fcc_neural_network_SMS_Text_Classifier_SaumyaSubham.ipynb

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  "metadata": {
    "colab": {
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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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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/SaumyaSubham/Neural_Network_SMS_Text_Classifier/blob/main/fcc_neural_network_SMS_Text_Classifier_SaumyaSubham.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eg62Pmz3o83v"
      },
      "source": [
        "First of All, Welcome Everyone\n",
        "\n",
        "In this challenge, we are going to create a machine learning model that will classify SMS messages as either \"ham\" or \"spam\". A \"ham\" message is a normal message sent by a friend. A \"spam\" message is an advertisement or a message sent by a company.\n",
        "\n",
        "We should create a function called `predict_message` that takes a message string as an argument and returns a list. The first element in the list should be a number between zero and one that indicates the likeliness of \"ham\" (0) or \"spam\" (1). The second element in the list should be the word \"ham\" or \"spam\", depending on which is most likely.\n",
        "\n",
        "For this challenge, we will use the [SMS Spam Collection dataset](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/). The dataset has already been grouped into train data and test data.\n",
        "\n",
        "The first two cells import the libraries and data. The final cell tests your model and function. Adding my code in between these cells.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RZOuS9LWQvv"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from tensorflow import keras\n",
        "!pip install tensorflow-datasets\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMHwYXHXCar3"
      },
      "source": [
        "# get data files\n",
        "!wget https://cdn.freecodecamp.org/project-data/sms/train-data.tsv\n",
        "!wget https://cdn.freecodecamp.org/project-data/sms/valid-data.tsv\n",
        "\n",
        "train_file_path = \"train-data.tsv\"\n",
        "test_file_path = \"valid-data.tsv\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_h508FEClxO"
      },
      "source": [
        "df_train = pd.read_table(train_file_path, header=0, names=['indicates', 'text'], usecols=['indicates', 'text'])\n",
        "df_test = pd.read_table(test_file_path, header=0, names=['indicates', 'text'], usecols=['indicates', 'text'])\n",
        "\n",
        "df_train['indicates'] = df_train['indicates'].replace(\"ham\", 0)\n",
        "df_train['indicates'] = df_train['indicates'].replace(\"spam\", 1)\n",
        "df_test['indicates'] = df_test['indicates'].replace(\"ham\", 0)\n",
        "df_test['indicates'] = df_test['indicates'].replace(\"spam\", 1)\n",
        "\n",
        "train_data = tf.data.Dataset.from_tensor_slices((df_train['text'].values, df_train['indicates'].values))\n",
        "test_data = tf.data.Dataset.from_tensor_slices((df_test['text'].values, df_test['indicates'].values))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQVcghZ-kGEQ"
      },
      "source": [
        "tokenizer = tfds.deprecated.text.Tokenizer()\n",
        "\n",
        "# To create vocabulary list from all data\n",
        "vocabulary_set = set()\n",
        "\n",
        "for text_tensor, _ in train_data.concatenate(test_data):\n",
        "  some_tokens = tokenizer.tokenize(text_tensor.numpy())\n",
        "  vocabulary_set.update(some_tokens)\n",
        "\n",
        "vocab_size = len(vocabulary_set)\n",
        "vocab_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3seUWrhkHmX"
      },
      "source": [
        "encoder = tfds.deprecated.text.TokenTextEncoder(vocabulary_set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dG-ykwFJGzDc"
      },
      "source": [
        "def encode(text_tensor, label):\n",
        "  encoded_text = encoder.encode(text_tensor.numpy())\n",
        "  return encoded_text, label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ao6NpbfnNATS"
      },
      "source": [
        "def encode_map_fn(text, label):\n",
        "  encoded_text, label = tf.py_function(encode, inp=[text, label], Tout=(tf.int64, tf.int64))\n",
        "\n",
        "  encoded_text.set_shape([None])\n",
        "  label.set_shape([])\n",
        "\n",
        "  return encoded_text, label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVOTpFtFgyDJ"
      },
      "source": [
        "train_data_encoded = train_data.map(encode_map_fn)\n",
        "test_data_encoded = test_data.map(encode_map_fn)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCzvf-0og7Al"
      },
      "source": [
        "BUFFER_SIZE = 1000\n",
        "BATCH_SIZE = 32\n",
        "train_dataset = (train_data_encoded.shuffle(BUFFER_SIZE).padded_batch(BATCH_SIZE))\n",
        "test_dataset = (test_data_encoded.padded_batch(BATCH_SIZE))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXgBVDMNNFhV"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(encoder.vocab_size, 32),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgMA7acNh73-"
      },
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8cRu6QRh_Vm"
      },
      "source": [
        "history = model.fit(train_dataset, epochs=10, validation_data=test_dataset, validation_steps=30)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "co_7g7iXNMbH"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss: {}'.format(test_loss))\n",
        "print('Test Accuracy: {}'.format(test_acc))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9tD9yACG6M9"
      },
      "source": [
        "# function to predict messages based on model\n",
        "# (should return list containing prediction and label, ex. [0.008318834938108921, 'ham'])\n",
        "def predict_message(pred_text):\n",
        "  pred_text = encoder.encode(pred_text)\n",
        "  pred_text = tf.cast(pred_text, tf.float32)\n",
        "  prediction = model.predict(tf.expand_dims(pred_text, 0)).tolist()\n",
        "  if prediction[0][0] < 0.5:\n",
        "    prediction.append('ham')\n",
        "  else:\n",
        "    prediction.append('spam')\n",
        "  return (prediction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvaEO4BDxoNc"
      },
      "source": [
        "def plot_graphs(history, metric):\n",
        "  plt.plot(history.history[metric])\n",
        "  plt.plot(history.history['val_'+metric], '')\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(metric)\n",
        "  plt.legend([metric, 'val_'+metric])\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "712k92AMxqRl"
      },
      "source": [
        "plot_graphs(history, 'accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VxMpcT-xrjh"
      },
      "source": [
        "plot_graphs(history, 'loss')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dxotov85SjsC"
      },
      "source": [
        "# Run this cell to test your function and model. Do not modify contents.\n",
        "def test_predictions():\n",
        "  test_messages = [\"how are you doing today\",\n",
        "                   \"sale today! to stop texts call 98912460324\",\n",
        "                   \"i dont want to go. can we try it a different day? available sat\",\n",
        "                   \"our new mobile video service is live. just install on your phone to start watching.\",\n",
        "                   \"you have won £1000 cash! call to claim your prize.\",\n",
        "                   \"i'll bring it tomorrow. don't forget the milk.\",\n",
        "                   \"wow, is your arm alright. that happened to me one time too\"\n",
        "                  ]\n",
        "\n",
        "  test_answers = [\"ham\", \"spam\", \"ham\", \"spam\", \"spam\", \"ham\", \"ham\"]\n",
        "  passed = True\n",
        "\n",
        "  for msg, ans in zip(test_messages, test_answers):\n",
        "    prediction = predict_message(msg)\n",
        "    if prediction[1] != ans:\n",
        "      passed = False\n",
        "\n",
        "  if passed:\n",
        "    print(\"You passed the challenge. Great job!\")\n",
        "  else:\n",
        "    print(\"You haven't passed yet. Keep trying.\")\n",
        "\n",
        "test_predictions()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}