03_models.ipynb

{
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  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "03_models.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyMKAIdw0SlO2mybAekE1Q91",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/evanh1393/dsi_capstone/blob/main/03_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPLzuRF9SwZq"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFa-HBP5SUDu"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from random import sample\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import keras\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Reshape, Dot, Concatenate, Dense, Dropout, Lambda, Activation, Add, Average, Flatten, Multiply\n",
        "from keras.layers.embeddings import Embedding\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.regularizers import l2\n",
        "from keras.utils import np_utils\n",
        "from keras.utils.vis_utils import plot_model"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDwaChgjGbdY"
      },
      "source": [
        "# Tensorboard\n",
        "%load_ext tensorboard"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "LFQJT-9_GdWR",
        "outputId": "7bbd0ee9-8cb5-489e-ba9f-1dfadd19d463"
      },
      "source": [
        "import tensorboard\n",
        "tensorboard.__version__"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.6.0'"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59lX_CgUSvXx"
      },
      "source": [
        "# Mounting Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pvLBjhmSyp_",
        "outputId": "212d626c-6388-4976-e139-7e5926c54957"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2J55v59S6v1"
      },
      "source": [
        "# Loading Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VAUcSmesNJx"
      },
      "source": [
        "DATA_PATH = '/content/drive/MyDrive/Colab Notebooks/one-m-capstone/data/main/'"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "da2_4lomSzro"
      },
      "source": [
        "df = pd.read_csv(DATA_PATH + 'combined.csv')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjhNjAHILTTP"
      },
      "source": [
        "movies = pd.read_csv(DATA_PATH + 'processed_movies.csv')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqEiTGkpTDkB"
      },
      "source": [
        "# Content Based Filtering Engines\n",
        "\n",
        "Content Filtering engines use item features in order to recommend items to a user. For our dataset we are going to use the genres feature of our movies in order to recommend other similar genres to users. One of the ways to achieve this is by simply grouping like products together. One way of doing this is to simply group. Another way is to group based off a mathematic distance. For our genres we are computing the cosine similarity between genres, which is a crude form of nlp.\n",
        "\n",
        "While this is not really necessary for our dataset, since all of our genres are effecitvely sorted, the same process used on genres here could be used in collaborative filtering as well. Insetead of turning the genres into vectors, you essentially create vectors based of user scores where every score from every user is compared to every score from every other user. These types of recommendation engines are known as **memory-based** models.\n",
        "\n",
        "The issue with using **memory-base** models is their load. For these to work often times large amounts of data have to be stored in memory. There are workarounds such as using generators, or parallel execution, but for the most part these methods tend to beocome more dubious as the size of your data increases.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ch8Wnfh2S-76"
      },
      "source": [
        "# Create a tf-idf matrix of genres found\n",
        "tf_vec = TfidfVectorizer()\n",
        "tfidf_matrix = tf_vec.fit_transform(movies['genres'])\n",
        "\n",
        "# Cosine similarity is done implicitly by the l2 normalization applied from the \n",
        "# tf-idf matrix, we can just use linear_kernel\n",
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIQt1WDgsHwo"
      },
      "source": [
        "def genre_recommendations(title):\n",
        "  # Looks at a title and returns the top 5 most similar titles\n",
        "  titles = movies['title']\n",
        "  indices = pd.Series(movies.index, index=movies['title'])\n",
        "  idx = indices[title]\n",
        "  sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "  sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "  top_scores = sim_scores[1:6]\n",
        "  movie_indices = [i[0] for i in top_scores]\n",
        "  return titles.iloc[movie_indices]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIWq3PtyMQ29",
        "outputId": "ad82499f-0858-4238-ef84-69811765d741"
      },
      "source": [
        "genre_recommendations('Heaven & Earth (1993)')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "461           Heaven & Earth (1993)\n",
              "1204       Full Metal Jacket (1987)\n",
              "1214    Boat, The (Das Boot) (1981)\n",
              "1222                   Glory (1989)\n",
              "1545               G.I. Jane (1997)\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnVrURoyMjfu"
      },
      "source": [
        "### Implementation\n",
        "\n",
        "When we are using this algorithm inside the web app, we will filter movie titles based on movies that have already been seen as to not recommend movies that have already been viewed by the user."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Waz-M-E7DslG"
      },
      "source": [
        "# Collaborative Modeling\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQ85DZ1SGyUu"
      },
      "source": [
        "user_enc = LabelEncoder()\n",
        "df['user'] = user_enc.fit_transform(df['user_id'].values)\n",
        "n_users = df['user'].nunique()\n",
        "\n",
        "item_enc = LabelEncoder()\n",
        "df['movie'] = item_enc.fit_transform(df['movie_id'].values)\n",
        "n_movies = df['movie'].nunique()\n",
        "\n",
        "df['rating'] = df['rating'].values.astype(np.float32)  # make it more workable with keras\n",
        "\n",
        "X = df[['user', 'movie']].values\n",
        "y = df[\"rating\"]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "X_train_array = [X_train[:, 0], X_train[:, 1]]\n",
        "X_test_array = [X_test[:, 0], X_test[:, 1]]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "td727Q_H_pSE",
        "outputId": "fc20e4ba-f6f4-4ae8-b3e0-8fb9ca7ce5e7"
      },
      "source": [
        "n_users"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6041"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayncs-S-_nmG",
        "outputId": "feebb9e4-5407-4d37-dc75-b145836dc553"
      },
      "source": [
        "n_movies"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3883"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0TfacFdGvWI"
      },
      "source": [
        "f_dim = 10 # this is the n-dimension of our embedding layers it is a hyper param\n",
        "\n",
        "def Recommender(n_users, n_movies, n_factors):\n",
        "    \n",
        "    user = Input(shape=(1,))\n",
        "    u = Embedding(n_users, n_factors, embeddings_initializer='he_normal', name='user_embedding',\n",
        "                  embeddings_regularizer=l2(1e-6))(user)\n",
        "    u = Reshape((n_factors,), name='user_reshape')(u)\n",
        "    \n",
        "    movie = Input(shape=(1,))\n",
        "    m = Embedding(n_movies, n_factors, embeddings_initializer='he_normal', name='movie_embedding',\n",
        "                  embeddings_regularizer=l2(1e-6))(movie)\n",
        "    m = Reshape((n_factors,), name='movie_reshape')(m)\n",
        "\n",
        "\n",
        "    x = Dot(axes=1)([u, m])\n",
        "    model = Model(inputs=[user, movie], outputs=x)\n",
        "    opt = Adam(lr=0.001)\n",
        "    model.compile(loss='mean_squared_error', optimizer=opt)    \n",
        "\n",
        "    return model"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b81X0SmKG-g2",
        "outputId": "d3a67b40-4777-4b6e-a117-a0967c849e39"
      },
      "source": [
        "model = Recommender(n_users, n_movies, f_dim)\n",
        "model.summary()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "user_embedding (Embedding)      (None, 1, 10)        60410       input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "movie_embedding (Embedding)     (None, 1, 10)        38830       input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "user_reshape (Reshape)          (None, 10)           0           user_embedding[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "movie_reshape (Reshape)         (None, 10)           0           movie_embedding[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dot (Dot)                       (None, 1)            0           user_reshape[0][0]               \n",
            "                                                                 movie_reshape[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 99,240\n",
            "Trainable params: 99,240\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtjiKsAJndWw"
      },
      "source": [
        "tf.keras.models.save_model(\n",
        "    model, DATA_PATH + 'implemented_model.keras'\n",
        ")"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SI2QFb4CMdW0",
        "outputId": "4359a2a5-54da-40fe-a2ae-7cabe5e18637"
      },
      "source": [
        "# Define the Keras TensorBoard callback.\n",
        "logdir=\"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)\n",
        "\n",
        "early_stop = EarlyStopping(patience=2)\n",
        "history = model.fit(x=X_train_array, y=y_train,\n",
        "    batch_size=128,\n",
        "    epochs=5,\n",
        "    verbose=1,\n",
        "    validation_data=(X_test_array,y_test),\n",
        "    callbacks=[early_stop,tensorboard_callback]\n",
        ")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "6253/6253 [==============================] - 12s 2ms/step - loss: 4.7138 - val_loss: 1.1266\n",
            "Epoch 2/5\n",
            "6253/6253 [==============================] - 11s 2ms/step - loss: 0.9663 - val_loss: 0.9122\n",
            "Epoch 3/5\n",
            "6253/6253 [==============================] - 11s 2ms/step - loss: 0.8954 - val_loss: 0.8959\n",
            "Epoch 4/5\n",
            "6253/6253 [==============================] - 11s 2ms/step - loss: 0.8860 - val_loss: 0.8902\n",
            "Epoch 5/5\n",
            "6253/6253 [==============================] - 11s 2ms/step - loss: 0.8800 - val_loss: 0.8868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "3--ewt-9IUGd",
        "outputId": "ac873d00-0628-411a-efeb-9027606a5dd6"
      },
      "source": [
        "plot_model(model)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "xPhX09a-JNOI",
        "outputId": "84cddef8-5eda-41d3-871a-2a5f5203cb4b"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfeAH57ENT22"
      },
      "source": [
        "# Improved Model\n",
        "\n",
        "The improved model is the result of adding more features onto our user and item vectors. Instead of simply embedding and flattening the ids, we embed and flatten other data that is related to the concept of our user and item, in addition we add a bias layer to the input which is added to the final large vector, and then sent to 2-layer neural network. The final output is sent to a sigmoid function, of which the that output is multiplied by 5 so we are better within the actual range of our output. The ultimate goal here was to create a model that learned relationships better.\n",
        "\n",
        "The next improvement would probably be to implement some Convolutional Layer that could better select the important features from our embeddings. In doing this we can better regularize our input features, and this would even be more of a \"matrix-decomp\" model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHuTG81TKnmW"
      },
      "source": [
        "user_enc = LabelEncoder()\n",
        "df['user'] = user_enc.fit_transform(df['user_id'].values)\n",
        "n_users = df['user'].nunique()\n",
        "\n",
        "item_enc = LabelEncoder()\n",
        "df['movie'] = item_enc.fit_transform(df['movie_id'].values)\n",
        "n_movies = df['movie'].nunique()\n",
        "\n",
        "genre_enc = LabelEncoder()\n",
        "df['genre'] = genre_enc.fit_transform(df['genres'].values)\n",
        "n_genres = df['genre'].nunique()\n",
        "\n",
        "job_enc = LabelEncoder()\n",
        "df['job'] = job_enc.fit_transform(df['occupation'])\n",
        "n_jobs = df['job'].nunique()\n",
        "\n",
        "age_enc = LabelEncoder()\n",
        "df['aged'] = age_enc.fit_transform(df['age'])\n",
        "n_ages = df['aged'].nunique()\n",
        "\n",
        "df['rating'] = df['rating'].values.astype(np.float32)  # make it more workable with keras\n",
        "\n",
        "X = df[['user', 'movie', 'genre', 'job', 'aged']].values\n",
        "y = df['rating']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "X_train_array = [X_train[:, 0], X_train[:, 1], X_train[:, 2], X_train[:, 3], X_train[:, 4]]\n",
        "X_test_array = [X_test[:, 0], X_test[:, 1], X_test[:, 2], X_test[:, 3], X_test[:, 4]]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-J-l_Gf3NYl4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5fa3bb2-1e87-4469-b552-de9196a50052"
      },
      "source": [
        "f_dim = 50\n",
        "def Recommender(n_users, n_movies, n_genres, n_jobs, n_ages, n_factors):\n",
        "    # USER\n",
        "    # user\n",
        "    user = Input(shape=(1,), name='user_input')\n",
        "    uv = Embedding(n_users, n_factors, name='user_embedding', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(user)\n",
        "    uv = Flatten(name='user_vector')(uv)\n",
        "\n",
        "    # job\n",
        "    job = Input(shape=(1,), name='job_input')\n",
        "    jv = Embedding(n_jobs, n_factors, name='job_embedding', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(job)\n",
        "    jv = Flatten(name='job_vector')(jv)\n",
        "\n",
        "    # age\n",
        "    age = Input(shape=(1,), name='age_input')\n",
        "    a = Embedding(n_ages, n_factors, name='age_embedding', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(age)\n",
        "    av = Flatten(name='age_vector')(a)\n",
        "\n",
        "    # ITEMS\n",
        "    # movies\n",
        "    movie = Input(shape=(1,), name='movie_input')\n",
        "    m = Embedding(n_movies, n_factors, name='movie_embedding', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(movie)\n",
        "    m = Flatten(name='movie_vector')(m)\n",
        "    # genres\n",
        "    genre = Input(shape=(1,), name='genre_input')\n",
        "    g = Embedding(n_genres, n_factors, name='genre_embedding', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(genre)\n",
        "    g = Flatten(name='genre_vector')(g)\n",
        "\n",
        "    # user vector creation\n",
        "    u = Concatenate(name='user_concat')([uv,jv,av])\n",
        "    u = Dropout(.2)(u)\n",
        "\n",
        "    # items vector creation\n",
        "    i = Concatenate(name='item_concat')([m,g])\n",
        "    i = Dropout(.2)(i)\n",
        "\n",
        "    # bias layers\n",
        "    uvb = Embedding(n_users, 1, name='user_bias', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(user)\n",
        "    jb = Embedding(n_jobs, 1, name='job_bias', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(job)\n",
        "                  \n",
        "    ab = Embedding(n_ages, 1, name='age_bias', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(age)\n",
        "    mb = Embedding(n_movies, 1, name='movie_bias', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(movie) \n",
        "    gb = Embedding(n_movies, 1, name='genre_bias', embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(genre)\n",
        "\n",
        "    # bias vectors\n",
        "    ub = Multiply(name='user_bias_mult')([uvb,jb,ab])\n",
        "    ib = Multiply(name='item_bias_mult')([mb,gb])\n",
        "    \n",
        "    # ANN\n",
        "    x = Concatenate()([u, i])\n",
        "    x = Dropout(.2)(x)\n",
        "    x = Add()([x, ub, ib])\n",
        "    x = Dense(1024, activation='relu', name='hidden_layer_1')(x)\n",
        "    x = Dropout(.5)(x)\n",
        "    x = Dense(128, activation='relu', name='hidden_layer_2')(x)\n",
        "    x = Dropout(.5)(x)\n",
        "    x = Dense(1, kernel_initializer='he_normal', activation='sigmoid')(x)\n",
        "    x = Lambda(lambda x: x * 5)(x)\n",
        "\n",
        "    model = Model(inputs=[user, movie, genre, job, age], outputs=x)\n",
        "    opt = Adam(learning_rate=0.001)\n",
        "    model.compile(loss='mean_squared_error', optimizer=opt)    \n",
        "\n",
        "    return model\n",
        "\n",
        "model = Recommender(n_users, n_movies, n_genres, n_jobs, n_ages, f_dim)\n",
        "model.summary()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "user_input (InputLayer)         [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "job_input (InputLayer)          [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "age_input (InputLayer)          [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "movie_input (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genre_input (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "user_embedding (Embedding)      (None, 1, 50)        302050      user_input[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "job_embedding (Embedding)       (None, 1, 50)        1050        job_input[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "age_embedding (Embedding)       (None, 1, 50)        400         age_input[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "movie_embedding (Embedding)     (None, 1, 50)        194150      movie_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "genre_embedding (Embedding)     (None, 1, 50)        15050       genre_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "user_vector (Flatten)           (None, 50)           0           user_embedding[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "job_vector (Flatten)            (None, 50)           0           job_embedding[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "age_vector (Flatten)            (None, 50)           0           age_embedding[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "movie_vector (Flatten)          (None, 50)           0           movie_embedding[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "genre_vector (Flatten)          (None, 50)           0           genre_embedding[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "user_concat (Concatenate)       (None, 150)          0           user_vector[0][0]                \n",
            "                                                                 job_vector[0][0]                 \n",
            "                                                                 age_vector[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "item_concat (Concatenate)       (None, 100)          0           movie_vector[0][0]               \n",
            "                                                                 genre_vector[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 150)          0           user_concat[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 100)          0           item_concat[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 250)          0           dropout[0][0]                    \n",
            "                                                                 dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "user_bias (Embedding)           (None, 1, 1)         6041        user_input[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "job_bias (Embedding)            (None, 1, 1)         21          job_input[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "age_bias (Embedding)            (None, 1, 1)         8           age_input[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "movie_bias (Embedding)          (None, 1, 1)         3883        movie_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "genre_bias (Embedding)          (None, 1, 1)         3883        genre_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 250)          0           concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "user_bias_mult (Multiply)       (None, 1, 1)         0           user_bias[0][0]                  \n",
            "                                                                 job_bias[0][0]                   \n",
            "                                                                 age_bias[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "item_bias_mult (Multiply)       (None, 1, 1)         0           movie_bias[0][0]                 \n",
            "                                                                 genre_bias[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 1, 250)       0           dropout_2[0][0]                  \n",
            "                                                                 user_bias_mult[0][0]             \n",
            "                                                                 item_bias_mult[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "hidden_layer_1 (Dense)          (None, 1, 1024)      257024      add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 1, 1024)      0           hidden_layer_1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "hidden_layer_2 (Dense)          (None, 1, 128)       131200      dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 1, 128)       0           hidden_layer_2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 1, 1)         129         dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda (Lambda)                 (None, 1, 1)         0           dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 914,889\n",
            "Trainable params: 914,889\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAHArRBYNhyg",
        "outputId": "085ed44e-0f76-484b-e955-c842c1df699c"
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss', min_delta=.005, patience=3)\n",
        "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)\n",
        "history = model.fit(x=X_train_array, y=y_train,\n",
        "    batch_size=128,\n",
        "    epochs=20,\n",
        "    verbose=1,\n",
        "    validation_data=(X_test_array,y_test),\n",
        "    callbacks=[early_stop, tensorboard_callback]\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6253/6253 [==============================] - 83s 13ms/step - loss: 0.8945 - val_loss: 0.8255\n",
            "Epoch 2/20\n",
            "6253/6253 [==============================] - 84s 13ms/step - loss: 0.8314 - val_loss: 0.8082\n",
            "Epoch 3/20\n",
            "6253/6253 [==============================] - 84s 13ms/step - loss: 0.8104 - val_loss: 0.7854\n",
            "Epoch 4/20\n",
            "6253/6253 [==============================] - 86s 14ms/step - loss: 0.7923 - val_loss: 0.7794\n",
            "Epoch 5/20\n",
            "6253/6253 [==============================] - 84s 13ms/step - loss: 0.7814 - val_loss: 0.7734\n",
            "Epoch 6/20\n",
            "6253/6253 [==============================] - 88s 14ms/step - loss: 0.7733 - val_loss: 0.7649\n",
            "Epoch 7/20\n",
            "6253/6253 [==============================] - 87s 14ms/step - loss: 0.7664 - val_loss: 0.7654\n",
            "Epoch 8/20\n",
            "6253/6253 [==============================] - 89s 14ms/step - loss: 0.7614 - val_loss: 0.7596\n",
            "Epoch 9/20\n",
            "6253/6253 [==============================] - 86s 14ms/step - loss: 0.7562 - val_loss: 0.7597\n",
            "Epoch 10/20\n",
            "6253/6253 [==============================] - 85s 14ms/step - loss: 0.7528 - val_loss: 0.7627\n",
            "Epoch 11/20\n",
            "6253/6253 [==============================] - 90s 14ms/step - loss: 0.7480 - val_loss: 0.7531\n",
            "Epoch 12/20\n",
            "6253/6253 [==============================] - 88s 14ms/step - loss: 0.7452 - val_loss: 0.7530\n",
            "Epoch 13/20\n",
            "6253/6253 [==============================] - 89s 14ms/step - loss: 0.7429 - val_loss: 0.7629\n",
            "Epoch 14/20\n",
            "6253/6253 [==============================] - 86s 14ms/step - loss: 0.7401 - val_loss: 0.7550\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "pD_TTDWaSLCU",
        "outputId": "64e71ce2-92f4-44c8-9b9c-001ca82e1755"
      },
      "source": [
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3g8_rPQQnhz",
        "outputId": "7173e024-995f-4caf-90d7-1bf6b760179e"
      },
      "source": [
        "tf.keras.models.save_model(\n",
        "    model, DATA_PATH + 'official_model.keras'\n",
        ")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "7bWQP28OVwR8",
        "outputId": "5ccdef85-7133-4a23-c064-d314168ed523"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}