Copy_of_Tweet_Emotion_Recognition_Learner.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": "3867ef56-a261-45b4-8b16-4aa0fe7d5051"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting nlp\n",
            "  Downloading nlp-0.4.0-py3-none-any.whl (1.7 MB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2021.10.8)\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: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2018.9)\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-2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dc176fd-9691-4eb4-e2c0-a99b3edce1fb"
      },
      "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')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_accuracy'), label='Validation')\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')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_loss'), label='Validation')\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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow version 2.7.0\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": "6b79e67c-8806-46bb-e443-981645d57587"
      },
      "source": [
        "dataset = nlp.load_dataset('emotion')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd9d88cd22ca4c3f81cee225e38fc01a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/3.41k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "938a276c64914f0a88b2056487bb6c9a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/6.01k [00:00<?, ?B/s]"
            ]
          },
          "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": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4b512c33eab64037a6c3709cb4d6acfb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.66M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c9635ecc4cf4839af6b18ec71c9e22c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/204k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d48df24455114df4a75228a5f7a84bf3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/207k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7ee9e4891454d1388a4ba90e6ae65a1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed13089c87e74583bff285da7bc2287a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55c489e7b7844a5682137541ddca8414",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "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"
      },
      "source": [
        "train = dataset['train']\n",
        "val = dataset['validation']\n",
        "test = dataset['test']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDYXMfZy5eiP"
      },
      "source": [
        "tweets, labels = get_tweet(train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e740f522-3303-4f34-faba-829c175f3a16"
      },
      "source": [
        "tweets[90], labels[90]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i was ready to meet mom in the airport and feel her ever supportive arms around me',\n",
              " 'love')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cckUvwBo5eif"
      },
      "source": [
        "tokenizer = Tokenizer(num_words=10000, oov_token='<UNK>')\n",
        "tokenizer.fit_on_texts(tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.texts_to_sequences([tweets[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVJ5EYDCTp-v",
        "outputId": "9d6ca5d6-27bf-43b3-8b7f-f98b92b83b39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 139, 3, 679]]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "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": "9878a539-7d10-49b1-f133-8b048d62d6a7"
      },
      "source": [
        "lengths = [len(t.split(' ')) for t in tweets]\n",
        "plt.hist(lengths, bins = len(set(lengths)))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOi5lIE3xIgx"
      },
      "source": [
        "maxlen = 50\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eglH77ky5ei0"
      },
      "source": [
        "padded_train_seq = get_sequences(tokenizer, tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGR473HA5ei7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43f365fd-7b56-4c4b-bead-cfca5c4a69ee"
      },
      "source": [
        "padded_train_seq[0]"
      ],
      "execution_count": null,
      "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": 33
        }
      ]
    },
    {
      "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": "160826ad-8f1f-4044-d917-4c762c52aee3"
      },
      "source": [
        "classes = set(labels)\n",
        "print(classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'anger', 'sadness', 'love', 'fear', 'joy', 'surprise'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "75075ea1-b0a9-49b1-bda0-0ba410c905a4"
      },
      "source": [
        "plt.hist(labels, bins = 11)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "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())\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87h96eZFZsKz",
        "outputId": "dc1cc805-72f3-44c2-836a-7896fa548fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': 0, 'fear': 3, 'joy': 4, 'love': 2, 'sadness': 1, 'surprise': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0caefb04-795d-4427-9c5d-873daa079959"
      },
      "source": [
        "index_to_class"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'anger', 1: 'sadness', 2: 'love', 3: 'fear', 4: 'joy', 5: 'surprise'}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpeDoA6gxIhE"
      },
      "source": [
        "names_to_ids = lambda labels:np.array([class_to_index.get(x) for x in labels])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0WJYsP5ejR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af0e0690-56dd-450d-e7c9-18d76852fbde"
      },
      "source": [
        "train_labels = names_to_ids(labels)\n",
        "print(train_labels[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v15KnrNC5ejW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDb0UCDjb9mb",
        "outputId": "a9cb64db-6a04-48e5-e211-9eb758d44a64"
      },
      "execution_count": null,
      "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",
      "metadata": {
        "id": "Ff7F3hCK5ejm"
      },
      "source": [
        "val_tweets, val_labels = get_tweet(val)\n",
        "val_seq = get_sequences(tokenizer, val_tweets)\n",
        "val_labels = names_to_ids(val_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlMKaZ3H5ejr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa5ec087-2129-403e-fe25-78bd282bfeef"
      },
      "source": [
        "val_tweets[0], val_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('im feeling quite sad and sorry for myself but ill snap out of it soon', 1)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzBqnWQ-5ejw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce9ca2f9-ad37-4746-c0ca-7d4b036dde6b"
      },
      "source": [
        "h = model.fit(padded_train_seq, train_labels, \n",
        "              validation_data = (val_seq, val_labels),\n",
        "              epochs  = 20,\n",
        "              callbacks = [\n",
        "                           tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2)\n",
        "                           ])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "500/500 [==============================] - 39s 62ms/step - loss: 1.3063 - accuracy: 0.4932 - val_loss: 1.0073 - val_accuracy: 0.6015\n",
            "Epoch 2/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.7462 - accuracy: 0.7070 - val_loss: 0.6493 - val_accuracy: 0.7580\n",
            "Epoch 3/20\n",
            "500/500 [==============================] - 29s 59ms/step - loss: 0.4134 - accuracy: 0.8506 - val_loss: 0.4776 - val_accuracy: 0.8430\n",
            "Epoch 4/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.2230 - accuracy: 0.9279 - val_loss: 0.4013 - val_accuracy: 0.8695\n",
            "Epoch 5/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.1561 - accuracy: 0.9503 - val_loss: 0.4084 - val_accuracy: 0.8745\n",
            "Epoch 6/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.1153 - accuracy: 0.9657 - val_loss: 0.3718 - val_accuracy: 0.8895\n",
            "Epoch 7/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.0962 - accuracy: 0.9711 - val_loss: 0.3909 - val_accuracy: 0.8900\n",
            "Epoch 8/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.0837 - accuracy: 0.9743 - val_loss: 0.4362 - val_accuracy: 0.8805\n",
            "Epoch 9/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.0674 - accuracy: 0.9801 - val_loss: 0.3944 - val_accuracy: 0.8945\n",
            "Epoch 10/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.0558 - accuracy: 0.9831 - val_loss: 0.4341 - val_accuracy: 0.8965\n",
            "Epoch 11/20\n",
            "500/500 [==============================] - 29s 59ms/step - loss: 0.0626 - accuracy: 0.9803 - val_loss: 0.3841 - val_accuracy: 0.8940\n",
            "Epoch 12/20\n",
            "500/500 [==============================] - 29s 58ms/step - loss: 0.0512 - accuracy: 0.9850 - val_loss: 0.4010 - val_accuracy: 0.8955\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": 392
        },
        "outputId": "80345ff8-c8f6-4733-9c04-936692cdfa6c"
      },
      "source": [
        "show_history(h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vRVJ_2SxIhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25207613-4f0d-499d-c28a-4f0992de0cad"
      },
      "source": [
        "_ = model.evaluate(test_seq, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1s 20ms/step - loss: 0.4520 - accuracy: 0.8770\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh638vHG5ej6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10845517-2b91-46e6-9634-c291cba9513f"
      },
      "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",
        "print(\"Predicted Emotions: \", pred_class)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence:  i feel tender when i have not done anything\n",
            "Emotion:  love\n",
            "Predicted Emotions:  love\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = np.argmax(model.predict_classes(test_seq), axis = -1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "MhOwKrJfhY3V",
        "outputId": "57461342-f2ff-4adb-de3e-cbfa2f13d8f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-66-7f1a885d2527>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_seq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'Sequential' object has no attribute 'predict_classes'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC8YQ0OexIhj"
      },
      "source": [
        "show_confusion_matrix(test_labels, preds, list(classes))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}