Transfer_Learning.ipynb

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  "metadata": {
    "colab": {
      "name": "Transfer_Learning.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "koIzoOjQv1Bp",
        "6GuBCLOXxCh5",
        "ZUVAsUwVQtgE",
        "sg4mEcObSqXu",
        "yHJ6CjV_ZTf7",
        "AoAnaSIYxOAp"
      ],
      "authorship_tag": "ABX9TyMg4BUw29LuTrlMjVZMPqzb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rajsoni03/NLP-Insincere-Questions-Classifier/blob/main/Transfer_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvP1INTsvdFq"
      },
      "source": [
        "<h1>NLP-Insincere-Questions-Classifier</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koIzoOjQv1Bp"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwiVS1LyqbLj",
        "outputId": "119dd40c-77e4-4443-f4d2-8e15e7e76bd2"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "import tensorflow_hub as hub\r\n",
        "\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "from imblearn.under_sampling import RandomUnderSampler\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "import os\r\n",
        "\r\n",
        "!pip install -q git+https://github.com/tensorflow/docs\r\n",
        "\r\n",
        "import tensorflow_docs as tfdocs\r\n",
        "import tensorflow_docs.modeling\r\n",
        "import tensorflow_docs.plots"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25h  Building wheel for tensorflow-docs (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GuBCLOXxCh5"
      },
      "source": [
        "## Download and Import the Quora Insincere Questions Dataset\r\n",
        "\r\n",
        "Dataset Link - https://www.kaggle.com/c/quora-insincere-questions-classification/data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8HdRKi3viBG",
        "outputId": "3230dad5-7676-47a2-d0dc-cfe2ae509ec4"
      },
      "source": [
        "# mount drive to notebook\r\n",
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trQdGeJUxu3D",
        "outputId": "d4ec8696-6237-4b18-e03e-5c9a949492e0"
      },
      "source": [
        "# clone data from kaggle\r\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = '/content/drive/MyDrive/AI Colab/Kaggle'\r\n",
        "!kaggle competitions download --force -c quora-insincere-questions-classification -f train.csv"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading train.csv.zip to /content\n",
            "100% 54.9M/54.9M [00:00<00:00, 57.0MB/s]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fP2Ov_4syZeu",
        "outputId": "30e4ec3f-6a8e-43a8-bfcc-8acf68b3d0aa"
      },
      "source": [
        "# read train.csv file\r\n",
        "df = pd.read_csv('/content/train.csv.zip', compression='zip', low_memory=False)\r\n",
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1306122, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "kCDXmyO1y7XT",
        "outputId": "fece143e-47f9-4cf6-ec65-6d3005cc7af8"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>qid</th>\n",
              "      <th>question_text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00002165364db923c7e6</td>\n",
              "      <td>How did Quebec nationalists see their province...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000032939017120e6e44</td>\n",
              "      <td>Do you have an adopted dog, how would you enco...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0000412ca6e4628ce2cf</td>\n",
              "      <td>Why does velocity affect time? Does velocity a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>000042bf85aa498cd78e</td>\n",
              "      <td>How did Otto von Guericke used the Magdeburg h...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0000455dfa3e01eae3af</td>\n",
              "      <td>Can I convert montra helicon D to a mountain b...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    qid  ... target\n",
              "0  00002165364db923c7e6  ...      0\n",
              "1  000032939017120e6e44  ...      0\n",
              "2  0000412ca6e4628ce2cf  ...      0\n",
              "3  000042bf85aa498cd78e  ...      0\n",
              "4  0000455dfa3e01eae3af  ...      0\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "15iJ3v2uy7a2",
        "outputId": "7705e696-6ab5-4358-9b5f-04cc52df3155"
      },
      "source": [
        "df['target'].plot(kind='hist', title='Target Distribution')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efb93f23d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "jaTcjvVtzE-B"
      },
      "source": [
        "There are class imbalance problem with dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUVAsUwVQtgE"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2biQ6_zy7dK",
        "outputId": "a79aad59-0db1-4154-9efc-a9409ff17031"
      },
      "source": [
        "# handle class imbalance problem\r\n",
        "rus  = RandomUnderSampler(random_state=42)\r\n",
        "df_final, _ = rus.fit_sample(df, df['target'])\r\n",
        "df_final = pd.DataFrame(df_final, columns=['qid', 'question_text','target'])\r\n",
        "df_final.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(161620, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6F9OvHegtAk6",
        "outputId": "11a57ff9-345b-4dae-b9d5-ac4b6f546289"
      },
      "source": [
        "df_final.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 161620 entries, 0 to 161619\n",
            "Data columns (total 3 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   qid            161620 non-null  object\n",
            " 1   question_text  161620 non-null  object\n",
            " 2   target         161620 non-null  object\n",
            "dtypes: object(3)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuwu9-mgtMtC",
        "outputId": "60060871-d059-46be-a420-a239d464da7b"
      },
      "source": [
        "# convert target type from obj to int\r\n",
        "df_final = df_final.astype({'target': 'int32'})\r\n",
        "df_final.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 161620 entries, 0 to 161619\n",
            "Data columns (total 3 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   qid            161620 non-null  object\n",
            " 1   question_text  161620 non-null  object\n",
            " 2   target         161620 non-null  int32 \n",
            "dtypes: int32(1), object(2)\n",
            "memory usage: 3.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6LxtA-LKJr2N",
        "outputId": "7b5f518c-a73a-473a-9174-d251d1a9d8e5"
      },
      "source": [
        "df_final['target'].plot(kind='hist', title='Target Distribution after undersampling');"
      ],
      "execution_count": 10,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkN1IqgAQm-W",
        "outputId": "aea56200-1444-490b-ba44-2d3457114528"
      },
      "source": [
        "# split data into training and validation set in ratio of 9:1\r\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(df_final['question_text'], df_final['target'], random_state=42, train_size=0.9, stratify=df_final.target.values)\r\n",
        "\r\n",
        "print('No. of samples Training data :', X_train.shape[0])\r\n",
        "print('No. of samples Validation data :', X_val.shape[0]) "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of samples Training data : 145458\n",
            "No. of samples Validation data : 16162\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sg4mEcObSqXu"
      },
      "source": [
        "## Define Function to Train and Evaluate Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yO1NKKAMYaFF"
      },
      "source": [
        "logdir = '/content/tensorboard_logs'\r\n",
        "os.mkdir(logdir)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQrjHZtySpcu"
      },
      "source": [
        "def train_and_evaluate_model(module_url, embed_size, name, trainable=False):\r\n",
        "  hub_layer = hub.KerasLayer(module_url, input_shape=[], output_shape=[embed_size], dtype=tf.string, trainable=trainable)\r\n",
        "\r\n",
        "  model = tf.keras.models.Sequential([\r\n",
        "                                      hub_layer,\r\n",
        "                                      tf.keras.layers.Dense(256, activation='relu'),\r\n",
        "                                      tf.keras.layers.Dense(64, activation='relu'),\r\n",
        "                                      tf.keras.layers.Dense(1, activation='sigmoid')\r\n",
        "  ])\r\n",
        "\r\n",
        "  model.summary()\r\n",
        "  model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3),\r\n",
        "                loss = 'binary_crossentropy',\r\n",
        "                metrics=['accuracy']\r\n",
        "                )\r\n",
        "  \r\n",
        "  history = model.fit(X_train, Y_train,\r\n",
        "                      validation_data = (X_val, Y_val),\r\n",
        "                      epochs = 20,\r\n",
        "                      batch_size = 32,\r\n",
        "                      verbose = 1,\r\n",
        "                      callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2, mode='min'),\r\n",
        "                                   tf.keras.callbacks.TensorBoard(log_dir=f'{logdir}/{name}')]\r\n",
        "                      )\r\n",
        "  return history"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHJ6CjV_ZTf7"
      },
      "source": [
        "## Train Various Text Classification Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ii0-cDWHy7fJ"
      },
      "source": [
        "histories = {}"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChQ4--SMZmGB"
      },
      "source": [
        "Model - 1 <br>\r\n",
        "[gnews-swivel-20dim](https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGOxhrccOUD-",
        "outputId": "7ce9f031-4b95-47c1-fad6-4b3599cd286a"
      },
      "source": [
        "histories['gnews-swivel-20dim'] = train_and_evaluate_model(\"https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\",\r\n",
        "                                                                     embed_size=20,\r\n",
        "                                                                     name='gnews-swivel-20dim',\r\n",
        "                                                                     trainable=False)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer (KerasLayer)     (None, 20)                400020    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               5376      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 421,909\n",
            "Trainable params: 21,889\n",
            "Non-trainable params: 400,020\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "4546/4546 [==============================] - 24s 5ms/step - loss: 0.4328 - accuracy: 0.8019 - val_loss: 0.4076 - val_accuracy: 0.8178\n",
            "Epoch 2/20\n",
            "4546/4546 [==============================] - 21s 5ms/step - loss: 0.4082 - accuracy: 0.8162 - val_loss: 0.4013 - val_accuracy: 0.8216\n",
            "Epoch 3/20\n",
            "4546/4546 [==============================] - 21s 5ms/step - loss: 0.4031 - accuracy: 0.8197 - val_loss: 0.4030 - val_accuracy: 0.8203\n",
            "Epoch 4/20\n",
            "4546/4546 [==============================] - 21s 5ms/step - loss: 0.3989 - accuracy: 0.8221 - val_loss: 0.4032 - val_accuracy: 0.8197\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQ2DqkHFuuCC"
      },
      "source": [
        "Model - 2 <br>\r\n",
        "[nnlm-en-dim50](\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGc1BXzHueAU",
        "outputId": "960908f2-3da7-4fce-bdc2-d25d0a6586f6"
      },
      "source": [
        "histories['nnlm-en-dim50'] = train_and_evaluate_model(\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\", \r\n",
        "                                                      embed_size=50, \r\n",
        "                                                      name='nnlm-en-dim50', \r\n",
        "                                                      trainable=False)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_1 (KerasLayer)   (None, 50)                48190600  \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 256)               13056     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 48,220,169\n",
            "Trainable params: 29,569\n",
            "Non-trainable params: 48,190,600\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.4411 - accuracy: 0.7949 - val_loss: 0.4062 - val_accuracy: 0.8191\n",
            "Epoch 2/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.3990 - accuracy: 0.8224 - val_loss: 0.3988 - val_accuracy: 0.8237\n",
            "Epoch 3/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.3894 - accuracy: 0.8269 - val_loss: 0.3956 - val_accuracy: 0.8251\n",
            "Epoch 4/20\n",
            "4546/4546 [==============================] - 21s 5ms/step - loss: 0.3795 - accuracy: 0.8327 - val_loss: 0.3945 - val_accuracy: 0.8259\n",
            "Epoch 5/20\n",
            "4546/4546 [==============================] - 21s 5ms/step - loss: 0.3740 - accuracy: 0.8348 - val_loss: 0.3963 - val_accuracy: 0.8231\n",
            "Epoch 6/20\n",
            "4546/4546 [==============================] - 21s 5ms/step - loss: 0.3677 - accuracy: 0.8388 - val_loss: 0.3968 - val_accuracy: 0.8270\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPQG7_9mwc68"
      },
      "source": [
        "Model - 3 <br>\r\n",
        "[nnlm-en-dim128](\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HlTnYAeLss5e",
        "outputId": "920bc7f1-6340-467b-97d6-3f3509a603ac"
      },
      "source": [
        "histories['nnlm-en-dim128'] = train_and_evaluate_model(\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1\",\r\n",
        "                                                       embed_size=128,\r\n",
        "                                                       name='nnlm-en-dim128',\r\n",
        "                                                       trainable=False)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7efb800e4d08> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7efb800e4d08> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7efb2a7cd620> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7efb2a7cd620> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_2 (KerasLayer)   (None, 128)               124642688 \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 256)               33024     \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 124,692,225\n",
            "Trainable params: 49,537\n",
            "Non-trainable params: 124,642,688\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.3874 - accuracy: 0.8284 - val_loss: 0.3514 - val_accuracy: 0.8508\n",
            "Epoch 2/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.3349 - accuracy: 0.8589 - val_loss: 0.3433 - val_accuracy: 0.8589\n",
            "Epoch 3/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.3193 - accuracy: 0.8664 - val_loss: 0.3392 - val_accuracy: 0.8581\n",
            "Epoch 4/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.3027 - accuracy: 0.8754 - val_loss: 0.3384 - val_accuracy: 0.8599\n",
            "Epoch 5/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.2945 - accuracy: 0.8786 - val_loss: 0.3415 - val_accuracy: 0.8578\n",
            "Epoch 6/20\n",
            "4546/4546 [==============================] - 22s 5ms/step - loss: 0.2798 - accuracy: 0.8856 - val_loss: 0.3479 - val_accuracy: 0.8569\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57LpQkS3yBID"
      },
      "source": [
        "Model - 4 <br>\r\n",
        "[universal-sentence-encoder](\"https://tfhub.dev/google/universal-sentence-encoder/4\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4P4EfBBDx_JZ",
        "outputId": "ae60e046-7763-4408-b3d5-3650d489e5ff"
      },
      "source": [
        "histories['universal-sentence-encoder'] = train_and_evaluate_model(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\r\n",
        "                                                                   embed_size=512,\r\n",
        "                                                                   name='universal-sentence-encoder',\r\n",
        "                                                                   trainable=False)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:7 out of the last 7 calls to <function recreate_function.<locals>.restored_function_body at 0x7efbc0e44950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:7 out of the last 7 calls to <function recreate_function.<locals>.restored_function_body at 0x7efbc0e44950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_3 (KerasLayer)   (None, 512)               256797824 \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 256,945,665\n",
            "Trainable params: 147,841\n",
            "Non-trainable params: 256,797,824\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "4546/4546 [==============================] - 63s 13ms/step - loss: 0.2841 - accuracy: 0.8897 - val_loss: 0.2542 - val_accuracy: 0.9009\n",
            "Epoch 2/20\n",
            "4546/4546 [==============================] - 60s 13ms/step - loss: 0.2299 - accuracy: 0.9133 - val_loss: 0.2377 - val_accuracy: 0.9093\n",
            "Epoch 3/20\n",
            "4546/4546 [==============================] - 60s 13ms/step - loss: 0.2092 - accuracy: 0.9220 - val_loss: 0.2385 - val_accuracy: 0.9084\n",
            "Epoch 4/20\n",
            "4546/4546 [==============================] - 60s 13ms/step - loss: 0.1897 - accuracy: 0.9290 - val_loss: 0.2460 - val_accuracy: 0.9078\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAHCXPDPyW0C"
      },
      "source": [
        "Model - 5 <br>\r\n",
        "[universal-sentence-encoder-large](\"https://tfhub.dev/google/universal-sentence-encoder-large/5\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jxRSboYyXI6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1b07172-3415-4e78-a580-e21ad334ef01"
      },
      "source": [
        "histories['universal-sentence-encoder-large'] = train_and_evaluate_model(\"https://tfhub.dev/google/universal-sentence-encoder-large/5\",\r\n",
        "                                                                         embed_size=512,\r\n",
        "                                                                         name='universal-sentence-encoder-large',\r\n",
        "                                                                         trainable=False)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:8 out of the last 8 calls to <function recreate_function.<locals>.restored_function_body at 0x7efa984972f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:8 out of the last 8 calls to <function recreate_function.<locals>.restored_function_body at 0x7efa984972f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_4 (KerasLayer)   (None, 512)               147354880 \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 147,502,721\n",
            "Trainable params: 147,841\n",
            "Non-trainable params: 147,354,880\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "4546/4546 [==============================] - 230s 46ms/step - loss: 0.2479 - accuracy: 0.9033 - val_loss: 0.2151 - val_accuracy: 0.9217\n",
            "Epoch 2/20\n",
            "4546/4546 [==============================] - 203s 45ms/step - loss: 0.1983 - accuracy: 0.9278 - val_loss: 0.2120 - val_accuracy: 0.9224\n",
            "Epoch 3/20\n",
            "4546/4546 [==============================] - 204s 45ms/step - loss: 0.1761 - accuracy: 0.9367 - val_loss: 0.2176 - val_accuracy: 0.9233\n",
            "Epoch 4/20\n",
            "4546/4546 [==============================] - 206s 45ms/step - loss: 0.1503 - accuracy: 0.9470 - val_loss: 0.2230 - val_accuracy: 0.9199\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoAnaSIYxOAp"
      },
      "source": [
        "## Evaluate Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9eOnzPOvrak",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "74227b95-26ae-4660-d1c0-393f5c73f2ce"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 8)\r\n",
        "plotter = tfdocs.plots.HistoryPlotter(metric = 'accuracy')\r\n",
        "plotter.plot(histories)\r\n",
        "plt.xlabel(\"Epochs\")\r\n",
        "plt.legend(bbox_to_anchor=(1.0, 1.0), loc='upper left')\r\n",
        "plt.title(\"Accuracy Curves for Models\")\r\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHmd-awrxfdS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "b750ef0f-c03e-48bc-b056-60385ef72f31"
      },
      "source": [
        "plotter = tfdocs.plots.HistoryPlotter(metric = 'loss')\r\n",
        "plotter.plot(histories)\r\n",
        "plt.xlabel(\"Epochs\")\r\n",
        "plt.legend(bbox_to_anchor=(1.0, 1.0), loc='upper left')\r\n",
        "plt.title(\"Loss Curves for Models\")\r\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nkv68ni2xxAY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wonrzYvOU-jm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}