TF2_SPAM_DETECTOR_RNN.ipynb

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    "colab": {
      "name": "TF2 SPAM DETECTOR RNN.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN6ZE9wiz3g8RlrAETA6OPb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CristianCristanchoT/SPAM-DETECTOR/blob/master/TF2_SPAM_DETECTOR_RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGvmP3VR28Wd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q tensorflow-gpu==2.0.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYdP5mFk3Eoj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from tensorflow.keras.layers import Input, Dense, Flatten, SimpleRNN, GRU, LSTM, GlobalMaxPooling1D, Embedding\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xs2sxBnQ_cr7",
        "colab_type": "code",
        "outputId": "720ca80b-ba90-4407-d4bd-d1ffa0eed09b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!wget https://lazyprogrammer.me/course_files/spam.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-02-10 21:26:48--  https://lazyprogrammer.me/course_files/spam.csv\n",
            "Resolving lazyprogrammer.me (lazyprogrammer.me)... 104.31.80.48, 104.31.81.48, 2606:4700:3035::681f:5130, ...\n",
            "Connecting to lazyprogrammer.me (lazyprogrammer.me)|104.31.80.48|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 503663 (492K) [text/csv]\n",
            "Saving to: ‘spam.csv’\n",
            "\n",
            "\rspam.csv              0%[                    ]       0  --.-KB/s               \rspam.csv            100%[===================>] 491.86K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2020-02-10 21:26:48 (15.3 MB/s) - ‘spam.csv’ saved [503663/503663]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJenm848_3de",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('spam.csv', encoding='ISO-8859-1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArUvi3dwAKna",
        "colab_type": "code",
        "outputId": "5010caca-5b8f-4606-a26a-4252ac69cb7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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            ],
            "text/plain": [
              "     v1  ... Unnamed: 4\n",
              "0   ham  ...        NaN\n",
              "1   ham  ...        NaN\n",
              "2  spam  ...        NaN\n",
              "3   ham  ...        NaN\n",
              "4   ham  ...        NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qkAOFv3ANWa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "IwQm3hKyAfwf",
        "colab_type": "code",
        "outputId": "3d4d654e-fdad-40eb-d06c-db8010c7ae1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
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              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "     v1                                                 v2\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6F53m_RpAhpg",
        "colab_type": "code",
        "outputId": "f25a7e99-590e-4673-86d9-46db8a36f81a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.columns = ['labels', 'data']\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  labels                                               data\n",
              "0    ham  Go until jurong point, crazy.. Available only ...\n",
              "1    ham                      Ok lar... Joking wif u oni...\n",
              "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3    ham  U dun say so early hor... U c already then say...\n",
              "4    ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCTAtEVQAs7F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['b_labels'] = df['labels'].map({'ham': 0, 'spam': 1})\n",
        "Y = df['b_labels'].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9r3dJK1kCTmY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train, df_test, Y_train, Y_test = train_test_split(df['data'], Y, test_size = 0.33)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HuOwxcjDJY2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_VOCAB_SIZE = 20000\n",
        "tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\n",
        "tokenizer.fit_on_texts(df_train)\n",
        "sequences_train = tokenizer.texts_to_sequences(df_train)\n",
        "sequences_test = tokenizer.texts_to_sequences(df_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wj33fwc_Ds0K",
        "colab_type": "code",
        "outputId": "57228a82-fb38-4b2c-e8bb-1a1cf44ca63c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "word2idx = tokenizer.word_index\n",
        "V = len(word2idx)\n",
        "print('Se han encontrado %s tokens unicos' % V)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Se han encontrado 7269 tokens unicos\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELPS4RTUEIG9",
        "colab_type": "code",
        "outputId": "c0e3b9ad-8424-4ce9-b18d-353f5f037a73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data_train = pad_sequences(sequences_train)\n",
        "print('Tamaño de el tensor de datos de entrenamiento:', data_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tamaño de el tensor de datos de entrenamiento: (3733, 189)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtMcU_EOEsGH",
        "colab_type": "code",
        "outputId": "efbb3e07-d2e2-4d7e-f45c-45cc74090331",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "T = data_train.shape[1]\n",
        "print(T)\n",
        "data_test = pad_sequences(sequences_test, maxlen = T)\n",
        "print('Tamaño de el tensor de datos de prueba:', data_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "189\n",
            "Tamaño de el tensor de datos de prueba: (1839, 189)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-FeQFP0KrjP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqVOYh3cFOYM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D = 20\n",
        "M = 15\n",
        "\n",
        "i = Input(shape=(T,))\n",
        "x = Embedding(V + 1, D)(i)\n",
        "x = LSTM(M, return_sequences=True)(x)\n",
        "x = GlobalMaxPooling1D()(x)\n",
        "x = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "model = Model(i, x)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S00ZA-ADHCvR",
        "colab_type": "code",
        "outputId": "8303648e-03c2-4efd-de47-90f78d36ed9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.compile(\n",
        "    loss= 'binary_crossentropy',\n",
        "    optimizer = 'adam',\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "\n",
        "print('A entrenar!!...')\n",
        "\n",
        "r = model.fit(\n",
        "    data_train,\n",
        "    Y_train,\n",
        "    epochs = 30,\n",
        "    validation_data = (data_test, Y_test)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A entrenar!!...\n",
            "Train on 3733 samples, validate on 1839 samples\n",
            "Epoch 1/30\n",
            "3733/3733 [==============================] - 4s 1ms/sample - loss: 0.0362 - accuracy: 0.9938 - val_loss: 0.0617 - val_accuracy: 0.9859\n",
            "Epoch 2/30\n",
            "3733/3733 [==============================] - 2s 477us/sample - loss: 0.0260 - accuracy: 0.9960 - val_loss: 0.0577 - val_accuracy: 0.9842\n",
            "Epoch 3/30\n",
            "3733/3733 [==============================] - 2s 495us/sample - loss: 0.0198 - accuracy: 0.9971 - val_loss: 0.0562 - val_accuracy: 0.9837\n",
            "Epoch 4/30\n",
            "3733/3733 [==============================] - 2s 472us/sample - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.0572 - val_accuracy: 0.9853\n",
            "Epoch 5/30\n",
            "3733/3733 [==============================] - 2s 523us/sample - loss: 0.0132 - accuracy: 0.9973 - val_loss: 0.0592 - val_accuracy: 0.9831\n",
            "Epoch 6/30\n",
            "3733/3733 [==============================] - 2s 478us/sample - loss: 0.0113 - accuracy: 0.9976 - val_loss: 0.0665 - val_accuracy: 0.9826\n",
            "Epoch 7/30\n",
            "3733/3733 [==============================] - 2s 470us/sample - loss: 0.0106 - accuracy: 0.9976 - val_loss: 0.0617 - val_accuracy: 0.9821\n",
            "Epoch 8/30\n",
            "3733/3733 [==============================] - 2s 475us/sample - loss: 0.0109 - accuracy: 0.9976 - val_loss: 0.0620 - val_accuracy: 0.9821\n",
            "Epoch 9/30\n",
            "3733/3733 [==============================] - 2s 477us/sample - loss: 0.0081 - accuracy: 0.9989 - val_loss: 0.0621 - val_accuracy: 0.9831\n",
            "Epoch 10/30\n",
            "3733/3733 [==============================] - 2s 478us/sample - loss: 0.0071 - accuracy: 0.9995 - val_loss: 0.0625 - val_accuracy: 0.9826\n",
            "Epoch 11/30\n",
            "3733/3733 [==============================] - 2s 487us/sample - loss: 0.0064 - accuracy: 0.9995 - val_loss: 0.0633 - val_accuracy: 0.9831\n",
            "Epoch 12/30\n",
            "3733/3733 [==============================] - 2s 468us/sample - loss: 0.0058 - accuracy: 0.9995 - val_loss: 0.0644 - val_accuracy: 0.9837\n",
            "Epoch 13/30\n",
            "3733/3733 [==============================] - 2s 478us/sample - loss: 0.0054 - accuracy: 0.9995 - val_loss: 0.0631 - val_accuracy: 0.9837\n",
            "Epoch 14/30\n",
            "3733/3733 [==============================] - 2s 472us/sample - loss: 0.0046 - accuracy: 0.9995 - val_loss: 0.0658 - val_accuracy: 0.9837\n",
            "Epoch 15/30\n",
            "3733/3733 [==============================] - 2s 474us/sample - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.0665 - val_accuracy: 0.9831\n",
            "Epoch 16/30\n",
            "3733/3733 [==============================] - 2s 481us/sample - loss: 0.0028 - accuracy: 0.9995 - val_loss: 0.0677 - val_accuracy: 0.9837\n",
            "Epoch 17/30\n",
            "3733/3733 [==============================] - 2s 472us/sample - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.0688 - val_accuracy: 0.9837\n",
            "Epoch 18/30\n",
            "3733/3733 [==============================] - 2s 480us/sample - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.0689 - val_accuracy: 0.9837\n",
            "Epoch 19/30\n",
            "3733/3733 [==============================] - 2s 486us/sample - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0697 - val_accuracy: 0.9837\n",
            "Epoch 20/30\n",
            "3733/3733 [==============================] - 2s 532us/sample - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0702 - val_accuracy: 0.9831\n",
            "Epoch 21/30\n",
            "3733/3733 [==============================] - 2s 467us/sample - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0720 - val_accuracy: 0.9837\n",
            "Epoch 22/30\n",
            "3733/3733 [==============================] - 2s 480us/sample - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.0720 - val_accuracy: 0.9842\n",
            "Epoch 23/30\n",
            "3733/3733 [==============================] - 2s 486us/sample - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0726 - val_accuracy: 0.9842\n",
            "Epoch 24/30\n",
            "3733/3733 [==============================] - 2s 480us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9837\n",
            "Epoch 25/30\n",
            "3733/3733 [==============================] - 2s 488us/sample - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9837\n",
            "Epoch 26/30\n",
            "3733/3733 [==============================] - 2s 477us/sample - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 0.9837\n",
            "Epoch 27/30\n",
            "3733/3733 [==============================] - 2s 490us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9848\n",
            "Epoch 28/30\n",
            "3733/3733 [==============================] - 2s 472us/sample - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9837\n",
            "Epoch 29/30\n",
            "3733/3733 [==============================] - 2s 485us/sample - loss: 9.3268e-04 - accuracy: 1.0000 - val_loss: 0.0813 - val_accuracy: 0.9842\n",
            "Epoch 30/30\n",
            "3733/3733 [==============================] - 2s 484us/sample - loss: 8.5852e-04 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9837\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oewp3UeCIUeO",
        "colab_type": "code",
        "outputId": "9006eb1c-945c-4142-b3ab-f98331dbdc69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.plot(r.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf68933d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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loZL9hd54OTPtQq0xJsEkVLIP+n1cM7Y/760rYXOZjZdjjEkcCZXsAa4d25+A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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FO70j5s1IYN8",
        "colab_type": "code",
        "outputId": "f8956e5c-ea75-48e3-9936-e6beb97f5264",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(r.history['accuracy'], label='accuracy')\n",
        "plt.plot(r.history['val_accuracy'], label='val_accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf68866940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fHUQkWkRy8hoNTPbe/4i7s4gUkSjc3YVvE9MQTr97QEQa+ny8Ns/+xhhjSkmB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\nMcav/w/dtNavJolM+QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDWHYrgEKZMU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}