DeepLearning_Keras.ipynb

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    "colab": {
      "name": "DeepLearning_Keras.ipynb",
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      "name": "python3",
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    "language_info": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUAIoibSfRLJ"
      },
      "source": [
        "# Deep Learning applied with Keras for movies recomendations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdN5PZQVfFlF"
      },
      "source": [
        "# libraries required data pre-processing\n",
        "\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "itXsbBfyfvSR",
        "outputId": "013f7983-f98c-4ad1-b431-2bc0c9e14d00"
      },
      "source": [
        "#reading .csv file from my Google Drive\n",
        "ratings = pd.read_csv('drive/MyDrive/ratings.csv')\n",
        "ratings.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
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            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224"
            ]
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          "metadata": {},
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          "height": 545
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        "id": "PoxczREwhWZS",
        "outputId": "898c2efc-d199-4dd8-eec0-9f1d171d7528"
      },
      "source": [
        "g = ratings.groupby('userId')['rating'].count()\n",
        "top_users = g.sort_values(ascending=False)[:15]\n",
        "\n",
        "g = ratings.groupby('movieId')['rating'].count()\n",
        "top_movies = g.sort_values(ascending=False)[:15]\n",
        "\n",
        "top_r = ratings.join(top_users, rsuffix='_r', how='inner', on='userId')\n",
        "top_r = top_r.join(top_movies, rsuffix='_r', how='inner', on='movieId')\n",
        "\n",
        "pd.crosstab(top_r.userId, top_r.movieId, top_r.rating, aggfunc=np.sum)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "movieId  1     50    110   260   296   318   ...  589   593   1196  2571  2858  2959\n",
              "userId                                       ...                                    \n",
              "68        2.5   3.0   2.5   5.0   2.0   3.0  ...   3.5   3.5   5.0   4.5   5.0   2.5\n",
              "182       4.0   4.5   3.5   3.5   5.0   4.5  ...   2.0   4.5   3.0   5.0   5.0   5.0\n",
              "249       4.0   4.0   5.0   5.0   4.0   4.5  ...   4.0   4.0   5.0   5.0   4.5   5.0\n",
              "274       4.0   4.0   4.5   3.0   5.0   4.5  ...   4.5   4.0   4.5   4.0   5.0   5.0\n",
              "288       4.5   NaN   5.0   5.0   5.0   5.0  ...   4.0   5.0   4.5   3.0   NaN   3.5\n",
              "307       4.0   4.5   3.5   3.5   4.5   4.5  ...   2.5   4.5   3.0   3.5   4.0   4.0\n",
              "380       5.0   4.0   4.0   5.0   5.0   3.0  ...   5.0   5.0   5.0   4.5   NaN   4.0\n",
              "387       NaN   4.5   3.5   4.5   5.0   3.5  ...   3.5   4.0   4.5   4.0   4.5   4.5\n",
              "414       4.0   5.0   5.0   5.0   5.0   5.0  ...   5.0   4.0   5.0   5.0   5.0   5.0\n",
              "448       5.0   4.0   NaN   5.0   5.0   NaN  ...   3.0   5.0   5.0   2.0   4.0   4.0\n",
              "474       4.0   4.0   3.0   4.0   4.0   5.0  ...   4.0   4.5   5.0   4.5   3.5   4.0\n",
              "599       3.0   3.5   3.5   5.0   5.0   4.0  ...   4.5   3.0   5.0   5.0   5.0   5.0\n",
              "603       4.0   NaN   1.0   4.0   5.0   NaN  ...   NaN   5.0   3.0   5.0   5.0   4.0\n",
              "606       2.5   4.5   3.5   4.5   5.0   3.5  ...   3.5   4.5   4.5   5.0   4.5   5.0\n",
              "610       5.0   4.0   4.5   5.0   5.0   3.0  ...   5.0   4.5   5.0   5.0   3.5   5.0\n",
              "\n",
              "[15 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3SyAx8DiN6W",
        "outputId": "0da9ce16-20e6-4a66-d36d-f590b0fd5afa"
      },
      "source": [
        "user_enc = LabelEncoder()\n",
        "ratings['user'] = user_enc.fit_transform(ratings['userId'].values)\n",
        "n_users = ratings['user'].nunique()\n",
        "\n",
        "item_enc = LabelEncoder()\n",
        "ratings['movie'] = item_enc.fit_transform(ratings['movieId'].values)\n",
        "n_movies = ratings['movie'].nunique()\n",
        "\n",
        "ratings['rating'] = ratings['rating'].values.astype(np.float32)\n",
        "min_rating = min(ratings['rating'])\n",
        "max_rating = max(ratings['rating'])\n",
        "\n",
        "n_users, n_movies, min_rating, max_rating"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(610, 9724, 0.5, 5.0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJ2TlMa2kQkB",
        "outputId": "9e959e49-73c3-4844-da5a-dd1c86a28a03"
      },
      "source": [
        "X = ratings[['user', 'movie']].values\n",
        "y = ratings['rating'].values\n",
        " \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        " \n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((90752, 2), (10084, 2), (90752,), (10084,))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGp8pigPkga3"
      },
      "source": [
        "n_factors = 50\n",
        "\n",
        "X_train_array = [X_train[:, 0], X_train[:, 1]]\n",
        "X_test_array = [X_test[:, 0], X_test[:, 1]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MZtU412lCY2"
      },
      "source": [
        "importing keras and tensorflow librarie`s function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjmHzOkPlB3e"
      },
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Input, Reshape, Dot\n",
        "from keras.layers.embeddings import Embedding\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.regularizers import l2\n",
        "\n",
        "def RecommenderV1(n_users, n_movies, n_factors):\n",
        "    user = Input(shape=(1,))\n",
        "    u = Embedding(n_users, n_factors, embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(user)\n",
        "    u = Reshape((n_factors,))(u)\n",
        "    \n",
        "    movie = Input(shape=(1,))\n",
        "    m = Embedding(n_movies, n_factors, embeddings_initializer='he_normal',\n",
        "                  embeddings_regularizer=l2(1e-6))(movie)\n",
        "    m = Reshape((n_factors,))(m)\n",
        "    \n",
        "    x = Dot(axes=1)([u, m])\n",
        "\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kq1CQ3LSl4VQ",
        "outputId": "1a9125ad-e03f-4790-df0b-fa82c3517193"
      },
      "source": [
        "model = RecommenderV1(n_users, n_movies, n_factors)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_7 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_8 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_6 (Embedding)         (None, 1, 50)        30500       input_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_7 (Embedding)         (None, 1, 50)        486200      input_8[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "reshape_6 (Reshape)             (None, 50)           0           embedding_6[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "reshape_7 (Reshape)             (None, 50)           0           embedding_7[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dot_3 (Dot)                     (None, 1)            0           reshape_6[0][0]                  \n",
            "                                                                 reshape_7[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 516,700\n",
            "Trainable params: 516,700\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4WPvaNjmG0G",
        "outputId": "85178da7-ed75-497d-8bb8-bae98c96eaad"
      },
      "source": [
        "history = model.fit(x=X_train_array, y=y_train, batch_size=64, epochs=5,\n",
        "                    verbose=1, validation_data=(X_test_array, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1418/1418 [==============================] - 11s 7ms/step - loss: 9.2812 - val_loss: 3.1590\n",
            "Epoch 2/5\n",
            "1418/1418 [==============================] - 10s 7ms/step - loss: 1.9477 - val_loss: 1.6140\n",
            "Epoch 3/5\n",
            "1418/1418 [==============================] - 10s 7ms/step - loss: 1.1021 - val_loss: 1.3509\n",
            "Epoch 4/5\n",
            "1418/1418 [==============================] - 11s 7ms/step - loss: 0.8488 - val_loss: 1.2719\n",
            "Epoch 5/5\n",
            "1418/1418 [==============================] - 10s 7ms/step - loss: 0.7236 - val_loss: 1.2504\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59zWakpLuty9"
      },
      "source": [
        "from keras.layers import Add, Activation, Lambda\n",
        "\n",
        "class EmbeddingLayer:\n",
        "    def __init__(self, n_items, n_factors):\n",
        "        self.n_items = n_items\n",
        "        self.n_factors = n_factors\n",
        "    \n",
        "    def __call__(self, x):\n",
        "        x = Embedding(self.n_items, self.n_factors, embeddings_initializer='he_normal',\n",
        "                      embeddings_regularizer=l2(1e-6))(x)\n",
        "        x = Reshape((self.n_factors,))(x)\n",
        "        return x\n",
        "\n",
        "def RecommenderV2(n_users, n_movies, n_factors, min_rating, max_rating):\n",
        "    user = Input(shape=(1,))\n",
        "    u = EmbeddingLayer(n_users, n_factors)(user)\n",
        "    ub = EmbeddingLayer(n_users, 1)(user)\n",
        "    \n",
        "    movie = Input(shape=(1,))\n",
        "    m = EmbeddingLayer(n_movies, n_factors)(movie)\n",
        "    mb = EmbeddingLayer(n_movies, 1)(movie)\n",
        "\n",
        "    x = Dot(axes=1)([u, m])\n",
        "    x = Add()([x, ub, mb])\n",
        "    x = Activation('sigmoid')(x)\n",
        "    x = Lambda(lambda x: x * (max_rating - min_rating) + min_rating)(x)\n",
        "\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ptB4s6aux21",
        "outputId": "0e3c05ec-cd2c-4a9a-eac2-21bfd0cead1f"
      },
      "source": [
        "model = RecommenderV2(n_users, n_movies, n_factors, min_rating, max_rating)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_9 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_10 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_8 (Embedding)         (None, 1, 50)        30500       input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_10 (Embedding)        (None, 1, 50)        486200      input_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "reshape_8 (Reshape)             (None, 50)           0           embedding_8[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "reshape_10 (Reshape)            (None, 50)           0           embedding_10[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "embedding_9 (Embedding)         (None, 1, 1)         610         input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_11 (Embedding)        (None, 1, 1)         9724        input_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dot_4 (Dot)                     (None, 1)            0           reshape_8[0][0]                  \n",
            "                                                                 reshape_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_9 (Reshape)             (None, 1)            0           embedding_9[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "reshape_11 (Reshape)            (None, 1)            0           embedding_11[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 1)            0           dot_4[0][0]                      \n",
            "                                                                 reshape_9[0][0]                  \n",
            "                                                                 reshape_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 1)            0           add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "lambda (Lambda)                 (None, 1)            0           activation[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 527,034\n",
            "Trainable params: 527,034\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0y-Ah0ebu13N",
        "outputId": "c1343f8a-700f-4400-c8ed-6336c915db02"
      },
      "source": [
        "history = model.fit(x=X_train_array, y=y_train, batch_size=64, epochs=5,\n",
        "                    verbose=1, validation_data=(X_test_array, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1418/1418 [==============================] - 11s 8ms/step - loss: 1.2000 - val_loss: 0.8596\n",
            "Epoch 2/5\n",
            "1418/1418 [==============================] - 11s 8ms/step - loss: 0.6960 - val_loss: 0.7505\n",
            "Epoch 3/5\n",
            "1418/1418 [==============================] - 11s 8ms/step - loss: 0.5220 - val_loss: 0.7297\n",
            "Epoch 4/5\n",
            "1418/1418 [==============================] - 11s 8ms/step - loss: 0.3945 - val_loss: 0.7367\n",
            "Epoch 5/5\n",
            "1418/1418 [==============================] - 11s 7ms/step - loss: 0.3037 - val_loss: 0.7519\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVMt_hBCvfEM"
      },
      "source": [
        "from keras.layers import Concatenate, Dense, Dropout\n",
        "\n",
        "def RecommenderNet(n_users, n_movies, n_factors, min_rating, max_rating):\n",
        "    user = Input(shape=(1,))\n",
        "    u = EmbeddingLayer(n_users, n_factors)(user)\n",
        "    \n",
        "    movie = Input(shape=(1,))\n",
        "    m = EmbeddingLayer(n_movies, n_factors)(movie)\n",
        "    \n",
        "    x = Concatenate()([u, m])\n",
        "    x = Dropout(0.05)(x)\n",
        "    \n",
        "    x = Dense(10, kernel_initializer='he_normal')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    \n",
        "    x = Dense(1, kernel_initializer='he_normal')(x)\n",
        "    x = Activation('sigmoid')(x)\n",
        "    x = Lambda(lambda x: x * (max_rating - min_rating) + min_rating)(x)\n",
        "\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSqFbOA1vhmM",
        "outputId": "cedb422b-be1e-48fc-c7b1-58df5555bcb5"
      },
      "source": [
        "model = RecommenderNet(n_users, n_movies, n_factors, min_rating, max_rating)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_5\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_11 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_12 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_12 (Embedding)        (None, 1, 50)        30500       input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "embedding_13 (Embedding)        (None, 1, 50)        486200      input_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "reshape_12 (Reshape)            (None, 50)           0           embedding_12[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "reshape_13 (Reshape)            (None, 50)           0           embedding_13[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 100)          0           reshape_12[0][0]                 \n",
            "                                                                 reshape_13[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 100)          0           concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 10)           1010        dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 10)           0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 10)           0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 1)            11          dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 1)            0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 1)            0           activation_2[0][0]               \n",
            "==================================================================================================\n",
            "Total params: 517,721\n",
            "Trainable params: 517,721\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sOfZvk3vlT8",
        "outputId": "3434b93d-e706-4945-ed66-67c0816dd793"
      },
      "source": [
        "history = model.fit(x=X_train_array, y=y_train, batch_size=64, epochs=5,\n",
        "                    verbose=1, validation_data=(X_test_array, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1418/1418 [==============================] - 11s 7ms/step - loss: 0.9230 - val_loss: 0.7909\n",
            "Epoch 2/5\n",
            "1418/1418 [==============================] - 10s 7ms/step - loss: 0.7652 - val_loss: 0.7773\n",
            "Epoch 3/5\n",
            "1418/1418 [==============================] - 10s 7ms/step - loss: 0.7287 - val_loss: 0.7780\n",
            "Epoch 4/5\n",
            "1418/1418 [==============================] - 11s 7ms/step - loss: 0.7094 - val_loss: 0.7809\n",
            "Epoch 5/5\n",
            "1418/1418 [==============================] - 10s 7ms/step - loss: 0.6985 - val_loss: 0.7835\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkXfB6up0EkE",
        "outputId": "2449c83e-9c76-44dd-a556-f7a1199b3a63"
      },
      "source": [
        "type(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "keras.callbacks.History"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
  ]
}