Task 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Your Model Learn Addition!\n",
    "## Task 1: Introduction\n",
    "\n",
    "Given the string \"54+7\", the model should return a prediction: \"61\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import TimeDistributed, Dense, Dropout, SimpleRNN, RepeatVector\n",
    "from tensorflow.keras.callbacks import EarlyStopping, LambdaCallback\n",
    "\n",
    "from termcolor import colored"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Generate Data\n",
    "___\n",
    "Note: If you are starting the notebook from this task, you can run cells from all previous tasks in the kernel by going to the top menu and then selecting Kernel > Restart and Run All\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "hP-OyQrivBNl",
    "outputId": "70a8ce77-3241-4ea9-dbc7-d9bebb3789f3"
   },
   "outputs": [],
   "source": [
    "all_chars = '0123456789+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_features = len(all_chars)\n",
    "\n",
    "char_to_index = dict((c, i) for i, c in enumerate(all_chars))\n",
    "index_to_char = dict((i, c) for i, c in enumerate(all_chars))\n",
    "\n",
    "print('Number of features:', num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L2b1Xg9hvBNr"
   },
   "outputs": [],
   "source": [
    "def generate_data():\n",
    "    first_num = np.random.randint(low=0,high=100)\n",
    "    second_num = np.random.randint(low=0,high=100)\n",
    "    example = str(first_num) + '+' + str(second_num)\n",
    "    label = str(first_num+second_num)\n",
    "    return example, label\n",
    "\n",
    "generate_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Create the Model\n",
    "___\n",
    "Note: If you are starting the notebook from this task, you can run cells from all previous tasks in the kernel by going to the top menu and then selecting Kernel > Restart and Run All\n",
    "___\n",
    "Consider these two reviews:\n",
    "\n",
    "Review 1: This movie is not terrible at all.\n",
    "\n",
    "Review 2: This movie is pretty decent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "colab_type": "code",
    "id": "iZKl0LtdvBNy",
    "outputId": "34a3c344-5985-4e3b-e1ac-392c18739308"
   },
   "outputs": [],
   "source": [
    "hidden_units = 128\n",
    "max_time_steps = 5\n",
    "\n",
    "model = Sequential([\n",
    "    SimpleRNN(hidden_units, input_shape=(None, num_features)),\n",
    "    RepeatVector(max_time_steps),\n",
    "    SimpleRNN(hidden_units, return_sequences=True),\n",
    "    TimeDistributed(Dense(num_features, activation='softmax'))\n",
    "])\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Vectorize and De-Vectorize Data\n",
    "___\n",
    "Note: If you are starting the notebook from this task, you can run cells from all previous tasks in the kernel by going to the top menu and then selecting Kernel > Restart and Run All\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ci29YaA6vBN1"
   },
   "outputs": [],
   "source": [
    "def vectorize_example(example, label):\n",
    "    \n",
    "    x = np.zeros((max_time_steps, num_features))\n",
    "    y = np.zeros((max_time_steps, num_features))\n",
    "    \n",
    "    diff_x = max_time_steps - len(example)\n",
    "    diff_y = max_time_steps - len(label)\n",
    "    \n",
    "    for i, c in enumerate(example):\n",
    "        x[diff_x+i, char_to_index[c]] = 1\n",
    "    for i in range(diff_x):\n",
    "        x[i, char_to_index['0']] = 1\n",
    "    for i, c in enumerate(label):\n",
    "        y[diff_y+i, char_to_index[c]] = 1\n",
    "    for i in range(diff_y):\n",
    "        y[i, char_to_index['0']] = 1\n",
    "        \n",
    "    return x, y\n",
    "\n",
    "e, l = generate_data()\n",
    "print('Text Example and Label:', e, l)\n",
    "x, y = vectorize_example(e, l)\n",
    "print('Vectorized Example and Label Shapes:', x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_nARKq-bvBN9"
   },
   "outputs": [],
   "source": [
    "def devectorize_example(example):\n",
    "    result = [index_to_char[np.argmax(vec)] for i, vec in enumerate(example)]\n",
    "    return ''.join(result)\n",
    "\n",
    "devectorize_example(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "KCGUxNwNvBOA",
    "outputId": "a721451a-7f7c-42be-bdf0-0c96381f2eb2"
   },
   "outputs": [],
   "source": [
    "devectorize_example(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Create Dataset\n",
    "___\n",
    "Note: If you are starting the notebook from this task, you can run cells from all previous tasks in the kernel by going to the top menu and then selecting Kernel > Restart and Run All\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "T5rdrhiVvBOI",
    "outputId": "426b3722-78cf-494f-8bbf-1cdf7eab3113"
   },
   "outputs": [],
   "source": [
    "def create_dataset(num_examples=2000):\n",
    "\n",
    "    x_train = np.zeros((num_examples, max_time_steps, num_features))\n",
    "    y_train = np.zeros((num_examples, max_time_steps, num_features))\n",
    "\n",
    "    for i in range(num_examples):\n",
    "        e, l = generate_data()\n",
    "        x, y = vectorize_example(e, l)\n",
    "        x_train[i] = x\n",
    "        y_train[i] = y\n",
    "    \n",
    "    return x_train, y_train\n",
    "\n",
    "x_train, y_train = create_dataset()\n",
    "print(x_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "RRwiuwcQvBOL",
    "outputId": "07591064-5c57-4adb-ccf6-c226457d2263"
   },
   "outputs": [],
   "source": [
    "devectorize_example(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cRUvtd0IvBOO",
    "outputId": "d07ce279-06ba-44c4-de91-0ec4fb551877"
   },
   "outputs": [],
   "source": [
    "devectorize_example(y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6: Training the Model\n",
    "___\n",
    "Note: If you are starting the notebook from this task, you can run cells from all previous tasks in the kernel by going to the top menu and then selecting Kernel > Restart and Run All\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2686
    },
    "colab_type": "code",
    "id": "S8HWRdiOvBOS",
    "outputId": "2365b11f-30e8-48cc-e940-4d3267771fbe"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "tlNtu_9ZvBOY",
    "outputId": "82198555-f0e8-4a7a-9125-cc61cf4f4a26"
   },
   "outputs": [],
   "source": []
  }
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