MxNet_Deep_Collaborative_Filtering_ml_20m_thesis_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "MxNet_Deep_Collaborative_Filtering.ml-20m.thesis.final.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anhdung28888/deep-cf/blob/master/MxNet_Deep_Collaborative_Filtering_ml_20m_thesis_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zPmmtCiOS67",
        "colab_type": "text"
      },
      "source": [
        "## Environment setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Unk_evBCVRr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "642f06b5-9093-4045-f302-f9bd97b4d6f1"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtZ0XpCpQ4F8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "655878a3-3086-47be-9c10-f311e71a48c4"
      },
      "source": [
        "# Confirm that you have CUDA 10\n",
        "!nvcc --version\n",
        "\n",
        "# Install CUDA10-compatible version of mxnet\n",
        "!cp /content/drive/My\\ Drive/Bigdata/ml-20m/mxnet_setup/mxnet_cu101-1.6.0-py2.py3-none-manylinux1_x86_64.whl mxnet_cu101-1.6.0-py2.py3-none-manylinux1_x86_64.whl # restore package for offline installation\n",
        "!pip install mxnet_cu101-1.6.0-py2.py3-none-manylinux1_x86_64.whl # !pip install mxnet-cu101 # online installation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2019 NVIDIA Corporation\n",
            "Built on Sun_Jul_28_19:07:16_PDT_2019\n",
            "Cuda compilation tools, release 10.1, V10.1.243\n",
            "Processing ./mxnet_cu101-1.6.0-py2.py3-none-manylinux1_x86_64.whl\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu101==1.6.0) (2.23.0)\n",
            "Collecting graphviz<0.9.0,>=0.8.1\n",
            "  Downloading https://files.pythonhosted.org/packages/53/39/4ab213673844e0c004bed8a0781a0721a3f6bb23eb8854ee75c236428892/graphviz-0.8.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu101==1.6.0) (1.18.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101==1.6.0) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101==1.6.0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101==1.6.0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101==1.6.0) (2020.6.20)\n",
            "Installing collected packages: graphviz, mxnet-cu101\n",
            "  Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "Successfully installed graphviz-0.8.4 mxnet-cu101-1.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsGQi4U4Q_Pa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "81211621-891e-4f2d-beb4-7d0c10b0ce43"
      },
      "source": [
        "%pylab inline\n",
        "import mxnet as mx\n",
        "import pandas\n",
        "import seaborn; seaborn.set_style('whitegrid')\n",
        "import logging\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.DEBUG)\n",
        "import os\n",
        "import urllib.request\n",
        "import zipfile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ocSs8MgOS68",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "a627d372-5b7c-40d2-a3f5-3020b6ad89ee"
      },
      "source": [
        "# Now load it up using a pandas dataframe\n",
        "# movielens-20m\n",
        "data = pandas.read_csv('/content/drive/My Drive/Bigdata/ml-20m/ratings.csv') #movielens-20m\n",
        "# data.head()\n",
        "print(\"user id min/max: \", data['userId'].min(), data['userId'].max())\n",
        "print(\"# unique users: \", numpy.unique(data['userId']).shape[0])\n",
        "print(\"\")\n",
        "print(\"movie id min/max: \", data['movieId'].min(), data['movieId'].max())\n",
        "print(\"# unique movies: \", numpy.unique(data['movieId']).shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "user id min/max:  1 138493\n",
            "# unique users:  138493\n",
            "\n",
            "movie id min/max:  1 131262\n",
            "# unique movies:  26744\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNoKkrq9DHDz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "9593380f-de50-4c96-c7bb-26dff1941098"
      },
      "source": [
        "plt.hist(data['rating'])\n",
        "plt.xlabel(\"Điểm xếp hạng\", fontsize=14)\n",
        "plt.ylabel(\"Số lượng\", fontsize=14)\n",
        "plt.title(\"Phân bố dữ liệu xếp hạng\", fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUyCvkFCOS7K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_users, n_movies = 138493, 131262 #movielen\n",
        "batch_size = 25000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4miVcBpaOS7O",
        "colab_type": "text"
      },
      "source": [
        "Shuffle and split data for training and testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydDvrar1OS7P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#movielens\n",
        "n = 19000000\n",
        "\n",
        "data = data.sample(frac=1).reset_index(drop=True) # Shuffle the data in place row-wise\n",
        "\n",
        "# Use the first 18M samples to train the model\n",
        "train_users = data['userId'].values[:n] - 1 # Offset by 1 so that the IDs start at 0\n",
        "train_movies = data['movieId'].values[:n] - 1 # Offset by 1 so that the IDs start at 0\n",
        "train_ratings = data['rating'].values[:n]\n",
        "\n",
        "# Use the remaining ~1M samples for validation of the model\n",
        "valid_users = data['userId'].values[n:] - 1 # Offset by 1 so that the IDs start at 0\n",
        "valid_movies = data['movieId'].values[n:] - 1 # Offset by 1 so that the IDs start at 0\n",
        "valid_ratings = data['rating'].values[n:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOdwGsTFOS72",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "genres = pandas.read_csv('/content/drive/My Drive/Bigdata/ml-20m/movies.csv')\n",
        "\n",
        "# Create id for each genre text\n",
        "labels_str = [label.split(\"|\")[0] for label in genres['genres']]\n",
        "label_set = numpy.unique(labels_str) # query unique labels\n",
        "label_idxs = {l: i for i, l in enumerate(label_set)} # assign id for each label\n",
        "\n",
        "# Map first genre to each movie\n",
        "labels = numpy.empty(n_movies)\n",
        "for movieId, label in zip(genres['movieId'], labels_str):\n",
        "    labels[movieId-1] = label_idxs[label]\n",
        "\n",
        "# Split movie-genre to train and test set\n",
        "train_genres = numpy.array([labels[int(j)] for j in train_movies])\n",
        "valid_genres = numpy.array([labels[int(j)] for j in valid_movies])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvUFJq9b45bI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Load tag relevance scores\n",
        "# genome_scores = pandas.read_csv('/content/drive/My Drive/Bigdata/ml-20m/genome-scores.csv')\n",
        "# # Find the highest relevant tag for each movie\n",
        "# idx = genome_scores.groupby(['movieId'])['relevance'].transform(max) == genome_scores['relevance'] \n",
        "# tags = genome_scores[idx]\n",
        "# # Save to later use\n",
        "# tags.to_csv('/content/drive/My Drive/Bigdata/ml-20m/df_tag.csv',index=False)\n",
        "\n",
        "# Reload most relevant tags\n",
        "tags = pandas.read_csv('/content/drive/My Drive/Bigdata/ml-20m/df_tag.csv')\n",
        "\n",
        "# Map tag to each movie\n",
        "label_tag = numpy.empty(n_movies)\n",
        "for movieId, tagid in zip(tags['movieId'], tags['tagId']):\n",
        "    label_tag[movieId-1] = tagid"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-sHZh9eOS7T",
        "colab_type": "text"
      },
      "source": [
        "## Funk MF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRBQdg4kOS7T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "90d4e215-a6f9-4a9b-e4b7-a5ebaec45911"
      },
      "source": [
        "#movielens with userid, itemid as integer\n",
        "X_train = mx.io.NDArrayIter({'user': train_users, 'movie': train_movies}, \n",
        "                           label=train_ratings, batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, 'movie': valid_movies}, \n",
        "                          label=valid_ratings, batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25)\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "y_pred = mx.symbol.sum_axis(data=(user * movie), axis=1)\n",
        "y_pred = mx.symbol.flatten(y_pred)\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=y_pred, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie'), symbol=y_pred)\n",
        "model.fit(X_train, \n",
        "          num_epoch=50, \n",
        "          optimizer='adam', \n",
        "          optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', \n",
        "          eval_data=X_eval, \n",
        "          batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 14263336.10 samples/sec\trmse=1.825848\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 14165321.16 samples/sec\trmse=1.181800\n",
            "INFO:root:Epoch[0] Train-rmse=2.119840\n",
            "INFO:root:Epoch[0] Time cost=1.456\n",
            "INFO:root:Epoch[0] Validation-rmse=1.060756\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 14632621.53 samples/sec\trmse=1.003288\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 14212506.64 samples/sec\trmse=0.937666\n",
            "INFO:root:Epoch[1] Batch [500-750]\tSpeed: 14247428.85 samples/sec\trmse=0.905599\n",
            "INFO:root:Epoch[1] Train-rmse=0.948315\n",
            "INFO:root:Epoch[1] Time cost=1.334\n",
            "INFO:root:Epoch[1] Validation-rmse=0.897679\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 14423154.16 samples/sec\trmse=0.888487\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 14368117.23 samples/sec\trmse=0.880021\n",
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            "INFO:root:Epoch[2] Train-rmse=0.880578\n",
            "INFO:root:Epoch[2] Time cost=1.329\n",
            "INFO:root:Epoch[2] Validation-rmse=0.873712\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 14570395.55 samples/sec\trmse=0.868393\n",
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            "INFO:root:Epoch[3] Train-rmse=0.864599\n",
            "INFO:root:Epoch[3] Time cost=1.331\n",
            "INFO:root:Epoch[3] Validation-rmse=0.862143\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 14355740.26 samples/sec\trmse=0.856359\n",
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            "INFO:root:Epoch[4] Train-rmse=0.852722\n",
            "INFO:root:Epoch[4] Time cost=1.333\n",
            "INFO:root:Epoch[4] Validation-rmse=0.851592\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 14223279.40 samples/sec\trmse=0.844960\n",
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            "INFO:root:Epoch[5] Train-rmse=0.841994\n",
            "INFO:root:Epoch[5] Time cost=1.345\n",
            "INFO:root:Epoch[5] Validation-rmse=0.843303\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 14145569.69 samples/sec\trmse=0.835792\n",
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            "INFO:root:Epoch[6] Train-rmse=0.833553\n",
            "INFO:root:Epoch[6] Time cost=1.343\n",
            "INFO:root:Epoch[6] Validation-rmse=0.837158\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 14506906.09 samples/sec\trmse=0.828484\n",
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            "INFO:root:Epoch[7] Train-rmse=0.826533\n",
            "INFO:root:Epoch[7] Time cost=1.316\n",
            "INFO:root:Epoch[7] Validation-rmse=0.831916\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 14451516.16 samples/sec\trmse=0.821828\n",
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            "INFO:root:Epoch[8] Train-rmse=0.819926\n",
            "INFO:root:Epoch[8] Time cost=1.341\n",
            "INFO:root:Epoch[8] Validation-rmse=0.826990\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 14416562.68 samples/sec\trmse=0.815265\n",
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            "INFO:root:Epoch[9] Train-rmse=0.813355\n",
            "INFO:root:Epoch[9] Time cost=1.331\n",
            "INFO:root:Epoch[9] Validation-rmse=0.822261\n",
            "INFO:root:Epoch[10] Batch [0-250]\tSpeed: 14357760.98 samples/sec\trmse=0.808656\n",
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            "INFO:root:Epoch[10] Train-rmse=0.806678\n",
            "INFO:root:Epoch[10] Time cost=1.328\n",
            "INFO:root:Epoch[10] Validation-rmse=0.817569\n",
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            "INFO:root:Epoch[11] Time cost=1.332\n",
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            "INFO:root:Epoch[12] Train-rmse=0.791910\n",
            "INFO:root:Epoch[12] Time cost=1.333\n",
            "INFO:root:Epoch[12] Validation-rmse=0.807593\n",
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            "INFO:root:Epoch[13] Time cost=1.336\n",
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            "INFO:root:Epoch[14] Batch [0-250]\tSpeed: 14389617.06 samples/sec\trmse=0.777977\n",
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            "INFO:root:Epoch[14] Train-rmse=0.775509\n",
            "INFO:root:Epoch[14] Time cost=1.328\n",
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            "INFO:root:Epoch[18] Time cost=1.334\n",
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            "INFO:root:Epoch[28] Time cost=1.336\n",
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            "INFO:root:Epoch[31] Time cost=1.333\n",
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            "INFO:root:Epoch[32] Time cost=1.332\n",
            "INFO:root:Epoch[32] Validation-rmse=0.798217\n",
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            "INFO:root:Epoch[33] Train-rmse=0.679654\n",
            "INFO:root:Epoch[33] Time cost=1.347\n",
            "INFO:root:Epoch[33] Validation-rmse=0.799396\n",
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            "INFO:root:Epoch[34] Time cost=1.346\n",
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            "INFO:root:Epoch[36] Time cost=1.343\n",
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            "INFO:root:Epoch[49] Validation-rmse=0.814494\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJnFfa_ebvC4",
        "colab_type": "text"
      },
      "source": [
        "#DCF (4 layers)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPCChhG0rwVN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "218708a4-86b0-47a2-d8d4-ec224a98e67a"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies}, \n",
        "                            label=train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies}, \n",
        "                           label=valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "\n",
        "# Since we are using a two layer neural network here, we will create two FullyConnected layers\n",
        "# with activation functions before the output layer\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 7690824.99 samples/sec\trmse=0.867094\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 7684557.45 samples/sec\trmse=0.860139\n",
            "INFO:root:Epoch[0] Train-rmse=1.074485\n",
            "INFO:root:Epoch[0] Time cost=2.559\n",
            "INFO:root:Epoch[0] Validation-rmse=0.853476\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 7670688.05 samples/sec\trmse=0.847852\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 7674458.50 samples/sec\trmse=0.839065\n",
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            "INFO:root:Epoch[1] Train-rmse=0.840045\n",
            "INFO:root:Epoch[1] Time cost=2.483\n",
            "INFO:root:Epoch[1] Validation-rmse=0.834759\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 7653667.35 samples/sec\trmse=0.827756\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 7684458.34 samples/sec\trmse=0.822500\n",
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            "INFO:root:Epoch[2] Train-rmse=0.823099\n",
            "INFO:root:Epoch[2] Time cost=2.481\n",
            "INFO:root:Epoch[2] Validation-rmse=0.824554\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 7684721.90 samples/sec\trmse=0.814005\n",
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            "INFO:root:Epoch[3] Train-rmse=0.810923\n",
            "INFO:root:Epoch[3] Time cost=2.483\n",
            "INFO:root:Epoch[3] Validation-rmse=0.824536\n",
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            "INFO:root:Epoch[4] Train-rmse=0.798590\n",
            "INFO:root:Epoch[4] Time cost=2.475\n",
            "INFO:root:Epoch[4] Validation-rmse=0.832707\n",
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            "INFO:root:Epoch[5] Time cost=2.483\n",
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            "INFO:root:Epoch[6] Train-rmse=0.780280\n",
            "INFO:root:Epoch[6] Time cost=2.486\n",
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            "INFO:root:Epoch[7] Time cost=2.477\n",
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            "INFO:root:Epoch[8] Time cost=2.474\n",
            "INFO:root:Epoch[8] Validation-rmse=0.827302\n",
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            "INFO:root:Epoch[9] Time cost=2.478\n",
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            "INFO:root:Epoch[10] Time cost=2.475\n",
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            "INFO:root:Epoch[12] Time cost=2.486\n",
            "INFO:root:Epoch[12] Validation-rmse=0.833030\n",
            "INFO:root:Epoch[13] Batch [0-250]\tSpeed: 7681420.76 samples/sec\trmse=0.754243\n",
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            "INFO:root:Epoch[20] Train-rmse=0.730379\n",
            "INFO:root:Epoch[20] Time cost=2.479\n",
            "INFO:root:Epoch[20] Validation-rmse=0.821027\n",
            "INFO:root:Epoch[21] Batch [0-250]\tSpeed: 7738433.49 samples/sec\trmse=0.728892\n",
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            "INFO:root:Epoch[21] Train-rmse=0.727419\n",
            "INFO:root:Epoch[21] Time cost=2.468\n",
            "INFO:root:Epoch[21] Validation-rmse=0.823595\n",
            "INFO:root:Epoch[22] Batch [0-250]\tSpeed: 7757495.41 samples/sec\trmse=0.725768\n",
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            "INFO:root:Epoch[22] Train-rmse=0.724565\n",
            "INFO:root:Epoch[22] Time cost=2.458\n",
            "INFO:root:Epoch[22] Validation-rmse=0.830384\n",
            "INFO:root:Epoch[23] Batch [0-250]\tSpeed: 7693375.50 samples/sec\trmse=0.722969\n",
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            "INFO:root:Epoch[23] Train-rmse=0.722002\n",
            "INFO:root:Epoch[23] Time cost=2.470\n",
            "INFO:root:Epoch[23] Validation-rmse=0.831408\n",
            "INFO:root:Epoch[24] Batch [0-250]\tSpeed: 7689667.65 samples/sec\trmse=0.721411\n",
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            "INFO:root:Epoch[24] Train-rmse=0.720000\n",
            "INFO:root:Epoch[24] Time cost=2.474\n",
            "INFO:root:Epoch[24] Validation-rmse=0.830818\n",
            "INFO:root:Epoch[25] Batch [0-250]\tSpeed: 7702833.94 samples/sec\trmse=0.719400\n",
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            "INFO:root:Epoch[25] Train-rmse=0.717772\n",
            "INFO:root:Epoch[25] Time cost=2.478\n",
            "INFO:root:Epoch[25] Validation-rmse=0.830365\n",
            "INFO:root:Epoch[26] Batch [0-250]\tSpeed: 7675262.92 samples/sec\trmse=0.717554\n",
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            "INFO:root:Epoch[26] Train-rmse=0.716170\n",
            "INFO:root:Epoch[26] Time cost=2.482\n",
            "INFO:root:Epoch[26] Validation-rmse=0.831961\n",
            "INFO:root:Epoch[27] Batch [0-250]\tSpeed: 7681258.71 samples/sec\trmse=0.716035\n",
            "INFO:root:Epoch[27] Batch [250-500]\tSpeed: 7668978.09 samples/sec\trmse=0.713692\n",
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            "INFO:root:Epoch[27] Train-rmse=0.714479\n",
            "INFO:root:Epoch[27] Time cost=2.483\n",
            "INFO:root:Epoch[27] Validation-rmse=0.834123\n",
            "INFO:root:Epoch[28] Batch [0-250]\tSpeed: 7682692.69 samples/sec\trmse=0.713945\n",
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            "INFO:root:Epoch[28] Train-rmse=0.712303\n",
            "INFO:root:Epoch[28] Time cost=2.484\n",
            "INFO:root:Epoch[28] Validation-rmse=0.835033\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 7542537.98 samples/sec\trmse=0.711261\n",
            "INFO:root:Epoch[29] Batch [250-500]\tSpeed: 7567490.48 samples/sec\trmse=0.708588\n",
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            "INFO:root:Epoch[29] Train-rmse=0.709564\n",
            "INFO:root:Epoch[29] Time cost=2.520\n",
            "INFO:root:Epoch[29] Validation-rmse=0.835954\n",
            "INFO:root:Epoch[30] Batch [0-250]\tSpeed: 7587936.25 samples/sec\trmse=0.709325\n",
            "INFO:root:Epoch[30] Batch [250-500]\tSpeed: 7577787.20 samples/sec\trmse=0.707044\n",
            "INFO:root:Epoch[30] Batch [500-750]\tSpeed: 7573301.50 samples/sec\trmse=0.706394\n",
            "INFO:root:Epoch[30] Train-rmse=0.707601\n",
            "INFO:root:Epoch[30] Time cost=2.515\n",
            "INFO:root:Epoch[30] Validation-rmse=0.836805\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 7694398.44 samples/sec\trmse=0.707361\n",
            "INFO:root:Epoch[31] Batch [250-500]\tSpeed: 7700118.82 samples/sec\trmse=0.704749\n",
            "INFO:root:Epoch[31] Batch [500-750]\tSpeed: 7630673.36 samples/sec\trmse=0.704941\n",
            "INFO:root:Epoch[31] Train-rmse=0.705704\n",
            "INFO:root:Epoch[31] Time cost=2.480\n",
            "INFO:root:Epoch[31] Validation-rmse=0.836112\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 7663872.94 samples/sec\trmse=0.705909\n",
            "INFO:root:Epoch[32] Batch [250-500]\tSpeed: 7612047.61 samples/sec\trmse=0.703580\n",
            "INFO:root:Epoch[32] Batch [500-750]\tSpeed: 7675615.75 samples/sec\trmse=0.703044\n",
            "INFO:root:Epoch[32] Train-rmse=0.704202\n",
            "INFO:root:Epoch[32] Time cost=2.491\n",
            "INFO:root:Epoch[32] Validation-rmse=0.835421\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 7678054.99 samples/sec\trmse=0.704905\n",
            "INFO:root:Epoch[33] Batch [250-500]\tSpeed: 7666865.25 samples/sec\trmse=0.702927\n",
            "INFO:root:Epoch[33] Batch [500-750]\tSpeed: 7693700.65 samples/sec\trmse=0.703118\n",
            "INFO:root:Epoch[33] Train-rmse=0.703688\n",
            "INFO:root:Epoch[33] Time cost=2.479\n",
            "INFO:root:Epoch[33] Validation-rmse=0.830018\n",
            "INFO:root:Epoch[34] Batch [0-250]\tSpeed: 7707336.21 samples/sec\trmse=0.705187\n",
            "INFO:root:Epoch[34] Batch [250-500]\tSpeed: 7651520.50 samples/sec\trmse=0.702973\n",
            "INFO:root:Epoch[34] Batch [500-750]\tSpeed: 7655998.74 samples/sec\trmse=0.703255\n",
            "INFO:root:Epoch[34] Train-rmse=0.703838\n",
            "INFO:root:Epoch[34] Time cost=2.482\n",
            "INFO:root:Epoch[34] Validation-rmse=0.833564\n",
            "INFO:root:Epoch[35] Batch [0-250]\tSpeed: 7677785.14 samples/sec\trmse=0.704355\n",
            "INFO:root:Epoch[35] Batch [250-500]\tSpeed: 7672895.08 samples/sec\trmse=0.700881\n",
            "INFO:root:Epoch[35] Batch [500-750]\tSpeed: 7693427.44 samples/sec\trmse=0.701044\n",
            "INFO:root:Epoch[35] Train-rmse=0.702096\n",
            "INFO:root:Epoch[35] Time cost=2.479\n",
            "INFO:root:Epoch[35] Validation-rmse=0.840976\n",
            "INFO:root:Epoch[36] Batch [0-250]\tSpeed: 7695828.30 samples/sec\trmse=0.702012\n",
            "INFO:root:Epoch[36] Batch [250-500]\tSpeed: 7665594.07 samples/sec\trmse=0.699403\n",
            "INFO:root:Epoch[36] Batch [500-750]\tSpeed: 7671507.40 samples/sec\trmse=0.698959\n",
            "INFO:root:Epoch[36] Train-rmse=0.700124\n",
            "INFO:root:Epoch[36] Time cost=2.481\n",
            "INFO:root:Epoch[36] Validation-rmse=0.843032\n",
            "INFO:root:Epoch[37] Batch [0-250]\tSpeed: 7676926.23 samples/sec\trmse=0.700234\n",
            "INFO:root:Epoch[37] Batch [250-500]\tSpeed: 7682024.03 samples/sec\trmse=0.697449\n",
            "INFO:root:Epoch[37] Batch [500-750]\tSpeed: 7666158.99 samples/sec\trmse=0.696854\n",
            "INFO:root:Epoch[37] Train-rmse=0.698178\n",
            "INFO:root:Epoch[37] Time cost=2.480\n",
            "INFO:root:Epoch[37] Validation-rmse=0.844229\n",
            "INFO:root:Epoch[38] Batch [0-250]\tSpeed: 7731189.69 samples/sec\trmse=0.698543\n",
            "INFO:root:Epoch[38] Batch [250-500]\tSpeed: 7669727.51 samples/sec\trmse=0.695857\n",
            "INFO:root:Epoch[38] Batch [500-750]\tSpeed: 7693420.66 samples/sec\trmse=0.695515\n",
            "INFO:root:Epoch[38] Train-rmse=0.696639\n",
            "INFO:root:Epoch[38] Time cost=2.473\n",
            "INFO:root:Epoch[38] Validation-rmse=0.845318\n",
            "INFO:root:Epoch[39] Batch [0-250]\tSpeed: 7676233.85 samples/sec\trmse=0.697217\n",
            "INFO:root:Epoch[39] Batch [250-500]\tSpeed: 7657049.79 samples/sec\trmse=0.694323\n",
            "INFO:root:Epoch[39] Batch [500-750]\tSpeed: 7650051.24 samples/sec\trmse=0.694220\n",
            "INFO:root:Epoch[39] Train-rmse=0.695255\n",
            "INFO:root:Epoch[39] Time cost=2.487\n",
            "INFO:root:Epoch[39] Validation-rmse=0.846441\n",
            "INFO:root:Epoch[40] Batch [0-250]\tSpeed: 7708097.67 samples/sec\trmse=0.695872\n",
            "INFO:root:Epoch[40] Batch [250-500]\tSpeed: 7669945.18 samples/sec\trmse=0.692908\n",
            "INFO:root:Epoch[40] Batch [500-750]\tSpeed: 7654366.84 samples/sec\trmse=0.692721\n",
            "INFO:root:Epoch[40] Train-rmse=0.693834\n",
            "INFO:root:Epoch[40] Time cost=2.481\n",
            "INFO:root:Epoch[40] Validation-rmse=0.845734\n",
            "INFO:root:Epoch[41] Batch [0-250]\tSpeed: 7652641.81 samples/sec\trmse=0.694583\n",
            "INFO:root:Epoch[41] Batch [250-500]\tSpeed: 7671067.40 samples/sec\trmse=0.691851\n",
            "INFO:root:Epoch[41] Batch [500-750]\tSpeed: 7671132.50 samples/sec\trmse=0.691644\n",
            "INFO:root:Epoch[41] Train-rmse=0.692694\n",
            "INFO:root:Epoch[41] Time cost=2.483\n",
            "INFO:root:Epoch[41] Validation-rmse=0.845214\n",
            "INFO:root:Epoch[42] Batch [0-250]\tSpeed: 7671884.58 samples/sec\trmse=0.693687\n",
            "INFO:root:Epoch[42] Batch [250-500]\tSpeed: 7697405.61 samples/sec\trmse=0.691283\n",
            "INFO:root:Epoch[42] Batch [500-750]\tSpeed: 7690380.51 samples/sec\trmse=0.691167\n",
            "INFO:root:Epoch[42] Train-rmse=0.692048\n",
            "INFO:root:Epoch[42] Time cost=2.477\n",
            "INFO:root:Epoch[42] Validation-rmse=0.843607\n",
            "INFO:root:Epoch[43] Batch [0-250]\tSpeed: 7662235.44 samples/sec\trmse=0.693203\n",
            "INFO:root:Epoch[43] Batch [250-500]\tSpeed: 7633148.57 samples/sec\trmse=0.690704\n",
            "INFO:root:Epoch[43] Batch [500-750]\tSpeed: 7650609.41 samples/sec\trmse=0.690973\n",
            "INFO:root:Epoch[43] Train-rmse=0.691626\n",
            "INFO:root:Epoch[43] Time cost=2.490\n",
            "INFO:root:Epoch[43] Validation-rmse=0.845972\n",
            "INFO:root:Epoch[44] Batch [0-250]\tSpeed: 7659931.58 samples/sec\trmse=0.693100\n",
            "INFO:root:Epoch[44] Batch [250-500]\tSpeed: 7647457.98 samples/sec\trmse=0.690799\n",
            "INFO:root:Epoch[44] Batch [500-750]\tSpeed: 7675204.50 samples/sec\trmse=0.691682\n",
            "INFO:root:Epoch[44] Train-rmse=0.691849\n",
            "INFO:root:Epoch[44] Time cost=2.484\n",
            "INFO:root:Epoch[44] Validation-rmse=0.845690\n",
            "INFO:root:Epoch[45] Batch [0-250]\tSpeed: 7685113.90 samples/sec\trmse=0.693484\n",
            "INFO:root:Epoch[45] Batch [250-500]\tSpeed: 7622390.66 samples/sec\trmse=0.691587\n",
            "INFO:root:Epoch[45] Batch [500-750]\tSpeed: 7685744.79 samples/sec\trmse=0.692116\n",
            "INFO:root:Epoch[45] Train-rmse=0.692382\n",
            "INFO:root:Epoch[45] Time cost=2.484\n",
            "INFO:root:Epoch[45] Validation-rmse=0.845109\n",
            "INFO:root:Epoch[46] Batch [0-250]\tSpeed: 7645341.37 samples/sec\trmse=0.693688\n",
            "INFO:root:Epoch[46] Batch [250-500]\tSpeed: 7656338.62 samples/sec\trmse=0.691565\n",
            "INFO:root:Epoch[46] Batch [500-750]\tSpeed: 7679715.01 samples/sec\trmse=0.690857\n",
            "INFO:root:Epoch[46] Train-rmse=0.692009\n",
            "INFO:root:Epoch[46] Time cost=2.485\n",
            "INFO:root:Epoch[46] Validation-rmse=0.850846\n",
            "INFO:root:Epoch[47] Batch [0-250]\tSpeed: 7669756.68 samples/sec\trmse=0.692080\n",
            "INFO:root:Epoch[47] Batch [250-500]\tSpeed: 7663742.99 samples/sec\trmse=0.690336\n",
            "INFO:root:Epoch[47] Batch [500-750]\tSpeed: 7689333.83 samples/sec\trmse=0.688964\n",
            "INFO:root:Epoch[47] Train-rmse=0.690434\n",
            "INFO:root:Epoch[47] Time cost=2.483\n",
            "INFO:root:Epoch[47] Validation-rmse=0.848930\n",
            "INFO:root:Epoch[48] Batch [0-250]\tSpeed: 7680851.34 samples/sec\trmse=0.690144\n",
            "INFO:root:Epoch[48] Batch [250-500]\tSpeed: 7673088.23 samples/sec\trmse=0.688877\n",
            "INFO:root:Epoch[48] Batch [500-750]\tSpeed: 7625914.10 samples/sec\trmse=0.687349\n",
            "INFO:root:Epoch[48] Train-rmse=0.688775\n",
            "INFO:root:Epoch[48] Time cost=2.486\n",
            "INFO:root:Epoch[48] Validation-rmse=0.846817\n",
            "INFO:root:Epoch[49] Batch [0-250]\tSpeed: 7674276.52 samples/sec\trmse=0.688762\n",
            "INFO:root:Epoch[49] Batch [250-500]\tSpeed: 7661751.71 samples/sec\trmse=0.687204\n",
            "INFO:root:Epoch[49] Batch [500-750]\tSpeed: 7639270.16 samples/sec\trmse=0.686140\n",
            "INFO:root:Epoch[49] Train-rmse=0.687359\n",
            "INFO:root:Epoch[49] Time cost=2.486\n",
            "INFO:root:Epoch[49] Validation-rmse=0.847022\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gIph93audQr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6aa0d574-0195-4b26-c5f1-3e788d91e43d"
      },
      "source": [
        "# Visualize network\n",
        "mx.viz.plot_network(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3dBRSy2Hx4Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "3027973f-537f-481c-f1fb-51c61a4836f3"
      },
      "source": [
        "# Print network summary\n",
        "mx.viz.print_summary(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "________________________________________________________________________________________________________________________\n",
            "Layer (type)                                        Output Shape            Param #     Previous Layer                  \n",
            "========================================================================================================================\n",
            "user(null)                                                                  0                                           \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding12(Embedding)                                                      3462325     user                            \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding13(Embedding)                                                      3281550                                     \n",
            "________________________________________________________________________________________________________________________\n",
            "concat2(Concat)                                                             0           embedding12                     \n",
            "                                                                                        embedding13                     \n",
            "________________________________________________________________________________________________________________________\n",
            "flatten6(Flatten)                                                           0           concat2                         \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected6(FullyConnected)                                             128         flatten6                        \n",
            "________________________________________________________________________________________________________________________\n",
            "activation4(Activation)                                                     0           fullyconnected6                 \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected7(FullyConnected)                                             64          activation4                     \n",
            "________________________________________________________________________________________________________________________\n",
            "activation5(Activation)                                                     0           fullyconnected7                 \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected8(FullyConnected)                                             32          activation5                     \n",
            "________________________________________________________________________________________________________________________\n",
            "activation6(Activation)                                                     0           fullyconnected8                 \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected9(FullyConnected)                                             1           activation6                     \n",
            "________________________________________________________________________________________________________________________\n",
            "linearregressionoutput6(LinearRegressionOutput)                             0           fullyconnected9                 \n",
            "========================================================================================================================\n",
            "Total params: 6744100\n",
            "________________________________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muWAwKcOUWGu",
        "colab_type": "text"
      },
      "source": [
        "## 5-layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vcp8SjBEUN_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "040c8f1a-ebd5-4db6-9d42-bbaf709a243e"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies}, \n",
        "                            label=train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies}, \n",
        "                           label=valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "\n",
        "# Since we are using a two layer neural network here, we will create two FullyConnected layers\n",
        "# with activation functions before the output layer\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=16)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Epoch[0] Batch [0-250]\tSpeed: 6970389.19 samples/sec\trmse=1.562601\n",
            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 7081610.17 samples/sec\trmse=0.866865\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 7065639.85 samples/sec\trmse=0.859361\n",
            "INFO:root:Epoch[0] Train-rmse=1.094008\n",
            "INFO:root:Epoch[0] Time cost=2.772\n",
            "INFO:root:Epoch[0] Validation-rmse=0.852288\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 7085401.93 samples/sec\trmse=0.845418\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 7089863.06 samples/sec\trmse=0.836562\n",
            "INFO:root:Epoch[1] Batch [500-750]\tSpeed: 7081281.14 samples/sec\trmse=0.831500\n",
            "INFO:root:Epoch[1] Train-rmse=0.837728\n",
            "INFO:root:Epoch[1] Time cost=2.689\n",
            "INFO:root:Epoch[1] Validation-rmse=0.832639\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 7067489.53 samples/sec\trmse=0.824740\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 7088478.90 samples/sec\trmse=0.820413\n",
            "INFO:root:Epoch[2] Batch [500-750]\tSpeed: 7075079.34 samples/sec\trmse=0.816967\n",
            "INFO:root:Epoch[2] Train-rmse=0.820625\n",
            "INFO:root:Epoch[2] Time cost=2.691\n",
            "INFO:root:Epoch[2] Validation-rmse=0.825085\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 7082677.80 samples/sec\trmse=0.811281\n",
            "INFO:root:Epoch[3] Batch [250-500]\tSpeed: 7087838.76 samples/sec\trmse=0.809078\n",
            "INFO:root:Epoch[3] Batch [500-750]\tSpeed: 7055524.74 samples/sec\trmse=0.806827\n",
            "INFO:root:Epoch[3] Train-rmse=0.809000\n",
            "INFO:root:Epoch[3] Time cost=2.690\n",
            "INFO:root:Epoch[3] Validation-rmse=0.826154\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 7103110.86 samples/sec\trmse=0.799962\n",
            "INFO:root:Epoch[4] Batch [250-500]\tSpeed: 7053022.78 samples/sec\trmse=0.798322\n",
            "INFO:root:Epoch[4] Batch [500-750]\tSpeed: 7073498.61 samples/sec\trmse=0.796401\n",
            "INFO:root:Epoch[4] Train-rmse=0.798206\n",
            "INFO:root:Epoch[4] Time cost=2.690\n",
            "INFO:root:Epoch[4] Validation-rmse=0.828752\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 7053307.44 samples/sec\trmse=0.790797\n",
            "INFO:root:Epoch[5] Batch [250-500]\tSpeed: 7042234.22 samples/sec\trmse=0.789787\n",
            "INFO:root:Epoch[5] Batch [500-750]\tSpeed: 7062752.03 samples/sec\trmse=0.788321\n",
            "INFO:root:Epoch[5] Train-rmse=0.789641\n",
            "INFO:root:Epoch[5] Time cost=2.700\n",
            "INFO:root:Epoch[5] Validation-rmse=0.831456\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 7072809.65 samples/sec\trmse=0.785054\n",
            "INFO:root:Epoch[6] Batch [250-500]\tSpeed: 7054499.44 samples/sec\trmse=0.783558\n",
            "INFO:root:Epoch[6] Batch [500-750]\tSpeed: 7069832.07 samples/sec\trmse=0.782547\n",
            "INFO:root:Epoch[6] Train-rmse=0.783700\n",
            "INFO:root:Epoch[6] Time cost=2.694\n",
            "INFO:root:Epoch[6] Validation-rmse=0.828832\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 7037184.78 samples/sec\trmse=0.779389\n",
            "INFO:root:Epoch[7] Batch [250-500]\tSpeed: 7054144.46 samples/sec\trmse=0.777274\n",
            "INFO:root:Epoch[7] Batch [500-750]\tSpeed: 7066761.74 samples/sec\trmse=0.777023\n",
            "INFO:root:Epoch[7] Train-rmse=0.777901\n",
            "INFO:root:Epoch[7] Time cost=2.698\n",
            "INFO:root:Epoch[7] Validation-rmse=0.828257\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 7032487.81 samples/sec\trmse=0.774451\n",
            "INFO:root:Epoch[8] Batch [250-500]\tSpeed: 7053537.07 samples/sec\trmse=0.773983\n",
            "INFO:root:Epoch[8] Batch [500-750]\tSpeed: 7060205.01 samples/sec\trmse=0.772594\n",
            "INFO:root:Epoch[8] Train-rmse=0.773683\n",
            "INFO:root:Epoch[8] Time cost=2.700\n",
            "INFO:root:Epoch[8] Validation-rmse=0.828636\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 7065588.44 samples/sec\trmse=0.771400\n",
            "INFO:root:Epoch[9] Batch [250-500]\tSpeed: 7086214.02 samples/sec\trmse=0.771284\n",
            "INFO:root:Epoch[9] Batch [500-750]\tSpeed: 7074286.98 samples/sec\trmse=0.770288\n",
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            "INFO:root:Epoch[48] Validation-rmse=0.839631\n",
            "INFO:root:Epoch[49] Batch [0-250]\tSpeed: 7061005.63 samples/sec\trmse=0.691873\n",
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            "INFO:root:Epoch[49] Train-rmse=0.690618\n",
            "INFO:root:Epoch[49] Time cost=2.697\n",
            "INFO:root:Epoch[49] Validation-rmse=0.840093\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loVWaAB5UsAE",
        "colab_type": "text"
      },
      "source": [
        "## 3-layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ej5lrJz8UqXQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "91d2ae5b-9267-43e8-fc09-fed86db77a9b"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies}, \n",
        "                            label=train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies}, \n",
        "                           label=valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "\n",
        "# Since we are using a two layer neural network here, we will create two FullyConnected layers\n",
        "# with activation functions before the output layer\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=16)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 9820352.55 samples/sec\trmse=0.876661\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 10960269.52 samples/sec\trmse=0.866327\n",
            "INFO:root:Epoch[0] Train-rmse=1.167111\n",
            "INFO:root:Epoch[0] Time cost=1.878\n",
            "INFO:root:Epoch[0] Validation-rmse=0.864061\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 10958853.71 samples/sec\trmse=0.861641\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 10920123.19 samples/sec\trmse=0.859319\n",
            "INFO:root:Epoch[1] Batch [500-750]\tSpeed: 11121099.52 samples/sec\trmse=0.858477\n",
            "INFO:root:Epoch[1] Train-rmse=0.859798\n",
            "INFO:root:Epoch[1] Time cost=1.734\n",
            "INFO:root:Epoch[1] Validation-rmse=0.859562\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 11071588.76 samples/sec\trmse=0.856035\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 11031900.87 samples/sec\trmse=0.850538\n",
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            "INFO:root:Epoch[2] Train-rmse=0.850409\n",
            "INFO:root:Epoch[2] Time cost=1.728\n",
            "INFO:root:Epoch[2] Validation-rmse=0.844955\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 10955991.14 samples/sec\trmse=0.838870\n",
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            "INFO:root:Epoch[3] Train-rmse=0.834871\n",
            "INFO:root:Epoch[3] Time cost=1.735\n",
            "INFO:root:Epoch[3] Validation-rmse=0.835559\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 10939572.86 samples/sec\trmse=0.828810\n",
            "INFO:root:Epoch[4] Batch [250-500]\tSpeed: 11000578.26 samples/sec\trmse=0.827004\n",
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            "INFO:root:Epoch[4] Train-rmse=0.827167\n",
            "INFO:root:Epoch[4] Time cost=1.738\n",
            "INFO:root:Epoch[4] Validation-rmse=0.832533\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 11030127.69 samples/sec\trmse=0.824035\n",
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            "INFO:root:Epoch[5] Train-rmse=0.822812\n",
            "INFO:root:Epoch[5] Time cost=1.725\n",
            "INFO:root:Epoch[5] Validation-rmse=0.831117\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 11071093.12 samples/sec\trmse=0.819167\n",
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            "INFO:root:Epoch[6] Train-rmse=0.816077\n",
            "INFO:root:Epoch[6] Time cost=1.722\n",
            "INFO:root:Epoch[6] Validation-rmse=0.827997\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 10991339.64 samples/sec\trmse=0.810046\n",
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            "INFO:root:Epoch[7] Train-rmse=0.807203\n",
            "INFO:root:Epoch[7] Time cost=1.729\n",
            "INFO:root:Epoch[7] Validation-rmse=0.827865\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 11015740.49 samples/sec\trmse=0.803019\n",
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            "INFO:root:Epoch[8] Train-rmse=0.801212\n",
            "INFO:root:Epoch[8] Time cost=1.721\n",
            "INFO:root:Epoch[8] Validation-rmse=0.828071\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 10944971.61 samples/sec\trmse=0.798562\n",
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            "INFO:root:Epoch[9] Train-rmse=0.797448\n",
            "INFO:root:Epoch[9] Time cost=1.744\n",
            "INFO:root:Epoch[9] Validation-rmse=0.828280\n",
            "INFO:root:Epoch[10] Batch [0-250]\tSpeed: 11067830.49 samples/sec\trmse=0.795627\n",
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            "INFO:root:Epoch[10] Time cost=1.731\n",
            "INFO:root:Epoch[10] Validation-rmse=0.829041\n",
            "INFO:root:Epoch[11] Batch [0-250]\tSpeed: 10966468.57 samples/sec\trmse=0.793280\n",
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            "INFO:root:Epoch[11] Time cost=1.727\n",
            "INFO:root:Epoch[11] Validation-rmse=0.830322\n",
            "INFO:root:Epoch[12] Batch [0-250]\tSpeed: 11152852.24 samples/sec\trmse=0.791631\n",
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            "INFO:root:Epoch[12] Train-rmse=0.790666\n",
            "INFO:root:Epoch[12] Time cost=1.719\n",
            "INFO:root:Epoch[12] Validation-rmse=0.830202\n",
            "INFO:root:Epoch[13] Batch [0-250]\tSpeed: 11084331.64 samples/sec\trmse=0.790066\n",
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            "INFO:root:Epoch[13] Train-rmse=0.789414\n",
            "INFO:root:Epoch[13] Time cost=1.721\n",
            "INFO:root:Epoch[13] Validation-rmse=0.830079\n",
            "INFO:root:Epoch[14] Batch [0-250]\tSpeed: 11077773.97 samples/sec\trmse=0.788878\n",
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            "INFO:root:Epoch[14] Train-rmse=0.788362\n",
            "INFO:root:Epoch[14] Time cost=1.715\n",
            "INFO:root:Epoch[14] Validation-rmse=0.830683\n",
            "INFO:root:Epoch[15] Batch [0-250]\tSpeed: 11096563.69 samples/sec\trmse=0.787945\n",
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            "INFO:root:Epoch[15] Time cost=1.726\n",
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            "INFO:root:Epoch[16] Batch [0-250]\tSpeed: 10946086.73 samples/sec\trmse=0.787182\n",
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            "INFO:root:Epoch[19] Time cost=1.733\n",
            "INFO:root:Epoch[19] Validation-rmse=0.830711\n",
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            "INFO:root:Epoch[20] Time cost=1.768\n",
            "INFO:root:Epoch[20] Validation-rmse=0.829694\n",
            "INFO:root:Epoch[21] Batch [0-250]\tSpeed: 10580912.03 samples/sec\trmse=0.783381\n",
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            "INFO:root:Epoch[21] Train-rmse=0.783128\n",
            "INFO:root:Epoch[21] Time cost=1.802\n",
            "INFO:root:Epoch[21] Validation-rmse=0.829627\n",
            "INFO:root:Epoch[22] Batch [0-250]\tSpeed: 10484883.46 samples/sec\trmse=0.782800\n",
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            "INFO:root:Epoch[22] Time cost=1.820\n",
            "INFO:root:Epoch[22] Validation-rmse=0.829243\n",
            "INFO:root:Epoch[23] Batch [0-250]\tSpeed: 10612249.52 samples/sec\trmse=0.782275\n",
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            "INFO:root:Epoch[23] Train-rmse=0.782027\n",
            "INFO:root:Epoch[23] Time cost=1.806\n",
            "INFO:root:Epoch[23] Validation-rmse=0.829502\n",
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            "INFO:root:Epoch[24] Train-rmse=0.781514\n",
            "INFO:root:Epoch[24] Time cost=1.815\n",
            "INFO:root:Epoch[24] Validation-rmse=0.829515\n",
            "INFO:root:Epoch[25] Batch [0-250]\tSpeed: 10553194.92 samples/sec\trmse=0.781358\n",
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            "INFO:root:Epoch[25] Time cost=1.799\n",
            "INFO:root:Epoch[25] Validation-rmse=0.829326\n",
            "INFO:root:Epoch[26] Batch [0-250]\tSpeed: 10568698.92 samples/sec\trmse=0.780897\n",
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            "INFO:root:Epoch[26] Time cost=1.754\n",
            "INFO:root:Epoch[26] Validation-rmse=0.829272\n",
            "INFO:root:Epoch[27] Batch [0-250]\tSpeed: 11004554.31 samples/sec\trmse=0.780492\n",
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            "INFO:root:Epoch[27] Train-rmse=0.780139\n",
            "INFO:root:Epoch[27] Time cost=1.727\n",
            "INFO:root:Epoch[27] Validation-rmse=0.829055\n",
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            "INFO:root:Epoch[28] Train-rmse=0.779732\n",
            "INFO:root:Epoch[28] Time cost=1.727\n",
            "INFO:root:Epoch[28] Validation-rmse=0.828541\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 11170003.11 samples/sec\trmse=0.779655\n",
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            "INFO:root:Epoch[29] Train-rmse=0.779321\n",
            "INFO:root:Epoch[29] Time cost=1.721\n",
            "INFO:root:Epoch[29] Validation-rmse=0.828369\n",
            "INFO:root:Epoch[30] Batch [0-250]\tSpeed: 11028085.98 samples/sec\trmse=0.779147\n",
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            "INFO:root:Epoch[30] Train-rmse=0.778897\n",
            "INFO:root:Epoch[30] Time cost=1.717\n",
            "INFO:root:Epoch[30] Validation-rmse=0.828134\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 11054850.43 samples/sec\trmse=0.778737\n",
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            "INFO:root:Epoch[31] Train-rmse=0.778495\n",
            "INFO:root:Epoch[31] Time cost=1.729\n",
            "INFO:root:Epoch[31] Validation-rmse=0.827725\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 11074451.25 samples/sec\trmse=0.778314\n",
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            "INFO:root:Epoch[32] Train-rmse=0.778100\n",
            "INFO:root:Epoch[32] Time cost=1.723\n",
            "INFO:root:Epoch[32] Validation-rmse=0.827597\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 11096455.66 samples/sec\trmse=0.777928\n",
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            "INFO:root:Epoch[33] Validation-rmse=0.827459\n",
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxrE_3RKOS7w",
        "colab_type": "text"
      },
      "source": [
        "# Genre DCF model (4-layer)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyUEYupPrjmT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b8524cb5-672f-4796-b102-9b95fc6f704f"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies, \n",
        "                             'movie_genre': train_genres}, \n",
        "                            label=train_ratings, batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies, \n",
        "                            'movie_genre': valid_genres}, \n",
        "                           label=valid_ratings, batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 6510291.91 samples/sec\trmse=0.866223\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 6471429.03 samples/sec\trmse=0.859040\n",
            "INFO:root:Epoch[0] Train-rmse=1.058635\n",
            "INFO:root:Epoch[0] Time cost=3.123\n",
            "INFO:root:Epoch[0] Validation-rmse=0.852627\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 6503622.91 samples/sec\trmse=0.845813\n",
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            "INFO:root:Epoch[1] Train-rmse=0.838423\n",
            "INFO:root:Epoch[1] Time cost=2.941\n",
            "INFO:root:Epoch[1] Validation-rmse=0.834360\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 6523664.53 samples/sec\trmse=0.828605\n",
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            "INFO:root:Epoch[2] Train-rmse=0.825627\n",
            "INFO:root:Epoch[2] Time cost=2.908\n",
            "INFO:root:Epoch[2] Validation-rmse=0.828147\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 6555783.08 samples/sec\trmse=0.817669\n",
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            "INFO:root:Epoch[3] Train-rmse=0.813363\n",
            "INFO:root:Epoch[3] Time cost=2.906\n",
            "INFO:root:Epoch[3] Validation-rmse=0.828446\n",
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            "INFO:root:Epoch[4] Train-rmse=0.800740\n",
            "INFO:root:Epoch[4] Time cost=2.908\n",
            "INFO:root:Epoch[4] Validation-rmse=0.833255\n",
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            "INFO:root:Epoch[5] Time cost=2.904\n",
            "INFO:root:Epoch[5] Validation-rmse=0.832937\n",
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            "INFO:root:Epoch[6] Train-rmse=0.789467\n",
            "INFO:root:Epoch[6] Time cost=2.904\n",
            "INFO:root:Epoch[6] Validation-rmse=0.831517\n",
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            "INFO:root:Epoch[7] Train-rmse=0.786027\n",
            "INFO:root:Epoch[7] Time cost=2.902\n",
            "INFO:root:Epoch[7] Validation-rmse=0.827475\n",
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            "INFO:root:Epoch[8] Time cost=2.902\n",
            "INFO:root:Epoch[8] Validation-rmse=0.825509\n",
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            "INFO:root:Epoch[9] Time cost=2.915\n",
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            "INFO:root:Epoch[10] Time cost=2.893\n",
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            "INFO:root:Epoch[13] Time cost=2.892\n",
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            "INFO:root:Epoch[14] Time cost=2.894\n",
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            "INFO:root:Epoch[17] Train-rmse=0.751045\n",
            "INFO:root:Epoch[17] Time cost=2.907\n",
            "INFO:root:Epoch[17] Validation-rmse=0.822460\n",
            "INFO:root:Epoch[18] Batch [0-250]\tSpeed: 6573179.86 samples/sec\trmse=0.748846\n",
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            "INFO:root:Epoch[18] Train-rmse=0.748215\n",
            "INFO:root:Epoch[18] Time cost=2.903\n",
            "INFO:root:Epoch[18] Validation-rmse=0.823320\n",
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            "INFO:root:Epoch[19] Train-rmse=0.745797\n",
            "INFO:root:Epoch[19] Time cost=2.917\n",
            "INFO:root:Epoch[19] Validation-rmse=0.825408\n",
            "INFO:root:Epoch[20] Batch [0-250]\tSpeed: 6531736.64 samples/sec\trmse=0.744206\n",
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            "INFO:root:Epoch[20] Train-rmse=0.743292\n",
            "INFO:root:Epoch[20] Time cost=2.911\n",
            "INFO:root:Epoch[20] Validation-rmse=0.827802\n",
            "INFO:root:Epoch[21] Batch [0-250]\tSpeed: 6550427.96 samples/sec\trmse=0.741982\n",
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            "INFO:root:Epoch[21] Train-rmse=0.740986\n",
            "INFO:root:Epoch[21] Time cost=2.913\n",
            "INFO:root:Epoch[21] Validation-rmse=0.830738\n",
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            "INFO:root:Epoch[22] Train-rmse=0.739079\n",
            "INFO:root:Epoch[22] Time cost=2.893\n",
            "INFO:root:Epoch[22] Validation-rmse=0.829413\n",
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            "INFO:root:Epoch[23] Train-rmse=0.737373\n",
            "INFO:root:Epoch[23] Time cost=2.901\n",
            "INFO:root:Epoch[23] Validation-rmse=0.827027\n",
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            "INFO:root:Epoch[24] Train-rmse=0.735356\n",
            "INFO:root:Epoch[24] Time cost=2.904\n",
            "INFO:root:Epoch[24] Validation-rmse=0.827968\n",
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            "INFO:root:Epoch[25] Train-rmse=0.733073\n",
            "INFO:root:Epoch[25] Time cost=2.907\n",
            "INFO:root:Epoch[25] Validation-rmse=0.829579\n",
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            "INFO:root:Epoch[26] Train-rmse=0.731016\n",
            "INFO:root:Epoch[26] Time cost=2.895\n",
            "INFO:root:Epoch[26] Validation-rmse=0.830800\n",
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            "INFO:root:Epoch[27] Train-rmse=0.729308\n",
            "INFO:root:Epoch[27] Time cost=2.907\n",
            "INFO:root:Epoch[27] Validation-rmse=0.832517\n",
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            "INFO:root:Epoch[28] Train-rmse=0.727879\n",
            "INFO:root:Epoch[28] Time cost=2.921\n",
            "INFO:root:Epoch[28] Validation-rmse=0.835626\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 6543390.67 samples/sec\trmse=0.726752\n",
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            "INFO:root:Epoch[29] Train-rmse=0.726252\n",
            "INFO:root:Epoch[29] Time cost=2.908\n",
            "INFO:root:Epoch[29] Validation-rmse=0.834928\n",
            "INFO:root:Epoch[30] Batch [0-250]\tSpeed: 6559825.27 samples/sec\trmse=0.725291\n",
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            "INFO:root:Epoch[30] Train-rmse=0.724638\n",
            "INFO:root:Epoch[30] Time cost=2.905\n",
            "INFO:root:Epoch[30] Validation-rmse=0.832115\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 6498405.79 samples/sec\trmse=0.724020\n",
            "INFO:root:Epoch[31] Batch [250-500]\tSpeed: 6538822.26 samples/sec\trmse=0.721811\n",
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            "INFO:root:Epoch[31] Train-rmse=0.723050\n",
            "INFO:root:Epoch[31] Time cost=2.916\n",
            "INFO:root:Epoch[31] Validation-rmse=0.831705\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 6547865.71 samples/sec\trmse=0.721959\n",
            "INFO:root:Epoch[32] Batch [250-500]\tSpeed: 6531541.35 samples/sec\trmse=0.719741\n",
            "INFO:root:Epoch[32] Batch [500-750]\tSpeed: 6569379.65 samples/sec\trmse=0.721645\n",
            "INFO:root:Epoch[32] Train-rmse=0.721151\n",
            "INFO:root:Epoch[32] Time cost=2.907\n",
            "INFO:root:Epoch[32] Validation-rmse=0.831122\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 6554165.30 samples/sec\trmse=0.720838\n",
            "INFO:root:Epoch[33] Batch [250-500]\tSpeed: 6549151.49 samples/sec\trmse=0.718079\n",
            "INFO:root:Epoch[33] Batch [500-750]\tSpeed: 6550416.50 samples/sec\trmse=0.720088\n",
            "INFO:root:Epoch[33] Train-rmse=0.719697\n",
            "INFO:root:Epoch[33] Time cost=2.905\n",
            "INFO:root:Epoch[33] Validation-rmse=0.830617\n",
            "INFO:root:Epoch[34] Batch [0-250]\tSpeed: 6548421.84 samples/sec\trmse=0.719209\n",
            "INFO:root:Epoch[34] Batch [250-500]\tSpeed: 6561301.33 samples/sec\trmse=0.716354\n",
            "INFO:root:Epoch[34] Batch [500-750]\tSpeed: 6545426.40 samples/sec\trmse=0.718564\n",
            "INFO:root:Epoch[34] Train-rmse=0.718067\n",
            "INFO:root:Epoch[34] Time cost=2.905\n",
            "INFO:root:Epoch[34] Validation-rmse=0.830127\n",
            "INFO:root:Epoch[35] Batch [0-250]\tSpeed: 6531451.85 samples/sec\trmse=0.717629\n",
            "INFO:root:Epoch[35] Batch [250-500]\tSpeed: 6521626.10 samples/sec\trmse=0.714955\n",
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            "INFO:root:Epoch[35] Train-rmse=0.716664\n",
            "INFO:root:Epoch[35] Time cost=2.912\n",
            "INFO:root:Epoch[35] Validation-rmse=0.830257\n",
            "INFO:root:Epoch[36] Batch [0-250]\tSpeed: 6536181.08 samples/sec\trmse=0.716736\n",
            "INFO:root:Epoch[36] Batch [250-500]\tSpeed: 6565185.91 samples/sec\trmse=0.713960\n",
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            "INFO:root:Epoch[36] Train-rmse=0.716017\n",
            "INFO:root:Epoch[36] Time cost=2.907\n",
            "INFO:root:Epoch[36] Validation-rmse=0.830312\n",
            "INFO:root:Epoch[37] Batch [0-250]\tSpeed: 6574628.95 samples/sec\trmse=0.715956\n",
            "INFO:root:Epoch[37] Batch [250-500]\tSpeed: 6551081.11 samples/sec\trmse=0.713814\n",
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            "INFO:root:Epoch[37] Train-rmse=0.715581\n",
            "INFO:root:Epoch[37] Time cost=2.905\n",
            "INFO:root:Epoch[37] Validation-rmse=0.832189\n",
            "INFO:root:Epoch[38] Batch [0-250]\tSpeed: 6542765.18 samples/sec\trmse=0.714989\n",
            "INFO:root:Epoch[38] Batch [250-500]\tSpeed: 6555228.97 samples/sec\trmse=0.712640\n",
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            "INFO:root:Epoch[38] Train-rmse=0.714452\n",
            "INFO:root:Epoch[38] Time cost=2.904\n",
            "INFO:root:Epoch[38] Validation-rmse=0.830533\n",
            "INFO:root:Epoch[39] Batch [0-250]\tSpeed: 6560977.82 samples/sec\trmse=0.713256\n",
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            "INFO:root:Epoch[39] Train-rmse=0.712576\n",
            "INFO:root:Epoch[39] Time cost=2.905\n",
            "INFO:root:Epoch[39] Validation-rmse=0.830705\n",
            "INFO:root:Epoch[40] Batch [0-250]\tSpeed: 6529467.09 samples/sec\trmse=0.711637\n",
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            "INFO:root:Epoch[40] Train-rmse=0.710753\n",
            "INFO:root:Epoch[40] Time cost=2.906\n",
            "INFO:root:Epoch[40] Validation-rmse=0.830737\n",
            "INFO:root:Epoch[41] Batch [0-250]\tSpeed: 6496829.08 samples/sec\trmse=0.710107\n",
            "INFO:root:Epoch[41] Batch [250-500]\tSpeed: 6380554.57 samples/sec\trmse=0.706968\n",
            "INFO:root:Epoch[41] Batch [500-750]\tSpeed: 6359492.39 samples/sec\trmse=0.710671\n",
            "INFO:root:Epoch[41] Train-rmse=0.709284\n",
            "INFO:root:Epoch[41] Time cost=2.975\n",
            "INFO:root:Epoch[41] Validation-rmse=0.831568\n",
            "INFO:root:Epoch[42] Batch [0-250]\tSpeed: 6395623.31 samples/sec\trmse=0.708626\n",
            "INFO:root:Epoch[42] Batch [250-500]\tSpeed: 6425844.57 samples/sec\trmse=0.705553\n",
            "INFO:root:Epoch[42] Batch [500-750]\tSpeed: 6377997.78 samples/sec\trmse=0.709056\n",
            "INFO:root:Epoch[42] Train-rmse=0.707788\n",
            "INFO:root:Epoch[42] Time cost=2.977\n",
            "INFO:root:Epoch[42] Validation-rmse=0.832458\n",
            "INFO:root:Epoch[43] Batch [0-250]\tSpeed: 6383146.06 samples/sec\trmse=0.707237\n",
            "INFO:root:Epoch[43] Batch [250-500]\tSpeed: 6372740.04 samples/sec\trmse=0.704380\n",
            "INFO:root:Epoch[43] Batch [500-750]\tSpeed: 6401118.95 samples/sec\trmse=0.707991\n",
            "INFO:root:Epoch[43] Train-rmse=0.706587\n",
            "INFO:root:Epoch[43] Time cost=2.981\n",
            "INFO:root:Epoch[43] Validation-rmse=0.832671\n",
            "INFO:root:Epoch[44] Batch [0-250]\tSpeed: 6415727.62 samples/sec\trmse=0.706222\n",
            "INFO:root:Epoch[44] Batch [250-500]\tSpeed: 6452799.21 samples/sec\trmse=0.702955\n",
            "INFO:root:Epoch[44] Batch [500-750]\tSpeed: 6538189.49 samples/sec\trmse=0.706580\n",
            "INFO:root:Epoch[44] Train-rmse=0.705303\n",
            "INFO:root:Epoch[44] Time cost=2.943\n",
            "INFO:root:Epoch[44] Validation-rmse=0.833441\n",
            "INFO:root:Epoch[45] Batch [0-250]\tSpeed: 6520112.70 samples/sec\trmse=0.704463\n",
            "INFO:root:Epoch[45] Batch [250-500]\tSpeed: 6506669.02 samples/sec\trmse=0.701454\n",
            "INFO:root:Epoch[45] Batch [500-750]\tSpeed: 6516279.64 samples/sec\trmse=0.704705\n",
            "INFO:root:Epoch[45] Train-rmse=0.703602\n",
            "INFO:root:Epoch[45] Time cost=2.924\n",
            "INFO:root:Epoch[45] Validation-rmse=0.835351\n",
            "INFO:root:Epoch[46] Batch [0-250]\tSpeed: 6502179.14 samples/sec\trmse=0.703547\n",
            "INFO:root:Epoch[46] Batch [250-500]\tSpeed: 6532786.54 samples/sec\trmse=0.700098\n",
            "INFO:root:Epoch[46] Batch [500-750]\tSpeed: 6530003.83 samples/sec\trmse=0.703834\n",
            "INFO:root:Epoch[46] Train-rmse=0.702549\n",
            "INFO:root:Epoch[46] Time cost=2.920\n",
            "INFO:root:Epoch[46] Validation-rmse=0.836186\n",
            "INFO:root:Epoch[47] Batch [0-250]\tSpeed: 6506128.04 samples/sec\trmse=0.702381\n",
            "INFO:root:Epoch[47] Batch [250-500]\tSpeed: 6496201.19 samples/sec\trmse=0.699190\n",
            "INFO:root:Epoch[47] Batch [500-750]\tSpeed: 6524198.70 samples/sec\trmse=0.702917\n",
            "INFO:root:Epoch[47] Train-rmse=0.701548\n",
            "INFO:root:Epoch[47] Time cost=2.923\n",
            "INFO:root:Epoch[47] Validation-rmse=0.837113\n",
            "INFO:root:Epoch[48] Batch [0-250]\tSpeed: 6526138.01 samples/sec\trmse=0.701890\n",
            "INFO:root:Epoch[48] Batch [250-500]\tSpeed: 6505210.99 samples/sec\trmse=0.698418\n",
            "INFO:root:Epoch[48] Batch [500-750]\tSpeed: 6559672.62 samples/sec\trmse=0.702363\n",
            "INFO:root:Epoch[48] Train-rmse=0.700945\n",
            "INFO:root:Epoch[48] Time cost=2.914\n",
            "INFO:root:Epoch[48] Validation-rmse=0.837032\n",
            "INFO:root:Epoch[49] Batch [0-250]\tSpeed: 6532657.93 samples/sec\trmse=0.701379\n",
            "INFO:root:Epoch[49] Batch [250-500]\tSpeed: 6530792.84 samples/sec\trmse=0.697854\n",
            "INFO:root:Epoch[49] Batch [500-750]\tSpeed: 6553162.58 samples/sec\trmse=0.702106\n",
            "INFO:root:Epoch[49] Train-rmse=0.700491\n",
            "INFO:root:Epoch[49] Time cost=2.913\n",
            "INFO:root:Epoch[49] Validation-rmse=0.839135\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acsyV1QRbuMy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7f80cd3f-63c4-4d9f-e4a4-037a69333d48"
      },
      "source": [
        "# Visualize network\n",
        "mx.viz.plot_network(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f9b0d5914e0>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZvoNeS-MZEy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "0bd4504f-52be-4e8e-a32b-1561a5119a36"
      },
      "source": [
        "# Print network summary\n",
        "mx.viz.print_summary(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "________________________________________________________________________________________________________________________\n",
            "Layer (type)                                        Output Shape            Param #     Previous Layer                  \n",
            "========================================================================================================================\n",
            "user(null)                                                                  0                                           \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding17(Embedding)                                                      3462325     user                            \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding18(Embedding)                                                      3281550                                     \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding19(Embedding)                                                      100                                         \n",
            "________________________________________________________________________________________________________________________\n",
            "concat3(Concat)                                                             0           embedding17                     \n",
            "                                                                                        embedding18                     \n",
            "                                                                                        embedding19                     \n",
            "________________________________________________________________________________________________________________________\n",
            "flatten7(Flatten)                                                           0           concat3                         \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected14(FullyConnected)                                            128         flatten7                        \n",
            "________________________________________________________________________________________________________________________\n",
            "activation10(Activation)                                                    0           fullyconnected14                \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected15(FullyConnected)                                            64          activation10                    \n",
            "________________________________________________________________________________________________________________________\n",
            "activation11(Activation)                                                    0           fullyconnected15                \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected16(FullyConnected)                                            32          activation11                    \n",
            "________________________________________________________________________________________________________________________\n",
            "activation12(Activation)                                                    0           fullyconnected16                \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected17(FullyConnected)                                            1           activation12                    \n",
            "________________________________________________________________________________________________________________________\n",
            "linearregressionoutput8(LinearRegressionOutput)                             0           fullyconnected17                \n",
            "========================================================================================================================\n",
            "Total params: 6744200\n",
            "________________________________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvTX7WwSYFcV",
        "colab_type": "text"
      },
      "source": [
        "## 3-layer Genre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFRay8-KX5qX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "42958164-de47-44e5-fa67-8aa7740a0667"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies, \n",
        "                             'movie_genre': train_genres}, \n",
        "                            label=train_ratings, batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies, \n",
        "                            'movie_genre': valid_genres}, \n",
        "                           label=valid_ratings, batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Epoch[0] Batch [0-250]\tSpeed: 8261213.48 samples/sec\trmse=1.674798\n",
            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 8426937.76 samples/sec\trmse=0.870670\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 8305218.29 samples/sec\trmse=0.862455\n",
            "INFO:root:Epoch[0] Train-rmse=1.133422\n",
            "INFO:root:Epoch[0] Time cost=2.350\n",
            "INFO:root:Epoch[0] Validation-rmse=0.860163\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 8335264.97 samples/sec\trmse=0.853780\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 8391346.94 samples/sec\trmse=0.845323\n",
            "INFO:root:Epoch[1] Batch [500-750]\tSpeed: 8354159.46 samples/sec\trmse=0.839644\n",
            "INFO:root:Epoch[1] Train-rmse=0.846146\n",
            "INFO:root:Epoch[1] Time cost=2.280\n",
            "INFO:root:Epoch[1] Validation-rmse=0.839047\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 8339133.59 samples/sec\trmse=0.833168\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 8313815.58 samples/sec\trmse=0.829774\n",
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            "INFO:root:Epoch[41] Train-rmse=0.724677\n",
            "INFO:root:Epoch[41] Time cost=2.251\n",
            "INFO:root:Epoch[41] Validation-rmse=0.823588\n",
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            "INFO:root:Epoch[42] Train-rmse=0.723906\n",
            "INFO:root:Epoch[42] Time cost=2.248\n",
            "INFO:root:Epoch[42] Validation-rmse=0.823803\n",
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            "INFO:root:Epoch[43] Time cost=2.254\n",
            "INFO:root:Epoch[43] Validation-rmse=0.824155\n",
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            "INFO:root:Epoch[44] Time cost=2.253\n",
            "INFO:root:Epoch[44] Validation-rmse=0.824686\n",
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            "INFO:root:Epoch[45] Time cost=2.254\n",
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            "INFO:root:Epoch[46] Batch [0-250]\tSpeed: 8455265.25 samples/sec\trmse=0.721120\n",
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            "INFO:root:Epoch[48] Time cost=2.256\n",
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            "INFO:root:Epoch[49] Time cost=2.247\n",
            "INFO:root:Epoch[49] Validation-rmse=0.827035\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3H1ca2jYMMN",
        "colab_type": "text"
      },
      "source": [
        "##5-layer Genre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xsAYhJ7YLYw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "28c07412-f062-457e-c2cb-79e8f33f1659"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies, \n",
        "                             'movie_genre': train_genres}, \n",
        "                            label=train_ratings, batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies, \n",
        "                            'movie_genre': valid_genres}, \n",
        "                           label=valid_ratings, batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=16)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 6134234.59 samples/sec\trmse=0.867991\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 6129390.98 samples/sec\trmse=0.861647\n",
            "INFO:root:Epoch[0] Train-rmse=1.129542\n",
            "INFO:root:Epoch[0] Time cost=3.168\n",
            "INFO:root:Epoch[0] Validation-rmse=0.859983\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 6114940.18 samples/sec\trmse=0.852630\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 6144602.94 samples/sec\trmse=0.841442\n",
            "INFO:root:Epoch[1] Batch [500-750]\tSpeed: 6155700.16 samples/sec\trmse=0.834818\n",
            "INFO:root:Epoch[1] Train-rmse=0.842859\n",
            "INFO:root:Epoch[1] Time cost=3.101\n",
            "INFO:root:Epoch[1] Validation-rmse=0.835320\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 6161273.23 samples/sec\trmse=0.829643\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 6149759.15 samples/sec\trmse=0.825776\n",
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            "INFO:root:Epoch[2] Train-rmse=0.825646\n",
            "INFO:root:Epoch[2] Time cost=3.098\n",
            "INFO:root:Epoch[2] Validation-rmse=0.826275\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 6104789.60 samples/sec\trmse=0.817634\n",
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            "INFO:root:Epoch[3] Train-rmse=0.813470\n",
            "INFO:root:Epoch[3] Time cost=3.115\n",
            "INFO:root:Epoch[3] Validation-rmse=0.823357\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 6090078.07 samples/sec\trmse=0.806256\n",
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            "INFO:root:Epoch[4] Train-rmse=0.802349\n",
            "INFO:root:Epoch[4] Time cost=3.113\n",
            "INFO:root:Epoch[4] Validation-rmse=0.826020\n",
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            "INFO:root:Epoch[5] Train-rmse=0.792626\n",
            "INFO:root:Epoch[5] Time cost=3.117\n",
            "INFO:root:Epoch[5] Validation-rmse=0.827633\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 6122219.25 samples/sec\trmse=0.786833\n",
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            "INFO:root:Epoch[6] Train-rmse=0.783969\n",
            "INFO:root:Epoch[6] Time cost=3.100\n",
            "INFO:root:Epoch[6] Validation-rmse=0.824954\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 6147337.81 samples/sec\trmse=0.779445\n",
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            "INFO:root:Epoch[7] Train-rmse=0.777679\n",
            "INFO:root:Epoch[7] Time cost=3.096\n",
            "INFO:root:Epoch[7] Validation-rmse=0.823329\n",
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            "INFO:root:Epoch[8] Train-rmse=0.773156\n",
            "INFO:root:Epoch[8] Time cost=3.096\n",
            "INFO:root:Epoch[8] Validation-rmse=0.822331\n",
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            "INFO:root:Epoch[9] Train-rmse=0.769634\n",
            "INFO:root:Epoch[9] Time cost=3.098\n",
            "INFO:root:Epoch[9] Validation-rmse=0.821877\n",
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            "INFO:root:Epoch[10] Time cost=3.095\n",
            "INFO:root:Epoch[10] Validation-rmse=0.823743\n",
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            "INFO:root:Epoch[11] Time cost=3.103\n",
            "INFO:root:Epoch[11] Validation-rmse=0.824891\n",
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            "INFO:root:Epoch[12] Train-rmse=0.759602\n",
            "INFO:root:Epoch[12] Time cost=3.096\n",
            "INFO:root:Epoch[12] Validation-rmse=0.826047\n",
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            "INFO:root:Epoch[13] Time cost=3.098\n",
            "INFO:root:Epoch[13] Validation-rmse=0.825805\n",
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            "INFO:root:Epoch[14] Time cost=3.096\n",
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            "INFO:root:Epoch[20] Time cost=3.096\n",
            "INFO:root:Epoch[20] Validation-rmse=0.825402\n",
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            "INFO:root:Epoch[21] Time cost=3.087\n",
            "INFO:root:Epoch[21] Validation-rmse=0.826811\n",
            "INFO:root:Epoch[22] Batch [0-250]\tSpeed: 6163495.42 samples/sec\trmse=0.727033\n",
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            "INFO:root:Epoch[22] Train-rmse=0.726040\n",
            "INFO:root:Epoch[22] Time cost=3.096\n",
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            "INFO:root:Epoch[23] Time cost=3.094\n",
            "INFO:root:Epoch[23] Validation-rmse=0.830324\n",
            "INFO:root:Epoch[24] Batch [0-250]\tSpeed: 6146571.00 samples/sec\trmse=0.721138\n",
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            "INFO:root:Epoch[24] Validation-rmse=0.831330\n",
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9by-JzlMsxh",
        "colab_type": "text"
      },
      "source": [
        "# Genre-Tag DCF model (4-layer)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDSqP-fgAGu7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split movie-tag to train and test set\n",
        "train_tags = numpy.array([label_tag[int(j)] for j in train_movies])\n",
        "valid_tags = numpy.array([label_tag[int(j)] for j in valid_movies])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "kyZnoGk8OS7-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4ac1a916-15e7-40b2-eeea-420eaa07831b"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies, \n",
        "                             'movie_genre': train_genres, \n",
        "                             'movie_tag': train_tags}, \n",
        "                            label=train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies, \n",
        "                            'movie_genre': valid_genres, \n",
        "                            'movie_tag': valid_tags}, \n",
        "                           label=valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) # user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) # Reduce from 25 to 20 # movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) # Reduce from 25 to 20\n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "# We need to add in a fourth embedding layer for tag\n",
        "movie_tag = mx.symbol.Variable(\"movie_tag\")\n",
        "movie_tag = mx.symbol.Embedding(data=movie_tag, input_dim=812, output_dim=5) # Set to 25\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre, movie_tag)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre','movie_tag'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Epoch[0] Train-rmse=3.175722\n",
            "INFO:root:Epoch[0] Time cost=0.351\n",
            "INFO:root:Epoch[0] Validation-rmse=1.128766\n",
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            "INFO:root:Epoch[1] Time cost=3.309\n",
            "INFO:root:Epoch[1] Validation-rmse=0.852619\n",
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            "INFO:root:Epoch[2] Validation-rmse=0.833634\n",
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            "INFO:root:Epoch[47] Train-rmse=0.682917\n",
            "INFO:root:Epoch[47] Time cost=3.315\n",
            "INFO:root:Epoch[47] Validation-rmse=0.842235\n",
            "INFO:root:Epoch[48] Batch [0-250]\tSpeed: 5746931.90 samples/sec\trmse=0.680904\n",
            "INFO:root:Epoch[48] Batch [250-500]\tSpeed: 5777951.85 samples/sec\trmse=0.682878\n",
            "INFO:root:Epoch[48] Batch [500-750]\tSpeed: 5766628.24 samples/sec\trmse=0.682583\n",
            "INFO:root:Epoch[48] Train-rmse=0.682152\n",
            "INFO:root:Epoch[48] Time cost=3.303\n",
            "INFO:root:Epoch[48] Validation-rmse=0.840371\n",
            "INFO:root:Epoch[49] Batch [0-250]\tSpeed: 5710940.70 samples/sec\trmse=0.680209\n",
            "INFO:root:Epoch[49] Batch [250-500]\tSpeed: 5777511.25 samples/sec\trmse=0.682656\n",
            "INFO:root:Epoch[49] Batch [500-750]\tSpeed: 5745183.71 samples/sec\trmse=0.682231\n",
            "INFO:root:Epoch[49] Train-rmse=0.681743\n",
            "INFO:root:Epoch[49] Time cost=3.312\n",
            "INFO:root:Epoch[49] Validation-rmse=0.837378\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9s0QKdRb64l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "25dbf529-a4b0-4f68-988a-4e298b6eb183"
      },
      "source": [
        "# Visualize network\n",
        "mx.viz.plot_network(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f9b0074ca58>"
            ],
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168,-810 168,-752 262,-752 262,-810\"/>\n<text text-anchor=\"middle\" x=\"215\" y=\"-784.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">FullyConnected</text>\n<text text-anchor=\"middle\" x=\"215\" y=\"-769.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">32</text>\n</g>\n<!-- fullyconnected20&#45;&gt;activation14 -->\n<g id=\"edge14\" class=\"edge\">\n<title>fullyconnected20&#45;&gt;activation14</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M215,-741.6321C215,-733.1148 215,-724.2539 215,-716.2088\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"215,-751.7731 210.5001,-741.773 215,-746.7731 215.0001,-741.7731 215.0001,-741.7731 215.0001,-741.7731 215,-746.7731 219.5001,-741.7731 215,-751.7731 215,-751.7731\"/>\n</g>\n<!-- activation15 -->\n<g id=\"node16\" class=\"node\">\n<title>activation15</title>\n<polygon fill=\"#ffffb3\" stroke=\"#000000\" points=\"262,-904 168,-904 168,-846 262,-846 262,-904\"/>\n<text 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RpJ4HgzQ1Ke",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "outputId": "725006ba-8546-425b-93a8-bd9f5fa55aba"
      },
      "source": [
        "# Print summary\n",
        "mx.viz.print_summary(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "________________________________________________________________________________________________________________________\n",
            "Layer (type)                                        Output Shape            Param #     Previous Layer                  \n",
            "========================================================================================================================\n",
            "user(null)                                                                  0                                           \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding20(Embedding)                                                      3462325     user                            \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding21(Embedding)                                                      3281550                                     \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding22(Embedding)                                                      100                                         \n",
            "________________________________________________________________________________________________________________________\n",
            "embedding23(Embedding)                                                      4060                                        \n",
            "________________________________________________________________________________________________________________________\n",
            "concat4(Concat)                                                             0           embedding20                     \n",
            "                                                                                        embedding21                     \n",
            "                                                                                        embedding22                     \n",
            "                                                                                        embedding23                     \n",
            "________________________________________________________________________________________________________________________\n",
            "flatten8(Flatten)                                                           0           concat4                         \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected18(FullyConnected)                                            128         flatten8                        \n",
            "________________________________________________________________________________________________________________________\n",
            "activation13(Activation)                                                    0           fullyconnected18                \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected19(FullyConnected)                                            64          activation13                    \n",
            "________________________________________________________________________________________________________________________\n",
            "activation14(Activation)                                                    0           fullyconnected19                \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected20(FullyConnected)                                            32          activation14                    \n",
            "________________________________________________________________________________________________________________________\n",
            "activation15(Activation)                                                    0           fullyconnected20                \n",
            "________________________________________________________________________________________________________________________\n",
            "fullyconnected21(FullyConnected)                                            1           activation15                    \n",
            "________________________________________________________________________________________________________________________\n",
            "linearregressionoutput9(LinearRegressionOutput)                             0           fullyconnected21                \n",
            "========================================================================================================================\n",
            "Total params: 6748260\n",
            "________________________________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmEz61HeYiz0",
        "colab_type": "text"
      },
      "source": [
        "## 3-layer Genre-Tag"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ImiHbk9YguY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c54f24a9-4d07-4b2e-eb7b-4013acac95e7"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies, \n",
        "                             'movie_genre': train_genres, \n",
        "                             'movie_tag': train_tags}, \n",
        "                            label=train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies, \n",
        "                            'movie_genre': valid_genres, \n",
        "                            'movie_tag': valid_tags}, \n",
        "                           label=valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) # user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) # Reduce from 25 to 20 # movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) # Reduce from 25 to 20\n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "# We need to add in a fourth embedding layer for tag\n",
        "movie_tag = mx.symbol.Variable(\"movie_tag\")\n",
        "movie_tag = mx.symbol.Embedding(data=movie_tag, input_dim=812, output_dim=5) # Set to 25\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre, movie_tag)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre','movie_tag'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 7178223.92 samples/sec\trmse=0.868798\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 7130810.59 samples/sec\trmse=0.862290\n",
            "INFO:root:Epoch[0] Train-rmse=1.111417\n",
            "INFO:root:Epoch[0] Time cost=2.749\n",
            "INFO:root:Epoch[0] Validation-rmse=0.861022\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 7115720.58 samples/sec\trmse=0.857313\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 7129937.83 samples/sec\trmse=0.848013\n",
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            "INFO:root:Epoch[1] Train-rmse=0.848757\n",
            "INFO:root:Epoch[1] Time cost=2.678\n",
            "INFO:root:Epoch[1] Validation-rmse=0.840956\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 7034929.90 samples/sec\trmse=0.835585\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 7031559.72 samples/sec\trmse=0.831526\n",
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            "INFO:root:Epoch[2] Train-rmse=0.832057\n",
            "INFO:root:Epoch[2] Time cost=2.706\n",
            "INFO:root:Epoch[2] Validation-rmse=0.833806\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 7027239.45 samples/sec\trmse=0.826386\n",
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            "INFO:root:Epoch[3] Train-rmse=0.822264\n",
            "INFO:root:Epoch[3] Time cost=2.709\n",
            "INFO:root:Epoch[3] Validation-rmse=0.825613\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 7038985.57 samples/sec\trmse=0.815034\n",
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            "INFO:root:Epoch[4] Train-rmse=0.811348\n",
            "INFO:root:Epoch[4] Time cost=2.694\n",
            "INFO:root:Epoch[4] Validation-rmse=0.824048\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 7085499.60 samples/sec\trmse=0.805529\n",
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            "INFO:root:Epoch[5] Train-rmse=0.802772\n",
            "INFO:root:Epoch[5] Time cost=2.675\n",
            "INFO:root:Epoch[5] Validation-rmse=0.824246\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 7071329.13 samples/sec\trmse=0.798142\n",
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            "INFO:root:Epoch[6] Train-rmse=0.796027\n",
            "INFO:root:Epoch[6] Time cost=2.680\n",
            "INFO:root:Epoch[6] Validation-rmse=0.825024\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 7128222.02 samples/sec\trmse=0.792574\n",
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            "INFO:root:Epoch[7] Train-rmse=0.790813\n",
            "INFO:root:Epoch[7] Time cost=2.674\n",
            "INFO:root:Epoch[7] Validation-rmse=0.826426\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 7077342.83 samples/sec\trmse=0.788014\n",
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            "INFO:root:Epoch[8] Train-rmse=0.786228\n",
            "INFO:root:Epoch[8] Time cost=2.680\n",
            "INFO:root:Epoch[8] Validation-rmse=0.827207\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 7137139.68 samples/sec\trmse=0.783241\n",
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            "INFO:root:Epoch[9] Train-rmse=0.781471\n",
            "INFO:root:Epoch[9] Time cost=2.673\n",
            "INFO:root:Epoch[9] Validation-rmse=0.825239\n",
            "INFO:root:Epoch[10] Batch [0-250]\tSpeed: 7145318.08 samples/sec\trmse=0.778341\n",
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            "INFO:root:Epoch[10] Train-rmse=0.776408\n",
            "INFO:root:Epoch[10] Time cost=2.668\n",
            "INFO:root:Epoch[10] Validation-rmse=0.824629\n",
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            "INFO:root:Epoch[11] Train-rmse=0.772227\n",
            "INFO:root:Epoch[11] Time cost=2.675\n",
            "INFO:root:Epoch[11] Validation-rmse=0.825550\n",
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            "INFO:root:Epoch[12] Time cost=2.672\n",
            "INFO:root:Epoch[12] Validation-rmse=0.827377\n",
            "INFO:root:Epoch[13] Batch [0-250]\tSpeed: 7073977.72 samples/sec\trmse=0.767083\n",
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            "INFO:root:Epoch[13] Train-rmse=0.766048\n",
            "INFO:root:Epoch[13] Time cost=2.673\n",
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            "INFO:root:Epoch[14] Time cost=2.675\n",
            "INFO:root:Epoch[14] Validation-rmse=0.828464\n",
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            "INFO:root:Epoch[15] Time cost=2.709\n",
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            "INFO:root:Epoch[18] Time cost=2.677\n",
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            "INFO:root:Epoch[19] Time cost=2.673\n",
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            "INFO:root:Epoch[20] Train-rmse=0.749129\n",
            "INFO:root:Epoch[20] Time cost=2.675\n",
            "INFO:root:Epoch[20] Validation-rmse=0.825185\n",
            "INFO:root:Epoch[21] Batch [0-250]\tSpeed: 7100026.95 samples/sec\trmse=0.748249\n",
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            "INFO:root:Epoch[21] Train-rmse=0.747458\n",
            "INFO:root:Epoch[21] Time cost=2.673\n",
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            "INFO:root:Epoch[22] Train-rmse=0.745948\n",
            "INFO:root:Epoch[22] Time cost=2.675\n",
            "INFO:root:Epoch[22] Validation-rmse=0.826275\n",
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            "INFO:root:Epoch[23] Time cost=2.671\n",
            "INFO:root:Epoch[23] Validation-rmse=0.826024\n",
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            "INFO:root:Epoch[24] Train-rmse=0.743298\n",
            "INFO:root:Epoch[24] Time cost=2.734\n",
            "INFO:root:Epoch[24] Validation-rmse=0.826166\n",
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            "INFO:root:Epoch[26] Time cost=2.674\n",
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            "INFO:root:Epoch[27] Train-rmse=0.739543\n",
            "INFO:root:Epoch[27] Time cost=2.674\n",
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            "INFO:root:Epoch[28] Train-rmse=0.738132\n",
            "INFO:root:Epoch[28] Time cost=2.674\n",
            "INFO:root:Epoch[28] Validation-rmse=0.825306\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 7101813.87 samples/sec\trmse=0.737194\n",
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            "INFO:root:Epoch[29] Train-rmse=0.737312\n",
            "INFO:root:Epoch[29] Time cost=2.675\n",
            "INFO:root:Epoch[29] Validation-rmse=0.824366\n",
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            "INFO:root:Epoch[30] Train-rmse=0.736457\n",
            "INFO:root:Epoch[30] Time cost=2.679\n",
            "INFO:root:Epoch[30] Validation-rmse=0.824179\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 7098984.83 samples/sec\trmse=0.735374\n",
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            "INFO:root:Epoch[31] Train-rmse=0.735616\n",
            "INFO:root:Epoch[31] Time cost=2.684\n",
            "INFO:root:Epoch[31] Validation-rmse=0.824802\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 7088910.19 samples/sec\trmse=0.734584\n",
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            "INFO:root:Epoch[32] Train-rmse=0.734783\n",
            "INFO:root:Epoch[32] Time cost=2.679\n",
            "INFO:root:Epoch[32] Validation-rmse=0.825351\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 7121759.75 samples/sec\trmse=0.733793\n",
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            "INFO:root:Epoch[33] Train-rmse=0.733931\n",
            "INFO:root:Epoch[33] Time cost=2.681\n",
            "INFO:root:Epoch[33] Validation-rmse=0.825317\n",
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            "INFO:root:Epoch[34] Train-rmse=0.733057\n",
            "INFO:root:Epoch[34] Time cost=2.678\n",
            "INFO:root:Epoch[34] Validation-rmse=0.825559\n",
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            "INFO:root:Epoch[35] Train-rmse=0.732175\n",
            "INFO:root:Epoch[35] Time cost=2.669\n",
            "INFO:root:Epoch[35] Validation-rmse=0.826475\n",
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            "INFO:root:Epoch[36] Train-rmse=0.731311\n",
            "INFO:root:Epoch[36] Time cost=2.672\n",
            "INFO:root:Epoch[36] Validation-rmse=0.826881\n",
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            "INFO:root:Epoch[37] Train-rmse=0.730539\n",
            "INFO:root:Epoch[37] Time cost=2.675\n",
            "INFO:root:Epoch[37] Validation-rmse=0.826437\n",
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            "INFO:root:Epoch[38] Time cost=2.670\n",
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            "INFO:root:Epoch[39] Time cost=2.665\n",
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            "INFO:root:Epoch[40] Time cost=2.669\n",
            "INFO:root:Epoch[40] Validation-rmse=0.826026\n",
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            "INFO:root:Epoch[41] Train-rmse=0.728023\n",
            "INFO:root:Epoch[41] Time cost=2.672\n",
            "INFO:root:Epoch[41] Validation-rmse=0.825726\n",
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            "INFO:root:Epoch[42] Train-rmse=0.727481\n",
            "INFO:root:Epoch[42] Time cost=2.667\n",
            "INFO:root:Epoch[42] Validation-rmse=0.825975\n",
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            "INFO:root:Epoch[43] Time cost=2.668\n",
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            "INFO:root:Epoch[44] Train-rmse=0.726490\n",
            "INFO:root:Epoch[44] Time cost=2.669\n",
            "INFO:root:Epoch[44] Validation-rmse=0.825361\n",
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            "INFO:root:Epoch[45] Time cost=2.675\n",
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            "INFO:root:Epoch[46] Time cost=2.675\n",
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            "INFO:root:Epoch[47] Time cost=2.676\n",
            "INFO:root:Epoch[47] Validation-rmse=0.825721\n",
            "INFO:root:Epoch[48] Batch [0-250]\tSpeed: 7142409.53 samples/sec\trmse=0.724566\n",
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            "INFO:root:Epoch[48] Time cost=2.669\n",
            "INFO:root:Epoch[48] Validation-rmse=0.825708\n",
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            "INFO:root:Epoch[49] Train-rmse=0.724462\n",
            "INFO:root:Epoch[49] Time cost=2.673\n",
            "INFO:root:Epoch[49] Validation-rmse=0.826118\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cPFlWQOYvv_",
        "colab_type": "text"
      },
      "source": [
        "##5-layer Genre-Tag"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A82bK-nqYuej",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8dd7addf-7f4d-4dd4-9bc2-561394d04578"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': train_users, \n",
        "                             'movie': train_movies, \n",
        "                             'movie_genre': train_genres, \n",
        "                             'movie_tag': train_tags}, \n",
        "                            label=train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': valid_users, \n",
        "                            'movie': valid_movies, \n",
        "                            'movie_genre': valid_genres, \n",
        "                            'movie_tag': valid_tags}, \n",
        "                           label=valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25)\n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "# We need to add in a fourth embedding layer for tag\n",
        "movie_tag = mx.symbol.Variable(\"movie_tag\")\n",
        "movie_tag = mx.symbol.Embedding(data=movie_tag, input_dim=812, output_dim=5) # Set to 25\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre, movie_tag)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=16)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre','movie_tag'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 5489089.83 samples/sec\trmse=0.867227\n",
            "INFO:root:Epoch[0] Batch [500-750]\tSpeed: 5483371.92 samples/sec\trmse=0.860954\n",
            "INFO:root:Epoch[0] Train-rmse=1.128696\n",
            "INFO:root:Epoch[0] Time cost=3.543\n",
            "INFO:root:Epoch[0] Validation-rmse=0.858809\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 5469668.88 samples/sec\trmse=0.850972\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 5471019.32 samples/sec\trmse=0.839827\n",
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            "INFO:root:Epoch[1] Train-rmse=0.841494\n",
            "INFO:root:Epoch[1] Time cost=3.479\n",
            "INFO:root:Epoch[1] Validation-rmse=0.834736\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 5460926.04 samples/sec\trmse=0.829152\n",
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            "INFO:root:Epoch[2] Train-rmse=0.824978\n",
            "INFO:root:Epoch[2] Time cost=3.474\n",
            "INFO:root:Epoch[2] Validation-rmse=0.825693\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 5464705.51 samples/sec\trmse=0.816920\n",
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            "INFO:root:Epoch[3] Train-rmse=0.812933\n",
            "INFO:root:Epoch[3] Time cost=3.477\n",
            "INFO:root:Epoch[3] Validation-rmse=0.823801\n",
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            "INFO:root:Epoch[4] Train-rmse=0.802606\n",
            "INFO:root:Epoch[4] Time cost=3.481\n",
            "INFO:root:Epoch[4] Validation-rmse=0.828096\n",
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            "INFO:root:Epoch[5] Train-rmse=0.795636\n",
            "INFO:root:Epoch[5] Time cost=3.479\n",
            "INFO:root:Epoch[5] Validation-rmse=0.828831\n",
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            "INFO:root:Epoch[6] Train-rmse=0.790699\n",
            "INFO:root:Epoch[6] Time cost=3.480\n",
            "INFO:root:Epoch[6] Validation-rmse=0.829868\n",
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            "INFO:root:Epoch[7] Time cost=3.478\n",
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            "INFO:root:Epoch[8] Time cost=3.483\n",
            "INFO:root:Epoch[8] Validation-rmse=0.824700\n",
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            "INFO:root:Epoch[9] Time cost=3.489\n",
            "INFO:root:Epoch[9] Validation-rmse=0.821536\n",
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            "INFO:root:Epoch[10] Time cost=3.486\n",
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            "INFO:root:Epoch[11] Time cost=3.500\n",
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            "INFO:root:Epoch[12] Time cost=3.484\n",
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            "INFO:root:Epoch[13] Time cost=3.480\n",
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            "INFO:root:Epoch[14] Time cost=3.480\n",
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            "INFO:root:Epoch[49] Validation-rmse=0.844590\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ni9cOjNgxGf",
        "colab_type": "text"
      },
      "source": [
        "# Cold-start scenario"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_5mFrWBgzzD",
        "colab_type": "text"
      },
      "source": [
        "## Scenario setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XqC26lIg4V4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Randomly take out 10% of movies from rating matrix to build cold-start validation set\n",
        "unique_mov_ids = numpy.unique(data['movieId'])\n",
        "cs_movie_ids = numpy.random.choice(unique_mov_ids, size=2674, replace=False)\n",
        "cs_ratings_eval = data[data['movieId'].isin(cs_movie_ids)]\n",
        "cs_ratings_train = data[~data['movieId'].isin(cs_movie_ids)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TsZTEE0pIF4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training set\n",
        "cs_train_users = cs_ratings_train['userId'].values[:] - 1 # Offset by 1 so that the IDs start at 0\n",
        "cs_train_movies = cs_ratings_train['movieId'].values[:] - 1 # Offset by 1 so that the IDs start at 0\n",
        "cs_train_ratings = cs_ratings_train['rating'].values[:]\n",
        "cs_train_genres = numpy.array([labels[int(j)] for j in cs_train_movies])\n",
        "cs_train_tags = numpy.array([label_tag[int(j)] for j in cs_train_movies])\n",
        "\n",
        "# Validate set\n",
        "cs_valid_users = cs_ratings_eval['userId'].values[:] - 1 # Offset by 1 so that the IDs start at 0\n",
        "cs_valid_movies = cs_ratings_eval['movieId'].values[:] - 1 # Offset by 1 so that the IDs start at 0\n",
        "cs_valid_ratings = cs_ratings_eval['rating'].values[:]\n",
        "cs_valid_genres = numpy.array([labels[int(j)] for j in cs_valid_movies])\n",
        "cs_valid_tags = numpy.array([label_tag[int(j)] for j in cs_valid_movies])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tlixw2DEpKKm",
        "colab_type": "text"
      },
      "source": [
        "## Funk MF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isG0rKScpNZE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b6b2071c-d162-475b-be3d-8ef5de6c98cd"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': cs_train_users, 'movie': cs_train_movies}, \n",
        "                           label=cs_train_ratings, batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': cs_valid_users, 'movie': cs_valid_movies}, \n",
        "                          label=cs_valid_ratings, batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25)\n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25)\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "y_pred = mx.symbol.sum_axis(data=(user * movie), axis=1)\n",
        "y_pred = mx.symbol.flatten(y_pred)\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=y_pred, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie'), symbol=y_pred)\n",
        "model.fit(X_train, \n",
        "          num_epoch=50, \n",
        "          optimizer='adam', \n",
        "          optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', \n",
        "          eval_data=X_eval, \n",
        "          batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Epoch[0] Batch [0-250]\tSpeed: 15855439.12 samples/sec\trmse=3.361134\n",
            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 16537373.81 samples/sec\trmse=1.794214\n",
            "INFO:root:Epoch[0] Train-rmse=2.140401\n",
            "INFO:root:Epoch[0] Time cost=1.220\n",
            "INFO:root:Epoch[0] Validation-rmse=3.700145\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 16153947.59 samples/sec\trmse=1.009323\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 16298250.83 samples/sec\trmse=0.939167\n",
            "INFO:root:Epoch[1] Train-rmse=0.953325\n",
            "INFO:root:Epoch[1] Time cost=1.131\n",
            "INFO:root:Epoch[1] Validation-rmse=3.700102\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 16539575.38 samples/sec\trmse=0.891286\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 15967021.24 samples/sec\trmse=0.882583\n",
            "INFO:root:Epoch[2] Train-rmse=0.883990\n",
            "INFO:root:Epoch[2] Time cost=1.129\n",
            "INFO:root:Epoch[2] Validation-rmse=3.700084\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 16505605.05 samples/sec\trmse=0.874326\n",
            "INFO:root:Epoch[3] Batch [250-500]\tSpeed: 16209825.43 samples/sec\trmse=0.872086\n",
            "INFO:root:Epoch[3] Train-rmse=0.872343\n",
            "INFO:root:Epoch[3] Time cost=1.121\n",
            "INFO:root:Epoch[3] Validation-rmse=3.700071\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 16302903.25 samples/sec\trmse=0.869109\n",
            "INFO:root:Epoch[4] Batch [250-500]\tSpeed: 16586899.25 samples/sec\trmse=0.867781\n",
            "INFO:root:Epoch[4] Train-rmse=0.867837\n",
            "INFO:root:Epoch[4] Time cost=1.118\n",
            "INFO:root:Epoch[4] Validation-rmse=3.700060\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 16317780.41 samples/sec\trmse=0.865138\n",
            "INFO:root:Epoch[5] Batch [250-500]\tSpeed: 16544857.38 samples/sec\trmse=0.863297\n",
            "INFO:root:Epoch[5] Train-rmse=0.863266\n",
            "INFO:root:Epoch[5] Time cost=1.119\n",
            "INFO:root:Epoch[5] Validation-rmse=3.700049\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 16365905.45 samples/sec\trmse=0.858692\n",
            "INFO:root:Epoch[6] Batch [250-500]\tSpeed: 16187204.07 samples/sec\trmse=0.855418\n",
            "INFO:root:Epoch[6] Train-rmse=0.855487\n",
            "INFO:root:Epoch[6] Time cost=1.128\n",
            "INFO:root:Epoch[6] Validation-rmse=3.700037\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 16302568.67 samples/sec\trmse=0.848667\n",
            "INFO:root:Epoch[7] Batch [250-500]\tSpeed: 16283075.46 samples/sec\trmse=0.845014\n",
            "INFO:root:Epoch[7] Train-rmse=0.845300\n",
            "INFO:root:Epoch[7] Time cost=1.125\n",
            "INFO:root:Epoch[7] Validation-rmse=3.700025\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 16538573.64 samples/sec\trmse=0.839177\n",
            "INFO:root:Epoch[8] Batch [250-500]\tSpeed: 16234017.97 samples/sec\trmse=0.836322\n",
            "INFO:root:Epoch[8] Train-rmse=0.836553\n",
            "INFO:root:Epoch[8] Time cost=1.123\n",
            "INFO:root:Epoch[8] Validation-rmse=3.700016\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 16339169.40 samples/sec\trmse=0.831617\n",
            "INFO:root:Epoch[9] Batch [250-500]\tSpeed: 16462619.30 samples/sec\trmse=0.828964\n",
            "INFO:root:Epoch[9] Train-rmse=0.829120\n",
            "INFO:root:Epoch[9] Time cost=1.124\n",
            "INFO:root:Epoch[9] Validation-rmse=3.700010\n",
            "INFO:root:Epoch[10] Batch [0-250]\tSpeed: 16252436.53 samples/sec\trmse=0.824027\n",
            "INFO:root:Epoch[10] Batch [250-500]\tSpeed: 16211880.49 samples/sec\trmse=0.821054\n",
            "INFO:root:Epoch[10] Train-rmse=0.821216\n",
            "INFO:root:Epoch[10] Time cost=1.126\n",
            "INFO:root:Epoch[10] Validation-rmse=3.700005\n",
            "INFO:root:Epoch[11] Batch [0-250]\tSpeed: 16439194.60 samples/sec\trmse=0.815445\n",
            "INFO:root:Epoch[11] Batch [250-500]\tSpeed: 16581454.03 samples/sec\trmse=0.812179\n",
            "INFO:root:Epoch[11] Train-rmse=0.812369\n",
            "INFO:root:Epoch[11] Time cost=1.118\n",
            "INFO:root:Epoch[11] Validation-rmse=3.700003\n",
            "INFO:root:Epoch[12] Batch [0-250]\tSpeed: 16257022.49 samples/sec\trmse=0.806149\n",
            "INFO:root:Epoch[12] Batch [250-500]\tSpeed: 16320055.98 samples/sec\trmse=0.802745\n",
            "INFO:root:Epoch[12] Train-rmse=0.802935\n",
            "INFO:root:Epoch[12] Time cost=1.123\n",
            "INFO:root:Epoch[12] Validation-rmse=3.700001\n",
            "INFO:root:Epoch[13] Batch [0-250]\tSpeed: 16312886.01 samples/sec\trmse=0.796472\n",
            "INFO:root:Epoch[13] Batch [250-500]\tSpeed: 16220025.68 samples/sec\trmse=0.793034\n",
            "INFO:root:Epoch[13] Train-rmse=0.793214\n",
            "INFO:root:Epoch[13] Time cost=1.129\n",
            "INFO:root:Epoch[13] Validation-rmse=3.699998\n",
            "INFO:root:Epoch[14] Batch [0-250]\tSpeed: 16021346.80 samples/sec\trmse=0.786715\n",
            "INFO:root:Epoch[14] Batch [250-500]\tSpeed: 16227033.87 samples/sec\trmse=0.783374\n",
            "INFO:root:Epoch[14] Train-rmse=0.783535\n",
            "INFO:root:Epoch[14] Time cost=1.129\n",
            "INFO:root:Epoch[14] Validation-rmse=3.699995\n",
            "INFO:root:Epoch[15] Batch [0-250]\tSpeed: 16075491.23 samples/sec\trmse=0.777204\n",
            "INFO:root:Epoch[15] Batch [250-500]\tSpeed: 16000859.42 samples/sec\trmse=0.774003\n",
            "INFO:root:Epoch[15] Train-rmse=0.774134\n",
            "INFO:root:Epoch[15] Time cost=1.138\n",
            "INFO:root:Epoch[15] Validation-rmse=3.699991\n",
            "INFO:root:Epoch[16] Batch [0-250]\tSpeed: 16107781.75 samples/sec\trmse=0.767968\n",
            "INFO:root:Epoch[16] Batch [250-500]\tSpeed: 16271008.02 samples/sec\trmse=0.764853\n",
            "INFO:root:Epoch[16] Train-rmse=0.764958\n",
            "INFO:root:Epoch[16] Time cost=1.133\n",
            "INFO:root:Epoch[16] Validation-rmse=3.699988\n",
            "INFO:root:Epoch[17] Batch [0-250]\tSpeed: 16350685.48 samples/sec\trmse=0.758865\n",
            "INFO:root:Epoch[17] Batch [250-500]\tSpeed: 16073421.32 samples/sec\trmse=0.755798\n",
            "INFO:root:Epoch[17] Train-rmse=0.755886\n",
            "INFO:root:Epoch[17] Time cost=1.126\n",
            "INFO:root:Epoch[17] Validation-rmse=3.699985\n",
            "INFO:root:Epoch[18] Batch [0-250]\tSpeed: 16505480.34 samples/sec\trmse=0.749853\n",
            "INFO:root:Epoch[18] Batch [250-500]\tSpeed: 16301605.57 samples/sec\trmse=0.746863\n",
            "INFO:root:Epoch[18] Train-rmse=0.746936\n",
            "INFO:root:Epoch[18] Time cost=1.121\n",
            "INFO:root:Epoch[18] Validation-rmse=3.699982\n",
            "INFO:root:Epoch[19] Batch [0-250]\tSpeed: 16114267.31 samples/sec\trmse=0.741056\n",
            "INFO:root:Epoch[19] Batch [250-500]\tSpeed: 16260319.94 samples/sec\trmse=0.738211\n",
            "INFO:root:Epoch[19] Train-rmse=0.738269\n",
            "INFO:root:Epoch[19] Time cost=1.127\n",
            "INFO:root:Epoch[19] Validation-rmse=3.699980\n",
            "INFO:root:Epoch[20] Batch [0-250]\tSpeed: 16195454.54 samples/sec\trmse=0.732681\n",
            "INFO:root:Epoch[20] Batch [250-500]\tSpeed: 16244188.30 samples/sec\trmse=0.730052\n",
            "INFO:root:Epoch[20] Train-rmse=0.730092\n",
            "INFO:root:Epoch[20] Time cost=1.133\n",
            "INFO:root:Epoch[20] Validation-rmse=3.699977\n",
            "INFO:root:Epoch[21] Batch [0-250]\tSpeed: 16381532.53 samples/sec\trmse=0.724919\n",
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            "INFO:root:Epoch[21] Train-rmse=0.722574\n",
            "INFO:root:Epoch[21] Time cost=1.121\n",
            "INFO:root:Epoch[21] Validation-rmse=3.699975\n",
            "INFO:root:Epoch[22] Batch [0-250]\tSpeed: 16136158.69 samples/sec\trmse=0.717891\n",
            "INFO:root:Epoch[22] Batch [250-500]\tSpeed: 16002988.84 samples/sec\trmse=0.715807\n",
            "INFO:root:Epoch[22] Train-rmse=0.715811\n",
            "INFO:root:Epoch[22] Time cost=1.136\n",
            "INFO:root:Epoch[22] Validation-rmse=3.699973\n",
            "INFO:root:Epoch[23] Batch [0-250]\tSpeed: 16489367.06 samples/sec\trmse=0.711644\n",
            "INFO:root:Epoch[23] Batch [250-500]\tSpeed: 15965368.09 samples/sec\trmse=0.709842\n",
            "INFO:root:Epoch[23] Train-rmse=0.709828\n",
            "INFO:root:Epoch[23] Time cost=1.134\n",
            "INFO:root:Epoch[23] Validation-rmse=3.699971\n",
            "INFO:root:Epoch[24] Batch [0-250]\tSpeed: 16146912.86 samples/sec\trmse=0.706164\n",
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            "INFO:root:Epoch[24] Train-rmse=0.704596\n",
            "INFO:root:Epoch[24] Time cost=1.122\n",
            "INFO:root:Epoch[24] Validation-rmse=3.699969\n",
            "INFO:root:Epoch[25] Batch [0-250]\tSpeed: 16334160.39 samples/sec\trmse=0.701391\n",
            "INFO:root:Epoch[25] Batch [250-500]\tSpeed: 16255439.79 samples/sec\trmse=0.700087\n",
            "INFO:root:Epoch[25] Train-rmse=0.700043\n",
            "INFO:root:Epoch[25] Time cost=1.129\n",
            "INFO:root:Epoch[25] Validation-rmse=3.699968\n",
            "INFO:root:Epoch[26] Batch [0-250]\tSpeed: 16370044.54 samples/sec\trmse=0.697236\n",
            "INFO:root:Epoch[26] Batch [250-500]\tSpeed: 16420484.26 samples/sec\trmse=0.696132\n",
            "INFO:root:Epoch[26] Train-rmse=0.696076\n",
            "INFO:root:Epoch[26] Time cost=1.121\n",
            "INFO:root:Epoch[26] Validation-rmse=3.699966\n",
            "INFO:root:Epoch[27] Batch [0-250]\tSpeed: 16298909.51 samples/sec\trmse=0.693603\n",
            "INFO:root:Epoch[27] Batch [250-500]\tSpeed: 15969443.23 samples/sec\trmse=0.692665\n",
            "INFO:root:Epoch[27] Train-rmse=0.692600\n",
            "INFO:root:Epoch[27] Time cost=1.133\n",
            "INFO:root:Epoch[27] Validation-rmse=3.699964\n",
            "INFO:root:Epoch[28] Batch [0-250]\tSpeed: 16322383.00 samples/sec\trmse=0.690402\n",
            "INFO:root:Epoch[28] Batch [250-500]\tSpeed: 16090588.13 samples/sec\trmse=0.689603\n",
            "INFO:root:Epoch[28] Train-rmse=0.689531\n",
            "INFO:root:Epoch[28] Time cost=1.128\n",
            "INFO:root:Epoch[28] Validation-rmse=3.699963\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 16021258.68 samples/sec\trmse=0.687561\n",
            "INFO:root:Epoch[29] Batch [250-500]\tSpeed: 16349135.47 samples/sec\trmse=0.686877\n",
            "INFO:root:Epoch[29] Train-rmse=0.686799\n",
            "INFO:root:Epoch[29] Time cost=1.131\n",
            "INFO:root:Epoch[29] Validation-rmse=3.699961\n",
            "INFO:root:Epoch[30] Batch [0-250]\tSpeed: 16103596.13 samples/sec\trmse=0.685018\n",
            "INFO:root:Epoch[30] Batch [250-500]\tSpeed: 16182917.14 samples/sec\trmse=0.684432\n",
            "INFO:root:Epoch[30] Train-rmse=0.684349\n",
            "INFO:root:Epoch[30] Time cost=1.137\n",
            "INFO:root:Epoch[30] Validation-rmse=3.699960\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 16290168.63 samples/sec\trmse=0.682727\n",
            "INFO:root:Epoch[31] Batch [250-500]\tSpeed: 15957466.88 samples/sec\trmse=0.682223\n",
            "INFO:root:Epoch[31] Train-rmse=0.682137\n",
            "INFO:root:Epoch[31] Time cost=1.139\n",
            "INFO:root:Epoch[31] Validation-rmse=3.699958\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 16139029.70 samples/sec\trmse=0.680650\n",
            "INFO:root:Epoch[32] Batch [250-500]\tSpeed: 15954310.53 samples/sec\trmse=0.680217\n",
            "INFO:root:Epoch[32] Train-rmse=0.680128\n",
            "INFO:root:Epoch[32] Time cost=1.136\n",
            "INFO:root:Epoch[32] Validation-rmse=3.699957\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 16138175.25 samples/sec\trmse=0.678757\n",
            "INFO:root:Epoch[33] Batch [250-500]\tSpeed: 16020397.06 samples/sec\trmse=0.678385\n",
            "INFO:root:Epoch[33] Train-rmse=0.678294\n",
            "INFO:root:Epoch[33] Time cost=1.138\n",
            "INFO:root:Epoch[33] Validation-rmse=3.699955\n",
            "INFO:root:Epoch[34] Batch [0-250]\tSpeed: 16083865.03 samples/sec\trmse=0.677023\n",
            "INFO:root:Epoch[34] Batch [250-500]\tSpeed: 16164765.37 samples/sec\trmse=0.676705\n",
            "INFO:root:Epoch[34] Train-rmse=0.676612\n",
            "INFO:root:Epoch[34] Time cost=1.132\n",
            "INFO:root:Epoch[34] Validation-rmse=3.699954\n",
            "INFO:root:Epoch[35] Batch [0-250]\tSpeed: 16102913.58 samples/sec\trmse=0.675429\n",
            "INFO:root:Epoch[35] Batch [250-500]\tSpeed: 16099808.20 samples/sec\trmse=0.675157\n",
            "INFO:root:Epoch[35] Train-rmse=0.675063\n",
            "INFO:root:Epoch[35] Time cost=1.140\n",
            "INFO:root:Epoch[35] Validation-rmse=3.699952\n",
            "INFO:root:Epoch[36] Batch [0-250]\tSpeed: 16343865.60 samples/sec\trmse=0.673957\n",
            "INFO:root:Epoch[36] Batch [250-500]\tSpeed: 16132067.52 samples/sec\trmse=0.673728\n",
            "INFO:root:Epoch[36] Train-rmse=0.673632\n",
            "INFO:root:Epoch[36] Time cost=1.129\n",
            "INFO:root:Epoch[36] Validation-rmse=3.699951\n",
            "INFO:root:Epoch[37] Batch [0-250]\tSpeed: 16442896.40 samples/sec\trmse=0.672594\n",
            "INFO:root:Epoch[37] Batch [250-500]\tSpeed: 15981252.46 samples/sec\trmse=0.672402\n",
            "INFO:root:Epoch[37] Train-rmse=0.672305\n",
            "INFO:root:Epoch[37] Time cost=1.127\n",
            "INFO:root:Epoch[37] Validation-rmse=3.699950\n",
            "INFO:root:Epoch[38] Batch [0-250]\tSpeed: 16369686.76 samples/sec\trmse=0.671329\n",
            "INFO:root:Epoch[38] Batch [250-500]\tSpeed: 16135701.80 samples/sec\trmse=0.671169\n",
            "INFO:root:Epoch[38] Train-rmse=0.671071\n",
            "INFO:root:Epoch[38] Time cost=1.120\n",
            "INFO:root:Epoch[38] Validation-rmse=3.699948\n",
            "INFO:root:Epoch[39] Batch [0-250]\tSpeed: 16278808.38 samples/sec\trmse=0.670150\n",
            "INFO:root:Epoch[39] Batch [250-500]\tSpeed: 16092336.46 samples/sec\trmse=0.670021\n",
            "INFO:root:Epoch[39] Train-rmse=0.669921\n",
            "INFO:root:Epoch[39] Time cost=1.130\n",
            "INFO:root:Epoch[39] Validation-rmse=3.699947\n",
            "INFO:root:Epoch[40] Batch [0-250]\tSpeed: 16310653.03 samples/sec\trmse=0.669049\n",
            "INFO:root:Epoch[40] Batch [250-500]\tSpeed: 16318146.08 samples/sec\trmse=0.668947\n",
            "INFO:root:Epoch[40] Train-rmse=0.668846\n",
            "INFO:root:Epoch[40] Time cost=1.131\n",
            "INFO:root:Epoch[40] Validation-rmse=3.699946\n",
            "INFO:root:Epoch[41] Batch [0-250]\tSpeed: 16511998.95 samples/sec\trmse=0.668019\n",
            "INFO:root:Epoch[41] Batch [250-500]\tSpeed: 16184955.40 samples/sec\trmse=0.667942\n",
            "INFO:root:Epoch[41] Train-rmse=0.667840\n",
            "INFO:root:Epoch[41] Time cost=1.124\n",
            "INFO:root:Epoch[41] Validation-rmse=3.699944\n",
            "INFO:root:Epoch[42] Batch [0-250]\tSpeed: 16332939.15 samples/sec\trmse=0.667053\n",
            "INFO:root:Epoch[42] Batch [250-500]\tSpeed: 15957233.76 samples/sec\trmse=0.666999\n",
            "INFO:root:Epoch[42] Train-rmse=0.666895\n",
            "INFO:root:Epoch[42] Time cost=1.132\n",
            "INFO:root:Epoch[42] Validation-rmse=3.699943\n",
            "INFO:root:Epoch[43] Batch [0-250]\tSpeed: 16068938.33 samples/sec\trmse=0.666145\n",
            "INFO:root:Epoch[43] Batch [250-500]\tSpeed: 15879873.56 samples/sec\trmse=0.666113\n",
            "INFO:root:Epoch[43] Train-rmse=0.666008\n",
            "INFO:root:Epoch[43] Time cost=1.137\n",
            "INFO:root:Epoch[43] Validation-rmse=3.699942\n",
            "INFO:root:Epoch[44] Batch [0-250]\tSpeed: 16094213.62 samples/sec\trmse=0.665291\n",
            "INFO:root:Epoch[44] Batch [250-500]\tSpeed: 16705635.69 samples/sec\trmse=0.665278\n",
            "INFO:root:Epoch[44] Train-rmse=0.665171\n",
            "INFO:root:Epoch[44] Time cost=1.120\n",
            "INFO:root:Epoch[44] Validation-rmse=3.699941\n",
            "INFO:root:Epoch[45] Batch [0-250]\tSpeed: 16371782.56 samples/sec\trmse=0.664486\n",
            "INFO:root:Epoch[45] Batch [250-500]\tSpeed: 16335280.03 samples/sec\trmse=0.664491\n",
            "INFO:root:Epoch[45] Train-rmse=0.664383\n",
            "INFO:root:Epoch[45] Time cost=1.125\n",
            "INFO:root:Epoch[45] Validation-rmse=3.699939\n",
            "INFO:root:Epoch[46] Batch [0-250]\tSpeed: 16421420.31 samples/sec\trmse=0.663725\n",
            "INFO:root:Epoch[46] Batch [250-500]\tSpeed: 16282246.14 samples/sec\trmse=0.663747\n",
            "INFO:root:Epoch[46] Train-rmse=0.663637\n",
            "INFO:root:Epoch[46] Time cost=1.120\n",
            "INFO:root:Epoch[46] Validation-rmse=3.699938\n",
            "INFO:root:Epoch[47] Batch [0-250]\tSpeed: 16152255.51 samples/sec\trmse=0.663006\n",
            "INFO:root:Epoch[47] Batch [250-500]\tSpeed: 16233967.70 samples/sec\trmse=0.663043\n",
            "INFO:root:Epoch[47] Train-rmse=0.662932\n",
            "INFO:root:Epoch[47] Time cost=1.133\n",
            "INFO:root:Epoch[47] Validation-rmse=3.699937\n",
            "INFO:root:Epoch[48] Batch [0-250]\tSpeed: 15246727.53 samples/sec\trmse=0.662325\n",
            "INFO:root:Epoch[48] Batch [250-500]\tSpeed: 15585542.90 samples/sec\trmse=0.662376\n",
            "INFO:root:Epoch[48] Train-rmse=0.662264\n",
            "INFO:root:Epoch[48] Time cost=1.192\n",
            "INFO:root:Epoch[48] Validation-rmse=3.699936\n",
            "INFO:root:Epoch[49] Batch [0-250]\tSpeed: 15338481.19 samples/sec\trmse=0.661679\n",
            "INFO:root:Epoch[49] Batch [250-500]\tSpeed: 15504250.09 samples/sec\trmse=0.661744\n",
            "INFO:root:Epoch[49] Train-rmse=0.661629\n",
            "INFO:root:Epoch[49] Time cost=1.187\n",
            "INFO:root:Epoch[49] Validation-rmse=3.699934\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMoZUspTouc6",
        "colab_type": "text"
      },
      "source": [
        "## DCF (4 layers)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxX_pTN_otC7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "62b68166-792d-4ab5-ac05-4f138df32473"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': cs_train_users, \n",
        "                             'movie': cs_train_movies}, \n",
        "                            label=cs_train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': cs_valid_users, \n",
        "                            'movie': cs_valid_movies}, \n",
        "                           label=cs_valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "\n",
        "# Create 4 FullyConnected layers\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Epoch[0] Batch [0-250]\tSpeed: 7562986.42 samples/sec\trmse=1.475015\n",
            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 7629649.53 samples/sec\trmse=0.865733\n",
            "INFO:root:Epoch[0] Train-rmse=1.072613\n",
            "INFO:root:Epoch[0] Time cost=2.487\n",
            "INFO:root:Epoch[0] Validation-rmse=1.455735\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 7642223.24 samples/sec\trmse=0.844711\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 7599698.50 samples/sec\trmse=0.835576\n",
            "INFO:root:Epoch[1] Train-rmse=0.837091\n",
            "INFO:root:Epoch[1] Time cost=2.418\n",
            "INFO:root:Epoch[1] Validation-rmse=1.404538\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 7446322.90 samples/sec\trmse=0.825552\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 7465761.09 samples/sec\trmse=0.820599\n",
            "INFO:root:Epoch[2] Train-rmse=0.821255\n",
            "INFO:root:Epoch[2] Time cost=2.457\n",
            "INFO:root:Epoch[2] Validation-rmse=1.356875\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 7449920.41 samples/sec\trmse=0.812915\n",
            "INFO:root:Epoch[3] Batch [250-500]\tSpeed: 7436261.99 samples/sec\trmse=0.809340\n",
            "INFO:root:Epoch[3] Train-rmse=0.809796\n",
            "INFO:root:Epoch[3] Time cost=2.453\n",
            "INFO:root:Epoch[3] Validation-rmse=1.327557\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 7422409.76 samples/sec\trmse=0.801100\n",
            "INFO:root:Epoch[4] Batch [250-500]\tSpeed: 7441408.39 samples/sec\trmse=0.798175\n",
            "INFO:root:Epoch[4] Train-rmse=0.798607\n",
            "INFO:root:Epoch[4] Time cost=2.463\n",
            "INFO:root:Epoch[4] Validation-rmse=1.322434\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 7393330.82 samples/sec\trmse=0.790746\n",
            "INFO:root:Epoch[5] Batch [250-500]\tSpeed: 7430880.46 samples/sec\trmse=0.787787\n",
            "INFO:root:Epoch[5] Train-rmse=0.788252\n",
            "INFO:root:Epoch[5] Time cost=2.455\n",
            "INFO:root:Epoch[5] Validation-rmse=1.306037\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 7576777.51 samples/sec\trmse=0.781188\n",
            "INFO:root:Epoch[6] Batch [250-500]\tSpeed: 7573922.92 samples/sec\trmse=0.779600\n",
            "INFO:root:Epoch[6] Train-rmse=0.779616\n",
            "INFO:root:Epoch[6] Time cost=2.409\n",
            "INFO:root:Epoch[6] Validation-rmse=1.291197\n",
            "INFO:root:Epoch[7] Batch [0-250]\tSpeed: 7614028.61 samples/sec\trmse=0.775486\n",
            "INFO:root:Epoch[7] Batch [250-500]\tSpeed: 7606271.90 samples/sec\trmse=0.774910\n",
            "INFO:root:Epoch[7] Train-rmse=0.774536\n",
            "INFO:root:Epoch[7] Time cost=2.401\n",
            "INFO:root:Epoch[7] Validation-rmse=1.287366\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 7608252.10 samples/sec\trmse=0.771871\n",
            "INFO:root:Epoch[8] Batch [250-500]\tSpeed: 7596271.87 samples/sec\trmse=0.770920\n",
            "INFO:root:Epoch[8] Train-rmse=0.771203\n",
            "INFO:root:Epoch[8] Time cost=2.412\n",
            "INFO:root:Epoch[8] Validation-rmse=1.279065\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 7639724.33 samples/sec\trmse=0.769504\n",
            "INFO:root:Epoch[9] Batch [250-500]\tSpeed: 7631964.09 samples/sec\trmse=0.769106\n",
            "INFO:root:Epoch[9] Train-rmse=0.769123\n",
            "INFO:root:Epoch[9] Time cost=2.392\n",
            "INFO:root:Epoch[9] Validation-rmse=1.273941\n",
            "INFO:root:Epoch[10] Batch [0-250]\tSpeed: 7616138.97 samples/sec\trmse=0.767574\n",
            "INFO:root:Epoch[10] Batch [250-500]\tSpeed: 7604034.65 samples/sec\trmse=0.765767\n",
            "INFO:root:Epoch[10] Train-rmse=0.766261\n",
            "INFO:root:Epoch[10] Time cost=2.398\n",
            "INFO:root:Epoch[10] Validation-rmse=1.265731\n",
            "INFO:root:Epoch[11] Batch [0-250]\tSpeed: 7582545.77 samples/sec\trmse=0.764449\n",
            "INFO:root:Epoch[11] Batch [250-500]\tSpeed: 7617604.09 samples/sec\trmse=0.762783\n",
            "INFO:root:Epoch[11] Train-rmse=0.763335\n",
            "INFO:root:Epoch[11] Time cost=2.403\n",
            "INFO:root:Epoch[11] Validation-rmse=1.262411\n",
            "INFO:root:Epoch[12] Batch [0-250]\tSpeed: 7611335.94 samples/sec\trmse=0.761271\n",
            "INFO:root:Epoch[12] Batch [250-500]\tSpeed: 7668632.60 samples/sec\trmse=0.759523\n",
            "INFO:root:Epoch[12] Train-rmse=0.760322\n",
            "INFO:root:Epoch[12] Time cost=2.398\n",
            "INFO:root:Epoch[12] Validation-rmse=1.254285\n",
            "INFO:root:Epoch[13] Batch [0-250]\tSpeed: 7638444.33 samples/sec\trmse=0.758438\n",
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            "INFO:root:Epoch[13] Train-rmse=0.757217\n",
            "INFO:root:Epoch[13] Time cost=2.403\n",
            "INFO:root:Epoch[13] Validation-rmse=1.243164\n",
            "INFO:root:Epoch[14] Batch [0-250]\tSpeed: 7635218.40 samples/sec\trmse=0.754106\n",
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            "INFO:root:Epoch[14] Train-rmse=0.753520\n",
            "INFO:root:Epoch[14] Time cost=2.395\n",
            "INFO:root:Epoch[14] Validation-rmse=1.238525\n",
            "INFO:root:Epoch[15] Batch [0-250]\tSpeed: 7612942.91 samples/sec\trmse=0.751100\n",
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            "INFO:root:Epoch[15] Train-rmse=0.749869\n",
            "INFO:root:Epoch[15] Time cost=2.394\n",
            "INFO:root:Epoch[15] Validation-rmse=1.245409\n",
            "INFO:root:Epoch[16] Batch [0-250]\tSpeed: 7635096.09 samples/sec\trmse=0.746506\n",
            "INFO:root:Epoch[16] Batch [250-500]\tSpeed: 7641049.30 samples/sec\trmse=0.745476\n",
            "INFO:root:Epoch[16] Train-rmse=0.745489\n",
            "INFO:root:Epoch[16] Time cost=2.395\n",
            "INFO:root:Epoch[16] Validation-rmse=1.243422\n",
            "INFO:root:Epoch[17] Batch [0-250]\tSpeed: 7613776.50 samples/sec\trmse=0.743314\n",
            "INFO:root:Epoch[17] Batch [250-500]\tSpeed: 7552520.97 samples/sec\trmse=0.741908\n",
            "INFO:root:Epoch[17] Train-rmse=0.742021\n",
            "INFO:root:Epoch[17] Time cost=2.407\n",
            "INFO:root:Epoch[17] Validation-rmse=1.241836\n",
            "INFO:root:Epoch[18] Batch [0-250]\tSpeed: 7659790.57 samples/sec\trmse=0.739773\n",
            "INFO:root:Epoch[18] Batch [250-500]\tSpeed: 7607333.62 samples/sec\trmse=0.738286\n",
            "INFO:root:Epoch[18] Train-rmse=0.738647\n",
            "INFO:root:Epoch[18] Time cost=2.393\n",
            "INFO:root:Epoch[18] Validation-rmse=1.249613\n",
            "INFO:root:Epoch[19] Batch [0-250]\tSpeed: 7613234.76 samples/sec\trmse=0.736616\n",
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            "INFO:root:Epoch[19] Train-rmse=0.735597\n",
            "INFO:root:Epoch[19] Time cost=2.400\n",
            "INFO:root:Epoch[19] Validation-rmse=1.245097\n",
            "INFO:root:Epoch[20] Batch [0-250]\tSpeed: 7627695.91 samples/sec\trmse=0.733353\n",
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            "INFO:root:Epoch[20] Time cost=2.392\n",
            "INFO:root:Epoch[20] Validation-rmse=1.253805\n",
            "INFO:root:Epoch[21] Batch [0-250]\tSpeed: 7632992.99 samples/sec\trmse=0.730741\n",
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            "INFO:root:Epoch[21] Train-rmse=0.729791\n",
            "INFO:root:Epoch[21] Time cost=2.395\n",
            "INFO:root:Epoch[21] Validation-rmse=1.256414\n",
            "INFO:root:Epoch[22] Batch [0-250]\tSpeed: 7567455.53 samples/sec\trmse=0.727950\n",
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            "INFO:root:Epoch[22] Time cost=2.470\n",
            "INFO:root:Epoch[22] Validation-rmse=1.264424\n",
            "INFO:root:Epoch[23] Batch [0-250]\tSpeed: 7648212.12 samples/sec\trmse=0.725663\n",
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            "INFO:root:Epoch[23] Time cost=2.395\n",
            "INFO:root:Epoch[23] Validation-rmse=1.264889\n",
            "INFO:root:Epoch[24] Batch [0-250]\tSpeed: 7629300.91 samples/sec\trmse=0.723142\n",
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            "INFO:root:Epoch[24] Train-rmse=0.722105\n",
            "INFO:root:Epoch[24] Time cost=2.394\n",
            "INFO:root:Epoch[24] Validation-rmse=1.272207\n",
            "INFO:root:Epoch[25] Batch [0-250]\tSpeed: 7615008.43 samples/sec\trmse=0.720850\n",
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            "INFO:root:Epoch[25] Train-rmse=0.719769\n",
            "INFO:root:Epoch[25] Time cost=2.397\n",
            "INFO:root:Epoch[25] Validation-rmse=1.273820\n",
            "INFO:root:Epoch[26] Batch [0-250]\tSpeed: 7627760.27 samples/sec\trmse=0.718749\n",
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            "INFO:root:Epoch[26] Train-rmse=0.717641\n",
            "INFO:root:Epoch[26] Time cost=2.398\n",
            "INFO:root:Epoch[26] Validation-rmse=1.272991\n",
            "INFO:root:Epoch[27] Batch [0-250]\tSpeed: 7622603.43 samples/sec\trmse=0.716555\n",
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            "INFO:root:Epoch[27] Train-rmse=0.715534\n",
            "INFO:root:Epoch[27] Time cost=2.396\n",
            "INFO:root:Epoch[27] Validation-rmse=1.267794\n",
            "INFO:root:Epoch[28] Batch [0-250]\tSpeed: 7626706.16 samples/sec\trmse=0.714428\n",
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            "INFO:root:Epoch[28] Train-rmse=0.713539\n",
            "INFO:root:Epoch[28] Time cost=2.392\n",
            "INFO:root:Epoch[28] Validation-rmse=1.261827\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 7612372.54 samples/sec\trmse=0.712400\n",
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            "INFO:root:Epoch[29] Train-rmse=0.711682\n",
            "INFO:root:Epoch[29] Time cost=2.398\n",
            "INFO:root:Epoch[29] Validation-rmse=1.274374\n",
            "INFO:root:Epoch[30] Batch [0-250]\tSpeed: 7650200.82 samples/sec\trmse=0.710875\n",
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            "INFO:root:Epoch[30] Train-rmse=0.710285\n",
            "INFO:root:Epoch[30] Time cost=2.398\n",
            "INFO:root:Epoch[30] Validation-rmse=1.284459\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 7648042.53 samples/sec\trmse=0.709739\n",
            "INFO:root:Epoch[31] Batch [250-500]\tSpeed: 7609237.07 samples/sec\trmse=0.708417\n",
            "INFO:root:Epoch[31] Train-rmse=0.709018\n",
            "INFO:root:Epoch[31] Time cost=2.398\n",
            "INFO:root:Epoch[31] Validation-rmse=1.292538\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 7649261.03 samples/sec\trmse=0.707976\n",
            "INFO:root:Epoch[32] Batch [250-500]\tSpeed: 7642515.11 samples/sec\trmse=0.706748\n",
            "INFO:root:Epoch[32] Train-rmse=0.707320\n",
            "INFO:root:Epoch[32] Time cost=2.389\n",
            "INFO:root:Epoch[32] Validation-rmse=1.298489\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 7602689.41 samples/sec\trmse=0.706294\n",
            "INFO:root:Epoch[33] Batch [250-500]\tSpeed: 7628057.69 samples/sec\trmse=0.704969\n",
            "INFO:root:Epoch[33] Train-rmse=0.705675\n",
            "INFO:root:Epoch[33] Time cost=2.401\n",
            "INFO:root:Epoch[33] Validation-rmse=1.293873\n",
            "INFO:root:Epoch[34] Batch [0-250]\tSpeed: 7602012.56 samples/sec\trmse=0.704664\n",
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            "INFO:root:Epoch[34] Train-rmse=0.704319\n",
            "INFO:root:Epoch[34] Time cost=2.406\n",
            "INFO:root:Epoch[34] Validation-rmse=1.287668\n",
            "INFO:root:Epoch[35] Batch [0-250]\tSpeed: 7644799.58 samples/sec\trmse=0.703298\n",
            "INFO:root:Epoch[35] Batch [250-500]\tSpeed: 7601833.99 samples/sec\trmse=0.702866\n",
            "INFO:root:Epoch[35] Train-rmse=0.703362\n",
            "INFO:root:Epoch[35] Time cost=2.398\n",
            "INFO:root:Epoch[35] Validation-rmse=1.278589\n",
            "INFO:root:Epoch[36] Batch [0-250]\tSpeed: 7634051.06 samples/sec\trmse=0.702255\n",
            "INFO:root:Epoch[36] Batch [250-500]\tSpeed: 7609482.24 samples/sec\trmse=0.701926\n",
            "INFO:root:Epoch[36] Train-rmse=0.702279\n",
            "INFO:root:Epoch[36] Time cost=2.402\n",
            "INFO:root:Epoch[36] Validation-rmse=1.272459\n",
            "INFO:root:Epoch[37] Batch [0-250]\tSpeed: 7584217.41 samples/sec\trmse=0.700819\n",
            "INFO:root:Epoch[37] Batch [250-500]\tSpeed: 7620272.39 samples/sec\trmse=0.700483\n",
            "INFO:root:Epoch[37] Train-rmse=0.700823\n",
            "INFO:root:Epoch[37] Time cost=2.404\n",
            "INFO:root:Epoch[37] Validation-rmse=1.275279\n",
            "INFO:root:Epoch[38] Batch [0-250]\tSpeed: 7615187.61 samples/sec\trmse=0.699269\n",
            "INFO:root:Epoch[38] Batch [250-500]\tSpeed: 7575818.44 samples/sec\trmse=0.699146\n",
            "INFO:root:Epoch[38] Train-rmse=0.699452\n",
            "INFO:root:Epoch[38] Time cost=2.406\n",
            "INFO:root:Epoch[38] Validation-rmse=1.280535\n",
            "INFO:root:Epoch[39] Batch [0-250]\tSpeed: 7621993.95 samples/sec\trmse=0.697980\n",
            "INFO:root:Epoch[39] Batch [250-500]\tSpeed: 7588285.49 samples/sec\trmse=0.698161\n",
            "INFO:root:Epoch[39] Train-rmse=0.698350\n",
            "INFO:root:Epoch[39] Time cost=2.405\n",
            "INFO:root:Epoch[39] Validation-rmse=1.282235\n",
            "INFO:root:Epoch[40] Batch [0-250]\tSpeed: 7613519.99 samples/sec\trmse=0.696729\n",
            "INFO:root:Epoch[40] Batch [250-500]\tSpeed: 7601029.46 samples/sec\trmse=0.697261\n",
            "INFO:root:Epoch[40] Train-rmse=0.697371\n",
            "INFO:root:Epoch[40] Time cost=2.401\n",
            "INFO:root:Epoch[40] Validation-rmse=1.298992\n",
            "INFO:root:Epoch[41] Batch [0-250]\tSpeed: 7601589.31 samples/sec\trmse=0.695876\n",
            "INFO:root:Epoch[41] Batch [250-500]\tSpeed: 7624390.35 samples/sec\trmse=0.696349\n",
            "INFO:root:Epoch[41] Train-rmse=0.696508\n",
            "INFO:root:Epoch[41] Time cost=2.397\n",
            "INFO:root:Epoch[41] Validation-rmse=1.300377\n",
            "INFO:root:Epoch[42] Batch [0-250]\tSpeed: 7674375.37 samples/sec\trmse=0.694659\n",
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            "INFO:root:Epoch[42] Train-rmse=0.695358\n",
            "INFO:root:Epoch[42] Time cost=2.390\n",
            "INFO:root:Epoch[42] Validation-rmse=1.305532\n",
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            "INFO:root:Epoch[44] Validation-rmse=1.321543\n",
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            "INFO:root:Epoch[49] Validation-rmse=1.346252\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1uQtCgosvj5",
        "colab_type": "text"
      },
      "source": [
        "## Genre (4 layers)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdbKk4MhsuKR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "73733b97-fcb2-4f05-9d3b-9d28b9199aa5"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': cs_train_users, \n",
        "                             'movie': cs_train_movies, \n",
        "                             'movie_genre': cs_train_genres}, \n",
        "                            label=cs_train_ratings, batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': cs_valid_users, \n",
        "                            'movie': cs_valid_movies, \n",
        "                            'movie_genre': cs_valid_genres}, \n",
        "                           label=cs_valid_ratings, batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 6546670.35 samples/sec\trmse=0.866661\n",
            "INFO:root:Epoch[0] Train-rmse=1.088658\n",
            "INFO:root:Epoch[0] Time cost=2.872\n",
            "INFO:root:Epoch[0] Validation-rmse=1.448978\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 6523289.54 samples/sec\trmse=0.854708\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 6517637.31 samples/sec\trmse=0.842470\n",
            "INFO:root:Epoch[1] Train-rmse=0.844299\n",
            "INFO:root:Epoch[1] Time cost=2.802\n",
            "INFO:root:Epoch[1] Validation-rmse=1.421868\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 6531129.65 samples/sec\trmse=0.830002\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 6500167.00 samples/sec\trmse=0.825290\n",
            "INFO:root:Epoch[2] Train-rmse=0.825641\n",
            "INFO:root:Epoch[2] Time cost=2.812\n",
            "INFO:root:Epoch[2] Validation-rmse=1.354134\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 6420713.70 samples/sec\trmse=0.817593\n",
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            "INFO:root:Epoch[3] Train-rmse=0.813770\n",
            "INFO:root:Epoch[3] Time cost=2.840\n",
            "INFO:root:Epoch[3] Validation-rmse=1.314069\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 6460918.13 samples/sec\trmse=0.807576\n",
            "INFO:root:Epoch[4] Batch [250-500]\tSpeed: 6456235.13 samples/sec\trmse=0.802983\n",
            "INFO:root:Epoch[4] Train-rmse=0.803558\n",
            "INFO:root:Epoch[4] Time cost=2.817\n",
            "INFO:root:Epoch[4] Validation-rmse=1.304946\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 6541894.91 samples/sec\trmse=0.797061\n",
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            "INFO:root:Epoch[5] Train-rmse=0.793734\n",
            "INFO:root:Epoch[5] Time cost=2.792\n",
            "INFO:root:Epoch[5] Validation-rmse=1.297902\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 6572421.77 samples/sec\trmse=0.788321\n",
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            "INFO:root:Epoch[6] Train-rmse=0.786575\n",
            "INFO:root:Epoch[6] Time cost=2.790\n",
            "INFO:root:Epoch[6] Validation-rmse=1.285847\n",
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            "INFO:root:Epoch[7] Train-rmse=0.781510\n",
            "INFO:root:Epoch[7] Time cost=2.801\n",
            "INFO:root:Epoch[7] Validation-rmse=1.270747\n",
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            "INFO:root:Epoch[8] Train-rmse=0.775154\n",
            "INFO:root:Epoch[8] Time cost=2.788\n",
            "INFO:root:Epoch[8] Validation-rmse=1.263539\n",
            "INFO:root:Epoch[9] Batch [0-250]\tSpeed: 6518784.80 samples/sec\trmse=0.772082\n",
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            "INFO:root:Epoch[9] Train-rmse=0.770015\n",
            "INFO:root:Epoch[9] Time cost=2.797\n",
            "INFO:root:Epoch[9] Validation-rmse=1.256807\n",
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            "INFO:root:Epoch[10] Train-rmse=0.765622\n",
            "INFO:root:Epoch[10] Time cost=2.800\n",
            "INFO:root:Epoch[10] Validation-rmse=1.251178\n",
            "INFO:root:Epoch[11] Batch [0-250]\tSpeed: 6548647.59 samples/sec\trmse=0.763616\n",
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            "INFO:root:Epoch[11] Train-rmse=0.762425\n",
            "INFO:root:Epoch[11] Time cost=2.784\n",
            "INFO:root:Epoch[11] Validation-rmse=1.238478\n",
            "INFO:root:Epoch[12] Batch [0-250]\tSpeed: 6586411.86 samples/sec\trmse=0.759156\n",
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            "INFO:root:Epoch[12] Time cost=2.793\n",
            "INFO:root:Epoch[12] Validation-rmse=1.230394\n",
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            "INFO:root:Epoch[13] Time cost=2.785\n",
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            "INFO:root:Epoch[15] Time cost=2.781\n",
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            "INFO:root:Epoch[16] Time cost=2.788\n",
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            "INFO:root:Epoch[17] Time cost=2.789\n",
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            "INFO:root:Epoch[18] Time cost=2.794\n",
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            "INFO:root:Epoch[27] Time cost=2.794\n",
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            "INFO:root:Epoch[28] Time cost=2.799\n",
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            "INFO:root:Epoch[30] Validation-rmse=1.222201\n",
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            "INFO:root:Epoch[32] Time cost=2.780\n",
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            "INFO:root:Epoch[33] Time cost=2.790\n",
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            "INFO:root:Epoch[35] Time cost=2.786\n",
            "INFO:root:Epoch[35] Validation-rmse=1.228312\n",
            "INFO:root:Epoch[36] Batch [0-250]\tSpeed: 6557103.13 samples/sec\trmse=0.700873\n",
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            "INFO:root:Epoch[36] Train-rmse=0.700517\n",
            "INFO:root:Epoch[36] Time cost=2.786\n",
            "INFO:root:Epoch[36] Validation-rmse=1.224016\n",
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            "INFO:root:Epoch[37] Time cost=2.783\n",
            "INFO:root:Epoch[37] Validation-rmse=1.225237\n",
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            "INFO:root:Epoch[38] Train-rmse=0.697842\n",
            "INFO:root:Epoch[38] Time cost=2.784\n",
            "INFO:root:Epoch[38] Validation-rmse=1.231384\n",
            "INFO:root:Epoch[39] Batch [0-250]\tSpeed: 6576515.87 samples/sec\trmse=0.697281\n",
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            "INFO:root:Epoch[39] Train-rmse=0.696706\n",
            "INFO:root:Epoch[39] Time cost=2.781\n",
            "INFO:root:Epoch[39] Validation-rmse=1.228141\n",
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            "INFO:root:Epoch[40] Time cost=2.787\n",
            "INFO:root:Epoch[40] Validation-rmse=1.220110\n",
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            "INFO:root:Epoch[41] Train-rmse=0.695800\n",
            "INFO:root:Epoch[41] Time cost=2.785\n",
            "INFO:root:Epoch[41] Validation-rmse=1.215622\n",
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            "INFO:root:Epoch[42] Time cost=2.782\n",
            "INFO:root:Epoch[42] Validation-rmse=1.207732\n",
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            "INFO:root:Epoch[43] Time cost=2.779\n",
            "INFO:root:Epoch[43] Validation-rmse=1.219179\n",
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            "INFO:root:Epoch[44] Time cost=2.782\n",
            "INFO:root:Epoch[44] Validation-rmse=1.225888\n",
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            "INFO:root:Epoch[45] Time cost=2.785\n",
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            "INFO:root:Epoch[46] Time cost=2.786\n",
            "INFO:root:Epoch[46] Validation-rmse=1.222772\n",
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            "INFO:root:Epoch[47] Time cost=2.783\n",
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            "INFO:root:Epoch[48] Time cost=2.788\n",
            "INFO:root:Epoch[48] Validation-rmse=1.217112\n",
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            "INFO:root:Epoch[49] Time cost=2.788\n",
            "INFO:root:Epoch[49] Validation-rmse=1.208109\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o750nGittGne",
        "colab_type": "text"
      },
      "source": [
        "## Genre-Tag (4 layers)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLNnVg6BtFYV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b808fb09-57f6-4446-f944-e0eb34f3257a"
      },
      "source": [
        "X_train = mx.io.NDArrayIter({'user': cs_train_users, \n",
        "                             'movie': cs_train_movies, \n",
        "                             'movie_genre': cs_train_genres, \n",
        "                             'movie_tag': cs_train_tags}, \n",
        "                            label=cs_train_ratings, \n",
        "                            batch_size=batch_size)\n",
        "X_eval = mx.io.NDArrayIter({'user': cs_valid_users, \n",
        "                            'movie': cs_valid_movies, \n",
        "                            'movie_genre': cs_valid_genres, \n",
        "                            'movie_tag': cs_valid_tags}, \n",
        "                           label=cs_valid_ratings, \n",
        "                           batch_size=batch_size)\n",
        "\n",
        "user = mx.symbol.Variable(\"user\")\n",
        "user = mx.symbol.Embedding(data=user, input_dim=n_users, output_dim=25) \n",
        "\n",
        "movie = mx.symbol.Variable(\"movie\")\n",
        "movie = mx.symbol.Embedding(data=movie, input_dim=n_movies, output_dim=25) \n",
        "\n",
        "# We need to add in a third embedding layer for genre\n",
        "movie_genre = mx.symbol.Variable(\"movie_genre\")\n",
        "movie_genre = mx.symbol.Embedding(data=movie_genre, input_dim=20, output_dim=5) # Set to 5\n",
        "\n",
        "# We need to add in a fourth embedding layer for tag\n",
        "movie_tag = mx.symbol.Variable(\"movie_tag\")\n",
        "movie_tag = mx.symbol.Embedding(data=movie_tag, input_dim=812, output_dim=25) # Set to 25\n",
        "\n",
        "y_true = mx.symbol.Variable(\"softmax_label\")\n",
        "\n",
        "nn = mx.symbol.concat(user, movie, movie_genre, movie_tag)\n",
        "nn = mx.symbol.flatten(nn)\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=128)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=64)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=32)\n",
        "nn = mx.symbol.Activation(data=nn, act_type='relu')\n",
        "nn = mx.symbol.FullyConnected(data=nn, num_hidden=1)\n",
        "\n",
        "y_pred = mx.symbol.LinearRegressionOutput(data=nn, label=y_true)\n",
        "\n",
        "model = mx.module.Module(context=mx.gpu(0), data_names=('user', 'movie', 'movie_genre','movie_tag'), symbol=y_pred)\n",
        "model.fit(X_train, num_epoch=50, optimizer='adam', optimizer_params=(('learning_rate', 0.001),),\n",
        "          eval_metric='rmse', eval_data=X_eval, batch_end_callback=mx.callback.Speedometer(batch_size, 250)) #num_epoch=5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "INFO:root:Epoch[0] Batch [250-500]\tSpeed: 5408607.30 samples/sec\trmse=0.864844\n",
            "INFO:root:Epoch[0] Train-rmse=1.068381\n",
            "INFO:root:Epoch[0] Time cost=3.483\n",
            "INFO:root:Epoch[0] Validation-rmse=1.255059\n",
            "INFO:root:Epoch[1] Batch [0-250]\tSpeed: 5397168.87 samples/sec\trmse=0.856675\n",
            "INFO:root:Epoch[1] Batch [250-500]\tSpeed: 5404164.03 samples/sec\trmse=0.846738\n",
            "INFO:root:Epoch[1] Train-rmse=0.847047\n",
            "INFO:root:Epoch[1] Time cost=3.382\n",
            "INFO:root:Epoch[1] Validation-rmse=1.241261\n",
            "INFO:root:Epoch[2] Batch [0-250]\tSpeed: 5378831.03 samples/sec\trmse=0.831352\n",
            "INFO:root:Epoch[2] Batch [250-500]\tSpeed: 5390820.28 samples/sec\trmse=0.825792\n",
            "INFO:root:Epoch[2] Train-rmse=0.826278\n",
            "INFO:root:Epoch[2] Time cost=3.393\n",
            "INFO:root:Epoch[2] Validation-rmse=1.206551\n",
            "INFO:root:Epoch[3] Batch [0-250]\tSpeed: 5384828.38 samples/sec\trmse=0.818045\n",
            "INFO:root:Epoch[3] Batch [250-500]\tSpeed: 5381750.69 samples/sec\trmse=0.813098\n",
            "INFO:root:Epoch[3] Train-rmse=0.813692\n",
            "INFO:root:Epoch[3] Time cost=3.395\n",
            "INFO:root:Epoch[3] Validation-rmse=1.191991\n",
            "INFO:root:Epoch[4] Batch [0-250]\tSpeed: 5375078.99 samples/sec\trmse=0.807557\n",
            "INFO:root:Epoch[4] Batch [250-500]\tSpeed: 5374937.92 samples/sec\trmse=0.803078\n",
            "INFO:root:Epoch[4] Train-rmse=0.803439\n",
            "INFO:root:Epoch[4] Time cost=3.394\n",
            "INFO:root:Epoch[4] Validation-rmse=1.175328\n",
            "INFO:root:Epoch[5] Batch [0-250]\tSpeed: 5394838.58 samples/sec\trmse=0.797701\n",
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            "INFO:root:Epoch[5] Train-rmse=0.794354\n",
            "INFO:root:Epoch[5] Time cost=3.394\n",
            "INFO:root:Epoch[5] Validation-rmse=1.171526\n",
            "INFO:root:Epoch[6] Batch [0-250]\tSpeed: 5386297.72 samples/sec\trmse=0.790680\n",
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            "INFO:root:Epoch[6] Train-rmse=0.787379\n",
            "INFO:root:Epoch[6] Time cost=3.391\n",
            "INFO:root:Epoch[6] Validation-rmse=1.159807\n",
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            "INFO:root:Epoch[7] Train-rmse=0.780731\n",
            "INFO:root:Epoch[7] Time cost=3.385\n",
            "INFO:root:Epoch[7] Validation-rmse=1.142185\n",
            "INFO:root:Epoch[8] Batch [0-250]\tSpeed: 5402438.87 samples/sec\trmse=0.778054\n",
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            "INFO:root:Epoch[8] Train-rmse=0.775871\n",
            "INFO:root:Epoch[8] Time cost=3.382\n",
            "INFO:root:Epoch[8] Validation-rmse=1.143421\n",
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            "INFO:root:Epoch[9] Train-rmse=0.771361\n",
            "INFO:root:Epoch[9] Time cost=3.384\n",
            "INFO:root:Epoch[9] Validation-rmse=1.157541\n",
            "INFO:root:Epoch[10] Batch [0-250]\tSpeed: 5393191.48 samples/sec\trmse=0.769831\n",
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            "INFO:root:Epoch[10] Train-rmse=0.767863\n",
            "INFO:root:Epoch[10] Time cost=3.387\n",
            "INFO:root:Epoch[10] Validation-rmse=1.133036\n",
            "INFO:root:Epoch[11] Batch [0-250]\tSpeed: 5399712.49 samples/sec\trmse=0.766150\n",
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            "INFO:root:Epoch[11] Train-rmse=0.764675\n",
            "INFO:root:Epoch[11] Time cost=3.394\n",
            "INFO:root:Epoch[11] Validation-rmse=1.146909\n",
            "INFO:root:Epoch[12] Batch [0-250]\tSpeed: 5415628.81 samples/sec\trmse=0.762640\n",
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            "INFO:root:Epoch[12] Time cost=3.383\n",
            "INFO:root:Epoch[12] Validation-rmse=1.142464\n",
            "INFO:root:Epoch[13] Batch [0-250]\tSpeed: 5390363.58 samples/sec\trmse=0.758382\n",
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            "INFO:root:Epoch[13] Train-rmse=0.756896\n",
            "INFO:root:Epoch[13] Time cost=3.389\n",
            "INFO:root:Epoch[13] Validation-rmse=1.144371\n",
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            "INFO:root:Epoch[14] Train-rmse=0.753556\n",
            "INFO:root:Epoch[14] Time cost=3.384\n",
            "INFO:root:Epoch[14] Validation-rmse=1.150366\n",
            "INFO:root:Epoch[15] Batch [0-250]\tSpeed: 5400498.96 samples/sec\trmse=0.751261\n",
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            "INFO:root:Epoch[15] Time cost=3.393\n",
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            "INFO:root:Epoch[16] Train-rmse=0.746023\n",
            "INFO:root:Epoch[16] Time cost=3.386\n",
            "INFO:root:Epoch[16] Validation-rmse=1.118879\n",
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            "INFO:root:Epoch[17] Time cost=3.385\n",
            "INFO:root:Epoch[17] Validation-rmse=1.124268\n",
            "INFO:root:Epoch[18] Batch [0-250]\tSpeed: 5409000.13 samples/sec\trmse=0.739420\n",
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            "INFO:root:Epoch[18] Time cost=3.382\n",
            "INFO:root:Epoch[18] Validation-rmse=1.132024\n",
            "INFO:root:Epoch[19] Batch [0-250]\tSpeed: 5407428.04 samples/sec\trmse=0.736580\n",
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            "INFO:root:Epoch[26] Train-rmse=0.716218\n",
            "INFO:root:Epoch[26] Time cost=3.393\n",
            "INFO:root:Epoch[26] Validation-rmse=1.135820\n",
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            "INFO:root:Epoch[27] Time cost=3.395\n",
            "INFO:root:Epoch[27] Validation-rmse=1.112890\n",
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            "INFO:root:Epoch[28] Train-rmse=0.712565\n",
            "INFO:root:Epoch[28] Time cost=3.400\n",
            "INFO:root:Epoch[28] Validation-rmse=1.118290\n",
            "INFO:root:Epoch[29] Batch [0-250]\tSpeed: 5414567.26 samples/sec\trmse=0.709560\n",
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            "INFO:root:Epoch[29] Train-rmse=0.710177\n",
            "INFO:root:Epoch[29] Time cost=3.384\n",
            "INFO:root:Epoch[29] Validation-rmse=1.120650\n",
            "INFO:root:Epoch[30] Batch [0-250]\tSpeed: 5387018.29 samples/sec\trmse=0.707562\n",
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            "INFO:root:Epoch[30] Train-rmse=0.708000\n",
            "INFO:root:Epoch[30] Time cost=3.394\n",
            "INFO:root:Epoch[30] Validation-rmse=1.127532\n",
            "INFO:root:Epoch[31] Batch [0-250]\tSpeed: 5388913.08 samples/sec\trmse=0.705391\n",
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            "INFO:root:Epoch[31] Train-rmse=0.706172\n",
            "INFO:root:Epoch[31] Time cost=3.386\n",
            "INFO:root:Epoch[31] Validation-rmse=1.130397\n",
            "INFO:root:Epoch[32] Batch [0-250]\tSpeed: 5389208.88 samples/sec\trmse=0.703664\n",
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            "INFO:root:Epoch[32] Train-rmse=0.704700\n",
            "INFO:root:Epoch[32] Time cost=3.385\n",
            "INFO:root:Epoch[32] Validation-rmse=1.129470\n",
            "INFO:root:Epoch[33] Batch [0-250]\tSpeed: 5388667.16 samples/sec\trmse=0.702292\n",
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            "INFO:root:Epoch[33] Train-rmse=0.703898\n",
            "INFO:root:Epoch[33] Time cost=3.388\n",
            "INFO:root:Epoch[33] Validation-rmse=1.127306\n",
            "INFO:root:Epoch[34] Batch [0-250]\tSpeed: 5398823.95 samples/sec\trmse=0.701906\n",
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            "INFO:root:Epoch[34] Train-rmse=0.703540\n",
            "INFO:root:Epoch[34] Time cost=3.386\n",
            "INFO:root:Epoch[34] Validation-rmse=1.114573\n",
            "INFO:root:Epoch[35] Batch [0-250]\tSpeed: 5417705.01 samples/sec\trmse=0.700484\n",
            "INFO:root:Epoch[35] Batch [250-500]\tSpeed: 5387966.07 samples/sec\trmse=0.703301\n",
            "INFO:root:Epoch[35] Train-rmse=0.701901\n",
            "INFO:root:Epoch[35] Time cost=3.385\n",
            "INFO:root:Epoch[35] Validation-rmse=1.124740\n",
            "INFO:root:Epoch[36] Batch [0-250]\tSpeed: 5392181.97 samples/sec\trmse=0.698507\n",
            "INFO:root:Epoch[36] Batch [250-500]\tSpeed: 5389597.79 samples/sec\trmse=0.701461\n",
            "INFO:root:Epoch[36] Train-rmse=0.700019\n",
            "INFO:root:Epoch[36] Time cost=3.390\n",
            "INFO:root:Epoch[36] Validation-rmse=1.126336\n",
            "INFO:root:Epoch[37] Batch [0-250]\tSpeed: 5407988.04 samples/sec\trmse=0.696714\n",
            "INFO:root:Epoch[37] Batch [250-500]\tSpeed: 5402678.26 samples/sec\trmse=0.699814\n",
            "INFO:root:Epoch[37] Train-rmse=0.698438\n",
            "INFO:root:Epoch[37] Time cost=3.384\n",
            "INFO:root:Epoch[37] Validation-rmse=1.132773\n",
            "INFO:root:Epoch[38] Batch [0-250]\tSpeed: 5407138.04 samples/sec\trmse=0.695192\n",
            "INFO:root:Epoch[38] Batch [250-500]\tSpeed: 5409398.60 samples/sec\trmse=0.698713\n",
            "INFO:root:Epoch[38] Train-rmse=0.697235\n",
            "INFO:root:Epoch[38] Time cost=3.384\n",
            "INFO:root:Epoch[38] Validation-rmse=1.140263\n",
            "INFO:root:Epoch[39] Batch [0-250]\tSpeed: 5396222.29 samples/sec\trmse=0.694282\n",
            "INFO:root:Epoch[39] Batch [250-500]\tSpeed: 5378685.35 samples/sec\trmse=0.698390\n",
            "INFO:root:Epoch[39] Train-rmse=0.696655\n",
            "INFO:root:Epoch[39] Time cost=3.392\n",
            "INFO:root:Epoch[39] Validation-rmse=1.142992\n",
            "INFO:root:Epoch[40] Batch [0-250]\tSpeed: 5313385.44 samples/sec\trmse=0.693663\n",
            "INFO:root:Epoch[40] Batch [250-500]\tSpeed: 5297720.95 samples/sec\trmse=0.697866\n",
            "INFO:root:Epoch[40] Train-rmse=0.696031\n",
            "INFO:root:Epoch[40] Time cost=3.442\n",
            "INFO:root:Epoch[40] Validation-rmse=1.133949\n",
            "INFO:root:Epoch[41] Batch [0-250]\tSpeed: 5317669.84 samples/sec\trmse=0.693070\n",
            "INFO:root:Epoch[41] Batch [250-500]\tSpeed: 5326999.72 samples/sec\trmse=0.696816\n",
            "INFO:root:Epoch[41] Train-rmse=0.694961\n",
            "INFO:root:Epoch[41] Time cost=3.436\n",
            "INFO:root:Epoch[41] Validation-rmse=1.129303\n",
            "INFO:root:Epoch[42] Batch [0-250]\tSpeed: 5317777.71 samples/sec\trmse=0.691979\n",
            "INFO:root:Epoch[42] Batch [250-500]\tSpeed: 5316523.42 samples/sec\trmse=0.695266\n",
            "INFO:root:Epoch[42] Train-rmse=0.693609\n",
            "INFO:root:Epoch[42] Time cost=3.439\n",
            "INFO:root:Epoch[42] Validation-rmse=1.134948\n",
            "INFO:root:Epoch[43] Batch [0-250]\tSpeed: 5403889.98 samples/sec\trmse=0.690703\n",
            "INFO:root:Epoch[43] Batch [250-500]\tSpeed: 5406961.83 samples/sec\trmse=0.693756\n",
            "INFO:root:Epoch[43] Train-rmse=0.692237\n",
            "INFO:root:Epoch[43] Time cost=3.385\n",
            "INFO:root:Epoch[43] Validation-rmse=1.143207\n",
            "INFO:root:Epoch[44] Batch [0-250]\tSpeed: 5385772.07 samples/sec\trmse=0.689402\n",
            "INFO:root:Epoch[44] Batch [250-500]\tSpeed: 5390642.91 samples/sec\trmse=0.692437\n",
            "INFO:root:Epoch[44] Train-rmse=0.691223\n",
            "INFO:root:Epoch[44] Time cost=3.389\n",
            "INFO:root:Epoch[44] Validation-rmse=1.142154\n",
            "INFO:root:Epoch[45] Batch [0-250]\tSpeed: 5347323.93 samples/sec\trmse=0.688934\n",
            "INFO:root:Epoch[45] Batch [250-500]\tSpeed: 5391176.16 samples/sec\trmse=0.691495\n",
            "INFO:root:Epoch[45] Train-rmse=0.690541\n",
            "INFO:root:Epoch[45] Time cost=3.397\n",
            "INFO:root:Epoch[45] Validation-rmse=1.131681\n",
            "INFO:root:Epoch[46] Batch [0-250]\tSpeed: 5395424.85 samples/sec\trmse=0.688263\n",
            "INFO:root:Epoch[46] Batch [250-500]\tSpeed: 5395613.64 samples/sec\trmse=0.690657\n",
            "INFO:root:Epoch[46] Train-rmse=0.689860\n",
            "INFO:root:Epoch[46] Time cost=3.387\n",
            "INFO:root:Epoch[46] Validation-rmse=1.125180\n",
            "INFO:root:Epoch[47] Batch [0-250]\tSpeed: 5399064.13 samples/sec\trmse=0.687973\n",
            "INFO:root:Epoch[47] Batch [250-500]\tSpeed: 5383129.91 samples/sec\trmse=0.689616\n",
            "INFO:root:Epoch[47] Train-rmse=0.689135\n",
            "INFO:root:Epoch[47] Time cost=3.387\n",
            "INFO:root:Epoch[47] Validation-rmse=1.128363\n",
            "INFO:root:Epoch[48] Batch [0-250]\tSpeed: 5395521.46 samples/sec\trmse=0.686958\n",
            "INFO:root:Epoch[48] Batch [250-500]\tSpeed: 5386758.16 samples/sec\trmse=0.688544\n",
            "INFO:root:Epoch[48] Train-rmse=0.688078\n",
            "INFO:root:Epoch[48] Time cost=3.393\n",
            "INFO:root:Epoch[48] Validation-rmse=1.133207\n",
            "INFO:root:Epoch[49] Batch [0-250]\tSpeed: 5382555.15 samples/sec\trmse=0.685816\n",
            "INFO:root:Epoch[49] Batch [250-500]\tSpeed: 5383087.91 samples/sec\trmse=0.687447\n",
            "INFO:root:Epoch[49] Train-rmse=0.686980\n",
            "INFO:root:Epoch[49] Time cost=3.395\n",
            "INFO:root:Epoch[49] Validation-rmse=1.139432\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}