Traffic_Sign_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Traffic Sign Classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZQGKN3d9POI"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2iWMvSm9Ruh"
      },
      "source": [
        "In this project, we will use Convolutional Neural Network to build train and test a traffic sign classification model. We will build this model using tensorflow and keras. It is a multiclass classification problem. This model can be used to make smarter cars."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "wf7-EsX99P7J",
        "outputId": "b0a838e2-872a-437e-db38-d6c4caeb811c"
      },
      "source": [
        "# Run this cell and select the kaggle.json file downloaded from the Kaggle account settings page.\r\n",
        "from google.colab import files\r\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-37d1b6f4-3dd3-4f24-b0ee-30c477bb05a7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-37d1b6f4-3dd3-4f24-b0ee-30c477bb05a7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"pianalytix\",\"key\":\"e384e332b6a329a802893747ab6d06db\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q93aviRocggV"
      },
      "source": [
        "We will start by connecting to Kaggle using Kaggle API which can be downloaded from your Kaggle account's settings and uploading it here(upload box)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6t6UlzY9ZPH"
      },
      "source": [
        "# Next, install the Kaggle API client.\r\n",
        "!pip install -q kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgZNe-6idYTM"
      },
      "source": [
        "Installing kaggle api using pip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdN3ZFKS9bzc"
      },
      "source": [
        "# The Kaggle API client expects this file to be in ~/.kaggle, so move it there.\r\n",
        "!mkdir -p ~/.kaggle\r\n",
        "!cp kaggle.json ~/.kaggle/\r\n",
        "\r\n",
        "# This permissions change avoids a warning on Kaggle tool startup.\r\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlTVvA3gck_p"
      },
      "source": [
        "Setting up Kaggle using Kaggle API."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEWoKy939e0S",
        "outputId": "52f7f027-ac8a-4575-f514-f86553882653"
      },
      "source": [
        "# Creating directory and changing the current working directory\r\n",
        "!mkdir traffic_sign_dataset\r\n",
        "%cd traffic_sign_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/traffic_sign_dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJKt8mPDcpV-"
      },
      "source": [
        "To store the data we will create a new directory and make it as current working directory."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzGxQr3T9e_q",
        "outputId": "af843f4e-f603-476a-8426-edf55ca4cad1"
      },
      "source": [
        "# Searching for dataset\r\n",
        "!kaggle datasets list -s gtsrb-german-traffic-sign"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.4)\n",
            "ref                                                         title                                               size  lastUpdated          downloadCount  \n",
            "----------------------------------------------------------  -------------------------------------------------  -----  -------------------  -------------  \n",
            "meowmeowmeowmeowmeow/gtsrb-german-traffic-sign              GTSRB - German Traffic Sign Recognition Benchmark  612MB  2018-11-25 18:12:34          21665  \n",
            "valentynsichkar/traffic-signs-preprocessed                  Traffic Signs Preprocessed                           4GB  2019-08-31 18:22:11           4383  \n",
            "eunjurho/german-traffic-sign-recognition-benchmark-cropped  german_traffic_sign_recognition_benchmark_cropped  206MB  2019-11-10 15:22:13            139  \n",
            "riyajoshi30/traffic                                         Traffic                                             12KB  2020-10-26 06:36:23              0  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKrC4jXFcuBi"
      },
      "source": [
        "Searching Kaggle for the required dataset using search option(-s) with title 'dogbreedidfromcomp'. We can also use different search options like searching competitions, notebooks, kernels, datasets, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJdl3GIP9fCs",
        "outputId": "e6320bcf-7d0b-4862-f782-fde599ee581a"
      },
      "source": [
        "# Downloading dataset and coming out of directory\r\n",
        "!kaggle datasets download meowmeowmeowmeowmeow/gtsrb-german-traffic-sign  \r\n",
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading gtsrb-german-traffic-sign.zip to /content/traffic_sign_dataset\n",
            " 99% 608M/612M [00:06<00:00, 99.8MB/s]\n",
            "100% 612M/612M [00:06<00:00, 99.4MB/s]\n",
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Jrsy575cx7e"
      },
      "source": [
        "After searching the data next step would be downloading the data into collab notebook using references found in search option."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOHRCQHJ9zu0",
        "outputId": "868dbc6e-93e1-4523-d9bf-9b58ae60c6db"
      },
      "source": [
        "# Unzipping downloaded file and removing unusable file\r\n",
        "!unzip traffic_sign_dataset/gtsrb-german-traffic-sign.zip -d traffic_sign_dataset\r\n",
        "!rm traffic_sign_dataset/gtsrb-german-traffic-sign.zip\r\n",
        "!rm -rf traffic_sign_dataset/Meta\r\n",
        "!rm -rf traffic_sign_dataset/meta\r\n",
        "!rm -rf traffic_sign_dataset/test\r\n",
        "!rm -rf traffic_sign_dataset/train\r\n",
        "!rm traffic_sign_dataset/Meta.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00010.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00011.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00012.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00013.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00014.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00015.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00016.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00017.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00018.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00019.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00020.png  \n",
            "  inflating: traffic_sign_dataset/train/5/00005_00053_00021.png  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5oG3Q8Ac9lA"
      },
      "source": [
        "We will unzip the data which is downloaded and remove the irrelevant files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnDWKmh39zx4"
      },
      "source": [
        "# Importing libraries\r\n",
        "import os\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from matplotlib.image import imread\r\n",
        "import seaborn as sns\r\n",
        "import random\r\n",
        "from PIL import Image\r\n",
        "from sklearn.model_selection import  train_test_split\r\n",
        "from tensorflow.keras.utils import to_categorical\r\n",
        "import tensorflow as tf\r\n",
        "from tensorflow.keras.models import Sequential\r\n",
        "from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPool2D"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RP-tswFNdrjj"
      },
      "source": [
        "Importing required libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        },
        "id": "3_ERdvte_YIw",
        "outputId": "f9222468-19bb-4f30-aa1d-5bc263bb8ae8"
      },
      "source": [
        "# Plotting 12 images to check dataset\r\n",
        "plt.figure(figsize=(12,12))\r\n",
        "path = \"traffic_sign_dataset/Test\"\r\n",
        "for i in range(1,17):\r\n",
        "    plt.subplot(4,4,i)\r\n",
        "    plt.tight_layout()\r\n",
        "    rand_img = imread(path +'/'+ random.choice(sorted(os.listdir(path))))\r\n",
        "    plt.imshow(rand_img)\r\n",
        "    plt.xlabel(rand_img.shape[1], fontsize = 10)#width of image\r\n",
        "    plt.ylabel(rand_img.shape[0], fontsize = 10)#height of image"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDzMeI-9d5Db"
      },
      "source": [
        "Visualizing some images of traffic sign from the test dataset. we can see here that the dimension of images are uneven."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaihvai8BTxH"
      },
      "source": [
        "# As size of images are different we have to make them equal so we will take mean of dimanesions\r\n",
        "dim1 = []\r\n",
        "dim2 = []\r\n",
        "\r\n",
        "for i in range(0,43):\r\n",
        "    labels = 'traffic_sign_dataset/Train' + '/{0}'.format(i)\r\n",
        "    image_path = os.listdir(labels)\r\n",
        "    for x in image_path:\r\n",
        "        img = imread(labels + '/' + x)\r\n",
        "        dim1.append(img.shape[0])\r\n",
        "        dim2.append(img.shape[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6-4uIV2edAf"
      },
      "source": [
        "For further processing we will require the images of same dimension. So, we will start storing the dimension of all the images from training dataset from all 43 classes.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k0kjKHqDYvZ",
        "outputId": "bd5fd445-f221-46f1-db54-8110e29b0d17"
      },
      "source": [
        "#Printing mean dimension of images\r\n",
        "print(\"Dimension 1 Mean : \",np.mean(dim1), \" Dimension 2 Mean : \",np.mean(dim2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimension 1 Mean :  50.328929582493814  Dimension 2 Mean :  50.83587951745773\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xm6X4AIfIdG"
      },
      "source": [
        "Now we will find out the mean value of both the dimensions and analyse them. Here, we can see that (50,50) is the average shape for all the images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYlG6oahDsgn"
      },
      "source": [
        "# Now we will reshape the images to (50,50)\r\n",
        "images = []\r\n",
        "label_id = []\r\n",
        "\r\n",
        "for i in range(43):\r\n",
        "    labels = 'traffic_sign_dataset/Train' + '/{0}'.format(i)\r\n",
        "    image_path = os.listdir(labels)\r\n",
        "    for x in image_path:\r\n",
        "        img = Image.open(labels + '/' + x)\r\n",
        "        img = img.resize((50,50))\r\n",
        "        img = np.array(img)\r\n",
        "        images.append(img)\r\n",
        "        label_id.append(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zr8Ip_8giBv"
      },
      "source": [
        "Now we will reshape the images into (50,50) and also store their label ids."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbY9MSaaEFeq"
      },
      "source": [
        "#Converting images into numpy array\r\n",
        "images = np.array(images)\r\n",
        "#The pixel value of each image ranges between 0 and 255\r\n",
        "#Dividing each image by 255 will scale the values between 0 and 1. This is also known as normalization.\r\n",
        "images = images/255 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p04xcb7cg01H"
      },
      "source": [
        "Now we will convert all the images into numpy array and normalize them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spCgQYCMESBQ",
        "outputId": "04b66171-95ef-4054-e06f-ef82170cb0e8"
      },
      "source": [
        "label_id = np.array(label_id)\r\n",
        "label_id.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39209,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKQomtbYg-_0"
      },
      "source": [
        "Storing the label ids into numpy array and printing the shape. Here we can observe that their are 39209 label ids."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQZXYvNzEVkw",
        "outputId": "819ffeb8-63bb-4fb0-9172-3dd99b0b207e"
      },
      "source": [
        "images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39209, 50, 50, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WDpgwyMhNpV"
      },
      "source": [
        "Checking the shape of the images. Here we can see that their are 39209 images with a shape of (50,50,3.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WC2sIMmEWYU",
        "outputId": "659e54bb-ffd6-44f1-cf69-2790c6481491"
      },
      "source": [
        "# Visualize the number of classes count\r\n",
        "label_counts = pd.DataFrame(label_id).value_counts()\r\n",
        "label_counts.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     2250\n",
              "1     2220\n",
              "13    2160\n",
              "12    2100\n",
              "38    2070\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7k6aiXfhrjs"
      },
      "source": [
        "Now we will observe images per class for checking whether the data is balanced or not. From the result we can say that data is balanced."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2hWQ3YIEc-o"
      },
      "source": [
        "#Splitting the data\r\n",
        "x_train, x_val, y_train, y_val = train_test_split(images, label_id , test_size = 0.2, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAoDpM03iqqI"
      },
      "source": [
        "The next step would be to split the data into training and validation with 80% of training data and 20% of validation data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdnFqZybEupr"
      },
      "source": [
        "#keras has a built-in function for one-hot encoding.\r\n",
        "y_train_cat = to_categorical(y_train)\r\n",
        "y_val_cat = to_categorical(y_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hfn9rGf1kGPv"
      },
      "source": [
        "Converting the classes column into categorical using to_categorical() function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_d1-ckeE2aw"
      },
      "source": [
        "model = Sequential()\r\n",
        "\r\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3), input_shape = x_train.shape[1:], activation = 'relu', padding = 'same'))\r\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\r\n",
        "model.add(Dropout(0.5))\r\n",
        "\r\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu'))\r\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\r\n",
        "model.add(Dropout(0.5))\r\n",
        "\r\n",
        "model.add(Flatten())\r\n",
        "model.add(Dense(128, activation = 'relu'))\r\n",
        "model.add(Dropout(0.5))\r\n",
        "model.add(Dense(43, activation = 'softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6UdL6JTvkW8E"
      },
      "source": [
        "Defining the model architecture. In this we will define all the layers with their input shape kernel size, activation, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0EWaNciJry-",
        "outputId": "6e4687ff-7de3-4183-d3b3-69f0646c9b53"
      },
      "source": [
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\r\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 50, 50, 64)        1792      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 25, 25, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 25, 25, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 23, 23, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 7744)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               991360    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 43)                5547      \n",
            "=================================================================\n",
            "Total params: 1,035,627\n",
            "Trainable params: 1,035,627\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl_aHjtOk9tw"
      },
      "source": [
        "Compiling the model using metrics, optimizer and loss as required and printing out the summary of the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocYgOv-0JsfT",
        "outputId": "bb26859e-491d-4bc2-cc60-9f3303539689"
      },
      "source": [
        "model.fit(x_train, y_train, epochs = 10, batch_size = 128, validation_data = (x_val, y_val), verbose = 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "246/246 - 184s - loss: 0.4137 - accuracy: 0.8650 - val_loss: 0.1094 - val_accuracy: 0.9783\n",
            "Epoch 2/10\n",
            "246/246 - 182s - loss: 0.3698 - accuracy: 0.8785 - val_loss: 0.0961 - val_accuracy: 0.9802\n",
            "Epoch 3/10\n",
            "246/246 - 182s - loss: 0.3428 - accuracy: 0.8871 - val_loss: 0.0826 - val_accuracy: 0.9832\n",
            "Epoch 4/10\n",
            "246/246 - 181s - loss: 0.3144 - accuracy: 0.8966 - val_loss: 0.0774 - val_accuracy: 0.9843\n",
            "Epoch 5/10\n",
            "246/246 - 183s - loss: 0.2891 - accuracy: 0.9043 - val_loss: 0.0681 - val_accuracy: 0.9856\n",
            "Epoch 6/10\n",
            "246/246 - 181s - loss: 0.2738 - accuracy: 0.9083 - val_loss: 0.0622 - val_accuracy: 0.9874\n",
            "Epoch 7/10\n",
            "246/246 - 182s - loss: 0.2535 - accuracy: 0.9163 - val_loss: 0.0671 - val_accuracy: 0.9879\n",
            "Epoch 8/10\n",
            "246/246 - 183s - loss: 0.2403 - accuracy: 0.9220 - val_loss: 0.0533 - val_accuracy: 0.9899\n",
            "Epoch 9/10\n",
            "246/246 - 181s - loss: 0.2325 - accuracy: 0.9229 - val_loss: 0.0545 - val_accuracy: 0.9902\n",
            "Epoch 10/10\n",
            "246/246 - 182s - loss: 0.2222 - accuracy: 0.9277 - val_loss: 0.0532 - val_accuracy: 0.9904\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f568f9c1400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoG9cFlhlMOC"
      },
      "source": [
        "Now we will fit the model and observe how our is getting trained on each epoch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "doO1VovGJ5i0",
        "outputId": "59121960-cb78-401f-dd0d-82480c4dea2a"
      },
      "source": [
        "evaluation = pd.DataFrame(model.history.history)\r\n",
        "evaluation[['accuracy', 'val_accuracy']].plot()\r\n",
        "evaluation[['loss', 'val_loss']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f568f07c160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LKSAcQGl_mJ"
      },
      "source": [
        "Next we will visualize the accuracy and loss per epoch. For this we will store the model history in the pandas dataframe and plot them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ixsgr0jlQTFP"
      },
      "source": [
        "test_path = 'traffic_sign_dataset/Test'\r\n",
        "!rm traffic_sign_dataset/Test/GT-final_test.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kokV342Nmlkk"
      },
      "source": [
        "Creating the variable which has path of test dataset. As we downloded the dataset we found out that their is a GT-final_test.csv file in the test images folder which cannot be processed. So, we will remove that file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TXtpJbTQXFU"
      },
      "source": [
        "#defining a function that will scale images\r\n",
        "from PIL import Image\r\n",
        "\r\n",
        "def scaling(test_images, test_path):\r\n",
        "    images = []\r\n",
        "\r\n",
        "    image_path = test_images\r\n",
        "    \r\n",
        "    for x in image_path:\r\n",
        "        img = Image.open(test_path + '/' + x)\r\n",
        "        img = img.resize((50,50))\r\n",
        "        img = np.array(img)\r\n",
        "        images.append(img)\r\n",
        "\r\n",
        "    #Converting images into numpy array\r\n",
        "    images = np.array(images)\r\n",
        "    #The pixel value of each image ranges between 0 and 255\r\n",
        "    #Dividing each image by 255 will scale the values between 0 and 1. This is also known as normalization.\r\n",
        "    images = images/255\r\n",
        "\r\n",
        "    return images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfonPVJznMRB"
      },
      "source": [
        "Next step would we creating a function to resize the test images converting them into a numpy array and normalize them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0drYIKsmQcga"
      },
      "source": [
        "test_images = scaling(sorted(os.listdir(test_path)),test_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pdn-LHM2nfx-"
      },
      "source": [
        "Calling the above created function on test images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AB0nhfiDQc6C",
        "outputId": "3d5e4307-a7c1-4ec6-b532-5f2884fc582e"
      },
      "source": [
        "test = pd.read_csv('traffic_sign_dataset/Test.csv')\r\n",
        "y_test = test['ClassId'].values\r\n",
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16,  1, 38, ...,  6,  7, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSLIQkB5no2Q"
      },
      "source": [
        "Next we will read label ids from Test.csv and store the values of the class id in y_test variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4OjbwyGQjcw",
        "outputId": "832fcc6d-3dcb-4214-846d-a565296a6789"
      },
      "source": [
        "y_pred = model.predict_classes(test_images);\r\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16,  1, 38, ..., 38,  7, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zu23-QJzoOFL"
      },
      "source": [
        "Now we will use the model to make predictions on our test images and save them in y_pred."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diVVLWzvEGNu"
      },
      "source": [
        "# Storing all lables\r\n",
        "all_lables = ['Speed limit (20km/h)','Speed limit (30km/h)','Speed limit (50km/h)','Speed limit (60km/h)',\r\n",
        "              'Speed limit (70km/h)','Speed limit (80km/h)','End of speed limit (80km/h)','Speed limit (100km/h)',\r\n",
        "              'Speed limit (120km/h)','No passing','No passing for vechiles over 3.5 metric tons',\r\n",
        "              'Right-of-way at the next intersection','Priority road','Yield','Stop','No vechiles',\r\n",
        "              'Vechiles over 3.5 metric tons prohibited','No entry','General caution','Dangerous curve to the left',\r\n",
        "              'Dangerous curve to the right','Double curve','Bumpy road','Slippery road','Road narrows on the right',\r\n",
        "              'Road work','Traffic signals','Pedestrians','Children crossing','Bicycles crossing','Beware of ice/snow',\r\n",
        "              'Wild animals crossing','End of all speed and passing limits','Turn right ahead','Turn left ahead',\r\n",
        "              'Ahead only','Go straight or right','Go straight or left','Keep right','Keep left','Roundabout mandatory',\r\n",
        "              'End of no passing','End of no passing by vechiles over 3.5 metric']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WoULg2ysot7D"
      },
      "source": [
        "Storing the labels according to the image classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKCGS13aQp-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62
        },
        "outputId": "e34476cc-e15c-4e61-a0c8-bcd95af328a2"
      },
      "source": [
        "# Visualize test image\r\n",
        "img = Image.open(test_path + '/00001.png')\r\n",
        "img"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=42x45 at 0x7F568F002DD8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JqaYFECo373"
      },
      "source": [
        "Let's visualize test image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agT8igHnHNDj",
        "outputId": "ba309936-1f18-4b3d-e462-fdbaa879a52d"
      },
      "source": [
        "# Original label\r\n",
        "print(\"Original label : \",all_lables[y_test[1]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original label :  Speed limit (30km/h)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIyb3C-XpEGZ"
      },
      "source": [
        "Finding out original label for the image above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9YQO0jqIj8j",
        "outputId": "609995fd-6f3d-43ed-86cb-b555e4df01e3"
      },
      "source": [
        "# Predicted label\r\n",
        "print(\"Predicted label : \",all_lables[y_pred[1]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted label :  Speed limit (30km/h)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pBItNkDZpMK0"
      },
      "source": [
        "Finding out the predicted label for the image above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwHeXF2OpWXi"
      },
      "source": [
        "## Conclusion:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcTEz8_dpWaw"
      },
      "source": [
        "We started with downloading the dataset, preprocessing it, created the model and found out the predictions using the model. During preprocessing we found that this dataset has 43 classes. Model reached an accuracy of 95%+ in just 50 epochs, we can further optimize the model using hyper parameter tuning and reach a higher accuracy. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxAFt2A1qKWM"
      },
      "source": [
        "# Scope:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M__KNJw4qMKT"
      },
      "source": [
        "This model can be used in self driving cars which will enable them to automatically recognize traffic signs similarly the driver alert system inside cars will help and protect drivers by understanding the traffic signs around them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INSToaqiIyJv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}