Tweet_Emotion_Recognition.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp7D0ktn5eiG"
      },
      "source": [
        "## Tweet Emotion Recognition: Natural Language Processing with TensorFlow\n",
        "\n",
        "---\n",
        "\n",
        "Dataset: [Tweet Emotion Dataset](https://github.com/dair-ai/emotion_dataset)\n",
        "\n",
        "This is a starter notebook for the guided project [Tweet Emotion Recognition with TensorFlow](https://www.coursera.org/projects/tweet-emotion-tensorflow)\n",
        "\n",
        "A complete version of this notebook is available in the course resources\n",
        "\n",
        "---\n",
        "\n",
        "## Task 1: Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cprXxkrMxIgT"
      },
      "source": [
        "## Task 2: Setup and Imports\n",
        "\n",
        "1. Installing Hugging Face's nlp package\n",
        "2. Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5agZRy-45i0g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "498124d5-b40a-4b6b-8c92-7e92b0df7856"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting nlp\n",
            "  Downloading nlp-0.4.0-py3-none-any.whl (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 4.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlp) (1.21.6)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from nlp) (2.23.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from nlp) (1.3.5)\n",
            "Requirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlp) (6.0.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from nlp) (4.64.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from nlp) (3.7.1)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from nlp) (0.3.5.1)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 48.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2.10)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->nlp) (1.15.0)\n",
            "Installing collected packages: xxhash, nlp\n",
            "Successfully installed nlp-0.4.0 xxhash-3.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc4cd6cb-29e0-4b79-ea96-b1f8f1b66312"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nlp\n",
        "import random\n",
        "\n",
        "\n",
        "def show_history(h):\n",
        "    epochs_trained = len(h.history['loss'])\n",
        "    plt.figure(figsize=(16, 6))\n",
        "\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('accuracy'), label='Training', color = 'aquamarine')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_accuracy'), label='Validation', color = 'mediumpurple')\n",
        "    plt.ylim([0., 1.])\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('loss'), label='Training', color = 'aquamarine')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_loss'), label='Validation', color = 'mediumpurple')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "def show_confusion_matrix(y_true, y_pred, classes):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    \n",
        "    cm = confusion_matrix(y_true, y_pred, normalize='true')\n",
        "\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sp = plt.subplot(1, 1, 1)\n",
        "    ctx = sp.matshow(cm)\n",
        "    plt.xticks(list(range(0, 6)), labels=classes)\n",
        "    plt.yticks(list(range(0, 6)), labels=classes)\n",
        "    plt.colorbar(ctx)\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "print('Using TensorFlow version', tf.__version__)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow version 2.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JsBpezExIga"
      },
      "source": [
        "## Task 3: Importing Data\n",
        "\n",
        "1. Importing the Tweet Emotion dataset\n",
        "2. Creating train, validation and test sets\n",
        "3. Extracting tweets and labels from the examples"
      ]
    },
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        "outputId": "81161d57-4a90-4195-9e2f-08e381f3ef8e"
      },
      "source": [
        "dataset = nlp.load_dataset('emotion')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/3.41k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
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            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset emotion/default (download: 1.97 MiB, generated: 2.09 MiB, post-processed: Unknown sizetotal: 4.06 MiB) to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
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              "model_id": "80d926ae716f43369be54f448152e696"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "6af97e712560414b9c36a699a6e1aad8"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s0h541FxIgc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b4df569-db8d-4549-e755-ad87f0928ce4"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 2000),\n",
              " 'train': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 16000),\n",
              " 'validation': Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}, num_rows: 2000)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "source": [
        "test = dataset['test']\n",
        "train = dataset['train']\n",
        "validation = dataset['validation']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDYXMfZy5eiP"
      },
      "source": [
        "def get_tweet(data):\n",
        "  tweets = [x['text'] for x in data]\n",
        "  labels = [x['label'] for x in data]\n",
        "  return tweets, labels"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR"
      },
      "source": [
        "tweets, labels = get_tweet(train)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc9ece38-0677-4630-bba6-9c2e8ec0403a"
      },
      "source": [
        "tweets[0], labels[0]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i didnt feel humiliated', 'sadness')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcAflLv6xIgp"
      },
      "source": [
        "## Task 4: Tokenizer\n",
        "\n",
        "1. Tokenizing the tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfX5-ResxIgq"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cckUvwBo5eif"
      },
      "source": [
        "tokenizer = Tokenizer(num_words = 10000, oov_token = '<OOV>')\n",
        "tokenizer.fit_on_texts(tweets)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.texts_to_sequences([tweets[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SuNOBxn76CXa",
        "outputId": "4d18b829-08f1-4acd-f959-9f2c463c971b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 139, 3, 679]]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "-9QsVcWG6V82",
        "outputId": "15e40afc-f709-4135-b8c7-146a8c46e68b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'i didnt feel humiliated'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3Bqm7b2xIgu"
      },
      "source": [
        "## Task 5: Padding and Truncating Sequences\n",
        "\n",
        "1. Checking length of the tweets\n",
        "2. Creating padded sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLvf_WFZxIgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "78398ac2-3a05-4e14-ff46-ca04afe57dff"
      },
      "source": [
        "lengths = [len(t.split(' ')) for t in tweets]\n",
        "plt.hist(lengths, bins = len(set(lengths)), color = 'lavender')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOi5lIE3xIgx"
      },
      "source": [
        "maxlen = 50\n",
        "\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9J_Iemf5eiq"
      },
      "source": [
        "def get_sequences(tokenizer,tweets):\n",
        "  sequences = tokenizer.texts_to_sequences(tweets)\n",
        "  padded = pad_sequences(sequences, truncating = 'post', padding = 'post', maxlen = maxlen)\n",
        "  return padded"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eglH77ky5ei0"
      },
      "source": [
        "padded_train_seq = get_sequences(tokenizer, tweets)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGR473HA5ei7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6941a1e-6d15-41c3-c45c-32ec4a60266a"
      },
      "source": [
        "padded_train_seq[0]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  2, 139,   3, 679,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BURhOX_KxIg8"
      },
      "source": [
        "## Task 6: Preparing the Labels\n",
        "\n",
        "1. Creating classes to index and index to classes dictionaries\n",
        "2. Converting text labels to numeric labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SufT2bpD5ejE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bef1c51-0027-4ae3-8821-dc7ff2f7f5ba"
      },
      "source": [
        "classes = set(labels)\n",
        "print(classes)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fear', 'joy', 'love', 'anger', 'surprise', 'sadness'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "49eb79c3-b53f-4bf7-d05d-c3d0013de2ec"
      },
      "source": [
        "plt.hist(labels, bins = 11, color = 'salmon')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNLF6rXL5ejN"
      },
      "source": [
        "class_to_index = dict((c,i) for i,c in enumerate(classes))\n",
        "index_to_class = dict((v,k) for k,v in class_to_index.items())"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df1bd957-3175-402d-c5db-9bee89a16647"
      },
      "source": [
        "class_to_index"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': 3, 'fear': 0, 'joy': 1, 'love': 2, 'sadness': 5, 'surprise': 4}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpeDoA6gxIhE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2623c7e3-f936-4405-9406-9e4c66e44b7c"
      },
      "source": [
        "index_to_class"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'fear', 1: 'joy', 2: 'love', 3: 'anger', 4: 'surprise', 5: 'sadness'}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0WJYsP5ejR"
      },
      "source": [
        "names_to_ids = lambda labels: np.array([class_to_index.get(x) for x in labels])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v15KnrNC5ejW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "535dcce1-5b4b-47d5-b00a-3d7b0b217497"
      },
      "source": [
        "train_labels = names_to_ids(labels)\n",
        "print(train_labels[0])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-v0Mnh8xIhP"
      },
      "source": [
        "## Task 7: Creating the Model\n",
        "\n",
        "1. Creating the model\n",
        "2. Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpewXxPQ5eji"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "                                    tf.keras.layers.Embedding(10000, 16, input_length=maxlen),\n",
        "                                    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20, return_sequences=True)),\n",
        "                                    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20)),\n",
        "                                    tf.keras.layers.Dense(6, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sP38ggU9IxeT",
        "outputId": "543028eb-7ba5-4107-eeaf-05287a170206"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 50, 16)            160000    \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 50, 40)           5920      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 40)               9760      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense (Dense)               (None, 6)                 246       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 175,926\n",
            "Trainable params: 175,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HST_CHjxIhR"
      },
      "source": [
        "## Task 8: Training the Model\n",
        "\n",
        "1. Preparing a validation set\n",
        "2. Training the model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_tweets, val_labels = get_tweet(validation)\n",
        "val_seq = get_sequences(tokenizer, val_tweets)\n",
        "val_labels = names_to_ids(val_labels)"
      ],
      "metadata": {
        "id": "-YihTOj1IwzD"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff7F3hCK5ejm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15747519-584d-4a50-c664-b73307bcba42"
      },
      "source": [
        "val_tweets[0], val_labels[0]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('im feeling quite sad and sorry for myself but ill snap out of it soon', 5)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlMKaZ3H5ejr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5c84880-bd0f-4232-cdcf-360ad8a8b786"
      },
      "source": [
        "h = model.fit(\n",
        "    padded_train_seq, train_labels,\n",
        "    validation_data=(val_seq, val_labels),\n",
        "    epochs=20,\n",
        "    callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience = 2)]\n",
        ")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "500/500 [==============================] - 35s 55ms/step - loss: 1.3083 - accuracy: 0.4776 - val_loss: 0.8021 - val_accuracy: 0.6990\n",
            "Epoch 2/20\n",
            "500/500 [==============================] - 26s 51ms/step - loss: 0.5558 - accuracy: 0.8099 - val_loss: 0.5863 - val_accuracy: 0.8035\n",
            "Epoch 3/20\n",
            "500/500 [==============================] - 26s 52ms/step - loss: 0.3328 - accuracy: 0.8902 - val_loss: 0.4909 - val_accuracy: 0.8470\n",
            "Epoch 4/20\n",
            "500/500 [==============================] - 24s 48ms/step - loss: 0.2330 - accuracy: 0.9265 - val_loss: 0.4817 - val_accuracy: 0.8455\n",
            "Epoch 5/20\n",
            "500/500 [==============================] - 24s 48ms/step - loss: 0.1673 - accuracy: 0.9476 - val_loss: 0.5063 - val_accuracy: 0.8610\n",
            "Epoch 6/20\n",
            "500/500 [==============================] - 25s 51ms/step - loss: 0.1284 - accuracy: 0.9606 - val_loss: 0.4259 - val_accuracy: 0.8810\n",
            "Epoch 7/20\n",
            "500/500 [==============================] - 25s 49ms/step - loss: 0.0958 - accuracy: 0.9710 - val_loss: 0.4518 - val_accuracy: 0.8770\n",
            "Epoch 8/20\n",
            "500/500 [==============================] - 26s 51ms/step - loss: 0.0846 - accuracy: 0.9726 - val_loss: 0.4373 - val_accuracy: 0.8780\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdsJyMTLxIhX"
      },
      "source": [
        "## Task 9: Evaluating the Model\n",
        "\n",
        "1. Visualizing training history\n",
        "2. Prepraring a test set\n",
        "3. A look at individual predictions on the test set\n",
        "4. A look at all predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENCfvXeLxIhX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "e245eb1d-171b-4866-e81b-a98e52f89242"
      },
      "source": [
        "show_history(h)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWuzoz8uxIha"
      },
      "source": [
        "test_tweets, test_labels = get_tweet(test)\n",
        "test_seq = get_sequences(tokenizer, test_tweets)\n",
        "test_labels = names_to_ids(test_labels)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vRVJ_2SxIhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06620b67-52e3-4489-fb6c-f7545c49cedf"
      },
      "source": [
        "_ = model.evaluate(test_seq, test_labels)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 2s 25ms/step - loss: 0.4105 - accuracy: 0.8775\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh638vHG5ej6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f8719a9-19f1-4c87-f533-d89c83962c5b"
      },
      "source": [
        "i = random.randint(0, len(test_labels) - 1)\n",
        "print('Sentence:', test_tweets[i])\n",
        "print('Emotion:', index_to_class[test_labels[i]])\n",
        "\n",
        "p = model.predict(np.expand_dims(test_seq[i], axis = 0))[0]\n",
        "pred_class = index_to_class[np.argmax(p).astype('uint8')]\n",
        "\n",
        "print('Predicted Emotion:', pred_class)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: i feel honoured that such a great man claims me as his friend\n",
            "Emotion: joy\n",
            "Predicted Emotion: joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHl5SVCFxIhh"
      },
      "source": [
        "preds = np.argmax(model.predict(test_seq), axis = -1)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC8YQ0OexIhj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "84c4ddb6-6394-4284-e6d6-0df0b4e4ceee"
      },
      "source": [
        "show_confusion_matrix(test_labels, preds, list(classes))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}