Model_1_Binary_Classification_Model_ipynX.ipynb

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      "mount_file_id": "1hOH516p2ptti8d0Q2euX3mH_7DV_3XVo",
      "authorship_tag": "ABX9TyNUg6BRD6W5MF1S6tLhNVI7",
      "include_colab_link": true
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pravincoder/Tensorflow_models/blob/main/Model_1_Binary_Classification_Model_ipynX.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction to BinaryClassifiaction Model\n",
        "\n",
        "### Problem :- Email Spam Detection\n",
        "Here we are going to create a model that can predict the mail is a Spam(1)/Not a Spam(0), using the provided subject of the email."
      ],
      "metadata": {
        "id": "UTUmS1fKvLZU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modules\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "id": "gixtLcxfvK5l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Set\n",
        "\n",
        "#### The dataset used in this example is sourced from Kaggle (original authors Almeida and Hidalgo, 2011) is provided below.\n",
        "* [Spam test Dataset](https://www.kaggle.com/datasets/team-ai/spam-text-message-classification)\n",
        "\n"
      ],
      "metadata": {
        "id": "_jayBaWXzazh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QN97wvpvBlt"
      },
      "outputs": [],
      "source": [
        "#Load Data\n",
        "dataset = pd.read_csv('/content/drive/MyDrive/SPAMtextmessage.csv')\n",
        "# Convert ham to 0 and spam to 1\n",
        "dataset['Category']= dataset['Category'].str.replace('ham','0')\n",
        "dataset['Category']= dataset['Category'].str.replace('spam','1')\n",
        "dataset['Category']= dataset['Category'].astype(int)\n",
        "sentences = dataset['Message'].tolist()\n",
        "labels = dataset['Category'].tolist()\n",
        "# Separate out the sentences and labels into training and test sets\n",
        "training_size = int(len(sentences) * 0.8)\n",
        "# Sentence variables\n",
        "training_sentences = sentences[0:training_size]\n",
        "testing_sentences = sentences[training_size:]\n",
        "# Labels variables\n",
        "training_labels = labels[0:training_size]\n",
        "testing_labels = labels[training_size:]\n",
        "# Make labels into numpy arrays for use with the network later\n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing the Data Labels\n",
        "\n",
        "As the is only 2 output in labels that is 0(Not Spam) and 1 (Spam) using a bar /pie chart is ideal for visualization."
      ],
      "metadata": {
        "id": "BBILL5O3bufs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "zero_count =int(labels.count(0))\n",
        "one_count = int(len(labels))-zero_count\n",
        "\n",
        "c = [zero_count,one_count]\n",
        "plt.title('Count of Spam & Not Spam (Labels)')\n",
        "plt.pie(c,labels=[f'Not Spam ,\\n count:{zero_count},\\n{zero_count/(int(len(labels))*0.01)}%',f'Spam ,\\n count:{one_count},\\n {one_count/int(len(labels)*0.01)}%'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "SC-fM1mbdVCx",
        "outputId": "42d638e9-957d-4296-8baa-27a90e80e1fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = 1000\n",
        "embedding_dim = 16\n",
        "max_length = 100\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index"
      ],
      "metadata": {
        "id": "lBRsE_M_Hmm5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "padded = pad_sequences(sequences,maxlen=max_length, padding=padding_type,\n",
        "                       truncating=trunc_type)\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences,maxlen=max_length,\n",
        "                               padding=padding_type, truncating=trunc_type)"
      ],
      "metadata": {
        "id": "ycrqFdU5smuY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbOUiHRLsnAV",
        "outputId": "629a6cf5-26f1-433a-d72a-583dbaa1483d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_6 (Embedding)     (None, 100, 16)           80000     \n",
            "                                                                 \n",
            " flatten_6 (Flatten)         (None, 1600)              0         \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 6)                 9606      \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 1)                 7         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 89,613\n",
            "Trainable params: 89,613\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 20\n",
        "history=model.fit(padded, training_labels_final, epochs=num_epochs,batch_size=128, validation_data=(testing_padded, testing_labels_final))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2L0swV9ktDYs",
        "outputId": "727f2f02-9a7f-4105-d3d5-6525766c7065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "35/35 [==============================] - 1s 12ms/step - loss: 0.4622 - accuracy: 0.8643 - val_loss: 0.3536 - val_accuracy: 0.8700\n",
            "Epoch 2/20\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 0.3190 - accuracy: 0.8649 - val_loss: 0.2635 - val_accuracy: 0.8700\n",
            "Epoch 3/20\n",
            "35/35 [==============================] - 0s 11ms/step - loss: 0.2145 - accuracy: 0.8649 - val_loss: 0.1685 - val_accuracy: 0.8700\n",
            "Epoch 4/20\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 0.1613 - accuracy: 0.8649 - val_loss: 0.1476 - val_accuracy: 0.8700\n",
            "Epoch 5/20\n",
            "35/35 [==============================] - 0s 10ms/step - loss: 0.1428 - accuracy: 0.9598 - val_loss: 0.1371 - val_accuracy: 0.9731\n",
            "Epoch 6/20\n",
            "35/35 [==============================] - 0s 11ms/step - loss: 0.1303 - accuracy: 0.9767 - val_loss: 0.1299 - val_accuracy: 0.9776\n",
            "Epoch 7/20\n",
            "35/35 [==============================] - 0s 11ms/step - loss: 0.1213 - accuracy: 0.9841 - val_loss: 0.1246 - val_accuracy: 0.9803\n",
            "Epoch 8/20\n",
            "35/35 [==============================] - 0s 11ms/step - loss: 0.1141 - accuracy: 0.9868 - val_loss: 0.1203 - val_accuracy: 0.9830\n",
            "Epoch 9/20\n",
            "35/35 [==============================] - 0s 10ms/step - loss: 0.1085 - accuracy: 0.9895 - val_loss: 0.1174 - val_accuracy: 0.9848\n",
            "Epoch 10/20\n",
            "35/35 [==============================] - 0s 11ms/step - loss: 0.1032 - accuracy: 0.9908 - val_loss: 0.1151 - val_accuracy: 0.9848\n",
            "Epoch 11/20\n",
            "35/35 [==============================] - 0s 11ms/step - loss: 0.0989 - accuracy: 0.9919 - val_loss: 0.1130 - val_accuracy: 0.9848\n",
            "Epoch 12/20\n",
            "35/35 [==============================] - 0s 10ms/step - loss: 0.0950 - accuracy: 0.9928 - val_loss: 0.1115 - val_accuracy: 0.9848\n",
            "Epoch 13/20\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 0.0912 - accuracy: 0.9944 - val_loss: 0.1109 - val_accuracy: 0.9848\n",
            "Epoch 14/20\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 0.0878 - accuracy: 0.9955 - val_loss: 0.1094 - val_accuracy: 0.9848\n",
            "Epoch 15/20\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 0.0849 - accuracy: 0.9962 - val_loss: 0.1086 - val_accuracy: 0.9848\n",
            "Epoch 16/20\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 0.0822 - accuracy: 0.9969 - val_loss: 0.1088 - val_accuracy: 0.9839\n",
            "Epoch 17/20\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 0.0799 - accuracy: 0.9980 - val_loss: 0.1080 - val_accuracy: 0.9848\n",
            "Epoch 18/20\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 0.0775 - accuracy: 0.9980 - val_loss: 0.1060 - val_accuracy: 0.9857\n",
            "Epoch 19/20\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 0.0752 - accuracy: 0.9982 - val_loss: 0.1065 - val_accuracy: 0.9848\n",
            "Epoch 20/20\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 0.0733 - accuracy: 0.9982 - val_loss: 0.1046 - val_accuracy: 0.9857\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(testing_padded,testing_labels_final)\n",
        "model_testpredition = model.predict(testing_padded,batch_size=128)\n",
        "print(model_testpredition[3])\n",
        "testing_padded[3]\n",
        "print(testing_sentences[3])\n",
        "print(testing_labels_final[3])"
      ],
      "metadata": {
        "id": "KuaVrt3lKAlA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a069ea00-6ab0-4c48-9062-5e9ca5dc1bb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35/35 [==============================] - 0s 2ms/step - loss: 0.1046 - accuracy: 0.9857\n",
            "9/9 [==============================] - 0s 3ms/step\n",
            "[0.60412675]\n",
            "Welcome to UK-mobile-date this msg is FREE giving you free calling to 08719839835. Future mgs billed at 150p daily. To cancel send \"go stop\" to 89123\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()"
      ],
      "metadata": {
        "id": "otwXPF0NKAz6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "9eeaaf00-f248-4781-ebfa-e55a23b2e2d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimal learning rate.\n",
        "model2= tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size,embedding_dim,input_length=max_length),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(20,activation='relu'),\n",
        "    tf.keras.layers.Dense(10,activation='relu'),\n",
        "    tf.keras.layers.Dense(1,activation='relu')\n",
        "])\n",
        "# Compile\n",
        "model2.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "# Learning rate Schedular\n",
        "lr_schedular = tf.keras.callbacks.LearningRateScheduler(lambda epoch:1e-4*10**(epoch/20))\n",
        "# fitting\n",
        "hist=model2.fit(padded,training_labels_final,epochs=100,callbacks=[lr_schedular],batch_size=500,validation_data=(testing_padded,testing_labels_final))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Icrcp9WujJSY",
        "outputId": "b8e979e6-5e8e-4a4c-9088-08c46ceec0f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "9/9 [==============================] - 2s 77ms/step - loss: 1.1798 - accuracy: 0.8649 - val_loss: 0.5462 - val_accuracy: 0.8700 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "9/9 [==============================] - 0s 21ms/step - loss: 0.5238 - accuracy: 0.8649 - val_loss: 0.4409 - val_accuracy: 0.8700 - lr: 1.1220e-04\n",
            "Epoch 3/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.4377 - accuracy: 0.8649 - val_loss: 0.4028 - val_accuracy: 0.8700 - lr: 1.2589e-04\n",
            "Epoch 4/100\n",
            "9/9 [==============================] - 0s 22ms/step - loss: 0.4090 - accuracy: 0.8649 - val_loss: 0.3873 - val_accuracy: 0.8700 - lr: 1.4125e-04\n",
            "Epoch 5/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.3964 - accuracy: 0.8649 - val_loss: 0.3788 - val_accuracy: 0.8700 - lr: 1.5849e-04\n",
            "Epoch 6/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.3876 - accuracy: 0.8649 - val_loss: 0.3714 - val_accuracy: 0.8700 - lr: 1.7783e-04\n",
            "Epoch 7/100\n",
            "9/9 [==============================] - 0s 20ms/step - loss: 0.3793 - accuracy: 0.8649 - val_loss: 0.3642 - val_accuracy: 0.8700 - lr: 1.9953e-04\n",
            "Epoch 8/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.3713 - accuracy: 0.8649 - val_loss: 0.3565 - val_accuracy: 0.8700 - lr: 2.2387e-04\n",
            "Epoch 9/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.3625 - accuracy: 0.8649 - val_loss: 0.3480 - val_accuracy: 0.8700 - lr: 2.5119e-04\n",
            "Epoch 10/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.3527 - accuracy: 0.8649 - val_loss: 0.3385 - val_accuracy: 0.8700 - lr: 2.8184e-04\n",
            "Epoch 11/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.3420 - accuracy: 0.8649 - val_loss: 0.3279 - val_accuracy: 0.8700 - lr: 3.1623e-04\n",
            "Epoch 12/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.3302 - accuracy: 0.8649 - val_loss: 0.3165 - val_accuracy: 0.8700 - lr: 3.5481e-04\n",
            "Epoch 13/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.3173 - accuracy: 0.8649 - val_loss: 0.3038 - val_accuracy: 0.8700 - lr: 3.9811e-04\n",
            "Epoch 14/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.3028 - accuracy: 0.8649 - val_loss: 0.2904 - val_accuracy: 0.8700 - lr: 4.4668e-04\n",
            "Epoch 15/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.2876 - accuracy: 0.8649 - val_loss: 0.2762 - val_accuracy: 0.8700 - lr: 5.0119e-04\n",
            "Epoch 16/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.2714 - accuracy: 0.8649 - val_loss: 0.2610 - val_accuracy: 0.8700 - lr: 5.6234e-04\n",
            "Epoch 17/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.2538 - accuracy: 0.8649 - val_loss: 0.2450 - val_accuracy: 0.8700 - lr: 6.3096e-04\n",
            "Epoch 18/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.2338 - accuracy: 0.8649 - val_loss: 0.2282 - val_accuracy: 0.8700 - lr: 7.0795e-04\n",
            "Epoch 19/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.2108 - accuracy: 0.8649 - val_loss: 0.2090 - val_accuracy: 0.8709 - lr: 7.9433e-04\n",
            "Epoch 20/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.1849 - accuracy: 0.8672 - val_loss: 0.1886 - val_accuracy: 0.8789 - lr: 8.9125e-04\n",
            "Epoch 21/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.1560 - accuracy: 0.9150 - val_loss: 0.1691 - val_accuracy: 0.9462 - lr: 0.0010\n",
            "Epoch 22/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.1234 - accuracy: 0.9650 - val_loss: 0.1407 - val_accuracy: 0.9668 - lr: 0.0011\n",
            "Epoch 23/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0898 - accuracy: 0.9778 - val_loss: 0.1120 - val_accuracy: 0.9785 - lr: 0.0013\n",
            "Epoch 24/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0557 - accuracy: 0.9841 - val_loss: 0.0827 - val_accuracy: 0.9839 - lr: 0.0014\n",
            "Epoch 25/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0310 - accuracy: 0.9897 - val_loss: 0.0772 - val_accuracy: 0.9848 - lr: 0.0016\n",
            "Epoch 26/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0196 - accuracy: 0.9933 - val_loss: 0.0663 - val_accuracy: 0.9848 - lr: 0.0018\n",
            "Epoch 27/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0122 - accuracy: 0.9953 - val_loss: 0.0654 - val_accuracy: 0.9865 - lr: 0.0020\n",
            "Epoch 28/100\n",
            "9/9 [==============================] - 0s 20ms/step - loss: 0.0075 - accuracy: 0.9969 - val_loss: 0.0748 - val_accuracy: 0.9865 - lr: 0.0022\n",
            "Epoch 29/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0720 - val_accuracy: 0.9857 - lr: 0.0025\n",
            "Epoch 30/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0833 - val_accuracy: 0.9883 - lr: 0.0028\n",
            "Epoch 31/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0018 - accuracy: 0.9993 - val_loss: 0.0843 - val_accuracy: 0.9865 - lr: 0.0032\n",
            "Epoch 32/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0796 - val_accuracy: 0.9848 - lr: 0.0035\n",
            "Epoch 33/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0806 - val_accuracy: 0.9874 - lr: 0.0040\n",
            "Epoch 34/100\n",
            "9/9 [==============================] - 0s 20ms/step - loss: 8.2721e-04 - accuracy: 0.9998 - val_loss: 0.0842 - val_accuracy: 0.9848 - lr: 0.0045\n",
            "Epoch 35/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1034 - val_accuracy: 0.9821 - lr: 0.0050\n",
            "Epoch 36/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 8.4314e-04 - accuracy: 0.9996 - val_loss: 0.0840 - val_accuracy: 0.9865 - lr: 0.0056\n",
            "Epoch 37/100\n",
            "9/9 [==============================] - 0s 20ms/step - loss: 4.2890e-04 - accuracy: 0.9998 - val_loss: 0.0933 - val_accuracy: 0.9848 - lr: 0.0063\n",
            "Epoch 38/100\n",
            "9/9 [==============================] - 0s 26ms/step - loss: 2.9251e-04 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9857 - lr: 0.0071\n",
            "Epoch 39/100\n",
            "9/9 [==============================] - 0s 33ms/step - loss: 1.9359e-04 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9865 - lr: 0.0079\n",
            "Epoch 40/100\n",
            "9/9 [==============================] - 0s 26ms/step - loss: 7.5384e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9865 - lr: 0.0089\n",
            "Epoch 41/100\n",
            "9/9 [==============================] - 0s 32ms/step - loss: 6.4168e-05 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9874 - lr: 0.0100\n",
            "Epoch 42/100\n",
            "9/9 [==============================] - 0s 27ms/step - loss: 2.9825e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9848 - lr: 0.0112\n",
            "Epoch 43/100\n",
            "9/9 [==============================] - 0s 28ms/step - loss: 2.0246e-05 - accuracy: 1.0000 - val_loss: 0.1208 - val_accuracy: 0.9865 - lr: 0.0126\n",
            "Epoch 44/100\n",
            "9/9 [==============================] - 0s 27ms/step - loss: 1.8501e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9821 - lr: 0.0141\n",
            "Epoch 45/100\n",
            "9/9 [==============================] - 0s 27ms/step - loss: 1.4413e-06 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9839 - lr: 0.0158\n",
            "Epoch 46/100\n",
            "9/9 [==============================] - 0s 28ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9874 - lr: 0.0178\n",
            "Epoch 47/100\n",
            "9/9 [==============================] - 0s 33ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1403 - val_accuracy: 0.9865 - lr: 0.0200\n",
            "Epoch 48/100\n",
            "9/9 [==============================] - 0s 29ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9848 - lr: 0.0224\n",
            "Epoch 49/100\n",
            "9/9 [==============================] - 0s 26ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1395 - val_accuracy: 0.9848 - lr: 0.0251\n",
            "Epoch 50/100\n",
            "9/9 [==============================] - 0s 27ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1395 - val_accuracy: 0.9848 - lr: 0.0282\n",
            "Epoch 51/100\n",
            "9/9 [==============================] - 0s 26ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0316\n",
            "Epoch 52/100\n",
            "9/9 [==============================] - 0s 27ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0355\n",
            "Epoch 53/100\n",
            "9/9 [==============================] - 0s 27ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0398\n",
            "Epoch 54/100\n",
            "9/9 [==============================] - 0s 29ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0447\n",
            "Epoch 55/100\n",
            "9/9 [==============================] - 0s 23ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0501\n",
            "Epoch 56/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0562\n",
            "Epoch 57/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0631\n",
            "Epoch 58/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0708\n",
            "Epoch 59/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0794\n",
            "Epoch 60/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.0891\n",
            "Epoch 61/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1000\n",
            "Epoch 62/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1122\n",
            "Epoch 63/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1259\n",
            "Epoch 64/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1413\n",
            "Epoch 65/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1585\n",
            "Epoch 66/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1778\n",
            "Epoch 67/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.1995\n",
            "Epoch 68/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.2239\n",
            "Epoch 69/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.2512\n",
            "Epoch 70/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.2818\n",
            "Epoch 71/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.3162\n",
            "Epoch 72/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.3548\n",
            "Epoch 73/100\n",
            "9/9 [==============================] - 0s 20ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.3981\n",
            "Epoch 74/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.4467\n",
            "Epoch 75/100\n",
            "9/9 [==============================] - 0s 21ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.5012\n",
            "Epoch 76/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.5623\n",
            "Epoch 77/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.6310\n",
            "Epoch 78/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.7079\n",
            "Epoch 79/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.7943\n",
            "Epoch 80/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 0.8913\n",
            "Epoch 81/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.0000\n",
            "Epoch 82/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.1220\n",
            "Epoch 83/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.2589\n",
            "Epoch 84/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.4125\n",
            "Epoch 85/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.5849\n",
            "Epoch 86/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.7783\n",
            "Epoch 87/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 1.9953\n",
            "Epoch 88/100\n",
            "9/9 [==============================] - 0s 21ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 2.2387\n",
            "Epoch 89/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 2.5119\n",
            "Epoch 90/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 2.8184\n",
            "Epoch 91/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 3.1623\n",
            "Epoch 92/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 3.5481\n",
            "Epoch 93/100\n",
            "9/9 [==============================] - 0s 20ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 3.9811\n",
            "Epoch 94/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 4.4668\n",
            "Epoch 95/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 5.0119\n",
            "Epoch 96/100\n",
            "9/9 [==============================] - 0s 17ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 5.6234\n",
            "Epoch 97/100\n",
            "9/9 [==============================] - 0s 19ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 6.3096\n",
            "Epoch 98/100\n",
            "9/9 [==============================] - 0s 16ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 7.0795\n",
            "Epoch 99/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 7.9433\n",
            "Epoch 100/100\n",
            "9/9 [==============================] - 0s 18ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9848 - lr: 8.9125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(hist.history).plot()\n",
        "plt.title('Model2 Graph')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Learning Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "jnq2cyVg1yr0",
        "outputId": "9c366f97-7ebf-48f8-e069-f2f2a05e681e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Learning Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.evaluate(testing_padded,testing_labels_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lj67Cuez1zeB",
        "outputId": "37f786d2-db7f-4635-c0c0-dbd613e5cc76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35/35 [==============================] - 0s 2ms/step - loss: 0.1394 - accuracy: 0.9848\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.13940833508968353, 0.9847533702850342]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot loss vs lr\n",
        "lr = 1e-4*(10**(tf.range(100)/20))\n",
        "plt.semilogx(lr,hist.history['loss'])\n",
        "plt.title('ideal lr is 1e-2/10**-2 ')\n",
        "plt.xlabel('Learning Rate')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "gxGcl3i11zoR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "da4ac578-749c-4581-c226-9a533cd14cbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Set lr = 0.01\n",
        "model3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size,embedding_dim,input_length=max_length),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(20,activation='relu'),\n",
        "    tf.keras.layers.Dense(10,activation= 'relu'),\n",
        "    tf.keras.layers.Dense(1,activation= 'sigmoid')\n",
        "])\n",
        "\n",
        "model3.compile(loss='binary_crossentropy',metrics=['accuracy'],\n",
        "              optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))\n",
        "h = model3.fit(padded,training_labels_final,batch_size=128,epochs=100,\n",
        "           validation_data=(testing_padded,testing_labels_final))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDc0uLrmNrDB",
        "outputId": "a81346dd-61ab-44fa-c6b2-3f72e2145fd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "35/35 [==============================] - 2s 20ms/step - loss: 0.3006 - accuracy: 0.8425 - val_loss: 0.1380 - val_accuracy: 0.8753\n",
            "Epoch 2/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 0.0526 - accuracy: 0.9796 - val_loss: 0.0521 - val_accuracy: 0.9865\n",
            "Epoch 3/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 0.0114 - accuracy: 0.9973 - val_loss: 0.0414 - val_accuracy: 0.9874\n",
            "Epoch 4/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 0.0029 - accuracy: 0.9991 - val_loss: 0.0627 - val_accuracy: 0.9883\n",
            "Epoch 5/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 0.0010 - accuracy: 0.9996 - val_loss: 0.0561 - val_accuracy: 0.9874\n",
            "Epoch 6/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.1463e-04 - accuracy: 1.0000 - val_loss: 0.0591 - val_accuracy: 0.9883\n",
            "Epoch 7/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 5.7325e-05 - accuracy: 1.0000 - val_loss: 0.0608 - val_accuracy: 0.9883\n",
            "Epoch 8/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 3.8897e-05 - accuracy: 1.0000 - val_loss: 0.0621 - val_accuracy: 0.9883\n",
            "Epoch 9/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.5300e-05 - accuracy: 1.0000 - val_loss: 0.0644 - val_accuracy: 0.9883\n",
            "Epoch 10/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 1.8066e-05 - accuracy: 1.0000 - val_loss: 0.0671 - val_accuracy: 0.9883\n",
            "Epoch 11/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 1.1908e-05 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 0.9883\n",
            "Epoch 12/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 9.0973e-06 - accuracy: 1.0000 - val_loss: 0.0700 - val_accuracy: 0.9883\n",
            "Epoch 13/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 7.7781e-06 - accuracy: 1.0000 - val_loss: 0.0709 - val_accuracy: 0.9883\n",
            "Epoch 14/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 6.3761e-06 - accuracy: 1.0000 - val_loss: 0.0717 - val_accuracy: 0.9883\n",
            "Epoch 15/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 5.6709e-06 - accuracy: 1.0000 - val_loss: 0.0722 - val_accuracy: 0.9883\n",
            "Epoch 16/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 4.6203e-06 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9874\n",
            "Epoch 17/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 4.0088e-06 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9883\n",
            "Epoch 18/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 3.5687e-06 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9883\n",
            "Epoch 19/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 3.2186e-06 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9883\n",
            "Epoch 20/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.8403e-06 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9883\n",
            "Epoch 21/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.5714e-06 - accuracy: 1.0000 - val_loss: 0.0763 - val_accuracy: 0.9874\n",
            "Epoch 22/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 2.3159e-06 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9883\n",
            "Epoch 23/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 2.1547e-06 - accuracy: 1.0000 - val_loss: 0.0773 - val_accuracy: 0.9883\n",
            "Epoch 24/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.0012e-06 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9883\n",
            "Epoch 25/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.7568e-06 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9883\n",
            "Epoch 26/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.6185e-06 - accuracy: 1.0000 - val_loss: 0.0785 - val_accuracy: 0.9865\n",
            "Epoch 27/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.5041e-06 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9865\n",
            "Epoch 28/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.4104e-06 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9874\n",
            "Epoch 29/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 1.2937e-06 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9865\n",
            "Epoch 30/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.2075e-06 - accuracy: 1.0000 - val_loss: 0.0800 - val_accuracy: 0.9865\n",
            "Epoch 31/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.1334e-06 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9865\n",
            "Epoch 32/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.0488e-06 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9865\n",
            "Epoch 33/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 9.8386e-07 - accuracy: 1.0000 - val_loss: 0.0810 - val_accuracy: 0.9865\n",
            "Epoch 34/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 9.3092e-07 - accuracy: 1.0000 - val_loss: 0.0813 - val_accuracy: 0.9865\n",
            "Epoch 35/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 8.6957e-07 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9865\n",
            "Epoch 36/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 8.2145e-07 - accuracy: 1.0000 - val_loss: 0.0819 - val_accuracy: 0.9865\n",
            "Epoch 37/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 7.7077e-07 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9865\n",
            "Epoch 38/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 7.3106e-07 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9865\n",
            "Epoch 39/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 6.9465e-07 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9865\n",
            "Epoch 40/100\n",
            "35/35 [==============================] - 0s 10ms/step - loss: 6.5620e-07 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9865\n",
            "Epoch 41/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 6.2332e-07 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9865\n",
            "Epoch 42/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 5.9536e-07 - accuracy: 1.0000 - val_loss: 0.0835 - val_accuracy: 0.9865\n",
            "Epoch 43/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 5.5887e-07 - accuracy: 1.0000 - val_loss: 0.0838 - val_accuracy: 0.9865\n",
            "Epoch 44/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 5.3266e-07 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9865\n",
            "Epoch 45/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 5.0626e-07 - accuracy: 1.0000 - val_loss: 0.0843 - val_accuracy: 0.9865\n",
            "Epoch 46/100\n",
            "35/35 [==============================] - 0s 14ms/step - loss: 4.8702e-07 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 0.9865\n",
            "Epoch 47/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 4.5839e-07 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9865\n",
            "Epoch 48/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 4.4211e-07 - accuracy: 1.0000 - val_loss: 0.0850 - val_accuracy: 0.9865\n",
            "Epoch 49/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 4.2465e-07 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9865\n",
            "Epoch 50/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 4.0020e-07 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9865\n",
            "Epoch 51/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 3.8245e-07 - accuracy: 1.0000 - val_loss: 0.0857 - val_accuracy: 0.9865\n",
            "Epoch 52/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 3.6808e-07 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 0.9865\n",
            "Epoch 53/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 3.5535e-07 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9865\n",
            "Epoch 54/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 3.3923e-07 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9865\n",
            "Epoch 55/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 3.2905e-07 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 0.9865\n",
            "Epoch 56/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 3.1433e-07 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9865\n",
            "Epoch 57/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 3.0064e-07 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9865\n",
            "Epoch 58/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 2.9066e-07 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9865\n",
            "Epoch 59/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.7711e-07 - accuracy: 1.0000 - val_loss: 0.0874 - val_accuracy: 0.9865\n",
            "Epoch 60/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.6631e-07 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 0.9865\n",
            "Epoch 61/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 2.5610e-07 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9865\n",
            "Epoch 62/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.4592e-07 - accuracy: 1.0000 - val_loss: 0.0879 - val_accuracy: 0.9865\n",
            "Epoch 63/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.3686e-07 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9865\n",
            "Epoch 64/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 2.2902e-07 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9865\n",
            "Epoch 65/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.2003e-07 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9865\n",
            "Epoch 66/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.1447e-07 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9865\n",
            "Epoch 67/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 2.0452e-07 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9865\n",
            "Epoch 68/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.9754e-07 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9865\n",
            "Epoch 69/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.9186e-07 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9865\n",
            "Epoch 70/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.8534e-07 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9865\n",
            "Epoch 71/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.7934e-07 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9865\n",
            "Epoch 72/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.7266e-07 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9865\n",
            "Epoch 73/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.6821e-07 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9865\n",
            "Epoch 74/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.6226e-07 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9865\n",
            "Epoch 75/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.5614e-07 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9865\n",
            "Epoch 76/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.5170e-07 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9865\n",
            "Epoch 77/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 1.4689e-07 - accuracy: 1.0000 - val_loss: 0.0906 - val_accuracy: 0.9865\n",
            "Epoch 78/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.4240e-07 - accuracy: 1.0000 - val_loss: 0.0907 - val_accuracy: 0.9865\n",
            "Epoch 79/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 1.3849e-07 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9865\n",
            "Epoch 80/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.3332e-07 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9865\n",
            "Epoch 81/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 1.2922e-07 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9865\n",
            "Epoch 82/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.2569e-07 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9865\n",
            "Epoch 83/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 1.2119e-07 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9865\n",
            "Epoch 84/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 1.1809e-07 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9865\n",
            "Epoch 85/100\n",
            "35/35 [==============================] - 0s 10ms/step - loss: 1.1474e-07 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9865\n",
            "Epoch 86/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 1.1057e-07 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9865\n",
            "Epoch 87/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 1.0770e-07 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9865\n",
            "Epoch 88/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 1.0436e-07 - accuracy: 1.0000 - val_loss: 0.0923 - val_accuracy: 0.9865\n",
            "Epoch 89/100\n",
            "35/35 [==============================] - 1s 16ms/step - loss: 1.0168e-07 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9865\n",
            "Epoch 90/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 9.8723e-08 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9865\n",
            "Epoch 91/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 9.5912e-08 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9865\n",
            "Epoch 92/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 9.3000e-08 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9865\n",
            "Epoch 93/100\n",
            "35/35 [==============================] - 0s 12ms/step - loss: 9.0672e-08 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9865\n",
            "Epoch 94/100\n",
            "35/35 [==============================] - 0s 13ms/step - loss: 8.8262e-08 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9865\n",
            "Epoch 95/100\n",
            "35/35 [==============================] - 0s 9ms/step - loss: 8.5026e-08 - accuracy: 1.0000 - val_loss: 0.0933 - val_accuracy: 0.9865\n",
            "Epoch 96/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 8.3588e-08 - accuracy: 1.0000 - val_loss: 0.0935 - val_accuracy: 0.9865\n",
            "Epoch 97/100\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 8.1598e-08 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9865\n",
            "Epoch 98/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 7.9041e-08 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9865\n",
            "Epoch 99/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 7.6544e-08 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9865\n",
            "Epoch 100/100\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 7.4437e-08 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9865\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "ans=model3.predict(testing_padded,batch_size=128)\n",
        "def Sample_Ans(num_sample=10):\n",
        "  for i in range(num_sample):\n",
        "    i=random.randrange(int(len(testing_padded)))\n",
        "    print(f'''\n",
        "\n",
        "      Prediction:{int(ans[i])},\n",
        "      Sentence: {testing_sentences[i]},\n",
        "      label:{testing_labels_final[i]}\n",
        "\n",
        "        ''')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0mGL5zoAAZb",
        "outputId": "ce3f50cd-2d96-4e1f-cad0-a866df1c901c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "pd.DataFrame(h.history).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "rkAl5u0vfrgB",
        "outputId": "60edd373-4382-4f45-acd4-1e7c402da9e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3.evaluate(testing_padded,testing_labels_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJT07uqdYmlg",
        "outputId": "68839a65-77ce-493d-b4c1-67c8630d77e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35/35 [==============================] - 0s 3ms/step - loss: 0.0940 - accuracy: 0.9865\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.09402327239513397, 0.9865471124649048]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sample_Ans()"
      ],
      "metadata": {
        "id": "EoPL95GrAEYJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "633672a7-e4c7-4c8a-cc7c-90dbde1e17c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "      Prediction:1,\n",
            "      Sentence: Urgent! Please call 09061213237 from landline. £5000 cash or a luxury 4* Canary Islands Holiday await collection. T&Cs SAE PO Box 177. M227XY. 150ppm. 16+,\n",
            "      label:1\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: You have to pls make a note of all she.s exposed to. Also find out from her school if anyone else was vomiting. Is there a dog or cat in the house? Let me know later.,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: I'm fine. Hope you are good. Do take care.,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: Nope thats fine. I might have a nap tho!,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: It's wylie, you in tampa or sarasota?,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: Very strange.  and  are watching the 2nd one now but i'm in bed. Sweet dreams, miss u,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:1,\n",
            "      Sentence: You won't believe it but it's true. It's Incredible Txts! Reply G now to learn truly amazing things that will blow your mind. From O2FWD only 18p/txt,\n",
            "      label:1\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: No sir. That's why i had an 8-hr trip on the bus last week. Have another audition next wednesday but i think i might drive this time.,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: Did u fix the teeth?if not do it asap.ok take care.,\n",
            "      label:0\n",
            "\n",
            "        \n",
            "\n",
            "\n",
            "      Prediction:0,\n",
            "      Sentence: A guy who gets used but is too dumb to realize it.,\n",
            "      label:0\n",
            "\n",
            "        \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matrix\n"
      ],
      "metadata": {
        "id": "7Ou4czjc4rRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "-li5lDk4ItQk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIBKqR_V5m0M",
        "outputId": "13313237-c839-4ae1-e84d-bc67fa69f555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.3733353e-10],\n",
              "       [9.7948637e-12],\n",
              "       [5.4030997e-09],\n",
              "       ...,\n",
              "       [5.5459701e-08],\n",
              "       [2.0192230e-08],\n",
              "       [2.3636755e-08]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.round(ans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4Bgvqg1_DE1",
        "outputId": "511918ce-b196-4657-e747-294b380eedee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1115, 1), dtype=float32, numpy=\n",
              "array([[0.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       ...,\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(testing_labels_final,tf.round(ans))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAtPLVQQ321j",
        "outputId": "684b64be-9cf4-44cf-ed87-e0d3d29013a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[964,   6],\n",
              "       [  9, 136]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    }
  ]
}