C3/W3/ungraded_labs/C3_W3_Lab_6_sarcasm_with_1D_convolutional.ipynb

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   "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": "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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