Digit_Localization_&_Classification_MNIST_Dataset.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Digit Localization & Classification: MNIST Dataset.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z62LitnkCZ7y"
      },
      "source": [
        "# ***Digit Localization & Classification: MNIST Dataset***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjtEf9CnGCFe"
      },
      "source": [
        "## *Import Libraries*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Kji4QuRCxRh"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import sklearn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import PIL, os, re, time, json\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn\n",
        "from tensorflow.keras.models import Model\n",
        "import tensorflow_datasets as tfds\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, AveragePooling2D, Input\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGEJPEk0I37n"
      },
      "source": [
        "## *Visualization Functions: Draw bounding boxes around MNIST Numbers*\n",
        "#### We are preparing a synthsized dataset from MNIST dataset where we are arranging the numbers on a 75 x 75 canvas; to further practice the bounding box technique."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYbtp88nBLV3"
      },
      "source": [
        "image_width = 75\n",
        "image_height = 75\n",
        "use_normalized_coordinates = True\n",
        "\n",
        "# Function 1\n",
        "# Bounding Box on Image Array\n",
        "def draw_bounding_boxes_on_image_array(image, boxes, color=[], thickness=1, display_str_list=()):\n",
        "    '''image: Numpy array object\n",
        "       Boxes: 2D numpy array of [N,4]: (ymin, xmin, ymax, xmax) -- Coordinates are normalized [0,1]\n",
        "       color: Color of bounding box (default: red)\n",
        "       thickness: line thickness (default: 4)\n",
        "       display_str_list: List of strings for each bounding box   '''\n",
        "    image_pil = PIL.Image.fromarray(image)\n",
        "    rgbimg = PIL.Image.new(mode=\"RGBA\", size=image_pil.size)\n",
        "    rgbimg.paste(image_pil)\n",
        "    draw_bounding_boxes_on_image(rgbimg, boxes, color, thickness, display_str_list)\n",
        "    return np.array(rgbimg)\n",
        "\n",
        "# Function 2\n",
        "def draw_bounding_boxes_on_image(image, boxes, color=[], thickness=1, display_str_list=()):\n",
        "    boxes_shape = boxes.shape\n",
        "    if not boxes_shape:\n",
        "        return \n",
        "    if (len(boxes_shape) != 2 or boxes_shape[1] != 4):\n",
        "        raise ValueError('Input must be of format [N,4]')\n",
        "    \n",
        "    for i in range(boxes_shape[0]):\n",
        "        draw_bounding_box_on_image(image, boxes[i, 1], boxes[i, 0], boxes[i, 3],boxes[i,2], color[i], thickness, display_str_list[i])\n",
        "\n",
        "# Function 3\n",
        "def draw_bounding_box_on_image(image, ymin, xmin, ymax, xmax, color='red', thickness=1, display_str = None, use_normalized_coordinates=True):\n",
        "    '''Bounding Box coordinates can be mentioned in absolute(pixels) or \n",
        "    normalized coordinates by setting the use_normalized_coordinates argument\n",
        "    Args:\n",
        "    image: a PIL.Image object.\n",
        "    ymin: ymin of bounding box.\n",
        "    xmin: xmin of bounding box.\n",
        "    ymax: ymax of bounding box.\n",
        "    xmax: xmax of bounding box.\n",
        "    color: color to draw bounding box. Default is red.\n",
        "    thickness: line thickness. Default value is 4.\n",
        "    display_str_list: string to display in box\n",
        "    use_normalized_coordinates: If True (default), treat coordinates\n",
        "      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat\n",
        "      coordinates as absolute.'''\n",
        "    \n",
        "    draw = PIL.ImageDraw.Draw(image)\n",
        "    image_width, image_height = image.size\n",
        "    if (use_normalized_coordinates):\n",
        "        (left, right, top, bottom) = (xmin*image_width, xmax*image_width,\n",
        "                                     ymin*image_height, ymax*image_height)\n",
        "    else:\n",
        "        (left, right, top, bottom) = (xmin, xmax, ymin, ymax)    \n",
        "    # Draw the bounding box like starting from one end and coming a full round to the same end: ALl the coordinates on the path need ot be mentioned in the list\n",
        "    draw.line([(left, top),(left, bottom), (right, bottom), (right, top), (left, top)], width= thickness, fill=color)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUuhjgDPQbAF"
      },
      "source": [
        "## *Visualize the data with boxes and predictions*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaF8-i2tQYE9"
      },
      "source": [
        "# Matplotlib configuration\n",
        "plt.rc('image', cmap='gray')\n",
        "plt.rc('grid', linewidth=0)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "\n",
        "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
        "  \n",
        "  # get one batch from each: 10000 validation digits, N training digits\n",
        "  batch_train_ds = training_dataset.unbatch().batch(N)\n",
        "  \n",
        "  # eager execution: loop through datasets normally\n",
        "  if tf.executing_eagerly():\n",
        "    for validation_digits, (validation_labels, validation_bboxes) in validation_dataset:\n",
        "      validation_digits = validation_digits.numpy()\n",
        "      validation_labels = validation_labels.numpy()\n",
        "      validation_bboxes = validation_bboxes.numpy()\n",
        "      break\n",
        "    for training_digits, (training_labels, training_bboxes) in batch_train_ds:\n",
        "      training_digits = training_digits.numpy()\n",
        "      training_labels = training_labels.numpy()\n",
        "      training_bboxes = training_bboxes.numpy()\n",
        "      break\n",
        "  \n",
        "  # these were one-hot encoded in the dataset\n",
        "  validation_labels = np.argmax(validation_labels, axis=1)\n",
        "  training_labels = np.argmax(training_labels, axis=1)\n",
        "  \n",
        "  return (training_digits, training_labels, training_bboxes,\n",
        "          validation_digits, validation_labels, validation_bboxes)\n",
        "\n",
        "# Create digits from local fonts for testing\n",
        "def create_digits_from_local_fonts(n):\n",
        "  font_labels = []\n",
        "  img = PIL.Image.new('LA', (75*n, 75), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
        "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
        "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
        "  d = PIL.ImageDraw.Draw(img)\n",
        "  for i in range(n):\n",
        "    font_labels.append(i%10)\n",
        "    d.text((7+i*75,0 if i<10 else -4), str(i%10), fill=(255,255), font=font1 if i<10 else font2)\n",
        "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
        "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [75, 75*n]), n, axis=1), axis=0), [n, 75*75])\n",
        "  return font_digits, font_labels\n",
        "\n",
        "def display_digits_with_boxes(digits, predictions, labels, pred_bboxes, bboxes, iou, title):\n",
        "    n = 10\n",
        "    indexes = np.random.choice(len(predictions), size= n)\n",
        "    n_digits = digits[indexes]\n",
        "    n_predictions = predictions[indexes]\n",
        "    n_labels = labels[indexes]\n",
        "    \n",
        "#     pred_bboxes: Bounding boxes as predicted by the model\n",
        "#     bboxes: Labels of bounding boxes\n",
        "    n_iou = []\n",
        "    if (len(iou)>0):\n",
        "        n_iou = iou[indexes]\n",
        "    if (len(pred_bboxes) > 0):\n",
        "        n_pred_bboxes = pred_bboxes[indexes,:]  \n",
        "    if (len(bboxes)>0):\n",
        "        n_bboxes = bboxes[indexes,:]\n",
        "    \n",
        "    n_digits = n_digits*255.0\n",
        "    print(n_digits.shape)\n",
        "    n_digits = n_digits.reshape(n,75,75)\n",
        "    fig = plt.figure(figsize=(20,4))\n",
        "    plt.title(title)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    \n",
        "    # Loop to show the bounding boxes on images\n",
        "    for i in range(n):\n",
        "        ax = fig.add_subplot(1, 10,i+1)\n",
        "        bboxes_to_plot = []\n",
        "        if (len(pred_bboxes) > i):\n",
        "            bboxes_to_plot.append(n_pred_bboxes[i]) \n",
        "        \n",
        "        if (len(bboxes) > i):\n",
        "            bboxes_to_plot.append(n_bboxes[i])\n",
        "        \n",
        "        # Creating the image to be shown with bounding boxes\n",
        "        img_to_draw = draw_bounding_boxes_on_image_array(image=n_digits[i], boxes=np.asarray(bboxes_to_plot), color=['red', 'green'], display_str_list=['true', 'pred'])\n",
        "        plt.xlabel(n_predictions[i])\n",
        "        plt.xticks([])\n",
        "        plt.yticks([])\n",
        "        \n",
        "        if (n_predictions[i] != n_labels[i]):\n",
        "            ax.xaxis.label.set_color('red')\n",
        "        \n",
        "        plt.imshow(img_to_draw)\n",
        "        if (len(iou) > i):\n",
        "            color=\"black\"\n",
        "            if (n_iou[i][0] < iou_threshold):\n",
        "                color=\"red\"\n",
        "            ax.text(0.2, -0.3, \"iou: %s\" %(n_iou[i][0]), color=color, transform=ax.transAxes)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFga1qGSW_--"
      },
      "source": [
        "## *Understanding numpy.array and constructing bounding boxes around a face*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "8A3Za6ChQdBr",
        "outputId": "9e715eec-997b-4168-ae46-20c677738732"
      },
      "source": [
        "pil_img = PIL.Image.open(\"portrait1.jpg\")\n",
        "draw = PIL.ImageDraw.Draw(pil_img)\n",
        "draw.line([(240,20), (240,450),(650,450),(650,20),(240,20)], width =7, fill=\"red\")\n",
        "# font = PIL.ImageFont.truetype(r'C:\\WINDOWS\\FONTS\\BAHNSCHRIFT.TTF',size= 50)\n",
        "draw.text((250,460), text=\"Human\" ,fill=\"red\", align=\"right\")\n",
        "test_img = np.asarray(pil_img)\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.imshow(test_img)\n",
        "plt.show()\n",
        "plt.imsave(\"Linked_portrait.jpg\", test_img)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46pv2G1KXTYz"
      },
      "source": [
        "## *Loading & Preprocessing Dataset*\n",
        "\n",
        "- `read_image_tfds`: randomly overlays the \"digit\" image on top of a larger canvas.\n",
        "- `get_training_dataset`: loads data and splits it to get the training set.\n",
        "- `get_validation_dataset`: loads and splits the data to get the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIUlo8fIXIKs",
        "outputId": "910eef94-3d9b-42bd-d114-9711c5c0bf6c"
      },
      "source": [
        "# Transform the number images in dataset by pasting it on a 75 x 75 canvas at random locations\n",
        "\n",
        "def read_image_tfds(image, label):\n",
        "    xmin = tf.random.uniform((), minval=0, maxval=48, dtype=tf.int32)\n",
        "    ymin = tf.random.uniform((), minval=0, maxval=48, dtype=tf.int32)\n",
        "    print(\"Read_Image_tfds:  Initial Image shape: \",image.shape)\n",
        "    image = tf.reshape(image, (28, 28, 1))\n",
        "    # Padding takes place here \n",
        "    image = tf.image.pad_to_bounding_box(image, ymin, xmin, 75, 75)\n",
        "    # Typecasting to float32 and normalization\n",
        "    image = tf.cast(image, tf.float32)/255.0\n",
        "    xmin = tf.cast(xmin, tf.float32)\n",
        "    ymin = tf.cast(ymin, tf.float32)\n",
        "    \n",
        "    xmax = (xmin + 28)/75\n",
        "    ymax = (ymin + 28)/75\n",
        "    xmin = xmin/75\n",
        "    ymin = ymin/75\n",
        "    return image, (tf.one_hot(label, 10), [xmin, ymin, xmax, ymax])\n",
        "\n",
        "# Loads and maps the training split of the dataset using the map function.\n",
        "def get_training_dataset():\n",
        "    dataset = tfds.load(\"mnist\", split=\"train\", as_supervised=True, try_gcs=True)\n",
        "    dataset = dataset.map(read_image_tfds, num_parallel_calls=16)\n",
        "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    dataset = dataset.batch(64, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
        "    dataset = dataset.prefetch(-1)  # fetch next batches while training on the current one (-1: autotune prefetch buffer size)\n",
        "    return dataset\n",
        "\n",
        "# Loads and maps the validation split of the dataset using the map function.\n",
        "def get_validation_dataset():\n",
        "    dataset = tfds.load(\"mnist\", split=\"test\", as_supervised=True, try_gcs=True)\n",
        "    dataset = dataset.map(read_image_tfds, num_parallel_calls=16)\n",
        "    dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    return dataset\n",
        "    \n",
        "training_dataset = get_training_dataset()\n",
        "validation_dataset = get_validation_dataset()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Read_Image_tfds:  Initial Image shape:  (28, 28, 1)\n",
            "Read_Image_tfds:  Initial Image shape:  (28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svy_xl5ZXukQ"
      },
      "source": [
        "## *Visualize Data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "wT22uqmVXWyz",
        "outputId": "d92e5908-3c64-4fbb-d9ef-d321493f7523"
      },
      "source": [
        "(training_digits, training_labels, training_bboxes,\n",
        "validation_digits, validation_labels, validation_bboxes) = dataset_to_numpy_util(training_dataset, validation_dataset, 10)\n",
        "\n",
        "display_digits_with_boxes(training_digits, training_labels, training_labels, np.array([]), training_bboxes, np.array([]), \"Training digits with labels\")\n",
        "display_digits_with_boxes(validation_digits, validation_labels, validation_labels, np.array([]), validation_bboxes, np.array([]), \"Validation digits with labels\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10, 75, 75, 1)\n",
            "(10, 75, 75, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x288 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9u6aLg5cjt-"
      },
      "source": [
        "## *Model creation*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAZDejEhX8vX",
        "outputId": "cd5e67a6-6677-4f5e-c1dd-ab37948f15a9"
      },
      "source": [
        "inputs = Input(shape= (75, 75, 1))\n",
        "x = Conv2D(16, (3,3), activation='relu', input_shape = (75,75,1))(inputs)\n",
        "x = AveragePooling2D((2,2))(x)\n",
        "\n",
        "x = Conv2D(32, 3, activation='relu')(x)\n",
        "x = AveragePooling2D((2,2))(x)\n",
        "\n",
        "x = Conv2D(64, (3,3), activation='relu')(x)\n",
        "x = AveragePooling2D((2,2))(x)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "\n",
        "classification_output = Dense(10, activation='softmax', name=\"Classification_Output\")(x)\n",
        "bounding_boxes_preds = Dense(4, name=\"Bounding_Box_Predictions\")(x)\n",
        "\n",
        "model = Model(inputs=inputs, outputs = [classification_output, bounding_boxes_preds])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 75, 75, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 73, 73, 16)   160         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 36, 36, 16)   0           conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 34, 34, 32)   4640        average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 17, 17, 32)   0           conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 15, 15, 64)   18496       average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 7, 7, 64)     0           conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 3136)         0           average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 128)          401536      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Classification_Output (Dense)   (None, 10)           1290        dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Bounding_Box_Predictions (Dense (None, 4)            516         dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 426,638\n",
            "Trainable params: 426,638\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g9iJAQs8fRPL",
        "outputId": "dea20fab-b89a-4664-d315-b241e066dd99"
      },
      "source": [
        "keras.utils.plot_model(model, show_layer_names=True, show_shapes=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odfpILpHfmP4"
      },
      "source": [
        "## *Compile and Train model*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuS_LrAlfgsJ"
      },
      "source": [
        "# Compile model\n",
        "\n",
        "model.compile(optimizer='adam', loss={\"Classification_Output\": 'categorical_crossentropy',\n",
        "                                      \"Bounding_Box_Predictions\": 'mse'}, \n",
        "                                metrics = {\"Classification_Output\": 'accuracy',\n",
        "                                           \"Bounding_Box_Predictions\": 'mse'})"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLRRVE9Ngh7N",
        "outputId": "a6d1d073-a6c1-4512-f82f-2f1553c9361f"
      },
      "source": [
        "# Train the model\n",
        "\n",
        "history = model.fit(training_dataset,\n",
        "                    steps_per_epoch=60000//64,\n",
        "                    epochs= 10,\n",
        "                    validation_data= validation_dataset,\n",
        "                    validation_steps=1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "937/937 [==============================] - 20s 19ms/step - loss: 0.9131 - Classification_Output_loss: 0.8980 - Bounding_Box_Predictions_loss: 0.0151 - Classification_Output_accuracy: 0.6915 - Bounding_Box_Predictions_mse: 0.0151 - val_loss: 0.3317 - val_Classification_Output_loss: 0.3219 - val_Bounding_Box_Predictions_loss: 0.0098 - val_Classification_Output_accuracy: 0.9036 - val_Bounding_Box_Predictions_mse: 0.0098\n",
            "Epoch 2/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.2549 - Classification_Output_loss: 0.2487 - Bounding_Box_Predictions_loss: 0.0062 - Classification_Output_accuracy: 0.9244 - Bounding_Box_Predictions_mse: 0.0062 - val_loss: 0.1808 - val_Classification_Output_loss: 0.1762 - val_Bounding_Box_Predictions_loss: 0.0045 - val_Classification_Output_accuracy: 0.9461 - val_Bounding_Box_Predictions_mse: 0.0045\n",
            "Epoch 3/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.1821 - Classification_Output_loss: 0.1784 - Bounding_Box_Predictions_loss: 0.0037 - Classification_Output_accuracy: 0.9462 - Bounding_Box_Predictions_mse: 0.0037 - val_loss: 0.1258 - val_Classification_Output_loss: 0.1230 - val_Bounding_Box_Predictions_loss: 0.0028 - val_Classification_Output_accuracy: 0.9615 - val_Bounding_Box_Predictions_mse: 0.0028\n",
            "Epoch 4/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.1460 - Classification_Output_loss: 0.1435 - Bounding_Box_Predictions_loss: 0.0025 - Classification_Output_accuracy: 0.9556 - Bounding_Box_Predictions_mse: 0.0025 - val_loss: 0.1179 - val_Classification_Output_loss: 0.1156 - val_Bounding_Box_Predictions_loss: 0.0023 - val_Classification_Output_accuracy: 0.9651 - val_Bounding_Box_Predictions_mse: 0.0023\n",
            "Epoch 5/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.1217 - Classification_Output_loss: 0.1198 - Bounding_Box_Predictions_loss: 0.0020 - Classification_Output_accuracy: 0.9633 - Bounding_Box_Predictions_mse: 0.0020 - val_loss: 0.0864 - val_Classification_Output_loss: 0.0848 - val_Bounding_Box_Predictions_loss: 0.0016 - val_Classification_Output_accuracy: 0.9733 - val_Bounding_Box_Predictions_mse: 0.0016\n",
            "Epoch 6/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.1107 - Classification_Output_loss: 0.1091 - Bounding_Box_Predictions_loss: 0.0016 - Classification_Output_accuracy: 0.9666 - Bounding_Box_Predictions_mse: 0.0016 - val_loss: 0.1074 - val_Classification_Output_loss: 0.1059 - val_Bounding_Box_Predictions_loss: 0.0015 - val_Classification_Output_accuracy: 0.9663 - val_Bounding_Box_Predictions_mse: 0.0015\n",
            "Epoch 7/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.0944 - Classification_Output_loss: 0.0930 - Bounding_Box_Predictions_loss: 0.0014 - Classification_Output_accuracy: 0.9715 - Bounding_Box_Predictions_mse: 0.0014 - val_loss: 0.0782 - val_Classification_Output_loss: 0.0770 - val_Bounding_Box_Predictions_loss: 0.0012 - val_Classification_Output_accuracy: 0.9751 - val_Bounding_Box_Predictions_mse: 0.0012\n",
            "Epoch 8/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.0867 - Classification_Output_loss: 0.0855 - Bounding_Box_Predictions_loss: 0.0012 - Classification_Output_accuracy: 0.9738 - Bounding_Box_Predictions_mse: 0.0012 - val_loss: 0.0748 - val_Classification_Output_loss: 0.0736 - val_Bounding_Box_Predictions_loss: 0.0012 - val_Classification_Output_accuracy: 0.9761 - val_Bounding_Box_Predictions_mse: 0.0012\n",
            "Epoch 9/10\n",
            "937/937 [==============================] - 12s 12ms/step - loss: 0.0801 - Classification_Output_loss: 0.0789 - Bounding_Box_Predictions_loss: 0.0012 - Classification_Output_accuracy: 0.9757 - Bounding_Box_Predictions_mse: 0.0012 - val_loss: 0.0755 - val_Classification_Output_loss: 0.0745 - val_Bounding_Box_Predictions_loss: 0.0011 - val_Classification_Output_accuracy: 0.9754 - val_Bounding_Box_Predictions_mse: 0.0011\n",
            "Epoch 10/10\n",
            "937/937 [==============================] - 12s 13ms/step - loss: 0.0743 - Classification_Output_loss: 0.0733 - Bounding_Box_Predictions_loss: 0.0011 - Classification_Output_accuracy: 0.9770 - Bounding_Box_Predictions_mse: 0.0011 - val_loss: 0.0555 - val_Classification_Output_loss: 0.0545 - val_Bounding_Box_Predictions_loss: 0.0010 - val_Classification_Output_accuracy: 0.9832 - val_Bounding_Box_Predictions_mse: 0.0010\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMKFRAAaicR8"
      },
      "source": [
        "## *Testing*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbbdAeEzhpjc",
        "outputId": "88dd2a95-a1a5-435f-97b9-749e4e3ab374"
      },
      "source": [
        "loss, Classification_Output_loss, Bounding_Box_Predictions_loss, Classification_Output_accuracy, Bounding_Box_Predictions_mse = model.evaluate(validation_dataset, steps=1)\n",
        "print(\"Accuracy: \", Classification_Output_accuracy*100,\"%\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 1s 1s/step - loss: 0.0588 - Classification_Output_loss: 0.0578 - Bounding_Box_Predictions_loss: 0.0010 - Classification_Output_accuracy: 0.9812 - Bounding_Box_Predictions_mse: 0.0010\n",
            "Accuracy:  98.11999797821045 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wasleYevmB4O"
      },
      "source": [
        "## *Individual Accuracy Plot*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "qqMM1Ludi3ZJ",
        "outputId": "a4f307ae-219a-4936-f277-07c209047bdf"
      },
      "source": [
        "df = pd.DataFrame(history.history)\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.plot(df[[\"Classification_Output_accuracy\", \"val_Classification_Output_accuracy\"]])\n",
        "plt.legend([\"Classification_Output_accuracy\", \"val_Classification_Output_accuracy\"])\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohA3vC0ZmFdS"
      },
      "source": [
        "## *All-In-One plot*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "TJ8YEEFhlOVN",
        "outputId": "2f7ffe41-f388-440d-b1d9-c99c56c090e4"
      },
      "source": [
        "df = pd.DataFrame(history.history)\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.plot(df)\n",
        "plt.legend(df.columns)\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ik4XKhAnltP"
      },
      "source": [
        "## *Intersection-Over-Union(iou)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQEwBuyNnNnx"
      },
      "source": [
        "def intersection_over_union(pred_box, true_box):\n",
        "    xmin_pred, ymin_pred, xmax_pred, ymax_pred =  np.split(pred_box, 4, axis = 1)\n",
        "    xmin_true, ymin_true, xmax_true, ymax_true = np.split(true_box, 4, axis = 1)\n",
        "\n",
        "    smoothing_factor = 1e-10\n",
        "\n",
        "    # Actual Logic for intersection\n",
        "    xmin_overlap = np.maximum(xmin_pred, xmin_true)\n",
        "    xmax_overlap = np.minimum(xmax_pred, xmax_true)\n",
        "    ymin_overlap = np.maximum(ymin_pred, ymin_true)\n",
        "    ymax_overlap = np.minimum(ymax_pred, ymax_true)\n",
        "\n",
        "    pred_box_area = (xmax_pred - xmin_pred) * (ymax_pred - ymin_pred)\n",
        "    true_box_area = (xmax_true - xmin_true) * (ymax_true - ymin_true)\n",
        "\n",
        "    overlap_area = np.maximum((xmax_overlap - xmin_overlap), 0)  * np.maximum((ymax_overlap - ymin_overlap), 0)\n",
        "    union_area = (pred_box_area + true_box_area) - overlap_area\n",
        "    \n",
        "    iou = (overlap_area + smoothing_factor) / (union_area + smoothing_factor)\n",
        "\n",
        "    return iou"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djricVUeqh8u"
      },
      "source": [
        "## *Visualize model predictions & actual labels*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "nCu3caQbqgGf",
        "outputId": "0fd35309-b0c1-41f4-97ca-b43e7f726bd7"
      },
      "source": [
        "# recognize validation digits\n",
        "predictions = model.predict(validation_digits, batch_size=64)\n",
        "predicted_labels = np.argmax(predictions[0], axis=1)\n",
        "\n",
        "predicted_bboxes = predictions[1]\n",
        "\n",
        "iou = intersection_over_union(predicted_bboxes, validation_bboxes)\n",
        "\n",
        "iou_threshold = 0.6\n",
        "\n",
        "print(\"Number of predictions where iou > threshold(%s): %s\" % (iou_threshold, (iou >= iou_threshold).sum()))\n",
        "print(\"Number of predictions where iou < threshold(%s): %s\" % (iou_threshold, (iou < iou_threshold).sum()))\n",
        "\n",
        "\n",
        "display_digits_with_boxes(validation_digits, predicted_labels, validation_labels, predicted_bboxes, validation_bboxes, iou, \"True and Predicted values\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of predictions where iou > threshold(0.6): 9301\n",
            "Number of predictions where iou < threshold(0.6): 699\n",
            "(10, 75, 75, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7F4TYkPGqwqh"
      },
      "source": [
        "#### **The Red boxes represent the predicted bounding boxes & Green boxes represent the actual bounding boxes**"
      ]
    }
  ]
}