C3/W3/ungraded_labs/C3_W3_Lab_6_sarcasm_with_1D_convolutional.ipynb

{
  "cells": [
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hobbang2/TensorFlowCertification/blob/main/C3/W3/ungraded_labs/C3_W3_Lab_6_sarcasm_with_1D_convolutional.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lKkd6qXch36"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/https-deeplearning-ai/tensorflow-1-public/blob/master/C3/W3/ungraded_labs/C3_W3_Lab_6_sarcasm_with_1D_convolutional.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YEdilk144fzb"
      },
      "source": [
        "# Ungraded Lab: Training a Sarcasm Detection Model using a Convolution Layer\n",
        "\n",
        "You will be doing the same steps here as the previous lab but will be using a convolution layer instead. As usual, try tweaking the parameters and observe how it affects the results. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pgwFC4oIch39"
      },
      "outputs": [],
      "source": [
        "# Install this package to use Colab's GPU for training\n",
        "!apt install --allow-change-held-packages libcudnn8=8.4.1.50-1+cuda11.6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmokcpHc5u1R"
      },
      "source": [
        "## Download the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dxezdGoV29Yz"
      },
      "outputs": [],
      "source": [
        "# Download the dataset\n",
        "!wget https://storage.googleapis.com/tensorflow-1-public/course3/sarcasm.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BTcGA2Po2_nN"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "# Load the JSON file\n",
        "with open(\"./sarcasm.json\", 'r') as f:\n",
        "    datastore = json.load(f)\n",
        "\n",
        "# Initialize the lists\n",
        "sentences = []\n",
        "labels = []\n",
        "\n",
        "# Collect sentences and labels into the lists\n",
        "for item in datastore:\n",
        "    sentences.append(item['headline'])\n",
        "    labels.append(item['is_sarcastic'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2zXSds45s2P"
      },
      "source": [
        "## Split the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "baDwTn9S3ENB"
      },
      "outputs": [],
      "source": [
        "training_size = 20000\n",
        "\n",
        "# Split the sentences\n",
        "training_sentences = sentences[0:training_size]\n",
        "testing_sentences = sentences[training_size:]\n",
        "\n",
        "# Split the labels\n",
        "training_labels = labels[0:training_size]\n",
        "testing_labels = labels[training_size:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdpLY-or5pTP"
      },
      "source": [
        "## Data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RHjZR4oi3LOq"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "vocab_size = 10000\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "# Initialize the Tokenizer class\n",
        "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\n",
        "\n",
        "# Generate the word index dictionary\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "# Generate and pad the training sequences\n",
        "training_sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "# Generate and pad the testing sequences\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "# Convert the labels lists into numpy arrays\n",
        "training_labels = np.array(training_labels)\n",
        "testing_labels = np.array(testing_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQBjPv_A5m1x"
      },
      "source": [
        "## Build and Compile the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jGwXGIXvFhXW"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Parameters\n",
        "embedding_dim = 16\n",
        "filters = 128\n",
        "kernel_size = 5\n",
        "dense_dim = 6\n",
        "\n",
        "# Model Definition with Conv1D\n",
        "model_conv = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Conv1D(filters, kernel_size, activation='relu'),\n",
        "    tf.keras.layers.GlobalMaxPooling1D(),\n",
        "    tf.keras.layers.Dense(dense_dim, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Set the training parameters\n",
        "model_conv.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "# Print the model summary\n",
        "model_conv.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcXC5QG45kM7"
      },
      "source": [
        "## Train the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oB6C55FO3z3q"
      },
      "outputs": [],
      "source": [
        "NUM_EPOCHS = 10\n",
        "\n",
        "# Train the model\n",
        "history_conv = model_conv.fit(training_padded, training_labels, epochs=NUM_EPOCHS, validation_data=(testing_padded, testing_labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g9DC6dmLF8DC"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot Utility\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "\n",
        "# Plot the accuracy and loss history\n",
        "plot_graphs(history_conv, 'accuracy')\n",
        "plot_graphs(history_conv, 'loss')"
      ]
    }
  ],
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