shape_identifier_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ShapeClassifierNet\n",
    "**My first neural network!** We're going to make a **basic neural network that takes in a drawing of a basic shape and classifies it**. This is the perfect first project for me, because unlike MNIST handwritten digit classification, where the work is basically done for me and there's not much I can glean from it, **ShapeClassifierNet** offers a rich learning experience for me. As a relatively unique idea, it's straightforward enough for me to be able to navigate alone, yet complex enough to provide genuine learning experiences.\n",
    "\n",
    "Note: this notebook is a much more cleaned up version of my original notebook. In the original notebook, I did tons of extra data exploration and just trying tons of things before I settled on my final ideas. I obviously didn't include it here because there would be no way to organize all of it. It would only clutter things, when what really matters are my final decisions. So **this notebook very accurately documents my journey, just without the all trial and error.**\n",
    "\n",
    "The only obstacle with this project was that I had absolutely no idea on how I was going to be able to gather this type of data. Luckily, I had an amazing idea during the Google workshop when I was at ShellHacks '23. While there, I was reminded of **Google QuickDraw**, which I had used years ago. QuickDraw provides free access to a large amount of the type of basic drawings I was looking for. We can pull out the drawings of just the basic shapes from this data, and train a model on identifying them. **I found 5 shapes (classes), and these are: triangle, square, circle, star, and line.**\n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-03 13:39:33.290557: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-10-03 13:39:33.313714: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-10-03 13:39:33.417469: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-10-03 13:39:33.418543: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-10-03 13:39:34.065290: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from math import radians, sqrt, sin, cos\n",
    "from collections import defaultdict\n",
    "from sklearn.utils import shuffle\n",
    "from PIL import Image, ImageDraw\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy\n",
    "import tensorflow as tf\n",
    "from time import time\n",
    "import numpy as np\n",
    "import datetime\n",
    "import requests\n",
    "import random\n",
    "import json\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory for downloaded QuickDraw data\n",
    "DATASET_PATH = 'quickdraw_dataset/'\n",
    "\n",
    "# Random state value for reproducibility\n",
    "RAND_STATE_VAL = 169\n",
    "random.seed(RAND_STATE_VAL)\n",
    "\n",
    "# Define classes of shapes\n",
    "classes = ['triangle', 'square', 'circle', 'star', 'line']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading\n",
    "As described above, we're going to be using data from QuickDraw, Google's pretty cool AI. We're able to download the data directly from the Google API by using a GET request. The data consists of many thousands of drawings drawn by users, and each one includes a tag indicating whether's Google AI was able to recognize the drawing. Looking at the unrecognized drawings, most of them don't match the class, which makes sense why Google's AI wasn't able to classify them, so very simply we're only going to use only the recognized drawings. As the data is requested from the web, we're going to store it in a dictionary corresponding to each shape/class. We need to keep the data separate by class for now while we preprocess and split, to maintain class balance, then we can combine them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading triangle data...\n",
      "triangle data already exists, loading from file...\n",
      "loading square data...\n",
      "square data already exists, loading from file...\n",
      "loading circle data...\n",
      "circle data already exists, loading from file...\n",
      "loading star data...\n",
      "star data already exists, loading from file...\n",
      "loading line data...\n",
      "line data already exists, loading from file...\n",
      "\n",
      "data loading process complete!\n"
     ]
    }
   ],
   "source": [
    "url = 'https://storage.googleapis.com/quickdraw_dataset/full/simplified/{}.ndjson'\n",
    "dataset = defaultdict(list)\n",
    "\n",
    "# Specific drawings to pull for demo later\n",
    "shape_drawings = {\n",
    "    'triangle': '5422932853522432',\n",
    "    'square': '5842683031453696',\n",
    "    'circle': '6387315482034176',\n",
    "    'star': '6621911364665344',\n",
    "    'line': '5804143316828160'\n",
    "}\n",
    "\n",
    "key_drawings = []\n",
    "\n",
    "# Creates dataset directory if it doesn't exist\n",
    "if os.path.exists(DATASET_PATH) is False:\n",
    "    print('dataset directory doesn\\'t exist, creating it...')\n",
    "    os.mkdir(DATASET_PATH)\n",
    "\n",
    "# Iterates through shapes and downloads each one's data\n",
    "for shape in classes:\n",
    "    print(f'loading {shape} data...')\n",
    "    shape_data_path = DATASET_PATH + shape + '.ndjson'\n",
    "\n",
    "    # If shape's data is downloaded, loads it and continues\n",
    "    if os.path.exists(shape_data_path) and os.path.isfile(shape_data_path):\n",
    "        print(f'{shape} data already exists, loading from file...')\n",
    "        \n",
    "        # NOTE: if the file exists, we are assuming it was downloaded\n",
    "        # by this code and will have only recognized drawings\n",
    "        infile = open(shape_data_path, 'r')\n",
    "        for line in infile:\n",
    "            data = json.loads(line)\n",
    "            dataset[shape].append(data['drawing'])\n",
    "            # Grab specific drawing for demo\n",
    "            if data.get('key_id') == shape_drawings[shape]:\n",
    "                key_drawings.append((data['key_id'], data['drawing']))\n",
    "\n",
    "        # Skip data downloading code\n",
    "        continue\n",
    "    \n",
    "    # Request data from Google\n",
    "    print(f'getting {shape} data from Google...')\n",
    "    response = requests.get(url.format(shape), stream=True)\n",
    "    response.raise_for_status()  # Raise an error if the request failed\n",
    "    print('download complete! parsing JSON now...')\n",
    "\n",
    "    # Open newline delimited JSON file to save data\n",
    "    outfile = open(shape_data_path, 'w+')\n",
    "    \n",
    "    for line in response.iter_lines():\n",
    "        # Decode line to string, then parse JSON to check the key values\n",
    "        data = json.loads(line.decode('utf-8'))\n",
    "\n",
    "        # Save drawing only if recognized is True\n",
    "        if data.get('recognized') is True:\n",
    "\n",
    "            # 1. Write to data to file\n",
    "            json_str = json.dumps(data)\n",
    "            outfile.write(json_str + '\\n')\n",
    "\n",
    "            # 2. Append data to loaded dataset\n",
    "            dataset[shape].append(data['drawing'])\n",
    "\n",
    "            # Grab drawing if it's a demo drawing\n",
    "            if data.get('key_id') == shape_drawings[shape]:\n",
    "                key_drawings.append((data['key_id'], data['drawing']))\n",
    "    print(f'{shape} data loaded!')\n",
    "\n",
    "del shape_drawings\n",
    "print('\\ndata loading process complete!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Size/Split\n",
    "We have quite a **massive dataset** (thank you Google), even though it's just 5 classes out of Quick Draw's 345 total classes 🤯 Let's look at how many drawings we have to figure out how to split them, while maintaining a balanced class distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Size of Each Shape\n",
      "\n",
      "triangle: 120,500\n",
      "square: 120,538\n",
      "circle: 118,808\n",
      "star: 132,814\n",
      "line: 140,874\n",
      "\n",
      "total: 633,534\n"
     ]
    }
   ],
   "source": [
    "class_sizes = {}\n",
    "total_count = 0\n",
    "\n",
    "print('Dataset Size of Each Shape\\n')\n",
    "\n",
    "for shape in classes:\n",
    "    line_count = len(dataset[shape])\n",
    "    class_sizes[shape] = line_count\n",
    "    total_count += line_count\n",
    "    print(f'{shape}: {line_count:,}')\n",
    "\n",
    "print(f'\\ntotal: {total_count:,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Data Sizes\n",
    "According to the computation above, we have a whopping **633,534 drawings (more than half a million)**, which is an **average of ~127 thousand drawings per shape**.\n",
    "\n",
    "| Shape    | Count   |\n",
    "|----------|---------|\n",
    "| triangle | 120,500 |\n",
    "| square   | 120,538 |\n",
    "| circle   | 118,808 |\n",
    "| star     | 132,814 |\n",
    "| line     | 140,874 |\n",
    "| **total**| 633,534 |\n",
    "\n",
    "## Data Split Configuration\n",
    "We're going to start with **about 20% of the dataset (120 thousand drawings)**, to make computation and tuning quicker; this is still a substantial amount of data. Because the classes has different sizes, we're going to use a constant number of drawings for each class instead of a percentage, to keep the classes perfectly balanced. We're basing the number off of the smallest class' size, which is the circle set. It has nearly 120 thousand drawings, and 20% of that is 24 thousand. Based on that number, **we're going to use exactly 24 thousand drawings from each class**.\n",
    "\n",
    "Putting it back together, our 20% subset across all classes contains **120,000 drawings** out of the 633,534 total. Because we have the privilege of access to so much data, I wanted to leverage that by allocating a large chunk to validation and testing:\n",
    "\n",
    "| Dataset    | Ratio | Size    | Per class |\n",
    "|------------|:-----:|:-------:|:----------|\n",
    "| Training   | 50%   | 60,000  | 12,000    |\n",
    "| Validation | 25%   | 30,000  | 6,000     |\n",
    "| Testing    | 25%   | 30,000  | 6,000     |\n",
    "| **Total**  | 100%  | 120,000 | 24,000    |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "triangle: 24,000\n",
      "square: 24,000\n",
      "circle: 24,000\n",
      "star: 24,000\n",
      "line: 24,000\n"
     ]
    }
   ],
   "source": [
    "CLASS_SIZE = 24_000  # 24,000 drawings = ~20% of the data\n",
    "\n",
    "# For every shape class, randomly sample ~20% of the dataset\n",
    "for shape in classes:\n",
    "    dataset[shape] = random.sample(dataset[shape], CLASS_SIZE)\n",
    "    print(f'{shape}: {len(dataset[shape]):,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train, validation, and test sets that hold all data across classes\n",
    "X_train, X_valid, X_test = [], [], []\n",
    "y_train, y_valid, y_test = [], [], []\n",
    "\n",
    "# Split each class' dataset separately\n",
    "for shape in classes:\n",
    "    X = dataset[shape]\n",
    "    y = [shape] * len(X)\n",
    "\n",
    "    # First split off train set, then split remainder into validation and test sets\n",
    "    tmp_X_train, X_temp, tmp_y_train, y_temp = train_test_split(X, y, test_size=0.5, random_state=RAND_STATE_VAL)\n",
    "    tmp_X_valid, tmp_X_test, tmp_y_valid, tmp_y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=RAND_STATE_VAL)\n",
    "\n",
    "    # Add newly split class set to datasets\n",
    "    X_train.extend(tmp_X_train)\n",
    "    X_valid.extend(tmp_X_valid)\n",
    "    X_test.extend(tmp_X_test)\n",
    "\n",
    "    y_train.extend(tmp_y_train)\n",
    "    y_valid.extend(tmp_y_valid)\n",
    "    y_test.extend(tmp_y_test)\n",
    "\n",
    "# Shuffle datasets\n",
    "X_train, y_train = shuffle(X_train, y_train, random_state=RAND_STATE_VAL)\n",
    "X_valid, y_valid = shuffle(X_valid, y_valid, random_state=RAND_STATE_VAL)\n",
    "X_test, y_test = shuffle(X_test, y_test, random_state=RAND_STATE_VAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train length: 60,000\n",
      "X_valid length: 30,000\n",
      "X_valid length: 30,000\n",
      "Total Dataset Size: 120,000\n"
     ]
    }
   ],
   "source": [
    "print(f'X_train length: {len(X_train):,}')\n",
    "print(f'X_valid length: {len(X_valid):,}')\n",
    "print(f'X_valid length: {len(X_valid):,}')\n",
    "print(f'Total Dataset Size: {len(X_train)+len(X_valid)+len(X_valid):,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Preprocessing\n",
    "Now that we have our data loaded and split, we can begin the essential preprocessing phase. To begin, let's randomly pull some drawings and display them for observation. This will hopefully give us some insights about the data and how we might need to process it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shape retrieval from data\n",
    "def random_shape_from_class(shape_class: str) -> list:\n",
    "    \"\"\" Gets random shape from given shape class and returns its stroke sequence. \"\"\"\n",
    "    return random.choice(dataset[shape_class])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shape drawing/image generation\n",
    "\n",
    "def convert_strokes_to_image(data, stroke_width=3):\n",
    "    \"\"\" Converts stroke data to an image using PIL at its full resolution, or resize if desired. \"\"\"\n",
    "\n",
    "    # Compute the bounds of the stroke data to determine the size of the image.\n",
    "    min_x = min([min(stroke[0]) for stroke in data])\n",
    "    max_x = max([max(stroke[0]) for stroke in data])\n",
    "    min_y = min([min(stroke[1]) for stroke in data])\n",
    "    max_y = max([max(stroke[1]) for stroke in data])\n",
    "\n",
    "    # Shift everything into the positive space\n",
    "    width, height = int(max_x - min_x + 1), int(max_y - min_y + 1)\n",
    "\n",
    "    # Initialize a blank white image\n",
    "    image = Image.new(\"RGB\", (width, height), \"white\")\n",
    "    draw = ImageDraw.Draw(image)\n",
    "\n",
    "    for stroke in data:\n",
    "        for i in range(len(stroke[0]) - 1):\n",
    "            x0, y0 = stroke[0][i] - min_x, stroke[1][i] - min_y\n",
    "            x1, y1 = stroke[0][i + 1] - min_x, stroke[1][i + 1] - min_y\n",
    "            draw.line([(x0, y0), (x1, y1)], fill=\"black\", width=stroke_width)\n",
    "\n",
    "    return image\n",
    "\n",
    "def display_multiple_drawings(shape: str = None, dimensions: tuple = (3, 5)):\n",
    "    \"\"\" Displays a grid of many drawings based on given arguments. \"\"\"\n",
    "\n",
    "    # this enables us to avoid checking if a shape is given every iteration\n",
    "    get_shape = (lambda: shape) if shape is not None else (lambda: random.choice(classes))\n",
    "\n",
    "    rows, cols = dimensions\n",
    "    _, axes = plt.subplots(rows, cols, figsize=(cols*2, rows*2))\n",
    "\n",
    "    # for 1 dimensional plots\n",
    "    if rows == 1 or cols == 1:\n",
    "        axes = [axes]\n",
    "\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            random_drawing = random_shape_from_class(get_shape())\n",
    "            image = convert_strokes_to_image(random_drawing)\n",
    "            ax = axes[r][c]\n",
    "            ax.imshow(image)\n",
    "            ax.axis('off')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selects multiple random shapes/drawings and displays them all\n",
    "display_multiple_drawings(dimensions=(3, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Augmentation\n",
    "While flipping through the dataset, one that becomes apparent is that everyone has the same idea when drawing triangles: the base is parallel to the bottom. I looked through 150+ drawings before I found a triangle drawing facing a different direction, and it'd probably take me even longer to find the next one. This is not a good pattern because if the model is only trained on triangles pointing upwards, then it won't be able to tell that a drawing is triangle simply because it's rotated 90°.\n",
    "\n",
    "This issue isn't difficult to address, all it takes is **data augmentation**. Very simply, we need to generate a few rotations for each drawing and add them to the dataset. This way, the neural network is exposed to a variation of orientations of each shape. This ensures it can easily identify a triangle even if it's \"upside down\".\n",
    "\n",
    "Likely not every shape will need to be rotated, or at least not rotated as many times as some other shapes will need. For example, a triangle rotated either 45° or 90°, however many times, looks quite different. On the other hand a square rotated 90° looks the same but rotated 45° looks very different; this requires different rotations from each shape. We're going to limit the smallest rotation to an increment of 45°, any more would be unnecessary and just pollute the dataset. Let's not forget that neural networks are still pretty smart, they can figure it out 🧠😎.\n",
    "\n",
    "What we're really doing is making sure each shape covers the a full variety. **A term I came up with to describe this is \"image space\"**. The triangles all (almost) only point upwards, meaning it's clearly missing lots of variation to cover the entire image space. Another thing is that drawings obviously don't look the exact same every time, just look at the drawings displayed above. From one triangle to the next, even in the same orientation, there is a significant amount of variation in the angles, where the lines meet, etc. That variation in drawing provides the core diversity that makes each drawing different. The goal of the rotation is to supplement only the missing variation. The rotations combined with the natural fluctuation of drawing results in a dataset with great dataset, hopefully leading to good training results..\n",
    "\n",
    "To decide how many augmentations each shape will need, we're going to observe each shape rotated at 45° increments and see what we can deduce from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shape/image rotation\n",
    "\n",
    "def rotate_point(x: int, y: int, angle: int) -> (int, int):\n",
    "    \"\"\" Rotate a single point (coord) around the origin by a given angle in degrees. \"\"\"\n",
    "    rad_angle = radians(angle)\n",
    "    x_rot = cos(rad_angle) * x - sin(rad_angle) * y\n",
    "    y_rot = sin(rad_angle) * x + cos(rad_angle) * y\n",
    "    return x_rot, y_rot\n",
    "\n",
    "def rotate_strokes(strokes: list, angle: int) -> list:\n",
    "    \"\"\" Rotate a series of strokes by a given angle in degrees and returns the rotated strokes. \"\"\"\n",
    "\n",
    "    if angle == 0:\n",
    "        return strokes\n",
    "\n",
    "    rotated_strokes = []\n",
    "\n",
    "    for stroke in strokes:\n",
    "        x_coords, y_coords = stroke\n",
    "        new_x_coords, new_y_coords = [], []\n",
    "\n",
    "        for x, y in zip(x_coords, y_coords):\n",
    "            new_x, new_y = rotate_point(x, y, angle)\n",
    "            new_x_coords.append(new_x)\n",
    "            new_y_coords.append(new_y)\n",
    "\n",
    "        rotated_strokes.append([new_x_coords, new_y_coords])\n",
    "\n",
    "    return rotated_strokes\n",
    "\n",
    "def demo_shape_rotations(axs, shape_drawing: list):\n",
    "    \"\"\" Displays given shape drawing on axs plot in rotations of 45°. \"\"\"\n",
    "    # First displays right angle views, then diagonal views\n",
    "    for i, angle in enumerate(list(range(0, 360, 90)) + list(range(45, 405, 90))):\n",
    "        rotated_strokes = rotate_strokes(shape_drawing, angle)\n",
    "        image = convert_strokes_to_image(rotated_strokes)\n",
    "        axs[i].imshow(image)\n",
    "        axs[i].set_title(f'{angle}°')\n",
    "        axs[i].axis('off')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x2500 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display rotation demo of every shape for analysis\n",
    "\n",
    "# Main display code\n",
    "fig = plt.figure(figsize=(25, 25))\n",
    "\n",
    "# Create the grid for the 5 shape, each shape occupies a row\n",
    "gs = fig.add_gridspec(5, 8)  # 5 rows, 8 columns\n",
    "\n",
    "for i, (shape_id, drawing) in enumerate(key_drawings):\n",
    "    axs = [fig.add_subplot(gs[i, j]) for j in range(8)]\n",
    "    demo_shape_rotations(axs, drawing)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rotation (data augmentation cont.)\n",
    "Looking at the drawings above, we can extrapolate many interesting details. Remember that we're **trying to address the problem of lack of variation**.\n",
    "\n",
    "**To combat this problem,** we very simply need to fill this absence with drawings in different orientations. The thing is, not every shape has this problem. Take the star for example. When rotated it provides some variation, but if you looked at the rest of the star dataset you'd realize that that variation already exists due to natural fluctuations that come from human drawing; we can't draw perfectly like robots. In the star's case, we wouldn't need to generate any rotated drawings.\n",
    "\n",
    "Let's look at one more shape, the square. The square looks the same in the right angle/upright orientations, however when it comes to the diagonals (45° orientations), it clearly looks different. More precisely, the diagonals look different relative only to the upright squares, but look the same compared to other diagonals. So for the square, we would only need to rotate each drawing 45° once, maybe a smaller angle as well.\n",
    "\n",
    "Below are the rotations/copies each shape requires to have complete variation (does not include the original orientation).\n",
    "\n",
    "| Shape    | Rotations          |\n",
    "|----------|--------------------|\n",
    "| triangle | +7: full range 45° |\n",
    "| square   | +3: 30°, 45°, 60°  |\n",
    "| circle   | +0: does not need  |\n",
    "| star     | +0: does not need  |\n",
    "| line     | +7: full range 45° |\n",
    "\n",
    "- **Triangle** benefits from rotating all the way around, so we're going to include the full range in increments of 45°.\n",
    "- **Square** on the other hand simply needs to be angled once (45°) to include diagonals. We're also going to include 30° and 60° (angled once as well) because I found that including smaller increments for the square provides even better variety.\n",
    "- **Circle** will not be getting any rotations because its dataset has variation, so rotating it does not make any difference.\n",
    "- **Star** will not be getting any rotations either for the same reason.\n",
    "- **Line** has decent variation, but there's definitely a bit more horizontal lines than any other orientation. Due to this, it's just better to rotate it, so we're going rotate it the full range in increments of 45° so that all orientations are balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rotations\n",
    "shape_rotations = {\n",
    "    'triangle': list(range(45, 360, 45)),  # 45° -> 360° in increments of 45°\n",
    "    'line': list(range(45, 360, 45)),  # 45° -> 360° in increments of 45°\n",
    "    'square': [30, 45, 60],  # angles 30°, 45°, 60°\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Balancing Augmented Data\n",
    "Earlier, we split our dataset into the training, cross validation, and testing sets. We'll now augment our data, but adjusting different amounts for each class. This is going to not only produce even more data, but skew the previously balanced class distribution because each class will be augmented different amounts. We're going to use **undersampling** to address this, which is to randomly sample data from the bigger classes until they're as small as the smallest class. For example if the smallest class has 1,000 rows and another class has 6,000 after augmentation, we're going to randomly sample 1,000 from the bigger class and only use that.\n",
    "\n",
    "### Augmented Data Usage\n",
    "Usually data augmentation consists of \"unnatural\" augmentations because they help **improve model robustness**, even if they don't reflect typical real-world conditions. The rationale is to **expose the model to a wide variety of conditions**, so it learns to focus on essential features and generalizes better. For example, randomly changing brightness, contrast, saturation, and hue might produce colors that are never seen in the real world, but make the model less sensitive to specific color conditions; allowing it to generalize better. But because these are unnatural scenarios, they are only applied to the training data to improve model robustness/generalization, and testing data is left clean because you're not gonna see a bright green dog in the real world.\n",
    "\n",
    "BUT in our specific case, we performed augmentation to **better mimic real world conditions**. The best reflection of this idea is our triangle dataset. I noticed that nearly all the triangles were drawn with the base parallel to the bottom. But in the real world, seeing a rotated triangle is not unnatural at all, so we would actually want to include it in the test set as well. So with this logic, both our training and testing sets will include the augmented and undersampled data above, which is an even mix of unaugmented and augmented data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation (rotation) and undersampling  functions\n",
    "\n",
    "def group_dataset_by_class(X_data: list[list], y_data: list[str]) -> dict:\n",
    "    \"\"\" Given a dataset and its corresponding labels,\n",
    "        groups it by class and returns the grouped sets. \"\"\"\n",
    "    class_data_dict = defaultdict(list)\n",
    "    for drawing, label in zip(X_data, y_data):\n",
    "        class_data_dict[label].append(drawing)\n",
    "\n",
    "    return class_data_dict\n",
    "\n",
    "def combine_class_data(class_grouped_data: dict, shuffle_return: bool = True) -> tuple[list]:\n",
    "    \"\"\" Given a dataset grouped by class, combines it back together\n",
    "        and returns the dataset and its corresponding labels. \"\"\"\n",
    "\n",
    "    # Original lists are not modified, creates new lists instead\n",
    "    X_combined, y_combined = [], []\n",
    "\n",
    "    for label, class_data in class_grouped_data.items():\n",
    "        X_combined.extend(class_data)\n",
    "        y_combined.extend([label]*len(class_data))\n",
    "\n",
    "    # Optional shuffle\n",
    "    if shuffle_return is True:\n",
    "        # Shuffle combined data because it's ordered by class\n",
    "        X_combined, y_combined = shuffle(X_combined, y_combined, random_state=RAND_STATE_VAL)\n",
    "\n",
    "    return X_combined, y_combined\n",
    "\n",
    "def rotate_strokes_class_set(X_data: list[list], rotation_angles: list[int], shuffle_return: bool = True) -> list[list]:\n",
    "    \"\"\" Given a dataset of a single class, returns an augmented dataset\n",
    "        where each stroke sequence is rotated by the specified angles. \"\"\"\n",
    "\n",
    "    # Original list is not modified, creates new list instead\n",
    "    X_augmented = deepcopy(X_data)\n",
    "\n",
    "    # Rotate every stroke sequence by every angle, and appends the rotated stroke sequences\n",
    "    for stroke_sequence in X_data:\n",
    "        for angle in rotation_angles:\n",
    "            rotated_stroke_sequence = rotate_strokes(stroke_sequence, angle)\n",
    "            X_augmented.append(rotated_stroke_sequence)\n",
    "\n",
    "    # Optional shuffle\n",
    "    if shuffle_return is True:\n",
    "        # Shuffle augmented data to mix angles together, and to mix with original data\n",
    "        X_augmented = shuffle(X_augmented, random_state=RAND_STATE_VAL)\n",
    "\n",
    "    return X_augmented\n",
    "\n",
    "def undersample_dataset(X_data: list[list], y_data: list[str], target_class_size: int, shuffle_return: bool = True) -> tuple[list]:\n",
    "    \"\"\" Given a dataset and their respective labels, returns a dataset\n",
    "        undersampled by class down to the given target class size. \"\"\"\n",
    "\n",
    "    # Original lists are not modified, creates new lists instead\n",
    "    X_undersampled, y_undersampled = [], []\n",
    "\n",
    "    # Group the data by class\n",
    "    class_grouped_data = group_dataset_by_class(X_data, y_data)\n",
    "\n",
    "    # Undersample each class\n",
    "    for label, class_data in class_grouped_data.items():\n",
    "        # Undersample ONLY if class is larger than target size\n",
    "        if len(class_data) > target_class_size:\n",
    "            class_data = random.sample(class_data, target_class_size)\n",
    "        # Append to new undersampled dataset\n",
    "        for drawing in class_data:\n",
    "            X_undersampled.append(drawing)\n",
    "            y_undersampled.append(label)\n",
    "\n",
    "    # Optional shuffle\n",
    "    if shuffle_return is True:\n",
    "        # Shuffle undersampled data because data is ordered by class\n",
    "        X_undersampled, y_undersampled = shuffle(X_undersampled, y_undersampled, random_state=RAND_STATE_VAL)\n",
    "\n",
    "    return X_undersampled, y_undersampled\n",
    "\n",
    "def undersample_class_set(X_data: list[list], target_class_size: int) -> list[list]:\n",
    "    \"\"\" Given a dataset of a single class, returns a dataset\n",
    "        undersampled down to the given target class size. \"\"\"\n",
    "\n",
    "    # Can not resize dataset if target class size is not smaller than it\n",
    "    if target_class_size >= len(X_data):\n",
    "        raise ValueError('Given target class size must be smaller than dataset.')\n",
    "\n",
    "    # Sample target size from given dataset (does not modify original list)\n",
    "    undersampled_data = random.sample(X_data, target_class_size)\n",
    "\n",
    "    return undersampled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform data augmentations\n",
    "num_of_classes = len(classes)\n",
    "train_class_size = len(X_train) // num_of_classes\n",
    "valid_class_size = len(X_valid) // num_of_classes\n",
    "test_class_size = len(X_test) // num_of_classes\n",
    "\n",
    "# === Training Set ===\n",
    "train_set_grouped_classes = group_dataset_by_class(X_train, y_train)\n",
    "\n",
    "# Iterate through each shape's angles and perform augmentation\n",
    "for shape, angles in shape_rotations.items():\n",
    "    # Augment current shape and undersample it to reduce size\n",
    "    augmented_data = rotate_strokes_class_set(train_set_grouped_classes[shape], angles)\n",
    "    train_set_grouped_classes[shape] = undersample_class_set(augmented_data, train_class_size)\n",
    "\n",
    "# Combine all classes back together after they've been separately augmented and undersampled\n",
    "X_train_augmented, y_train_augmented = combine_class_data(train_set_grouped_classes)\n",
    "del train_set_grouped_classes  # delete, same as augmented, just grouped by class\n",
    "\n",
    "# === Validation Set ===\n",
    "valid_set_grouped_classes = group_dataset_by_class(X_valid, y_valid)\n",
    "\n",
    "# Iterate through each shape's angles and perform augmentation\n",
    "for shape, angles in shape_rotations.items():\n",
    "    # Augment current shape and undersample it to reduce size\n",
    "    augmented_data = rotate_strokes_class_set(valid_set_grouped_classes[shape], angles)\n",
    "    valid_set_grouped_classes[shape] = undersample_class_set(augmented_data, valid_class_size)\n",
    "\n",
    "# Combine all classes back together after they've been separately augmented and undersampled\n",
    "X_valid_augmented, y_valid_augmented = combine_class_data(valid_set_grouped_classes)\n",
    "del valid_set_grouped_classes  # delete, same as augmented, just grouped by class\n",
    "\n",
    "# === Testing Set ===\n",
    "test_set_grouped_classes = group_dataset_by_class(X_test, y_test)\n",
    "\n",
    "# Iterate through each shape's angles and perform augmentation\n",
    "for shape, angles in shape_rotations.items():\n",
    "    # Augment current shape and undersample it to reduce size\n",
    "    augmented_data = rotate_strokes_class_set(test_set_grouped_classes[shape], angles)\n",
    "    test_set_grouped_classes[shape] = undersample_class_set(augmented_data, test_class_size)\n",
    "\n",
    "# Combine all classes back together after they've been separately augmented and undersampled\n",
    "X_test_augmented, y_test_augmented = combine_class_data(test_set_grouped_classes)\n",
    "del test_set_grouped_classes  # delete, same as augmented, just grouped by class\n",
    "\n",
    "# === Dataset Versions we have now ===\n",
    "\"\"\"\n",
    "Original data:\n",
    "  - X_train\n",
    "  - X_valid\n",
    "  - X_test\n",
    "\n",
    "Augmented & undersampled data, final clean result:\n",
    "  - X_train_augmented\n",
    "  - X_valid_augmented\n",
    "  - X_test_augmented\n",
    "  \n",
    "Y Labels Set: always has the same name as X sets,\n",
    "with the only difference being that it starts with `y_` instead of `X_`\n",
    "\"\"\";\n",
    "# ===================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Resizing\n",
    "We're very close to finishing up with preprocessing and nearing training 😳. This next very important step is to resize the images to a much smaller dimension. This is crucial to expedite computational processing, reduce memory footprint, and especially to ensure consistent input shape for our machine learning model, without sacrificing significant information from the original data.\n",
    "\n",
    "Some common sizes I've found for input images for deep learning are 224x224 and 256x256, but the more basic your images are, the smaller you can make your images (without sacrificing detail/information). I found that 48x48 is a good size that's quite small but still displays very clear shapes; it's just a bit bigger than MNIST's handwritten digits image size.\n",
    "\n",
    "Below is a modified version of the `convert_strokes_to_image` function that first resizes the drawing strokes to the given target size before drawing it. This new function, `convert_strokes_to_resized_image` is very powerful. It first performs many calculations to figure out how to resize the image and center it, then draws it on a bigger canvas first before resizing down to give the blurred antialias effect. Although we won't be feeding image files directly into our model, this intermediary image format between the stroke sequence data and the input to the model is crucial because it's placing the drawing into a frame/canvas; effectively deciding on a position and size relative to its borders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_strokes_to_resized_image(data, target_size: tuple[int, int], stroke_width=5, antialias_factor=4) -> Image.Image:\n",
    "    \"\"\" Converts stroke data to an image using PIL at given dimensions.\n",
    "    We draw the image on a larger canvas first then resize down to give the anti-aliasing effect.\n",
    "    \"\"\"\n",
    "\n",
    "    # Compute the bounds of the stroke data to determine the size of the image.\n",
    "    min_x = min([min(stroke[0]) for stroke in data])\n",
    "    max_x = max([max(stroke[0]) for stroke in data])\n",
    "    min_y = min([min(stroke[1]) for stroke in data])\n",
    "    max_y = max([max(stroke[1]) for stroke in data])\n",
    "\n",
    "    original_width = int(max_x - min_x + 1)\n",
    "    original_height = int(max_y - min_y + 1)\n",
    "\n",
    "    # Calculate the scaling factors for x and y axes\n",
    "    scale_factor = min((target_size[0] * antialias_factor) / original_width, (target_size[1] * antialias_factor) / original_height)\n",
    "\n",
    "    # Initialize a blank white image of a larger resolution for antialiasing\n",
    "    intermediate_image = Image.new(\"RGB\", (target_size[0] * antialias_factor, target_size[1] * antialias_factor), \"white\")\n",
    "    draw = ImageDraw.Draw(intermediate_image)\n",
    "\n",
    "    # Calculate offsets to center the drawing in the intermediate canvas\n",
    "    centered_width = original_width * scale_factor\n",
    "    centered_height = original_height * scale_factor\n",
    "    offset_x = (target_size[0] * antialias_factor - centered_width) / 2\n",
    "    offset_y = (target_size[1] * antialias_factor - centered_height) / 2\n",
    "\n",
    "    for stroke in data:\n",
    "        for i in range(len(stroke[0]) - 1):\n",
    "            x0, y0 = (stroke[0][i] - min_x) * scale_factor + offset_x, (stroke[1][i] - min_y) * scale_factor + offset_y\n",
    "            x1, y1 = (stroke[0][i + 1] - min_x) * scale_factor + offset_x, (stroke[1][i + 1] - min_y) * scale_factor + offset_y\n",
    "            draw.line([(x0, y0), (x1, y1)], fill=\"black\", width=int(stroke_width * sqrt(antialias_factor)))\n",
    "\n",
    "    # Resize the image down to target size to achieve the antialiasing effect\n",
    "    final_image = intermediate_image.resize(target_size, Image.ANTIALIAS)\n",
    "\n",
    "    return final_image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image to Tensors\n",
    "Our images have a dimension of 48x48, but our model can't normally accept more than 1 dimension as input. A solution to this is a Tensor. **TensorFlow has a datatype called Tensors that is the most efficient type for models** and allows the model to accept data of whatever dimension without resizing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# === Image and Tensor Dataset Conversion ===\n",
    "def convert_strokes_dataset_to_images(X_data: list[list], image_size: tuple[int, int], stroke_width=5, antialias_factor=4) -> list[Image.Image]:\n",
    "    \"\"\" Given a dataset of stroke sequences, resizes and draws each one\n",
    "        and returns it as a list of PIL images. \"\"\"\n",
    "    return [convert_strokes_to_resized_image(drawing, image_size, stroke_width, antialias_factor) for drawing in X_data]\n",
    "\n",
    "# Get reference to Tensor type for typehinting\n",
    "Tensor = type(tf.image.rgb_to_grayscale(convert_strokes_to_image(X_train[0])))\n",
    "# * to display Tensor, convert to numpy() first then show with plt\n",
    "\n",
    "def image_to_tensor(image: Image.Image) -> Tensor:\n",
    "    \"\"\" Converts given PIL image to a Tensor with one color channel. \"\"\"\n",
    "    return tf.image.rgb_to_grayscale(image)\n",
    "\n",
    "def convert_images_dataset_to_tensors(X_data: list[Image.Image]) -> list[Tensor]:\n",
    "    \"\"\" Given a dataset of PIL images, converts and returns it as Tensors. \"\"\"\n",
    "    return [image_to_tensor(image) for image in X_data]\n",
    "\n",
    "def convert_strokes_dataset_to_tensors(X_data: list[list], image_size: tuple[int, int], stroke_width=5, antialias_factor=4) -> list[Tensor]:\n",
    "    \"\"\" Given a dataset of stroke sequences, resizes and draws each one as PIL images,\n",
    "        then converts each to a Tensor and returns the final Tensor dataset. \"\"\"\n",
    "    return [image_to_tensor(convert_strokes_to_resized_image(drawing, image_size, stroke_width, antialias_factor)) for drawing in X_data]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 48, 1)\n"
     ]
    }
   ],
   "source": [
    "# Convert dataset into Tensors\n",
    "\n",
    "DIM = 48  # Image dimension (one side)\n",
    "\n",
    "X_train_tensors = convert_strokes_dataset_to_tensors(X_train_augmented, (DIM, DIM))\n",
    "X_valid_tensors = convert_strokes_dataset_to_tensors(X_valid_augmented, (DIM, DIM))\n",
    "X_test_tensors = convert_strokes_dataset_to_tensors(X_test_augmented, (DIM, DIM))\n",
    "\n",
    "# === New Dataset Versions ===\n",
    "\"\"\"\n",
    "Tensor data:\n",
    "  - X_train_tensors\n",
    "  - X_valid_tensors\n",
    "  - X_test_tensors\n",
    "\"\"\";\n",
    "# ==============================\n",
    "\n",
    "print(X_train_tensors[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice now that the shape of one tensor is (48, 48, 1). The third value is the number of color channels. Since we're using grayscale images, the channel is just 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Normalization\n",
    "We're at the last step of preprocessing! Now that we have our data as images/tensors with pixel values, we can normalize them. At the moment, if you were to look at the range of the pixel values of any image, it would range from 0 to 255. But in machine learning, especially for neural networks, keeping the range of your data small helps the model converge faster and reach a better local minimum. To do this we have to perform normalization.\n",
    "\n",
    "This is done by taking every pixel and squeezing it into a range of 0 to 1. To squeeze it, we simply divide the pixel's value by 255, effectively scaling it to a range of 0 to 1. This normalization step is applied to all 3 sets: training, validation, and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_tensor_dataset(X_data: list[Tensor], max_value: int = 255) -> list[Tensor]:\n",
    "    \"\"\" Given a dataset of Tensors, returns the normalized dataset. \"\"\"\n",
    "    return [(tf.cast(tensor, tf.float32) / float(max_value)) for tensor in X_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize each set\n",
    "X_train_tensors = normalize_tensor_dataset(X_train_tensors)\n",
    "X_valid_tensors = normalize_tensor_dataset(X_valid_tensors)\n",
    "X_test_tensors = normalize_tensor_dataset(X_test_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our X data has been completely preprocessed, we can stack the tensors. Currently, our drawings data is stored as a list of tensors, but we can combine this into a Tensor stack which is a much way to store the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data to a single Tensor\n",
    "X_train_tensor = tf.stack(X_train_tensors, axis=0)\n",
    "X_valid_tensor = tf.stack(X_valid_tensors, axis=0)\n",
    "X_test_tensor = tf.stack(X_test_tensors, axis=0)\n",
    "\n",
    "\"\"\" These tensors have the shape (m_rows, height, width, channels),\n",
    "which is the format expected when training models in TensorFlow. \"\"\";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for our labels, there's just one processing step we have to apply to them. The labels have been stored as strings the entire time, but our model can't process strings. Instead we have to encode the shape names as integers, where each shape name corresponds to an integer. After we train our model and make an inference with it, we can convert the output integer into its corresponding string name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Label: circle, Encoded Label: 0\n",
      "Original Label: line, Encoded Label: 1\n",
      "Original Label: square, Encoded Label: 2\n",
      "Original Label: star, Encoded Label: 3\n",
      "Original Label: triangle, Encoded Label: 4\n",
      "['circle', 'line', 'square', 'star', 'triangle']\n"
     ]
    }
   ],
   "source": [
    "# Encode the string classes (shape names) into integers\n",
    "encoder = LabelEncoder()\n",
    "y_train_encoded = encoder.fit_transform(y_train_augmented)\n",
    "y_valid_encoded = encoder.transform(y_valid_augmented)\n",
    "y_test_encoded = encoder.transform(y_test_augmented)\n",
    "\n",
    "encoder_mappings = [None] * len(encoder.classes_)  # index: label\n",
    "\n",
    "for original_label, encoded_label in zip(encoder.classes_, range(len(encoder.classes_))):\n",
    "    print(f\"Original Label: {original_label}, Encoded Label: {encoded_label}\")\n",
    "    encoder_mappings[encoded_label] = original_label\n",
    "\n",
    "print(encoder_mappings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Time 🏋\n",
    "At last, it's time to train! But not quite. Before we can actually start training the model, we have to define the model itself. The model needs a suitable architecture, optimizer & loss function, set initial hyperparameters, etc.\n",
    "\n",
    "## Model Architecture\n",
    "Arguably the most important part is model architecture, because that's literally the model itself. Because our task, identifying drawings of basic shapes, is a **computer vision task**, we're going to employ the use of a **Convolutional Neural Network (CNN)**. A CNN is a is a type of artificial intelligence model designed for computer vision (CV). Through layers that scan small sections of an image, it can automatically and adaptively learn spatial hierarchies of features. This means a CNN can start by detecting edges, then shapes by combining edges, and even more complex structures as it goes deeper, making it highly effective for tasks like object recognition in photos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model and Layers\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.regularizers import l2\n",
    "\n",
    "# Optimizer and Loss function\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
    "\n",
    "# Model Evaluation & Checkpointing\n",
    "from sklearn.metrics import classification_report\n",
    "from keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After lots of hyper parameter tuning (not shown) and training, this is the model architecture we settled on. It features 3 convolutional layers for feature extraction, and then 2 large dense layers with dropout for classification. We also regularized it with L2 regularization to reduce overfitting, and finally compiled it with the Adam optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 46, 46, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPooling  (None, 23, 23, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 21, 21, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPooling  (None, 10, 10, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_8 (MaxPooling  (None, 4, 4, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 256)               524544    \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 5)                 645       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 650,757\n",
      "Trainable params: 650,757\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "image_dimension = (48, 48, 1)  # 48 x 48 images, w/ 1 color channel (grayscale)\n",
    "lambda_val = 0.0002  # Lambda value for regularization\n",
    "\n",
    "model = Sequential([\n",
    "    # == Input ==\n",
    "    Input(shape=image_dimension),  # Specifies shape of inputs (images)\n",
    "\n",
    "    # == Feature Extraction (initial) ==\n",
    "    Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_regularizer=l2(lambda_val)),  # Conv. layer learns the significant features in the input\n",
    "    MaxPooling2D(pool_size=(2, 2)),  # Reduces dimensionality of the input to alleviate the computational load\n",
    "\n",
    "    # == Feature Extraction (deeper) ==\n",
    "    Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_regularizer=l2(lambda_val)),  # 2nd conv. learns more complex patterns\n",
    "    MaxPooling2D(pool_size=(2, 2)),  # Also reduces overfitting because it's reducing dimensions\n",
    "\n",
    "    # == Feature Extraction (EVEN deeper) ==\n",
    "    Conv2D(128, kernel_size=(3, 3), activation='relu', kernel_regularizer=l2(lambda_val)),  # Added another conv. to introduce more complexity\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "\n",
    "    # == Classification ==\n",
    "    Flatten(),  # Flattens 2D outputs from previous layers to prepare data for fully connected layers\n",
    "\n",
    "    Dense(256, activation='relu', kernel_regularizer=l2(lambda_val)),     # Increased neurons in first layer\n",
    "    Dropout(0.5),                      # Adjusted dropout for added complexity\n",
    "    Dense(128, activation='relu', kernel_regularizer=l2(lambda_val)),     # Add another dense layer for more complexity\n",
    "    Dropout(0.5),                      # Dropout for the added dense layer\n",
    "\n",
    "    # == Output ==\n",
    "    Dense(5, activation='linear')  # Softmax, but we do linear first then softmax after for numerical accuracy\n",
    "])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# Compile Model\n",
    "model.compile(optimizer=Adam(0.0005), loss=SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training !!\n",
    "Now that we have a model set up, we're actually ready for training!!! We're going to log our progress so that we could display them with TensorFlow graphs. We also set up a checkpoint callback, which monitors the validation loss and continuously saves the parameters every time the validation loss is at its lowest. This allows us to keep hold of the best performing parameters before the loss starts increasing. Sometimes we reach the best validation loss early on, so to prevent unnecessary training we also added early stopping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.3593 - accuracy: 0.9049\n",
      "Epoch 1: val_loss improved from inf to 0.17158, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.3593 - accuracy: 0.9049 - val_loss: 0.1716 - val_accuracy: 0.9707\n",
      "Epoch 2/20\n",
      "1872/1875 [============================>.] - ETA: 0s - loss: 0.1789 - accuracy: 0.9703\n",
      "Epoch 2: val_loss improved from 0.17158 to 0.13808, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.1788 - accuracy: 0.9703 - val_loss: 0.1381 - val_accuracy: 0.9800\n",
      "Epoch 3/20\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.1479 - accuracy: 0.9777\n",
      "Epoch 3: val_loss improved from 0.13808 to 0.12051, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 26s 14ms/step - loss: 0.1478 - accuracy: 0.9777 - val_loss: 0.1205 - val_accuracy: 0.9829\n",
      "Epoch 4/20\n",
      "1875/1875 [==============================] - ETA: 0s - loss: 0.1306 - accuracy: 0.9801\n",
      "Epoch 4: val_loss improved from 0.12051 to 0.12007, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.1306 - accuracy: 0.9801 - val_loss: 0.1201 - val_accuracy: 0.9818\n",
      "Epoch 5/20\n",
      "1874/1875 [============================>.] - ETA: 0s - loss: 0.1225 - accuracy: 0.9818\n",
      "Epoch 5: val_loss improved from 0.12007 to 0.10663, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.1225 - accuracy: 0.9818 - val_loss: 0.1066 - val_accuracy: 0.9843\n",
      "Epoch 6/20\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.1135 - accuracy: 0.9833\n",
      "Epoch 6: val_loss improved from 0.10663 to 0.10600, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.1134 - accuracy: 0.9834 - val_loss: 0.1060 - val_accuracy: 0.9851\n",
      "Epoch 7/20\n",
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.1095 - accuracy: 0.9838\n",
      "Epoch 7: val_loss improved from 0.10600 to 0.09957, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.1096 - accuracy: 0.9837 - val_loss: 0.0996 - val_accuracy: 0.9852\n",
      "Epoch 8/20\n",
      "1872/1875 [============================>.] - ETA: 0s - loss: 0.1061 - accuracy: 0.9844\n",
      "Epoch 8: val_loss improved from 0.09957 to 0.09846, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.1061 - accuracy: 0.9844 - val_loss: 0.0985 - val_accuracy: 0.9858\n",
      "Epoch 9/20\n",
      "1875/1875 [==============================] - ETA: 0s - loss: 0.1011 - accuracy: 0.9855\n",
      "Epoch 9: val_loss did not improve from 0.09846\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.1011 - accuracy: 0.9855 - val_loss: 0.1071 - val_accuracy: 0.9838\n",
      "Epoch 10/20\n",
      "1875/1875 [==============================] - ETA: 0s - loss: 0.0989 - accuracy: 0.9853\n",
      "Epoch 10: val_loss improved from 0.09846 to 0.09643, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0989 - accuracy: 0.9853 - val_loss: 0.0964 - val_accuracy: 0.9858\n",
      "Epoch 11/20\n",
      "1874/1875 [============================>.] - ETA: 0s - loss: 0.0962 - accuracy: 0.9860\n",
      "Epoch 11: val_loss did not improve from 0.09643\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0962 - accuracy: 0.9859 - val_loss: 0.0976 - val_accuracy: 0.9854\n",
      "Epoch 12/20\n",
      "1875/1875 [==============================] - ETA: 0s - loss: 0.0946 - accuracy: 0.9866\n",
      "Epoch 12: val_loss improved from 0.09643 to 0.09494, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0946 - accuracy: 0.9866 - val_loss: 0.0949 - val_accuracy: 0.9862\n",
      "Epoch 13/20\n",
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.0918 - accuracy: 0.9870\n",
      "Epoch 13: val_loss improved from 0.09494 to 0.09152, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0919 - accuracy: 0.9869 - val_loss: 0.0915 - val_accuracy: 0.9864\n",
      "Epoch 14/20\n",
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.0899 - accuracy: 0.9868\n",
      "Epoch 14: val_loss improved from 0.09152 to 0.09071, saving model to CompareHyper/15/training_checkpoint.h5\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0898 - accuracy: 0.9869 - val_loss: 0.0907 - val_accuracy: 0.9866\n",
      "Epoch 15/20\n",
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.0892 - accuracy: 0.9873\n",
      "Epoch 15: val_loss did not improve from 0.09071\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0891 - accuracy: 0.9873 - val_loss: 0.0909 - val_accuracy: 0.9871\n",
      "Epoch 16/20\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.0871 - accuracy: 0.9880\n",
      "Epoch 16: val_loss did not improve from 0.09071\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0871 - accuracy: 0.9879 - val_loss: 0.0912 - val_accuracy: 0.9868\n",
      "Epoch 17/20\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.0876 - accuracy: 0.9877\n",
      "Epoch 17: val_loss did not improve from 0.09071\n",
      "Restoring model weights from the end of the best epoch: 14.\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0877 - accuracy: 0.9877 - val_loss: 0.0923 - val_accuracy: 0.9867\n",
      "Epoch 17: early stopping\n"
     ]
    }
   ],
   "source": [
    "# === Training ===\n",
    "tuning_logs = 'CompareHyper/'\n",
    "\n",
    "# Check last model tuning iteration and increment it\n",
    "all_dirs = [d for d in os.listdir(tuning_logs) if os.path.isdir(os.path.join(tuning_logs, d))]\n",
    "all_dirs = list(filter(lambda x: x.isnumeric(), all_dirs))\n",
    "all_dirs = list(map(lambda x: int(x), all_dirs))\n",
    "model_iteration = max(all_dirs, default=0) + 1\n",
    "model_stats_dir = tuning_logs + str(model_iteration) + '/'\n",
    "\n",
    "# Tensorboard\n",
    "log_dir = model_stats_dir + 'logs' + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")  # Directory to save tensorboard logs\n",
    "tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "# Early stopping callback\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)\n",
    "\n",
    "# Checkpoint callback\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "    filepath=model_stats_dir+'training_checkpoint.h5',\n",
    "    monitor='val_loss',     # Monitor the validation loss\n",
    "    verbose=1,              # Log a message when saving\n",
    "    save_best_only=True,    # Only save the best model\n",
    "    mode='min'              # The best model is the one with minimal validation loss\n",
    ")\n",
    "\n",
    "# == TRAIN !!! ==\n",
    "history = model.fit(X_train_tensor, y_train_encoded,\n",
    "                    epochs=20,\n",
    "                    batch_size=32,  # 64 next\n",
    "                    validation_data=(X_valid_tensor, y_valid_encoded),\n",
    "                    callbacks=[tensorboard_callback, checkpoint_callback, early_stopping],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== BEST CHECKPOINT (Validation Loss) ===\n",
      "Epoch: 14\n",
      "Train Acc.: 0.9869\n",
      "Valid. Acc.: 0.9866\n",
      "Train Loss: 0.0898\n",
      "Valid. Loss: 0.0907\n",
      "\n",
      "=== FINAL EPOCH ===\n",
      "Epoch: 17\n",
      "Train Acc.: 0.9877\n",
      "Valid. Acc.: 0.9867\n",
      "Train Loss: 0.0877\n",
      "Valid. Loss: 0.0923\n"
     ]
    }
   ],
   "source": [
    "def extract_metrics(history_data: dict):\n",
    "    # Metrics for final epoch\n",
    "    total_epoch_count = len(history.history['accuracy'])\n",
    "    final_train_acc = history_data['accuracy'][-1]\n",
    "    final_val_acc = history_data['val_accuracy'][-1]\n",
    "    final_train_loss = history_data['loss'][-1]\n",
    "    final_val_loss = history_data['val_loss'][-1]\n",
    "\n",
    "    # Metrics for epoch with best validation loss (i.e., checkpoint)\n",
    "    best_epoch_idx = np.argmin(history_data['val_loss'])\n",
    "    checkpoint_train_acc = history_data['accuracy'][best_epoch_idx]\n",
    "    checkpoint_val_acc = history_data['val_accuracy'][best_epoch_idx]\n",
    "    checkpoint_train_loss = history_data['loss'][best_epoch_idx]\n",
    "    checkpoint_val_loss = history_data['val_loss'][best_epoch_idx]\n",
    "\n",
    "    # Display results\n",
    "    print(\"=== BEST CHECKPOINT (Validation Loss) ===\")\n",
    "    print(f\"Epoch: {best_epoch_idx + 1}\")\n",
    "    print(f\"Train Acc.: {checkpoint_train_acc:.4f}\")\n",
    "    print(f\"Valid. Acc.: {checkpoint_val_acc:.4f}\")\n",
    "    print(f\"Train Loss: {checkpoint_train_loss:.4f}\")\n",
    "    print(f\"Valid. Loss: {checkpoint_val_loss:.4f}\")\n",
    "    \n",
    "    print(\"\\n=== FINAL EPOCH ===\")\n",
    "    print(f\"Epoch: {total_epoch_count}\")\n",
    "    print(f\"Train Acc.: {final_train_acc:.4f}\")\n",
    "    print(f\"Valid. Acc.: {final_val_acc:.4f}\")\n",
    "    print(f\"Train Loss: {final_train_loss:.4f}\")\n",
    "    print(f\"Valid. Loss: {final_val_loss:.4f}\")\n",
    "\n",
    "# Extract metrics, save history, and save final params\n",
    "extract_metrics(history.history)\n",
    "\n",
    "with open(model_stats_dir+'model_history.json', 'w+') as outfile:\n",
    "    json.dump(history.history, outfile, indent=4)\n",
    "\n",
    "model.save(model_stats_dir+'final_epoch_params.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots loss and accuracy for both training and loss\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 5))  # Create a figure with two subplots side by side\n",
    "\n",
    "# Plot training & validation loss values on the first subplot\n",
    "axs[0].plot(history.history['loss'])\n",
    "axs[0].plot(history.history['val_loss'])\n",
    "axs[0].set_title('Model loss')\n",
    "axs[0].set_ylabel('Loss')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "# Plot training & validation accuracy on the second subplot\n",
    "axs[1].plot(history.history['accuracy'])\n",
    "axs[1].plot(history.history['val_accuracy'])\n",
    "axs[1].set_title('Model accuracy')\n",
    "axs[1].set_ylabel('Accuracy')\n",
    "axs[1].set_xlabel('Epoch')\n",
    "axs[1].legend(['Train', 'Validation'], loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation and Trying it\n",
    "Below is the final evaluation on the test set. We achieved a loss of 0.0941, and an accuracy of 0.986, which is a pretty good result. To try out the model, you can run the last cell to randomly pick a drawing from the test it and have the model run inference on it. I include the outputs of every class, so you can see how confident the model was, and what other shapes it might've considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "938/938 [==============================] - 4s 4ms/step - loss: 0.0941 - accuracy: 0.9857\n",
      "938/938 [==============================] - 3s 3ms/step\n",
      "Loss: 0.09414088726043701, Accuracy: 0.9857333302497864\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      circle       0.99      0.99      0.99      6000\n",
      "        line       0.98      0.99      0.98      6000\n",
      "      square       0.99      0.99      0.99      6000\n",
      "        star       0.99      0.98      0.98      6000\n",
      "    triangle       0.98      0.98      0.98      6000\n",
      "\n",
      "    accuracy                           0.99     30000\n",
      "   macro avg       0.99      0.99      0.99     30000\n",
      "weighted avg       0.99      0.99      0.99     30000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# === Evaluations ===\n",
    "\n",
    "loss, accuracy = model.evaluate(X_test_tensor, y_test_encoded)\n",
    "\n",
    "# Evaluations of each class\n",
    "y_pred = model.predict(X_test_tensor)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "\n",
    "print(f'Loss: {loss}, Accuracy: {accuracy}\\n')\n",
    "print(classification_report(y_test_encoded, y_pred_classes, target_names=encoder.classes_))\n",
    "\n",
    "# Loss: 0.09414088726043701, Accuracy: 0.9857333302497864"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 31ms/step\n",
      "\n",
      "                        circle    line    square    star    triangle    \n",
      "Confidence (percentage): 0.0      0.0      0.0      100.0    0.0      \n",
      "Label: star, Prediction: star\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_i = random.randint(0, len(X_test_tensors)-1)\n",
    "img_tensor, label = X_test_tensors[rand_i], y_test_augmented[rand_i]\n",
    "\n",
    "tensor_input = tf.expand_dims(img_tensor, 0)  # Add batch size dimension\n",
    "\n",
    "# Make prediction\n",
    "pred_logits = model.predict(tensor_input)\n",
    "prediction = tf.nn.softmax(pred_logits)\n",
    "probabilities = prediction.numpy()[0]*100\n",
    "highest_choice = np.argmax(prediction)\n",
    "pred_class = encoder_mappings[highest_choice]\n",
    "print()\n",
    "\n",
    "# predictions\n",
    "print('                        ', end='')\n",
    "for index, elem in enumerate(encoder_mappings):\n",
    "    print(elem, end='    ')\n",
    "print()\n",
    "\n",
    "print('Confidence (percentage):', end=' ')\n",
    "for prob in probabilities:\n",
    "    print(f'{prob:<8.1f}', end=' ')\n",
    "    #             <8.3f}\n",
    "print()\n",
    "\n",
    "print(f'Label: {label}, Prediction: {pred_class}')\n",
    "\n",
    "plt.imshow(img_tensor.numpy(), cmap='gray')\n",
    "plt.axis('off')  # Hide axes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion and Reflections\n",
    "In this notebook, we embarked on an image classification journey, aiming to classify drawings using a Convolutional Neural Network (CNN). Through rigorous experimentation, our model achieved a noteworthy accuracy of 98.6% on the test set. This success underscores the efficacy of the CNN model, especially with three convolutional layers. We also found L2 regularization and dropout layers to be particularly effective in mitigating overfitting.\n",
    "\n",
    "However, the journey wasn't without challenges. The model had difficulty towards the end of fine tuning, despite having such a large complete dataset. To advance this work, I strongly believe that performing further data augmentation could improve model robustness and refine accuracy.\n",
    "\n",
    "Thank you for following along this exploration of CNNs in image classification. Thank you for your time and patience in going through the entire notebook, I hope you find it interesting!"
   ]
  }
 ],
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