BBCTextClassifier_using_tokenizer.ipynb

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      "name": "BBCTextClassifier-using-tokenizer.ipynb",
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      "name": "python3",
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      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rashida048/Some-NLP-Projects/blob/master/BBCTextClassifier_using_tokenizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "id": "1XKnEG9UQd9s",
        "colab_type": "code",
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          "height": 564
        },
        "outputId": "47db03f7-af6b-44be-f027-7502a3f17ffc"
      },
      "source": [
        "!pip install tensorflow==2.00-beta0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.00-beta0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6c/19/0d0c7f240db7bcd6b83783b9a89a67f38584d100e23ad5ae93114be92232/tensorflow-2.0.0b0-cp36-cp36m-manylinux1_x86_64.whl (87.9MB)\n",
            "\u001b[K     |████████████████████████████████| 87.9MB 36kB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.00-beta0) (3.10.0)\n",
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            "Collecting tb-nightly<1.14.0a20190604,>=1.14.0a20190603\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a4/96/571b875cd81dda9d5dfa1422a4f9d749e67c0a8d4f4f0b33a4e5f5f35e27/tb_nightly-1.14.0a20190603-py3-none-any.whl (3.1MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1MB 28.5MB/s \n",
            "\u001b[?25hCollecting tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/dd/99c47dd007dcf10d63fd895611b063732646f23059c618a373e85019eb0e/tf_estimator_nightly-1.14.0.dev2019060501-py2.py3-none-any.whl (496kB)\n",
            "\u001b[K     |████████████████████████████████| 501kB 48.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.00-beta0) (0.1.8)\n",
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            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==2.00-beta0) (2.8.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow==2.00-beta0) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow==2.00-beta0) (3.1.1)\n",
            "Installing collected packages: tb-nightly, tf-estimator-nightly, tensorflow\n",
            "  Found existing installation: tensorflow 1.15.0\n",
            "    Uninstalling tensorflow-1.15.0:\n",
            "      Successfully uninstalled tensorflow-1.15.0\n",
            "Successfully installed tb-nightly-1.14.0a20190603 tensorflow-2.0.0b0 tf-estimator-nightly-1.14.0.dev2019060501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DO18Oe1QlHL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "b383a225-b232-48c4-c673-2b29458281e5"
      },
      "source": [
        "import csv\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv \\\n",
        "    -O /tmp/bbc-text.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "--2019-12-16 19:55:58--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.203.128, 2607:f8b0:400c:c07::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.203.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5057493 (4.8M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/bbc-text.csv’\n",
            "\n",
            "\r/tmp/bbc-text.csv     0%[                    ]       0  --.-KB/s               \r/tmp/bbc-text.csv   100%[===================>]   4.82M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2019-12-16 19:55:58 (61.6 MB/s) - ‘/tmp/bbc-text.csv’ saved [5057493/5057493]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NxD-AR1SBKP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 250\n",
        "trunc_type = 'post'\n",
        "padding_type = 'post'\n",
        "oov_tok = '<OOV>'\n",
        "training_portion = 0.8"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdTQK8trSdJ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cfcce7df-ba4e-47ef-cf4f-8e1f37b8bd17"
      },
      "source": [
        "sentences = []\n",
        "labels = []\n",
        "stopwords = [ \"a\", \"about\", \"above\", \"after\", \"again\", \"against\", \"all\", \"am\", \"an\", \"and\", \"any\", \"are\", \"as\", \"at\", \"be\", \"because\", \"been\", \"before\", \"being\", \"below\", \"between\", \"both\", \"but\", \"by\", \"could\", \"did\", \"do\", \"does\", \"doing\", \"down\", \"during\", \"each\", \"few\", \"for\", \"from\", \"further\", \"had\", \"has\", \"have\", \"having\", \"he\", \"he'd\", \"he'll\", \"he's\", \"her\", \"here\", \"here's\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"how's\", \"i\", \"i'd\", \"i'll\", \"i'm\", \"i've\", \"if\", \"in\", \"into\", \"is\", \"it\", \"it's\", \"its\", \"itself\", \"let's\", \"me\", \"more\", \"most\", \"my\", \"myself\", \"nor\", \"of\", \"on\", \"once\", \"only\", \"or\", \"other\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\", \"same\", \"she\", \"she'd\", \"she'll\", \"she's\", \"should\", \"so\", \"some\", \"such\", \"than\", \"that\", \"that's\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"there\", \"there's\", \"these\", \"they\", \"they'd\", \"they'll\", \"they're\", \"they've\", \"this\", \"those\", \"through\", \"to\", \"too\", \"under\", \"until\", \"up\", \"very\", \"was\", \"we\", \"we'd\", \"we'll\", \"we're\", \"we've\", \"were\", \"what\", \"what's\", \"when\", \"when's\", \"where\", \"where's\", \"which\", \"while\", \"who\", \"who's\", \"whom\", \"why\", \"why's\", \"with\", \"would\", \"you\", \"you'd\", \"you'll\", \"you're\", \"you've\", \"your\", \"yours\", \"yourself\", \"yourselves\" ]\n",
        "print(len(stopwords))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "153\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksTPArWNSoUj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "eb5cc822-ee68-4998-c5a6-7430a7b80b97"
      },
      "source": [
        "with open(\"/tmp/bbc-text.csv\", 'r') as csvfile:\n",
        "  reader = csv.reader(csvfile, delimiter=',')\n",
        "  next(reader)\n",
        "  for row in reader:\n",
        "    labels.append(row[0])\n",
        "    sentence = row[1]\n",
        "    for word in stopwords:\n",
        "      token = \" \" + word + \" \"\n",
        "      sentence = sentence.replace(token, \" \")\n",
        "    sentences.append(sentence)\n",
        "\n",
        "print(len(labels))\n",
        "print(len(sentences))\n",
        "print(sentences[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2225\n",
            "2225\n",
            "tv future hands viewers home theatre systems  plasma high-definition tvs  digital video recorders moving living room  way people watch tv will radically different five years  time.  according expert panel gathered annual consumer electronics show las vegas discuss new technologies will impact one favourite pastimes. us leading trend  programmes content will delivered viewers via home networks  cable  satellite  telecoms companies  broadband service providers front rooms portable devices.  one talked-about technologies ces digital personal video recorders (dvr pvr). set-top boxes  like us s tivo uk s sky+ system  allow people record  store  play  pause forward wind tv programmes want.  essentially  technology allows much personalised tv. also built-in high-definition tv sets  big business japan us  slower take off europe lack high-definition programming. not can people forward wind adverts  can also forget abiding network channel schedules  putting together a-la-carte entertainment. us networks cable satellite companies worried means terms advertising revenues well  brand identity  viewer loyalty channels. although us leads technology moment  also concern raised europe  particularly growing uptake services like sky+.  happens today  will see nine months years  time uk   adam hume  bbc broadcast s futurologist told bbc news website. likes bbc  no issues lost advertising revenue yet. pressing issue moment commercial uk broadcasters  brand loyalty important everyone.  will talking content brands rather network brands   said tim hanlon  brand communications firm starcom mediavest.  reality broadband connections  anybody can producer content.  added:  challenge now hard promote programme much choice.   means  said stacey jolna  senior vice president tv guide tv group  way people find content want watch simplified tv viewers. means networks  us terms  channels take leaf google s book search engine future  instead scheduler help people find want watch. kind channel model might work younger ipod generation used taking control gadgets play them. might not suit everyone  panel recognised. older generations comfortable familiar schedules channel brands know getting. perhaps not want much choice put hands  mr hanlon suggested.  end  kids just diapers pushing buttons already - everything possible available   said mr hanlon.  ultimately  consumer will tell market want.   50 000 new gadgets technologies showcased ces  many enhancing tv-watching experience. high-definition tv sets everywhere many new models lcd (liquid crystal display) tvs launched dvr capability built  instead external boxes. one example launched show humax s 26-inch lcd tv 80-hour tivo dvr dvd recorder. one us s biggest satellite tv companies  directtv  even launched branded dvr show 100-hours recording capability  instant replay  search function. set can pause rewind tv 90 hours. microsoft chief bill gates announced pre-show keynote speech partnership tivo  called tivotogo  means people can play recorded programmes windows pcs mobile devices. reflect increasing trend freeing multimedia people can watch want  want.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWLyEBkZVx0c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "0d0f7a60-3d63-4f81-aa00-39e5518bce04"
      },
      "source": [
        "train_size = int(training_portion * len(labels))\n",
        "\n",
        "train_sentences = sentences[:train_size]\n",
        "train_labels = labels[:train_size]\n",
        "\n",
        "validation_sentences = sentences[train_size:]\n",
        "validation_labels = labels[train_size:]\n",
        "\n",
        "print(train_size)\n",
        "print(len(train_sentences))\n",
        "print(len(train_labels))\n",
        "print(len(validation_sentences))\n",
        "print(len(validation_labels))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1780\n",
            "1780\n",
            "1780\n",
            "445\n",
            "445\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THR3difjWwnx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "cdc6ee5b-f1be-452d-becb-cafbcb69bb77"
      },
      "source": [
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_tok)\n",
        "tokenizer.fit_on_texts(train_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "train_sequences = tokenizer.texts_to_sequences(train_sentences)\n",
        "train_padded = pad_sequences(train_sequences, padding=padding_type, maxlen=max_length)\n",
        "\n",
        "print(len(train_sequences[0]))\n",
        "print(len(train_padded[0]))\n",
        "\n",
        "print(len(train_sequences[1]))\n",
        "print(len(train_padded[1]))\n",
        "\n",
        "print(len(train_sequences[10]))\n",
        "print(len(train_padded[10]))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "449\n",
            "250\n",
            "200\n",
            "250\n",
            "192\n",
            "250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esct7MBBdMtk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "457703c8-4f79-4a13-fd0c-7e4cb5037007"
      },
      "source": [
        "validation_sequences = tokenizer.texts_to_sequences(validation_sentences)\n",
        "validation_padded = pad_sequences(validation_sequences, padding = padding_type, maxlen=max_length)\n",
        "\n",
        "print(len(validation_labels))\n",
        "print(validation_padded.shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "445\n",
            "(445, 250)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oku_n7lBeVA3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_tokenizer = Tokenizer()\n",
        "label_tokenizer.fit_on_texts(labels)\n",
        "\n",
        "training_label_seq = np.array(label_tokenizer.texts_to_sequences(train_labels))\n",
        "validation_label_seq = np.array(label_tokenizer.texts_to_sequences(validation_labels))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_T9XNwhVfQRS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "f37e1aad-f3f5-4682-c9e5-8feda80d34cd"
      },
      "source": [
        "print(training_label_seq[0])\n",
        "print(training_label_seq[1])\n",
        "print(training_label_seq[2])\n",
        "print(training_label_seq.shape)\n",
        "\n",
        "print(validation_label_seq[0])\n",
        "print(validation_label_seq[1])\n",
        "print(validation_label_seq[2])\n",
        "print(validation_label_seq.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4]\n",
            "[2]\n",
            "[1]\n",
            "(1780, 1)\n",
            "[5]\n",
            "[4]\n",
            "[3]\n",
            "(445, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1uEr_JdfQ4u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "50097ec6-210f-42bf-f9f7-f874100cc37f"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "                             tf.keras.layers.GlobalAveragePooling1D(),\n",
        "                             tf.keras.layers.Dense(24, activation='relu'),\n",
        "                             tf.keras.layers.Dense(6, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 250, 16)           160000    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d_1 ( (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 24)                408       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 6)                 150       \n",
            "=================================================================\n",
            "Total params: 160,558\n",
            "Trainable params: 160,558\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z5Ot8gckh0-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "outputId": "8e2314a4-16e3-4f7d-b39f-9400a5c49c13"
      },
      "source": [
        "num_epochs = 20\n",
        "history = model.fit(train_padded, training_label_seq, epochs = num_epochs, validation_data=(validation_padded, validation_label_seq), verbose=2)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Train on 1780 samples, validate on 445 samples\n",
            "Epoch 1/20\n",
            "1780/1780 - 1s - loss: 1.7607 - accuracy: 0.2944 - val_loss: 1.7180 - val_accuracy: 0.3213\n",
            "Epoch 2/20\n",
            "1780/1780 - 0s - loss: 1.6519 - accuracy: 0.4506 - val_loss: 1.5750 - val_accuracy: 0.5438\n",
            "Epoch 3/20\n",
            "1780/1780 - 0s - loss: 1.4646 - accuracy: 0.5562 - val_loss: 1.3616 - val_accuracy: 0.5573\n",
            "Epoch 4/20\n",
            "1780/1780 - 0s - loss: 1.2304 - accuracy: 0.5753 - val_loss: 1.1426 - val_accuracy: 0.6854\n",
            "Epoch 5/20\n",
            "1780/1780 - 0s - loss: 1.0108 - accuracy: 0.7230 - val_loss: 0.9487 - val_accuracy: 0.7865\n",
            "Epoch 6/20\n",
            "1780/1780 - 0s - loss: 0.8094 - accuracy: 0.8579 - val_loss: 0.7680 - val_accuracy: 0.9101\n",
            "Epoch 7/20\n",
            "1780/1780 - 0s - loss: 0.6234 - accuracy: 0.9399 - val_loss: 0.6080 - val_accuracy: 0.9213\n",
            "Epoch 8/20\n",
            "1780/1780 - 0s - loss: 0.4674 - accuracy: 0.9590 - val_loss: 0.4822 - val_accuracy: 0.9348\n",
            "Epoch 9/20\n",
            "1780/1780 - 0s - loss: 0.3478 - accuracy: 0.9702 - val_loss: 0.3928 - val_accuracy: 0.9326\n",
            "Epoch 10/20\n",
            "1780/1780 - 0s - loss: 0.2606 - accuracy: 0.9820 - val_loss: 0.3250 - val_accuracy: 0.9393\n",
            "Epoch 11/20\n",
            "1780/1780 - 0s - loss: 0.1989 - accuracy: 0.9871 - val_loss: 0.2757 - val_accuracy: 0.9438\n",
            "Epoch 12/20\n",
            "1780/1780 - 0s - loss: 0.1545 - accuracy: 0.9899 - val_loss: 0.2407 - val_accuracy: 0.9483\n",
            "Epoch 13/20\n",
            "1780/1780 - 0s - loss: 0.1222 - accuracy: 0.9938 - val_loss: 0.2121 - val_accuracy: 0.9551\n",
            "Epoch 14/20\n",
            "1780/1780 - 0s - loss: 0.0973 - accuracy: 0.9955 - val_loss: 0.1913 - val_accuracy: 0.9596\n",
            "Epoch 15/20\n",
            "1780/1780 - 0s - loss: 0.0786 - accuracy: 0.9972 - val_loss: 0.1775 - val_accuracy: 0.9573\n",
            "Epoch 16/20\n",
            "1780/1780 - 0s - loss: 0.0646 - accuracy: 0.9983 - val_loss: 0.1630 - val_accuracy: 0.9596\n",
            "Epoch 17/20\n",
            "1780/1780 - 0s - loss: 0.0538 - accuracy: 0.9983 - val_loss: 0.1530 - val_accuracy: 0.9596\n",
            "Epoch 18/20\n",
            "1780/1780 - 0s - loss: 0.0449 - accuracy: 0.9994 - val_loss: 0.1447 - val_accuracy: 0.9618\n",
            "Epoch 19/20\n",
            "1780/1780 - 0s - loss: 0.0381 - accuracy: 0.9994 - val_loss: 0.1374 - val_accuracy: 0.9618\n",
            "Epoch 20/20\n",
            "1780/1780 - 0s - loss: 0.0324 - accuracy: 0.9994 - val_loss: 0.1325 - val_accuracy: 0.9618\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRfdUrTygIvJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "5016b00b-d4fc-46af-c6df-2bc2df08edf0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GrXY2YExMsEQQi44fghUvwLBvQsf6cwq8sGQnGZ3acOnArlEIzhgTaa4mAhGZJCJbRGSb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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eN8YkGmMSo6Ojq6NW5Y6vPwybBDEdYNpoxp6Ry0UdYnjuuzX8sXWP09UppU6B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\nGhBKKaXc0oBQSinllgaEUkopt/4fyn6rFLHTAZIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeuoCJmaj7hx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}