzK0+++wO1JjNe7i7cflUCoxNCevnsFMW5qEZB\nuSC4uRpIGhhoez0qIYRRCSFYLBZ0Oh33XpfE8x/t4t2vs8gprMFigbdXHiD7WBW3XynQ6XQcPHqC\nHVll3Dg5Fi93I9sPlFBrMhMd6kNJlYmFKzKpvjyen42K6jaGtjaLGtxW+jzVKCgXtPa7gNTkCKLW\neHO4QFtp7vYrBRv3FrIpvQijQc+AcB/eWy1paW2jydzKfdcOZt3uQgx6HQ/MGkZdg5mXPk9n6foc\nRsQHE+jn3ulz0g9X8NbKTOIj/Zk1KZboMJ9eP1dF6Q1qnoLSJ+j1OmZMjAFgeFwQaSn9efim4YQE\neLB2dwGLVmUBEB7oyQ8HS3lteQYF5XWMGBRMgI8bUSHe3Dg5ltY2C6u+P2p739r6ZrbsL+bNFZm0\ntFg4cOQEz320m6qTTQ44S0WxP3WnoPQZyXFBPHpLiu1bvK+XK4//YiQ7DpZSVdfEiPhgfL2MPLlk\nJ+k5Feh0cMXoH7uKxg4OZdW2fLbsL+akyUxRRT0lJ0wAuBh0zJ+dTGF5HZ+tz2H9ngKmjIhk7+Fy\n6hrMjE4IITzQyyHnrSjnk2oUlD4lITqg02s/L1d+NrrzGMFz942jrsGMr5crvp6utu0GvZ4ZE2N4\nc0Umew6V4+ZqYMjAfgyK8iclPojIYG/iI/34ens+G/YUsiWjmJq6ZgD2Hq7gf+8YRXVdM57uLrgZ\nDfY/WUWxA9UoKBcdf2832+JBpxqVEMLLv70MvQ683I1dBpZdjQamjIjkqy1HoAmuSx3A0ZKT7M+t\n5PP1OazbXUCgrzvzZg4lMti7289QFGdm10ZBCPEyMA6wAA9KKXd22DcX+AXQCuySUj4khDAC7wHR\n1u13SSnz7BmjopzKz8v1tPunjozkSHEtIwYFMzE5gqKKejJyK1mz8zgGvY6y6gae/mAXMyfFctOV\nib0UtaKcH3YbaBZCTALipZTjgXuABR32+QK/ByZIKS8DBgshxgG3AtXWbc8Az9krPkU5V94eRh66\nMZmJydoyoxFBXqQOC0cH3HfdEH5zfRJGg55P1x7mzj9/y8IVmVTWaAn/ms2tAGTnV/HS5+kcLam1\nve8PB0tZvT2/189HUTqy553CVGAFgJQySwgRIITwlVLWAs3Wf95CiDrAEzhhPeYD6/FrgcV2jE9R\nzps7r0rghgkxBPho3VKDovz5cnMeB46cYGd2GfJYFSEBnuQU1hAX6cfRYm3RoaLKep66awwGvY73\nv8mmsbmV1KHh+J7hbkVR7MWejUIYsLvD63LrtlopZaMQ4s9AHtAAfCalPCSECLOWQ0rZJoSwCCFc\npZTNPX1IQIAnLi7nPqgXHOycz5uruM6eo2ML7fBzcDD8boA22W7Vljz+uSKDWlMNl4T5kFNQg4eb\ngdRh4WzaW8CS1dmkiBAam7W7iJKaJmIHBHZ6b4vFwr+3HmFIbBADwn1/UjwvfrwbF4OeB29O6Xa/\no69XT5w1LnDe2M5nXL050GwbsbN2H/0RGATUAuuFEMmnO6YnVVWmcw4oONiH8vKT53y8vai4zp6z\nxhYc7MNYEUzEXWNwMegID/SiuLIeo0FPPz93qmob2HuonPRD5bZjdmYWMyii8y95Zl4lb32ZgZ+3\nK0/dORq/HgbKT9Q2YnTRY2pqYeOeAgAmDw/v8risM18vZ4wLnDe2c42rp4bEno1CEdqdQbsIoNj6\ncyKQJ6WsABBCbAZGdjhmn3XQWXe6uwRFuVBEhfz4JFLHP9C//nkSL3y+l9zCWsYNCWW3LEcer7Iu\nMmTBxaAN+63brf2Br6lr5uWl+4gK9aa40kSTuZVbLx9EYnQApkYzTy7ega+XK2MSf7xv+W5fETdN\nie+lM1UudPac0bwGmAUghBgBFEkp25uzo0CiEKI9Cf4o4LD1mBut264FNtgxPkVxODdXAw/dmMzs\nyXHcMjWeuP5+FJTX849P9/K7N7aSV1RLeXUD+3MrGRjuy6iEEI6V1bE1o4T8kpMUV5h46fN0Nu8r\nYt3uAuobWyiuNLFq21FcDDq8PYxszSjB3NLW7efvyCrl4zWHaGuz9PKZK87KbncKUsptQojdQoht\nQBswVwhxJ1AjpfxSCPEPYIMQogXYJqXcLIQwAD8TQmwBmoA77RWfojgLL3cjV429BAAR5U9WfhXZ\nx6oB+Mdne/H3csUCXD4yknFDQimZMBC9Xkc/HzeOFJ/k9S8yWLI6G1ejHi93F1paLTSZW0mJDyI0\nwJNvdhxjzc5jTBs/oMtnf7XlCMWVJqJCvW1PUykXN7uOKUgpHztl074O+94G3j6lfCtwlz1jUhRn\nNiSmHyu2HCEpph+XJoXx3upsKmubSIkPYlSClga8Y/fToCh/Hr01hec/2Utdg5lrxkaDDlZsPsL4\nIWHE9vdj+8ESlm/Kw8fTlYnJEbS1WWhoaqGmvpniSm1M7ovv8hidEIKHm5rPerFTNUBRnEhshB9/\nuWcMYf08cTHoGTkoGL1eh0Hfc09vZLA3f7g1hZ3ZZVwxJgpXo4Hk2CAuCfVGp9PxyM0p/P3jPby/\nOpvGphY27C2kDbjWeucQ6OtGZW0T3/xwjBusSQWVi5fKkmonGzeu+0nlXn31RYqKCu0cjXIhiQz2\ntg0wG10Mp20Q2vUP9ub6CTG4u7qg1+mIDvOxpRXvH+TF/NnJuLka+Gx9DqVVDZRXNbBsUy4Ad1+T\niLeHkfV7CmhsbiE9p4LjZXVnHXebRY1L9AWqUbCD4uIi1q799ieVffDBR4iI6G/niJSL3cBwXx6c\nNYzIYC9mTIzBoNdRW9+Mm9FAfJQ/U0dGUt/YwgufpbNg2X6eXLyDf3y6l8bmlk7v02axUFHd0Glb\nQ1MLH66R/PrFTaTnVPTmaSl20Oe7j5auz2Fndlm3+wwGHa2tZ//tZnRCCLOnxPW4/6WX/k5W1gEm\nTBjNFVdcTXFxEa+88ibPPfcXysvLaGho4O677yM1dQLz5t3Hww8/yoYN66ivr+PYsXxKSoqYO3c+\n48ennnVsitITcUkAf7lnLAAVtU18l15IXKQfLgY9U0b0Z/X2fPKKagn0dSPQz4Os/CrW7ipg+qUD\nAGhpbWPhikz2Hq5g9uQ4QgI8WLYxl9IqE+03Ccs35jIsNlCtd30B6/ONgiPccsscvvhiKQMHxnLs\n2FHefHMRVVUnGDNmHFdfPZ3CwgKeeOIxUlMndDqurKyUF15YQFbWXj744CPVKCh2c31aLNsyihg5\nKBgAH09Xrk0dwLbMEubNGIq/txuPLtzGtzuOERPhy8GjVRwqqCbHurLd0g056AAXFz3x/f1Iigmk\nqKKe7QdLWfSvgxwuqOGmKXGM6rDmtamxhbe+yuTSpDDGDQnrLizFCfT5RmH2lLgev9X3xgzFxMQh\nAPj4+JKVdYCVK79Ap9NTW1vTpeywYcMBCAsLo67u7Pt0FeWnio8K4NUHJuDu+mOKmGnjB3R6bPWq\nsZewfFMeL3yWbts2ZEAAN0yM5eWl6bgaDTwwc5htUaPSEyZ+yCpl+8FSAJasziYmwpd+vtrSplsy\nisk8coKDR6vwdHdhWGxQL5ypcrb6fKPgaEajEYD//OcbamtreeONRdTW1nLvvXO6lDUYfvwFtahB\nO8XOzvT46dSRkaQfrsDD3YWpIyKJCvEmwMcNnU7H3+4fj4tB32kxodB+ntw0JZ7y6gYCfd1ZuiGH\nd1YeYP7s4bga9WxKL8TFoEOv0/Hmikz+es9YSk+Y2CXLuXlqHO6uPcfTZrGwbncBnm4upA4NP2/X\nQOlKNQp2oNfraW1t7bSturqa8PAI9Ho9mzatx2w2Oyg6Rflp3F1d+NPto7rd5+Vu7HZ7+/KmFouF\nvKIadsly/vbJHiYMC6e40sS4waEkxfRj0aosFq06yPGyOhqbW3F3NXDz1O5TcVSdbOKzdYfZmV2G\nTgcDwnw4VlqHqamFKSP6256yUs4P1SjYQXT0QKTMJjw8An9/fwDS0qbw2GMPc/BgJtOmXUdISAhL\nlvzTwZEqin3odDruu24I7t9KtuwvJr9E66adNDyCQVH+bM0oISu/CgBPNxf+s+s4FguY2yxEh3gx\nUoSg1+lY+FUmB46cACCsnyclJ0wsWL6f8mptfYrC8jp+cYXoskKecu50F3o3RXn5yXM+gb6W9dDe\nnDUucN7YLva4LBYLGXmV7MupxNWoZ/bkOHQ6HcWV9Tz74W5SBgUzdnAoL3YYtwCtayvAx42iinri\nIv0YmxjKxOQIXl22j4NHq/BydyHAx52C8jrGJIZw7/TBtrkdoD0ppdfrzutTUH3t/zI42Kfbi6Pu\nFBRFsRudTsew2KAug8rhgV68NC8Vo3UtlPmzk7FYLMQNCGTjzmOs3p5PUUU944eEcc+0RNudwJwr\nBMs25XLNuGhCAzx4Zdl+dmSV0djcytwbhlJYUcfX3+eTfrgCvV7HwDAf7romkdB+nr1+7hcqdafQ\nh1p+e3PWuMB5Y1NxnZ32uOoazOQV1ZA0MPC0XUNN5lZe/yKDA0dOENvfl/ySOlpa24gI8sJFr+NY\nWR0ebi78dsZQEqIDzktszuZ83ymoGc2Kojgdbw8jw2KDzjhW4GY08NsZQ0mMDiC3sBaji44HZw3j\nr/eM4am7x3DPtETMLa289VUmtfXa0iwWi4VaU9dlWkpOmFi6IYfquia7nNOFQnUfKYpyQWufL7Fh\nbyHJcYGdssimDg2nrsHM5+tzWPT1QW6YEMOyjblk5VcxJjGEGybEEGodwP77J3uoqWsm62gVj902\nAjfXrsv8VtQ08N2+IpIGBjIoyr83T7PXqEZBUZQLnpurwbYmxal+NjqK/bmVZOadIDNPe5LJ18uV\nHVll7MouJzHan5zCWprMrQwM9+FI8UneXnmAeTOGdrpTWf39Ud75MoOW1jZWbctn3JBQ7p0+uM+l\n9FCNgqIofZpep+OBmcP4/kAJh45XExfpR1pKf/bIcpZ/l8eBo1UE+7szfXw845PCeHnpPtJzKli6\nIcc2d6K+0czilZm4GfXMSovl+8wSth8oJSU+mNEdUnkcOl7NnkPl3DAhhoLyOv69Pd+WfLC+qYWJ\nyRGkDe+P0cV5e+5Vo+BAs2ZdywcffM7y5UtJSRlBUtIw2z6TycTtt9/EsmX/6vH4jRvXkZY2lX//\n+194eXkzadLk3ghbUS44bq4G0lL6k5byY0biUQkhpAwKorK2iWA/d9skuLk3JPHMh7tZs/M4taZm\nbkyLY/uBEhqbW7lxcixXjI4iOS6QP73zAys259nWvKitb+b1LzKoazBz4mQTh45VUWvSJqnqdGDQ\n6/l07WF2ZZfxh1tHOO3cCtUoOIE5c+4862Pa03OnpU3lmmuuPf9BKcpFwKDXE+Lv0Wmbp7uR+Tcm\n88aKTLYfKGXvoQpcDDrcXQ1Msi5ZGhrgSerQMDbvL+axt7/HzWjAYNBR12DG082FXdbMzLMnxzE+\nKQwPVwNN5lYWf53FvtxK1uw83mN3l6P1+Ubhi5xV7C3L6HafQa+j9RwWLE8JGcqMuOk97r/77tt4\n9tkXCQsLo6SkmMcff4Tg4BAaGhpobGxk/vzfM3hwkq38M888RVraVIYPT+FPf3oUi6WVxMShtv1r\n1qxm2bLPMRj0DBgQyx/+8Cdbeu4lS/5JW1sb/v7+zJx5E2+++SoZGftoaWll5szZXHXVNObNu4/R\no8eyZ88uqqur+fvfXyYsTGWpVJSeBPl78MQdo9iyv5jlm3I5aTIz/bKBeHZI73Fd6kAyj5ygsbmV\nWlMzzeY2BkX6ccfVCTz30R4GRflz5Zgo2x2Iq9HA3dMSeWLRD3y5Oa/LoLizcN6OrQvYxImT2br1\nOwA2b97ExImTmT79el577W3uv38eH3/8frfHffvtamJiYvnkk0+Ijx9k297Q0MCLL77GwoWLOXbs\nKLm5OdxyyxyGDx/BXXf90lYuPX0PeXm5LFy4mAUL3mLx4ncwmeoB8PLy4tVXFzJu3KV89916O569\novQNep2OickRPHffOO6Zlsgd1wzutD/Qz50X56ay4MEJvPbgBP5wawoPzBpGeKAXL/zmUubekNQl\nL5OPpytzrhSYW9pYuOIAzWYtR1pNfTMVNZ0XLzqdrRnFPP/JHprMrWcufJb6/J3CjLjpPX6rt9dk\nlIkTJ/P6668wc+ZstmzZxLx58/nssw/59NMPMZvNuLu7d3vc0aN5DB8+EoCUlJG27b6+vjz++CMA\n5Ocfoaamutvjs7MPMnz4CAA8PDwYMCCG48ePA5CcnAJASEgINTVd03YritI9T3cjqUPDcXdzoae/\nFkYXA+KSHyfHuRq7Ps7abqQIIW14BBvTi/hwjeT6y2J4+oNdnDSZmZzSH3SQV1TD8bJ6BoT5MHVk\nJGMHh9qOLyiv4/1vJB5uBrs8+dTnGwVHiImJpbKynNLSEk6ePMnmzRsJCgrhiSf+Snb2QV5//ZVu\nj7NYsA0+tVm7tcxmMy+99DzvvfcJgYFBPProQz1+rk6no+ME9ZYWs+39VFpuRXEeN0+NJ6+olq0Z\nJezKLqfJ3Iq3h5F1ewoArWs7JMCD3MIacgpr8PNyJSE6gLIqE++sPEBLaxt3XZ1kl6eYVKNgJ+PH\nX8Y777zJhAmTqK6uIjZWe7Rt06YNtLS0dHvMJZdEk52dBVzPnj27ADCZ6jEYDAQGBlFaWkJ2dhYt\nLS24urp2Sc+dkDCE999/lzlz7sRkMlFYWEBkpHMOZinKxczVaODRW1N448tMsvKrSB0axi+uEOzP\nrSTAx43oUG+MLgZyC2t49sPdfLhGMiw2kLW7CmhtszA5pT/D4+2zSJFqFOxk0qTJ3H//3bz33qc0\nNjbw9NNPsmHDWmbOnM3atWv4+uuVXY656qpp/PGPv+OOO+4gMXEoOp0OPz9/Ro8ey7333k5cXDy3\n3jqHBQte4rXX3kbKbBYseBEvL28AkpOHI0QCc+f+kpaWFu6/fx4eHh5dPkdRFMfzdDcyf3YyuYU1\nxEX6YdDrO815AIjtr82p2LC3kOJKE0F+7sxKi+1S7nxSCfH6UIIre3PWuMB5Y1NxnR1njQscF1t9\no5kPvpEMCPPh8lGRtsyy/21cKnW2oijKBcjL3civr086c8HzRD2SqiiKotioRkFRFEWxUY2CoiiK\nYmPXMQUhxMvAOMACPCil3Gnd3h/4uEPRGOAxYCOwGHADDMB8KeVue8aoKIqi/MhudwpCiElAvJRy\nPHAPsKB9n5SyUEqZJqVMAy4HjgErgYeBL6WUk9EaiWfsFZ+iKIrSlT27j6YCKwCklFlAgBDCt5ty\ndwLLpZR1QAUQaN0eYH2tKIqi9BJ7dh+FAR27fsqt22pPKXcvcIX155eBHUKI2wFf4LIzfUhAgCcu\nLj3nGTmT4GCfcz7WnlRcZ89ZY1NxnR1njQucN7bzGVdvzlPoMlFCCDEeyJZStjcUvweWSimfEUJM\nB14AZpzuTauqTOcckLNOlFFxnT1njU3FdXacNS5w3tj+i8lr3W63Z6NQhHZn0C4CKD6lzHRgbYfX\nqcD/WH/+D/DmmT6kp1l5P9XF0PKfT84aFzhvbCqus+OscYHzxnY+47LnmMIaYBaAEGIEUCSlPLU5\nGw3s6/A6BxjbYd9hO8anKIqinMKuuY+EEH8DJgJtwFwgBaiRUn5p3Z8BXC6lLLW+DgfeBTytb/GA\nlHK/3QJUFEVROrngE+IpiqIo54+a0awoiqLYqEZBURRFsVGNgqIoimKjGgVFURTFpk8vsiOE8ATe\nA0IBd+CvaI/AfoiWcK8YmCOlbBJC3AY8hPak1DtSyncdENcSwAiYgV9IKUuEEGZga4fDp0opOy/O\nbN+4ZgEjgUprsX9IKb/uzet1mtjuAIKtRfoB24FngQx+nE1fLqW80Z6xWePzADKtca3DwXXsNHE5\ntI71EFcaTlDHuolrGk5Qv4QQacD/AQesmzKA57FTHevTjQJwLbBLSvm8ECIabULcVuANKeX/CSGe\nBe4WQnwA/C8wBmgGdgohvpRSnujFuL5H+09cKoSYi5Yc8FG0R3jT7BTHT4lrG/C4lHJVeyEhhBe9\ne726jU1KOahDTIuBRdaXshevWbv/AdrP/y84vo51F9fTOL6OdRcXOEcd6xRXxz/2TlC/NkkpZ3WI\nZwl2qmN9ulGQUn7e4WUUUID2reR+67Z/Ab8DJLBTSlkDIITYija7+l+9GNdvgEbrtnJghD0++xzi\n6s5YevF6nSk2IYQA/KWUO4QQA+wVQ0+EEAnAYOBr66Y0HFzHeojL4XWsh7i60+t1rKe4HF2/epCG\nnepYn24U2gkhtgGRWNNqSCmbrLvKgHC0dBzlHQ5p395rcUkp663bDGgT/f5iLeYuhPgEiEbLJvtS\nb8aF9m1ynhDiYbTrMg8HXa9uYmv3IPBah9dhQohlaKlV3pBSdly7wx5eRLsud1hfezlJHesUlxPV\nsVOvFzhHHesuLnB8/QIYLIRYidaN9WfsWMcuioFmKeWlwHXAR3ROzNdT3qT/Kp/ST9UxLiGEzvrL\n+iGwXkq5zlrsd8B9aJlkbxNCjOrNuKzxPCalnAKkA091c0ivXK9TY7NeM1fgMinlBmuRSuAJ4BZr\nub9aZ8rbhTWj7/dSyiM9FHFIHespLkfXsR7icngdO831cmj9sjqM1hD8HK3BepfOX+jPax3r03cK\nQoiRQJmU8riUMl0I4QKcFEJ4SCkbgP5oiftOTd7XH21QqTfjCkbLCntYSvnn9rJSyrc6HLcOGArs\n6sW4MqSUZdYiK4GFwDJ68XqdJrZgIBnY0V7Oml9rifVlhRBiF5BA12SM58s0IMaa1TcSaALqHF3H\nuotLCFEA3I4D61h3cQG/klKmW/c7qo71dL10OLZ+IaUsBNq7T3OFECXAaHvVsT7dKKDlXYoGHhJC\nhALewDfATLRvwTOtr38AFgkh/IEWtH64h3o5rp8BzVLKJ9sLWfsynwRuQ3vKIBXtl6U343pbCPGI\nlDIPrR8zk96/Xj3FVsEpSRWFEJOBa6WUD1sHK4cDh+wVlJTypg6f/RRwFLgUB9exHuIKxcF1rIe4\nfi2EyHNkHesuLinlWiHEH3Fg/bJ+5m1AuJTyBSFEGNr/4xLsVMf6eqPwFvCuEGIz4IHWj7oL+EAI\n8SsgH3hfSmkWQjwGfIu2nvSf2wdrejGux9H6djdayxyUUv5GCHEc7ZtKG7BSSrmjuze0Y1x1wOdC\nCJP157uklA29fL26jU1K2Wa9dc/tUG4zcIcQ4nu0P3LPWb9p9aYncXwd685cHF/HuvM6zlHHuuMM\n9Wsl8IkQ4ueAK/BrYC92qmMqIZ6iKIpic1EMNCuKoig/jWoUFEVRFBvVKCiKoig2qlFQFEVRbFSj\noCiKotioRkFRHEgIcacQ4iNHx6Eo7VSjoCiKotioeQqK8hMIIX4LzEab8JmNls9+FbAaLdUGwM1S\nykIhxDS0FMYm67/7rNvHAq+gpTU+gZZyYiYwA6hFy9CZD8yQUqpfTMUh1J2CopyBEGIMcAMwUUo5\nHqgGLgdigCVSygnARuARoS0GtAiYKaWcjNZoPG19q4+AX0opJwGb0PLtAAxBS0g3EkjCQSmtFQX6\nfpoLRTkf0oA4YIOWKggvtGRjlVLK9hW4tqLlmRkElEop29d72AjcL4QIQsvJnwkgpXwFtDEFtBz4\nJuvrQsDf/qekKN1TjYKinFkTWk6gee0brIut7OlQRoeWb+bUbp+O23u6M2/p5hhFcQjVfaQoZ7YV\nuFoI4Q0ghPgNWqK0ACFEirXMZcB+tIyZIUKIS6zbLwe2Sykr0VItj7a+xyPW91EUp6IaBUU5Aynl\nLuANYKMQYgtad1INUAjcKYRYj5am+GVrfvt70LJ+bgSmoq37CzAHeFUIsQktFbh6FFVxOurpI0U5\nB9buoy1SykhHx6Io55O6U1AURVFs1J2CoiiKYqPuFBRFURQb1SgoiqIoNqpRUBRFUWxUo6AoiqLY\nqEZBURRFsfl/zMKE3WCUHnYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OdKZjB6mVIpV",
        "colab_type": "code",
        "outputId": "1bf3b228-7e95-4564-96cb-7ac2b85cb24c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_result_deep = AutoRec.evaluate(users_items_matrix_train_average_noisy, users_items_matrix_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 1s 133us/sample - loss: 1.0258 - masked_rmse_clip: 0.8774\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "srsVRT8bAu-c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Gaussian Deep AE CF"
      ]
    },
    {
      "metadata": {
        "id": "8ZaMxFLuplMB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layers = [256, 512, 256]\n",
        "dropout = 0.8\n",
        "\n",
        "activation = 'selu'\n",
        "last_activation = 'selu'\n",
        "regularizer_encode = 0.001\n",
        "regularizer_decode = 0.001"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y2PYRVbrngp8",
        "colab_type": "code",
        "outputId": "eeee6d84-23db-41f2-f6a2-c438efb885d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# Build model\n",
        "Deep_AE_denoise = Deep_AE_model(users_items_matrix_train_zero_noisy, layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode)\n",
        "Deep_AE_denoise.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip]) \n",
        "Deep_AE_denoise.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "UserRating (InputLayer)      (None, 3952)              0         \n",
            "_________________________________________________________________\n",
            "EncLayer0 (Dense)            (None, 256)               1011968   \n",
            "_________________________________________________________________\n",
            "LatentSpace (Dense)          (None, 512)               131584    \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "DecLayer0 (Dense)            (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "UserScorePred (Dense)        (None, 3952)              1015664   \n",
            "=================================================================\n",
            "Total params: 2,290,544\n",
            "Trainable params: 2,290,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2IrVVyYBpnN3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "hist_Deep_AE_denoise = Deep_AE_denoise.fit(x=users_items_matrix_train_zero_noisy, y=users_items_matrix_train_zero,\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  validation_data=[users_items_matrix_train_zero_noisy, users_items_matrix_validate], verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EdZi6gYE7EP1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#plot_model(Deep_AE_user, to_file='model.png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EXTsR-m73LxO",
        "colab_type": "code",
        "outputId": "9ba91aeb-ffc3-4315-dc00-9691d93d44c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Deep_AE_denoise, 20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9uyfOUQkhrORIbQ7/OSxRWFRRUgEzMqeypaEM76xVLF8TYMHUHIpzrTvwtRBi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\nBUAIISxKCoAQQliUFAAhhLCo/w8NYvp6zNolKQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4b3oHDEq3Lu9",
        "colab_type": "code",
        "outputId": "eb0585e9-a157-470b-eb3f-3891de2733d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Deep_AE_denoise, 20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kK6WWAKVAQiQDGwkMw2BxTu8pDe7f+DDbw0NEHM5ht3H9+ar7+XpdHdH4hBDi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YerhiUcPgMwI",
        "colab_type": "code",
        "outputId": "30ee4a58-9205-47da-8303-1a589a77866f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_result_deep = Deep_AE_denoise.evaluate(users_items_matrix_train_zero_noisy, users_items_matrix_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 1s 135us/sample - loss: 1.0798 - masked_rmse_clip: 0.8753\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uRZ20B1kgMzr",
        "colab_type": "code",
        "outputId": "d6f05d68-ec87-4dd7-e66b-430769a55d9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "predict_deep = Deep_AE_denoise.predict(users_items_matrix_train_zero_noisy)\n",
        "loss = K.eval(masked_rmse_clip( \n",
        "    K.constant((users_items_matrix_train_zero)), \n",
        "    K.constant(predict_deep)))\n",
        "np.mean(loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.68468785"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "metadata": {
        "id": "rbFBt2fLA2El",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Dropout Noise"
      ]
    },
    {
      "metadata": {
        "id": "Lv1MjvDhgNE3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "  def Deep_AE_DropNoise_model(X, layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode, noise):\n",
        "    '''\n",
        "    Build Deep AE for CF\n",
        "    '''\n",
        "\n",
        "    # Input\n",
        "    input_layer = x = Input(shape=(X.shape[1],), name='UserRating')\n",
        "    \n",
        "    # Dropout Noise\n",
        "    x = Dropout(rate = noise)(x)\n",
        "    \n",
        "    # Encoder\n",
        "    # -----------------------------\n",
        "    k = int(len(layers)/2)\n",
        "    i = 0\n",
        "    for l in layers[:k]:\n",
        "      x = Dense(l, activation=activation,\n",
        "                      name='EncLayer{}'.format(i), kernel_regularizer=regularizers.l2(regularizer_encode))(x)\n",
        "      i = i+1\n",
        "      \n",
        "      \n",
        "    # Latent Space\n",
        "    # -----------------------------\n",
        "    x = Dense(layers[k], activation=activation, \n",
        "                                name='LatentSpace', kernel_regularizer=regularizers.l2(regularizer_encode))(x)\n",
        "    \n",
        "    # Dropout\n",
        "    x = Dropout(rate = dropout)(x)\n",
        "    \n",
        "    # Decoder\n",
        "    # -----------------------------\n",
        "    for l in layers[k+1:]:\n",
        "      i = i-1\n",
        "      x = Dense(l, activation=activation, \n",
        "                      name='DecLayer{}'.format(i), kernel_regularizer=regularizers.l2(regularizer_decode))(x)\n",
        "    # Output\n",
        "\n",
        "    output_layer = Dense(X.shape[1], activation=last_activation, name='UserScorePred', kernel_regularizer=regularizers.l2(regularizer_decode))(x)\n",
        "\n",
        "    # this model maps an input to its reconstruction\n",
        "    model = Model(input_layer, output_layer)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hCNs1U-mxazK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layers = [256, 512, 256]\n",
        "dropout = 0.8\n",
        "activation = 'selu'\n",
        "last_activation = 'selu'\n",
        "regularizer_encode = 0.001\n",
        "regularizer_decode = 0.001\n",
        "dropN = 0.1\n",
        "# Build model\n",
        "Deep_AE_denoise_dropN = Deep_AE_DropNoise_model(users_items_matrix_train_zero, layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode, dropN)\n",
        "Deep_AE_denoise_dropN.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip]) \n",
        "Deep_AE_denoise_dropN.summary()\n",
        "hist_Deep_AE_denoise_dropN = Deep_AE_denoise_dropN.fit(x=users_items_matrix_train_zero, y=users_items_matrix_train_zero,\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  validation_data=[users_items_matrix_train_zero, users_items_matrix_validate], verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-EtziWmsxaw9",
        "colab_type": "code",
        "outputId": "e6d0bf3f-83c4-45a9-8dcf-c7980e2296c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Deep_AE_denoise_dropN, 30)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bdbuqOOeUDNISwoa8HCGEOBqDPVMIBgqVUuuVUp90//NnxUbKzLipfZ5vqd7u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\noahuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZzTcRLP8Eqnn",
        "colab_type": "code",
        "outputId": "03aa4634-7715-400d-b1b4-1dc8566a5a28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Deep_AE_denoise_dropN, 30)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CodCAn5k6dRp79uwGYPPm9wHo6urEbreTlZVNbW0Ne/bsJhQKDTjk9OzZc9myZVP0c11U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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wW3I_5cOxate",
        "colab_type": "code",
        "outputId": "879e88ca-2c1e-46c3-8ffd-0cc80afd440d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_result_deep = Deep_AE_denoise_dropN.evaluate(users_items_matrix_train_zero, users_items_matrix_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 1s 154us/sample - loss: 0.9822 - masked_rmse_clip: 0.8554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y_CXpxd7ASDL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hP0RmB6dkgTP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Hybrid"
      ]
    },
    {
      "metadata": {
        "id": "QqY3NYh4MbBX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Preprocessing the side information"
      ]
    },
    {
      "metadata": {
        "id": "K0JIjYmBkmIM",
        "colab_type": "code",
        "outputId": "5fc922db-0696-4918-bb2e-a559a8553456",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "user_df = pd.read_csv('ml1m_users.csv',sep='\\t', encoding='latin-1', \n",
        "                      usecols=['user_emb_id', 'gender', 'age', 'occupation'])\n",
        "\n",
        "user_df.head(5)"
      ],
      "execution_count": 0,
      "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>user_emb_id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>occupation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>56</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>M</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>45</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>M</td>\n",
              "      <td>25</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_emb_id gender  age  occupation\n",
              "0            0      F    1          10\n",
              "1            1      M   56          16\n",
              "2            2      M   25          15\n",
              "3            3      M   45           7\n",
              "4            4      M   25          20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "t4DaP281kmPo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Transform side information to onehot encoding\n",
        "user_df['age'] = preprocessing.LabelEncoder().fit(user_df['age']).transform(user_df['age'])\n",
        "user_df['gender'] = preprocessing.LabelEncoder().fit(user_df['gender']).transform(user_df['gender'])\n",
        "onehot_df = preprocessing.OneHotEncoder(handle_unknown='ignore', sparse=False).fit(user_df[['gender', 'age', 'occupation']]).transform(user_df[['gender', 'age', 'occupation']])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "67Aya8pmkmNR",
        "colab_type": "code",
        "outputId": "e4302ab2-1b3e-4983-ae59-77e512d46e61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "onehot_df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6040, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "id": "2-7oFaVXBXY4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Concatenate content to input keep output the same"
      ]
    },
    {
      "metadata": {
        "id": "bZ1DPBFko_4_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Concatenate the one hot encoding side information to the rating matrix\n",
        "user_items_user_info = np.concatenate((users_items_matrix_train_zero, onehot_df), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7XRSIrkLyHUK",
        "colab_type": "code",
        "outputId": "c4b7747f-a80a-4787-8644-8e8e41ec9aee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "user_items_user_info.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6040, 3982)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "bOPPi_0ppAJP",
        "colab_type": "code",
        "outputId": "b99218b9-1268-4340-c2f0-25f30cc46c6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "cell_type": "code",
      "source": [
        "layers = [256, 512, 256]\n",
        "dropout = 0.8\n",
        "activation = 'selu'\n",
        "last_activation = 'selu'\n",
        "regularizer_encode = 0.001\n",
        "regularizer_decode = 0.001\n",
        "# Build model\n",
        "Deep_AE_concate = Deep_AE_model(user_items_user_info, layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode, 30)\n",
        "Deep_AE_concate.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip]) \n",
        "Deep_AE_concate.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "UserRating (InputLayer)      (None, 3982)              0         \n",
            "_________________________________________________________________\n",
            "EncLayer0 (Dense)            (None, 256)               1019648   \n",
            "_________________________________________________________________\n",
            "LatentSpace (Dense)          (None, 512)               131584    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "DecLayer0 (Dense)            (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "UserScorePred (Dense)        (None, 3952)              1015664   \n",
            "=================================================================\n",
            "Total params: 2,298,224\n",
            "Trainable params: 2,298,224\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K9-Lzc5bpAQL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "hist_Deep_AE_concate = Deep_AE_concate.fit(x=user_items_user_info, y=users_items_matrix_train_zero,\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  validation_data=[user_items_user_info, users_items_matrix_validate], verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dkVz73izpAUV",
        "colab_type": "code",
        "outputId": "558e37b8-5c77-47a5-930f-ce0d31eadcb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Deep_AE_concate, 20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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URyWEEMMz3KKQ0Fq/CXwUuFtr/TQg40MP4vzScwg4/ezt3kZmhsGrO6rl9lQh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\nipuCmZlV3BTMzKzipmBmZhU3BTMzq/wPZOFSkLbEVJ0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cQFeiVUWugqK",
        "colab_type": "code",
        "outputId": "5f6a60fe-7214-4bdb-ea81-26c68a6a380e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Deep_AE_concate, 20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZfqCRRpmZTQgxAYy2TWGX7wnkSSc5MonT0xfwfl0RlR3V5MUHbswik8loW3j4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\nTWl7BdsadvLo3n/Q0tvKfNs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5aenoZXdJEyYTZKfGkpsZR64tlsTYcOYVpMgAf+KkyG+P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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8TDeU1X2ugzf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plot_model(Deep_AE_concate, to_file='model.png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VszXLGXx7R87",
        "colab_type": "code",
        "outputId": "b5672aa2-c49b-4bbe-980d-600d1778cbe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_result_deep = Deep_AE_user.evaluate([users_items_matrix_train_zero, userIDs], users_items_matrix_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6040/6040 [==============================] - 1s 145us/sample - loss: 0.9974 - masked_rmse_clip: 0.8582\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "j0SduainBItn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Other Experiments"
      ]
    },
    {
      "metadata": {
        "id": "kyrjTWQ1fxan",
        "colab_type": "code",
        "outputId": "baa94ca3-af92-4fa9-9534-fe10aacd64ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "cell_type": "code",
      "source": [
        "user_df.hist(column=['age', 'gender'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fda67b21630>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fda67acdbe0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEHCAYAAABP3uaxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyd7MLAP/Hy3CiwjVE0UmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pK6Bs_oxjHsN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Concatenate the one hot encoding side information to the rating matrix\n",
        "user_items_user_validate_info = np.concatenate((users_items_matrix_validate, onehot_df), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pJXwHeo-iExD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_male_two = user_items_user_info[(user_items_user_info[:, 3953]==1) & (user_items_user_info[:, 3956]==1)]\n",
        "validate_male_two = user_items_user_validate_info[(user_items_user_validate_info[:, 3953]==1) & (user_items_user_validate_info[:, 3956]==1)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vU0ahKDXiyh_",
        "colab_type": "code",
        "outputId": "4cb5853c-636e-44b9-863a-a693b515368e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "validate_male_two.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1538, 3982)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "metadata": {
        "id": "L6c6zyRzj8Sf",
        "colab_type": "code",
        "outputId": "02aa0a42-e9e1-4623-f668-1c58f9f64b2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        " validate_male_two[:, :3952].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1538, 3952)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "metadata": {
        "id": "_7UtR_fZiz9A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layers = [256, 512, 256]\n",
        "dropout = 0.8\n",
        "activation = 'selu'\n",
        "last_activation = 'selu'\n",
        "regularizer_encode = 0.002\n",
        "regularizer_decode = 0.002\n",
        "# Build model\n",
        "Deep_AE_concate = Deep_AE_model(train_male_two[:, :3952], layers, activation, last_activation, dropout, regularizer_encode, regularizer_decode)\n",
        "Deep_AE_concate.compile(optimizer = Adam(lr=0.0001), loss=masked_mse, metrics=[masked_rmse_clip]) \n",
        "Deep_AE_concate.summary()\n",
        "hist_Deep_AE_concate = Deep_AE_concate.fit(x=train_male_two[:, :3952], y=train_male_two[:, :3952],\n",
        "                  epochs=500,\n",
        "                  batch_size=256,\n",
        "                  validation_data=[train_male_two[:, :3952], validate_male_two[:, :3952]], verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jzM2I-09jpSn",
        "colab_type": "code",
        "outputId": "b34f7898-01ed-445d-a914-74cccb61a5b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_error(hist_Deep_AE_concate, 20)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LaWioJzHR3W+4efMm7Hb7KfdJSZlAVlYmALt37wSgtbUFi8VCZGQUFRXlZGVlYrfbTznk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\ncYRuu2uzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "As5hD4ZSkiCU",
        "colab_type": "code",
        "outputId": "bf99754c-3d7d-48f4-d539-c18c60d89d0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "show_rmse(hist_Deep_AE_concate, 200)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YmAHArrJm9lS4KS5M7yMcYew2i7HKGWBSXhonzytg7tRsfJ1+Wtpig86/eWoL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\nmCijoFAoFAoTZRQUCoVCYfL/AdcryknCUEghAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hwmfsx6ymtGf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}