Tweet_Emotion_Recognition.ipynb

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  "cells": [
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      "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",
        "\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",
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        },
        "outputId": "0ce04a82-b7fd-401b-a2c1-bffdcd5f6e2f"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting nlp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/09/e3/bcdc59f3434b224040c1047769c47b82705feca2b89ebbc28311e3764782/nlp-0.4.0-py3-none-any.whl (1.7MB)\n",
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            "Successfully installed nlp-0.4.0 xxhash-2.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "417937e2-c953-47ae-e125-f094bc846459"
      },
      "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",
          "text": [
            "Using TensorFlow version 2.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "c1435fd7-51df-4d2b-b5e2-c8dacef656c3"
      },
      "source": [
        "dataset=nlp.load_dataset('emotion')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2933472798145278d6993c02e094682",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3406.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b6a9980207e49bab1941fa03ae01a1d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=6011.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92db4087b21a4923a7127ddd06fd3f9a",
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              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1658616.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "764b2a20e6c34ed2a587948cebfdcf55",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=204240.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b5ae2459c164fe78c58ae5a10088125",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=206760.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c281f46d673c4f8dbc3d4acbfd766d41",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0aafb39a91a247f4879db9e0b14dc205",
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          },
          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2418af5002204e99afe2b03f568816db",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s0h541FxIgc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0579ad35-618d-40a4-b3d7-0a134e1f15ec"
      },
      "source": [
        "dataset"
      ],
      "execution_count": null,
      "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": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "source": [
        "train=dataset['train']\n",
        "val=dataset['validation']\n",
        "test=dataset['test']"
      ],
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR"
      },
      "source": [
        "tweets,labels=get_tweet(train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cafaae8-0c70-4a93-c787-ba5e8278cf52"
      },
      "source": [
        "tweets[0],labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i didnt feel humiliated', 'sadness')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "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=1000,oov_token='<UNK>')\n",
        "tokenizer.fit_on_texts(tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MAx3tMa_yoU",
        "outputId": "030cf302-6a45-4783-fbeb-fa11baab1c23"
      },
      "source": [
        "tokenizer.texts_to_sequences([tweets[0]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 139, 3, 679]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "1fFjf2DFABSG",
        "outputId": "8264aaf9-5184-4ea4-9519-e6720a2dd848"
      },
      "source": [
        "tweets[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'i didnt feel humiliated'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "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": "ad555f85-6b42-499e-998c-587d1b878325"
      },
      "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": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOi5lIE3xIgx"
      },
      "source": [
        "maxlen=50\n",
        "\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": "f9bb5351-c2e2-4287-f702-01460c841292"
      },
      "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": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "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": "6dbc4e40-9775-44ac-ac77-91cda6c35529"
      },
      "source": [
        "classes=set(labels)\n",
        "print(classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'sadness', 'joy', 'surprise', 'anger', 'love', 'fear'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "029257d3-a682-43ef-9b30-1e4a3fe02847"
      },
      "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": {
            "tags": [],
            "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4d857a4-226a-4b89-8ae5-6559fedd28df"
      },
      "source": [
        "class_to_index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': 3, 'fear': 5, 'joy': 1, 'love': 4, 'sadness': 0, 'surprise': 2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpeDoA6gxIhE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cfe08c6-032f-43ec-f2c8-9a2d5ef45ca6"
      },
      "source": [
        "index_to_class"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'sadness', 1: 'joy', 2: 'surprise', 3: 'anger', 4: 'love', 5: 'fear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v15KnrNC5ejW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a33d288-f05f-4a1e-82ab-79c5b7e985fc"
      },
      "source": [
        "train_labels=names_to_ids(labels)\n",
        "print(train_labels[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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(1000,16,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",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKyjmKgGHl7L",
        "outputId": "31efcd93-f990-42ff-fd10-0d7e87728cde"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 50, 16)            16000     \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 50, 40)            5920      \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 40)                9760      \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 246       \n",
            "=================================================================\n",
            "Total params: 31,926\n",
            "Trainable params: 31,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "2c75eb37-ac10-4ccf-ba95-3e997dff5cc5"
      },
      "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', 0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzBqnWQ-5ejw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5ec6aa4-f046-4142-be4f-6434ceb4b05e"
      },
      "source": [
        "h=model.fit(\n",
        "    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",
        "    ]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "500/500 [==============================] - 39s 59ms/step - loss: 1.2556 - accuracy: 0.5144 - val_loss: 0.7509 - val_accuracy: 0.7385\n",
            "Epoch 2/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.6203 - accuracy: 0.7868 - val_loss: 0.6201 - val_accuracy: 0.7800\n",
            "Epoch 3/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.4978 - accuracy: 0.8242 - val_loss: 0.5204 - val_accuracy: 0.8280\n",
            "Epoch 4/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.4342 - accuracy: 0.8477 - val_loss: 0.4956 - val_accuracy: 0.8305\n",
            "Epoch 5/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.3873 - accuracy: 0.8664 - val_loss: 0.4615 - val_accuracy: 0.8370\n",
            "Epoch 6/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.3522 - accuracy: 0.8732 - val_loss: 0.4385 - val_accuracy: 0.8440\n",
            "Epoch 7/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.3323 - accuracy: 0.8782 - val_loss: 0.4200 - val_accuracy: 0.8570\n",
            "Epoch 8/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.3063 - accuracy: 0.8871 - val_loss: 0.4075 - val_accuracy: 0.8595\n",
            "Epoch 9/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.2873 - accuracy: 0.8891 - val_loss: 0.4183 - val_accuracy: 0.8615\n",
            "Epoch 10/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.2743 - accuracy: 0.8974 - val_loss: 0.4412 - val_accuracy: 0.8590\n",
            "Epoch 11/20\n",
            "500/500 [==============================] - 28s 56ms/step - loss: 0.2752 - accuracy: 0.8951 - val_loss: 0.4095 - val_accuracy: 0.8505\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "jmmvT87DSGfr",
        "outputId": "8171378b-b486-4c55-aceb-36049c7ebc4a"
      },
      "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": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHaKSNc6TQGr"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86QKFuiWTz3k",
        "outputId": "b7899059-cae2-4556-af26-b361ff106244"
      },
      "source": [
        "_=model.evaluate(test_seq,test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 1s 20ms/step - loss: 0.3748 - accuracy: 0.8600\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aR-muGmgT_kN",
        "outputId": "d258df54-4998-4011-df0f-fa3791d1d360"
      },
      "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",
        "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 Emotion:',pred_class)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sentence: i was feeling fairly keen\n",
            "Emotion: joy\n",
            "Predicted Emotion: joy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShcEaWxUVJHv",
        "outputId": "f10cf965-ef29-4154-db8b-69dda34e622e"
      },
      "source": [
        "preds=model.predict_classes(test_seq)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:455: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ja_n3Sw0VW5M",
        "outputId": "0a4c1f26-a84a-4fcb-b0a4-2dfa5d24b77e"
      },
      "source": [
        "show_confusion_matrix(test_labels,preds,list(classes))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQEGTVc0VjkK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}