Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfaba980",
   "metadata": {},
   "source": [
    "### Step 1 : Install Libraries\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93c3ef62",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
      "  Using cached tensorflow-2.10.0-cp310-cp310-win_amd64.whl (455.9 MB)\n",
      "Requirement already satisfied: pandas in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (1.4.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (1.22.2)\n",
      "Requirement already satisfied: sklearn in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (0.0)\n",
      "Requirement already satisfied: keras in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (2.10.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (2.1.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (1.14.1)\n",
      "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (0.27.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (21.3)\n",
      "\n",
      "Requirement already satisfied: setuptools in c:\\program files\\windowsapps\\pythonsoftwarefoundation.python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\\lib\\site-packages (from tensorflow) (63.2.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (3.7.0)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (1.3.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (4.1.1)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (3.19.6)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (14.0.6)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: tensorboard<2.11,>=2.10 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (2.10.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (22.10.26)\n",
      "Requirement already satisfied: tensorflow-estimator<2.11,>=2.10.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (2.10.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorflow) (1.50.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from sklearn) (1.1.1)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\patel\\\\AppData\\\\Local\\\\Packages\\\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\\\LocalCache\\\\local-packages\\\\Python310\\\\site-packages\\\\tensorflow\\\\include\\\\external\\\\com_github_grpc_grpc\\\\src\\\\core\\\\ext\\\\filters\\\\client_channel\\\\lb_policy\\\\grpclb\\\\client_load_reporting_filter.h'\n",
      "HINT: This error might have occurred since this system does not have Windows Long Path support enabled. You can find information on how to enable this at https://pip.pypa.io/warnings/enable-long-paths\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (2.14.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (2.0.2)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (2.27.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from packaging->tensorflow) (3.0.7)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn->sklearn) (1.1.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn->sklearn) (1.8.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn->sklearn) (3.1.0)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (5.2.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (4.9)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (2.0.12)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (1.26.8)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\patel\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow) (3.2.2)\n",
      "Installing collected packages: tensorflow\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 22.3.1\n",
      "[notice] To update, run: C:\\Users\\patel\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow pandas numpy sklearn keras\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb86e36c",
   "metadata": {},
   "source": [
    "### Step 2: Import Libarires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2cd98b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from PIL import Image\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44ec611",
   "metadata": {},
   "source": [
    "### Step 3 : Add Training Data Set into Program and Normalize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14411aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39209, 30, 30, 3) (39209,)\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "labels = []\n",
    "classes = 43\n",
    "cur_path = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\Train\\\\\"\n",
    "data_folder = 'data'\n",
    "\n",
    "# Loading training dataset\n",
    "for i in range(classes):\n",
    "    path = os.path.join(cur_path,str(i))\n",
    "    images = os.listdir(path)\n",
    "\n",
    "    for a in images:\n",
    "        try:\n",
    "            image = Image.open(path + '\\\\'+ a)\n",
    "            image = image.resize((30,30))\n",
    "            image = np.array(image)\n",
    "            data.append(image)\n",
    "            labels.append(i)\n",
    "        except:\n",
    "            print(\"Error loading image\")\n",
    "\n",
    "# Converting lists into numpy arrays\n",
    "data = np.array(data)\n",
    "labels = np.array(labels)\n",
    "\n",
    "# Normalizing data via Min-Max normalizer\n",
    "scaler = MinMaxScaler()\n",
    "ascolumns = data.reshape(-1, 3)\n",
    "t = scaler.fit_transform(ascolumns)\n",
    "data = t.reshape(data.shape)\n",
    "print(data.shape, labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef5703bc",
   "metadata": {},
   "source": [
    "### Step 3: Split Data into Train Set and Validation Set (Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88be1ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(data, labels, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d11427b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31367, 30, 30, 3) (7842, 30, 30, 3) (31367,) (7842,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf33a337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the labels into one hot encoding\n",
    "y_train = to_categorical(y_train, 43)\n",
    "y_val = to_categorical(y_val, 43)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893e6f99",
   "metadata": {},
   "source": [
    "### Step 4: Model Build Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db9e1223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_5 (Conv2D)           (None, 26, 26, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 13, 13, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 5, 5, 64)          0         \n",
      "                                                                 \n",
      " flatten_4 (Flatten)         (None, 1600)              0         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 256)               409856    \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 43)                11051     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 441,835\n",
      "Trainable params: 441,835\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "# Building the model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.25))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(rate=0.5))\n",
    "model.add(Dense(43, activation='softmax'))\n",
    "model.summary()\n",
    "print(len(model.layers))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a372f288",
   "metadata": {},
   "source": [
    "### Step 5: Compilation of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cf92b30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "981/981 [==============================] - 26s 25ms/step - loss: 1.4013 - accuracy: 0.6081 - val_loss: 0.2773 - val_accuracy: 0.9401\n",
      "Epoch 2/15\n",
      "981/981 [==============================] - 25s 26ms/step - loss: 0.3786 - accuracy: 0.8848 - val_loss: 0.1121 - val_accuracy: 0.9772\n",
      "Epoch 3/15\n",
      "981/981 [==============================] - 26s 27ms/step - loss: 0.2336 - accuracy: 0.9286 - val_loss: 0.0707 - val_accuracy: 0.9860\n",
      "Epoch 4/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.1697 - accuracy: 0.9476 - val_loss: 0.0493 - val_accuracy: 0.9903\n",
      "Epoch 5/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.1417 - accuracy: 0.9559 - val_loss: 0.0328 - val_accuracy: 0.9930\n",
      "Epoch 6/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.1172 - accuracy: 0.9629 - val_loss: 0.0316 - val_accuracy: 0.9918\n",
      "Epoch 7/15\n",
      "981/981 [==============================] - 26s 27ms/step - loss: 0.1009 - accuracy: 0.9684 - val_loss: 0.0292 - val_accuracy: 0.9929\n",
      "Epoch 8/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.0983 - accuracy: 0.9691 - val_loss: 0.0254 - val_accuracy: 0.9938\n",
      "Epoch 9/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.0883 - accuracy: 0.9712 - val_loss: 0.0232 - val_accuracy: 0.9943\n",
      "Epoch 10/15\n",
      "981/981 [==============================] - 27s 28ms/step - loss: 0.0775 - accuracy: 0.9758 - val_loss: 0.0267 - val_accuracy: 0.9935\n",
      "Epoch 11/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.0809 - accuracy: 0.9752 - val_loss: 0.0229 - val_accuracy: 0.9944\n",
      "Epoch 12/15\n",
      "981/981 [==============================] - 26s 27ms/step - loss: 0.0758 - accuracy: 0.9765 - val_loss: 0.0200 - val_accuracy: 0.9945\n",
      "Epoch 13/15\n",
      "981/981 [==============================] - 26s 27ms/step - loss: 0.0696 - accuracy: 0.9789 - val_loss: 0.0240 - val_accuracy: 0.9944\n",
      "Epoch 14/15\n",
      "981/981 [==============================] - 27s 27ms/step - loss: 0.0681 - accuracy: 0.9796 - val_loss: 0.0249 - val_accuracy: 0.9941\n",
      "Epoch 15/15\n",
      "981/981 [==============================] - 26s 26ms/step - loss: 0.0692 - accuracy: 0.9781 - val_loss: 0.0213 - val_accuracy: 0.9954\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "epochs = 15\n",
    "history = model.fit(X_train, y_train, batch_size=32, epochs=epochs, validation_data=(X_val, y_val))\n",
    "model.save('traffic_classifier.h5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ba0db4",
   "metadata": {},
   "source": [
    "### Plotting graphs for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eacf0df1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0)\n",
    "plt.plot(history.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history.history['loss'], label='training loss')\n",
    "plt.plot(history.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5606f02a",
   "metadata": {},
   "source": [
    "### Step 6:Import Test Data for Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d7a7022c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing the model\n",
    "test_path = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "path = os.path.join(test_path)\n",
    "data = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "y_test = pd.read_csv(data +'Test.csv')\n",
    "labels = y_test[\"ClassId\"].values\n",
    "imgs = y_test[\"Path\"].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for img in imgs:\n",
    "    image = Image.open(path + img)\n",
    "    image = image.resize((30,30))\n",
    "    data.append(np.array(image))\n",
    "X_test = np.array(data)\n",
    "\n",
    "# Normalizing test set\n",
    "ascolumns = X_test.reshape(-1, 3)\n",
    "t = scaler.transform(ascolumns)\n",
    "X_test = t.reshape(X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1d869e",
   "metadata": {},
   "source": [
    "### Step 7: Prediction and Accuracy on Test Data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "345ab920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 2s 5ms/step\n",
      "Confusion Matrix:\n",
      "[[ 45   9   0 ...   0   0   0]\n",
      " [  0 717   2 ...   0   0   0]\n",
      " [  0   7 741 ...   1   0   0]\n",
      " ...\n",
      " [  0   0   0 ...  89   0   0]\n",
      " [  0   0   0 ...   0  51   0]\n",
      " [  0   0   0 ...   0   0  90]]\n",
      "Accuracy: 0.962866\n",
      "Precision: 0.949239\n",
      "Recall: 0.944408\n",
      "F1 score: 0.944361\n"
     ]
    }
   ],
   "source": [
    "# Predicting on test set\n",
    "pred = np.argmax(model.predict(X_test),axis=1)\n",
    "cm = confusion_matrix(labels, pred)\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(labels, pred)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(labels, pred, average='macro')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(labels, pred, average='macro')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(labels, pred, average='macro')\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7512565d",
   "metadata": {},
   "source": [
    "### Model with Different Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "85a8c16b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_7 (Conv2D)           (None, 26, 26, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPooling  (None, 13, 13, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " flatten_5 (Flatten)         (None, 5408)              0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 256)               1384704   \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 43)                11051     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,398,187\n",
      "Trainable params: 1,398,187\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "7\n",
      "Epoch 1/10\n",
      "981/981 [==============================] - 22s 22ms/step - loss: 1.2357 - accuracy: 0.6594 - val_loss: 0.3144 - val_accuracy: 0.9300\n",
      "Epoch 2/10\n",
      "981/981 [==============================] - 22s 22ms/step - loss: 0.4283 - accuracy: 0.8760 - val_loss: 0.1893 - val_accuracy: 0.9527\n",
      "Epoch 3/10\n",
      "981/981 [==============================] - 23s 23ms/step - loss: 0.2988 - accuracy: 0.9122 - val_loss: 0.1409 - val_accuracy: 0.9676\n",
      "Epoch 4/10\n",
      "981/981 [==============================] - 22s 22ms/step - loss: 0.2366 - accuracy: 0.9299 - val_loss: 0.1015 - val_accuracy: 0.9748\n",
      "Epoch 5/10\n",
      "981/981 [==============================] - 22s 23ms/step - loss: 0.1972 - accuracy: 0.9406 - val_loss: 0.0936 - val_accuracy: 0.9772\n",
      "Epoch 6/10\n",
      "981/981 [==============================] - 22s 23ms/step - loss: 0.1743 - accuracy: 0.9480 - val_loss: 0.0903 - val_accuracy: 0.9832\n",
      "Epoch 7/10\n",
      "981/981 [==============================] - 22s 22ms/step - loss: 0.1669 - accuracy: 0.9500 - val_loss: 0.0691 - val_accuracy: 0.9838\n",
      "Epoch 8/10\n",
      "981/981 [==============================] - 22s 22ms/step - loss: 0.1446 - accuracy: 0.9571 - val_loss: 0.0774 - val_accuracy: 0.9832\n",
      "Epoch 9/10\n",
      "981/981 [==============================] - 22s 22ms/step - loss: 0.1436 - accuracy: 0.9577 - val_loss: 0.0527 - val_accuracy: 0.9878\n",
      "Epoch 10/10\n",
      "981/981 [==============================] - 23s 23ms/step - loss: 0.1265 - accuracy: 0.9631 - val_loss: 0.0566 - val_accuracy: 0.9874\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 2s 5ms/step\n",
      "Confusion Matrix:\n",
      "[[ 46   5   0 ...   0   0   0]\n",
      " [  1 665  28 ...   0   0   0]\n",
      " [  0  13 705 ...   0   0   0]\n",
      " ...\n",
      " [  0   0   0 ...  82   0   0]\n",
      " [  0   0   0 ...   0  44   0]\n",
      " [  0   0   0 ...   0   1  89]]\n",
      "Accuracy: 0.931591\n",
      "Precision: 0.920880\n",
      "Recall: 0.902539\n",
      "F1 score: 0.906067\n"
     ]
    }
   ],
   "source": [
    "# Building the model_1\n",
    "model_1 = Sequential()\n",
    "model_1.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_1.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_1.add(Dropout(rate=0.25))\n",
    "model_1.add(Flatten())\n",
    "model_1.add(Dense(256, activation='relu'))\n",
    "model_1.add(Dropout(rate=0.5))\n",
    "model_1.add(Dense(43, activation='softmax'))\n",
    "model_1.summary()\n",
    "print(len(model_1.layers))\n",
    "\n",
    "\n",
    "# Compilation of the model_1\n",
    "model_1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "epochs = 10\n",
    "history_1 = model_1.fit(X_train, y_train, batch_size=32, epochs=epochs, validation_data=(X_val, y_val))\n",
    "model_1.save('traffic_classifier_1.h5')\n",
    "\n",
    "# Plotting graphs for accuracy\n",
    "plt.figure(0)\n",
    "plt.plot(history_1.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history_1.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history_1.history['loss'], label='training loss')\n",
    "plt.plot(history_1.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Testing the model_1\n",
    "test_path = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "path = os.path.join(test_path)\n",
    "data = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "y_test = pd.read_csv(data +'Test.csv')\n",
    "labels = y_test[\"ClassId\"].values\n",
    "imgs = y_test[\"Path\"].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for img in imgs:\n",
    "    image = Image.open(path + '\\\\' + img)\n",
    "    image = image.resize((30,30))\n",
    "    data.append(np.array(image))\n",
    "\n",
    "X_test = np.array(data)\n",
    "\n",
    "# Normalizing test set\n",
    "ascolumns = X_test.reshape(-1, 3)\n",
    "t = scaler.transform(ascolumns)\n",
    "X_test = t.reshape(X_test.shape)\n",
    "\n",
    "# Predicting on test set\n",
    "pred = np.argmax(model_1.predict(X_test),axis=1)\n",
    "\n",
    "# Performance evaluation\n",
    "cm = confusion_matrix(labels, pred)\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(labels, pred)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(labels, pred, average='macro')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(labels, pred, average='macro')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(labels, pred, average='macro')\n",
    "print('F1 score: %f' % f1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "553ce056",
   "metadata": {},
   "source": [
    "### Model 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a6a43a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_11 (Conv2D)          (None, 26, 26, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_10 (MaxPoolin  (None, 13, 13, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_18 (Dropout)        (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " flatten_8 (Flatten)         (None, 5408)              0         \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 256)               1384704   \n",
      "                                                                 \n",
      " dropout_19 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 43)                11051     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,398,187\n",
      "Trainable params: 1,398,187\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "7\n",
      "Epoch 1/10\n",
      "1961/1961 [==============================] - 32s 16ms/step - loss: 1.2610 - accuracy: 0.6453 - val_loss: 0.3688 - val_accuracy: 0.9146\n",
      "Epoch 2/10\n",
      "1961/1961 [==============================] - 31s 16ms/step - loss: 0.5250 - accuracy: 0.8432 - val_loss: 0.1919 - val_accuracy: 0.9606\n",
      "Epoch 3/10\n",
      "1961/1961 [==============================] - 31s 16ms/step - loss: 0.4078 - accuracy: 0.8782 - val_loss: 0.1674 - val_accuracy: 0.9666\n",
      "Epoch 4/10\n",
      "1961/1961 [==============================] - 31s 16ms/step - loss: 0.3476 - accuracy: 0.8960 - val_loss: 0.1155 - val_accuracy: 0.9745\n",
      "Epoch 5/10\n",
      "1961/1961 [==============================] - 31s 16ms/step - loss: 0.3088 - accuracy: 0.9090 - val_loss: 0.0972 - val_accuracy: 0.9813\n",
      "Epoch 6/10\n",
      "1961/1961 [==============================] - 32s 16ms/step - loss: 0.2764 - accuracy: 0.9170 - val_loss: 0.0929 - val_accuracy: 0.9797\n",
      "Epoch 7/10\n",
      "1961/1961 [==============================] - 32s 16ms/step - loss: 0.2513 - accuracy: 0.9257 - val_loss: 0.0785 - val_accuracy: 0.9806\n",
      "Epoch 8/10\n",
      "1961/1961 [==============================] - 31s 16ms/step - loss: 0.2461 - accuracy: 0.9289 - val_loss: 0.0794 - val_accuracy: 0.9818\n",
      "Epoch 9/10\n",
      "1961/1961 [==============================] - 35s 18ms/step - loss: 0.2343 - accuracy: 0.9304 - val_loss: 0.0701 - val_accuracy: 0.9851\n",
      "Epoch 10/10\n",
      "1961/1961 [==============================] - 36s 18ms/step - loss: 0.2164 - accuracy: 0.9357 - val_loss: 0.0728 - val_accuracy: 0.9855\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 3s 6ms/step\n",
      "Confusion Matrix:\n",
      "[[ 51   7   0 ...   0   0   0]\n",
      " [  2 695  12 ...   0   0   0]\n",
      " [  0  18 712 ...   0   0   0]\n",
      " ...\n",
      " [  0   0   0 ...  88   0   0]\n",
      " [  0   0   0 ...   0  51   0]\n",
      " [  0   0   0 ...   0   0  89]]\n",
      "Accuracy: 0.936184\n",
      "Precision: 0.920444\n",
      "Recall: 0.925258\n",
      "F1 score: 0.920786\n"
     ]
    }
   ],
   "source": [
    "model_2 = Sequential()\n",
    "model_2.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_2.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_2.add(Dropout(rate=0.5))\n",
    "model_2.add(Flatten())\n",
    "model_2.add(Dense(256, activation='relu'))\n",
    "model_2.add(Dropout(rate=0.5))\n",
    "model_2.add(Dense(43, activation='softmax'))\n",
    "model_2.summary()\n",
    "print(len(model_2.layers))\n",
    "\n",
    "\n",
    "# Compilation of the model_2\n",
    "model_2.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "epochs = 10\n",
    "history_2 = model_2.fit(X_train, y_train, batch_size=16, epochs=epochs, validation_data=(X_val, y_val))\n",
    "model_2.save('traffic_classifier_2.h5')\n",
    "\n",
    "# Plotting graphs for accuracy\n",
    "plt.figure(0)\n",
    "plt.plot(history_2.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history_2.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history_2.history['loss'], label='training loss')\n",
    "plt.plot(history_2.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Testing the model_2\n",
    "test_path = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "path = os.path.join(test_path)\n",
    "data = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "y_test = pd.read_csv(data +'Test.csv')\n",
    "labels = y_test[\"ClassId\"].values\n",
    "imgs = y_test[\"Path\"].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for img in imgs:\n",
    "    image = Image.open(path + '\\\\' + img)\n",
    "    image = image.resize((30,30))\n",
    "    data.append(np.array(image))\n",
    "\n",
    "X_test = np.array(data)\n",
    "\n",
    "# Normalizing test set\n",
    "ascolumns = X_test.reshape(-1, 3)\n",
    "t = scaler.transform(ascolumns)\n",
    "X_test = t.reshape(X_test.shape)\n",
    "\n",
    "# Predicting on test set\n",
    "pred = np.argmax(model_2.predict(X_test),axis=1)\n",
    "\n",
    "# Performance evaluation\n",
    "cm = confusion_matrix(labels, pred)\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(labels, pred)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(labels, pred, average='macro')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(labels, pred, average='macro')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(labels, pred, average='macro')\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e11850",
   "metadata": {},
   "source": [
    "### Model 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32dc027f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_12\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_18 (Conv2D)          (None, 26, 26, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_17 (MaxPoolin  (None, 13, 13, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_28 (Dropout)        (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " conv2d_19 (Conv2D)          (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_18 (MaxPoolin  (None, 5, 5, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_29 (Dropout)        (None, 5, 5, 64)          0         \n",
      "                                                                 \n",
      " conv2d_20 (Conv2D)          (None, 5, 5, 128)         8320      \n",
      "                                                                 \n",
      " max_pooling2d_19 (MaxPoolin  (None, 2, 2, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_30 (Dropout)        (None, 2, 2, 128)         0         \n",
      "                                                                 \n",
      " flatten_11 (Flatten)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_22 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " dropout_31 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_23 (Dense)            (None, 43)                11051     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 171,627\n",
      "Trainable params: 171,627\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "13\n",
      "Epoch 1/10\n",
      "981/981 [==============================] - 27s 27ms/step - loss: 2.6751 - accuracy: 0.2458 - val_loss: 1.8275 - val_accuracy: 0.4489\n",
      "Epoch 2/10\n",
      "981/981 [==============================] - 27s 28ms/step - loss: 1.6562 - accuracy: 0.4676 - val_loss: 1.1669 - val_accuracy: 0.6415\n",
      "Epoch 3/10\n",
      "981/981 [==============================] - 29s 30ms/step - loss: 1.2890 - accuracy: 0.5770 - val_loss: 0.8247 - val_accuracy: 0.7478\n",
      "Epoch 4/10\n",
      "981/981 [==============================] - 27s 27ms/step - loss: 1.0997 - accuracy: 0.6422 - val_loss: 0.6738 - val_accuracy: 0.8059\n",
      "Epoch 5/10\n",
      "981/981 [==============================] - 27s 27ms/step - loss: 0.9784 - accuracy: 0.6768 - val_loss: 0.5393 - val_accuracy: 0.8484\n",
      "Epoch 6/10\n",
      "981/981 [==============================] - 27s 27ms/step - loss: 0.8807 - accuracy: 0.7120 - val_loss: 0.4860 - val_accuracy: 0.8619\n",
      "Epoch 7/10\n",
      "981/981 [==============================] - 28s 28ms/step - loss: 0.8154 - accuracy: 0.7322 - val_loss: 0.3857 - val_accuracy: 0.8859\n",
      "Epoch 8/10\n",
      "981/981 [==============================] - 29s 29ms/step - loss: 0.7721 - accuracy: 0.7476 - val_loss: 0.3471 - val_accuracy: 0.9037\n",
      "Epoch 9/10\n",
      "981/981 [==============================] - 28s 29ms/step - loss: 0.7343 - accuracy: 0.7607 - val_loss: 0.3012 - val_accuracy: 0.9200\n",
      "Epoch 10/10\n",
      "981/981 [==============================] - 28s 29ms/step - loss: 0.6985 - accuracy: 0.7733 - val_loss: 0.2813 - val_accuracy: 0.9218\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 3s 6ms/step\n",
      "Confusion Matrix:\n",
      "[[ 15  12   0 ...   0   0   0]\n",
      " [  0 646  13 ...   0   0   0]\n",
      " [  0   3 541 ...   0   0   0]\n",
      " ...\n",
      " [  0   0   1 ...  84   0   0]\n",
      " [  0   0   0 ...   0  46   6]\n",
      " [  0   0   0 ...   0   1  86]]\n",
      "Accuracy: 0.874109\n",
      "Precision: 0.878127\n",
      "Recall: 0.821166\n",
      "F1 score: 0.834557\n"
     ]
    }
   ],
   "source": [
    "model_3 = Sequential()\n",
    "model_3.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_3.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_3.add(Dropout(rate=0.50))\n",
    "model_3.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_3.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_3.add(Dropout(rate=0.50))\n",
    "model_3.add(Conv2D(filters=128, kernel_size=(1,1), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_3.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_3.add(Dropout(rate=0.25))\n",
    "model_3.add(Flatten())\n",
    "model_3.add(Dense(256, activation='relu'))\n",
    "model_3.add(Dropout(rate=0.5))\n",
    "model_3.add(Dense(43, activation='softmax'))\n",
    "model_3.summary()\n",
    "print(len(model_3.layers))\n",
    "\n",
    "\n",
    "# Compilation of the model_3\n",
    "model_3.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "epochs = 10\n",
    "history_3 = model_3.fit(X_train, y_train, batch_size=32, epochs=epochs, validation_data=(X_val, y_val))\n",
    "model_3.save('traffic_classifier_3.h5')\n",
    "\n",
    "# Plotting graphs for accuracy\n",
    "plt.figure(0)\n",
    "plt.plot(history_3.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history_3.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history_3.history['loss'], label='training loss')\n",
    "plt.plot(history_3.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Testing the model_3\n",
    "test_path = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "path = os.path.join(test_path)\n",
    "data = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "y_test = pd.read_csv(data +'Test.csv')\n",
    "labels = y_test[\"ClassId\"].values\n",
    "imgs = y_test[\"Path\"].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for img in imgs:\n",
    "    image = Image.open(path + '\\\\' + img)\n",
    "    image = image.resize((30,30))\n",
    "    data.append(np.array(image))\n",
    "\n",
    "X_test = np.array(data)\n",
    "\n",
    "# Normalizing test set\n",
    "ascolumns = X_test.reshape(-1, 3)\n",
    "t = scaler.transform(ascolumns)\n",
    "X_test = t.reshape(X_test.shape)\n",
    "\n",
    "# Predicting on test set\n",
    "pred = np.argmax(model_3.predict(X_test),axis=1)\n",
    "\n",
    "# Performance evaluation\n",
    "cm = confusion_matrix(labels, pred)\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(labels, pred)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(labels, pred, average='macro')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(labels, pred, average='macro')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(labels, pred, average='macro')\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6f0e2a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_13\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_21 (Conv2D)          (None, 26, 26, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_20 (MaxPoolin  (None, 13, 13, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_32 (Dropout)        (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " conv2d_22 (Conv2D)          (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_21 (MaxPoolin  (None, 5, 5, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_33 (Dropout)        (None, 5, 5, 64)          0         \n",
      "                                                                 \n",
      " conv2d_23 (Conv2D)          (None, 5, 5, 128)         8320      \n",
      "                                                                 \n",
      " max_pooling2d_22 (MaxPoolin  (None, 2, 2, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_34 (Dropout)        (None, 2, 2, 128)         0         \n",
      "                                                                 \n",
      " flatten_12 (Flatten)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_24 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " dropout_35 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_25 (Dense)            (None, 43)                11051     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 171,627\n",
      "Trainable params: 171,627\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "13\n",
      "Epoch 1/10\n",
      "491/491 [==============================] - 28s 55ms/step - loss: 2.9057 - accuracy: 0.1921 - val_loss: 2.0413 - val_accuracy: 0.3943\n",
      "Epoch 2/10\n",
      "491/491 [==============================] - 24s 49ms/step - loss: 1.9099 - accuracy: 0.3878 - val_loss: 1.5090 - val_accuracy: 0.5115\n",
      "Epoch 3/10\n",
      "491/491 [==============================] - 25s 51ms/step - loss: 1.5270 - accuracy: 0.4959 - val_loss: 1.1798 - val_accuracy: 0.6465\n",
      "Epoch 4/10\n",
      "491/491 [==============================] - 27s 54ms/step - loss: 1.2787 - accuracy: 0.5740 - val_loss: 0.8685 - val_accuracy: 0.7530\n",
      "Epoch 5/10\n",
      "491/491 [==============================] - 28s 57ms/step - loss: 1.1252 - accuracy: 0.6234 - val_loss: 0.7295 - val_accuracy: 0.7915\n",
      "Epoch 6/10\n",
      "491/491 [==============================] - 24s 48ms/step - loss: 1.0005 - accuracy: 0.6628 - val_loss: 0.5859 - val_accuracy: 0.8279\n",
      "Epoch 7/10\n",
      "491/491 [==============================] - 23s 47ms/step - loss: 0.9207 - accuracy: 0.6918 - val_loss: 0.5197 - val_accuracy: 0.8500\n",
      "Epoch 8/10\n",
      "491/491 [==============================] - 23s 46ms/step - loss: 0.8454 - accuracy: 0.7158 - val_loss: 0.4412 - val_accuracy: 0.8842\n",
      "Epoch 9/10\n",
      "491/491 [==============================] - 23s 47ms/step - loss: 0.7916 - accuracy: 0.7373 - val_loss: 0.3924 - val_accuracy: 0.8888\n",
      "Epoch 10/10\n",
      "491/491 [==============================] - 23s 47ms/step - loss: 0.7494 - accuracy: 0.7483 - val_loss: 0.3418 - val_accuracy: 0.9056\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bsrVKKdWcuHLW0CjgJmCziCQ6n/sb0BHAGDMbuAa4W0QqgGLgetMEJx9f1COSjm38mbtmH5P6tLe7HKWUalAuCwJjzBrgtNv/GGNeA15zVQ0NxdNDmDEylmeWbSPxYA79O4TaXZJSSjUYvbK4jq4b0oEgXy/m6Q5mSqkWRoOgjgJ9vZg2pANfbs4gI7fY7nKUUqrBaBDUwy0jY3EYw9tr6767kVJKNXUaBPXQoY0/l/Rqx8KfD1BUVmF3OUop1SA0COpp5ug4covL+SQhze5SlFKqQWgQ1NOgTq3pFxPC/DX7cDia3ExXpZSqNw2CehIRbhsdR3JWISt3HbG7HKWUOmcaBGdhUp/2tAv2Y96a/XaXopRS50yD4Cx4e3pw88hOrNmTxY5DNdfRU0qp5kWD4Cz9YWhHWnl76gVmSqlmT4PgLIX6+3D1oGg+S0wnq6DU7nKUUuqsaRCcg1tHxVFW4eC99XqBmVKq+dIgOAfnRQRyQbcI3lufQmlFpd3lKKXUWdEgOEczR3cmq6CMpYnpdpeilFJnRYPgHI3qEkb3dkHMXbOPJriVglJKnZEGwTkSEW4bFceOQ/ms29vkNldTSqkz0iBoAFP6RxEW4MO8n3QqqVKq+dEgaAB+3p7cMLwT3+04wr6sQrvLUUqpetEgaCA3De+Et4cH87VVoJRqZjQIGkhEkC9T+kfx8cZUcovK7S5HKaXqTIOgAd02Ko7i8koW/nLA7lKUUqrONAgaUM+oYEZ0DuPttfspr3TYXY5SStWJBkEDmzk6jozcEr7ecsjuUpRSqk40CBrYhd3bEhvmz1xdlVQp1UxoEDQwDw/h1lFxJB7MYVPKMbvLUUqpM9IgcIFrBsUQ7OelF5gppZoFDQIXCPD1YvrQjny95RBpOcV2l6OUUqelQeAit4yMBeDttfttrUMppc7EvYKgvKTRThUV2opLe7dj4c8HKCytaLTzKqVUfblPEOxaAa/2h+y9jXbK20bHkV9SwccbDzbaOZVSqr5cFgQi0kFEfhCRbSKyVUQeqOUYEZFXRWSPiPwmIgNdVQ8RXaGyDBZeDyW5LjtNdQM7tmZAx1Dmr92Pw6F7FSilmiZXtggqgL8aY3oCw4F7RaRnjWMuBeKdtzuBWS6rpnUsXPcOHE2GRbeBo3G2lpw5Oo6U7CK+23GkUc6nlFL15bIgMMZkGGMSnPfzge1AdI3DrgDeMZb1QKiItHdVTcSOhkkvwp5v4ZsnXHaa6ib2akdUiB9z1yQ3yvmUUqq+GmWMQERigQHAhhovRQPVO9BT+X1YICJ3ishGEdmYmZl5bsUMvhWG3AHrXoPE98/ts+rAy9ODW0bGsj75KFvTG6dLSiml6sPlQSAigcAnwJ+NMXln8xnGmDnGmMHGmMERERHnXtTE5yBuDHz+AByomU0N7/qhHfH38WTemv0uP5dSStWXS4NARLyxQmCBMebTWg5JAzpUexzjfM61PL3h2rchOBo+vBFyU116upBW3lw7KIbPk9I5kt94U1iVUqouXDlrSIC5wHZjzEunOGwpcLNz9tBwINcYk+Gqmk7i3wamfwDlxbBwOpQVufR0t46Ko9zh4L11KS49j1JK1ZcrWwSjgJuAC0Uk0XmbJCJ3ichdzmO+BJKBPcCbwD0urOf32naHa+bBoc2w5B4wrpviGRsewPjukby34QAl5Y0zY0kpperCy1UfbIxZA8gZjjHAva6qoU66ToCLn7ZmEbXtCWP/n8tOddvoWL598zCf/ZrG9UM7uuw8SilVH+5zZfHpjPwT9L0efvgnbFvqstOM6BxGj/bBzFmdTF6J7muslGoaNAgARODyVyBmCCz+o9VV5JLTCP/vkm4cyC7i2lnrSNeVSZVSTYAGQRVvP5j2HviFWoPHBed4vcIpXNC9Lf+5dSjpOcVMfeMnvbZAKWU7DYLqgtrB9PehMAs+ugkqylxymtHx4Xx89wg8RLhu9jpW7XJN6CilVF1oENQUNQCufB0OrIMvHnTZTKLu7YJZfM8oOoYFcNt/fuGDnw+45DxKKXUmGgS16X01jHkYfn0XNsx22Wnahfjx8V0jGNUlnEc/3cyLy3diXDiFVSmlaqNBcCrj/gbdJ8Pyv8Ge71x2mkBfL+beMpjrh3TgtR/28OBHSZRVOFx2PqWUqkmD4FQ8PGDqv61rCxbdCll7XHYqb08PnruqDw9N6MriX9O4Zd7P5Bbr9FKlVOPQIDgd30C4/n3w8IKF06A4x2WnEhHuuzCef03rz8aUo1wzay2px1y77IVSSoEGwZm17mRNKz2WYm1oU+na/YevHBDN27cN5VBeCVPfWMvmVJ1eqpRyLQ2Cuug0Ei77X9j7XaNsaDPyvHA+uXskPp4eTJuzjh90dzOllAtpENTVoFtg2F2w/nVIeNflp+saGcTie0bSOSKAmW//woINumqpUso1NAjqY8I/ofM4WPYXOLDe5adrG+zHh3eOYGzXCP6+eAvPf7UDh0OnlyqlGpYGQX14esE18yG0g7WhTc7BM7/nHAX4evHmzYO5YVhHZq/aywMfJlJaoctYK6UajgZBffm3gekfQkWpc0ObQpef0svTg2ev7M2jl3bn86R0bnrrZ3KKXLP8hVLK/WgQnI2IrtaGNke2wuK7wOH6C8BEhLvGnser0weQeDCHq2at5eBRnV6qlDp3GgRnK/5iuPgZ2L4UVv13o512Sr8o3p05lOyCMqa+8RNJB3Ma7dxKqZapTkEgIg+ISLBzb+G5IpIgIhNcXVyTN+I+6H8DrHoeti5utNMO6xzGJ3ePxM/bk+vnrOebbYcb7dxKqZanri2C24wxecAEoDXWXsTPu6yq5kIEJr8MMUNh8d2QkdRop+7SNpDF94yia2Qgf3x3I++s299o51ZKtSx1DYKqvYcnAe8aY7Zyhv2I3YaXr3XlsX8bWPgHKGi8i78ignxZeOdwLuzelieWbOWfX2zT6aVKqXqraxBsEpEVWEGwXESCAF0is0pQJExfCEXZ8MEN1oyiRuLv48W/bxrMzSM68ebqfdy/8FdKynV6qVKq7uoaBDOBR4EhxpgiwBu41WVVNUft+8HUWZD6s3XBWSPuK+DpITw9pRd/n9SDLzZncMNbGzhaqNNLlVJ1U9cgGAHsNMbkiMiNwOOAroZWU6+pMPYRSFwA615v1FOLCHeM6cwbNwxkc1ouV89aS0q2669xUEo1f3UNgllAkYj0A/4K7AXecVlVzdnYR6HHFPjmv2D3N41++kl92vP+7cPIKSpj6htrSThwrNFrUEo1L3UNggpj7aF4BfCaMeZ1IMh1ZTVjHh4wdTa07WUtW525q9FLGBzbhk/uHkmgrxfT56zn6y2HGr0GpVTzUdcgyBeRx7CmjX4hIh5Y4wSqNj4BMP198PSBhddDceP/q7xzRCCL7xlJj/bB3L1gE/PW7Gv0GpRSzUNdg2AaUIp1PcEhIAZ4wWVVtQShHeH6BZBzAD6e4fINbWoTFujLwjuGM6FnJM8s28bTn2+lUqeXKqVqqFMQOH/5LwBCRGQyUGKM0TGCM+k43LrgLHklrHjclhJa+Xjyxg2DuG1UHPN/2s89CzZRXKbTS5VSJ9R1iYnrgJ+Ba4HrgA0ico0rC2sxBt4Ew++BDbMgwZ7s9PQQnri8J09M7smKbYeZ/uZ6sgsa71oHpVTTVteuob9jXUNwizHmZmAo8F+ne4OIzBORIyKy5RSvjxORXBFJdN5cvwekXS7+B5w3HpY9CCnrbCvjttFxzLphENsz8rhq1lqSMwtsq0Up1XTUNQg8jDHV107IrsN7/wNMPMMxq40x/Z23Z+pYS/Pj6WUtW926k3NDmwO2lTKxdzsW3jmc/JIKJr26mpdW7KSwtPHHL5RSTUddg+BrEVkuIjNEZAbwBfDl6d5gjPkROHqO9bUcrUJh+gdQWQ4LroWj9s3iGdixNcvuH83FPdvx6vd7uODFlXy88aCuU6SUm6rrYPHDwBygr/M2xxjzSAOcf4SIJInIVyLSqwE+r2kLj4dp70JeBvx7DGxbYlspUaGt+L/pA/jk7hG0D23Fw4t+Y8rra9iQnG1bTUope4hx4Zo4IhILLDPG9K7ltWDAYYwpEJFJwCvGmPhTfM6dwJ0AHTt2HJSSkuKymhvFsf3w8a2QngDD7rI2uPHyta0ch8Pw+W/p/PdXO0jPLWFir3Y8Nqk7ncICbKtJKdWwRGSTMWZwra+dLghEJB+o7QABjDEm+AwnjuUUQVDLsfuBwcaYrNMdN3jwYLNx48YzfVzTV1EG3zxhzSaKGgDXzIc2cbaWVFxWyVurk5m1ai8VlYYZo2K578IuBPvptYNKNXenC4LTdg0ZY4KMMcG13ILOFAJ1KKqdiIjz/lBnLe7TL+HlA5c+b+1lkJ0M/x4L25baWlIrH0/uHx/PDw+N44r+Uby5OplxL6zk3fUpVFTqquNKtVQu6xoSkYXAOCAcOAw8iXNZCmPMbBG5D7gbqACKgQeNMWvP9LktpkVQ3bH91tXH6b82ia6iKlvScvnHsm1s2HeUrpGB/P2ynoztGmF3WUqps3DWXUNNUYsMArA2s/nmSWdX0UC4dj60jrW7KowxLN96mOe+2k5KdhHjukXw90k9iI/UNQeVak40CJqTbUthyX3W/Stfhx6X21uPU1mFg3fW7eeV73ZTVFbJH4Z25C8Xd6VNgI/dpSml6kCDoLk5ug8W3ersKrrb2VXUNH7hHi0s41/f7mLBhgP4+3jypwvjuWVkLD5edb0kRSllBw2C5qiJdhVV2X04n39+uZ2VOzPpFObPY5f24JJekTjH/5VSTcxZzxpSNvLytWYVXfcuZO+1LkDbvszuqo6LjwziP7cO5e3bhuLj6cFd721i+pvr2ZKmO5gq1dxoEDR1PafAH1dBm87w4Q3w9WPWNQhNxNiuEXz1wPn848re7DpcwOWvreHhj5M4kldid2lKqTrSrqHmoqLUeQHabGdX0X+sReyakNzicl7/YQ/zf9qHt6cHd489jzvGdMbP29Pu0pRyezpG0JJUzSoS4Io3oMdkuyv6nZTsQp77cgdfbz1EVIgfj1zanSn9onT8QCkb6RhBS1LVVdQ6ztlV9Lcm1VUE0CksgNk3DeLDO4fTJtCHBz5IZOoba9mU0vh7NyulzkyDoDlqEwczV8DQP8L612H+RDjW9BbiG9Y5jKX3jubFa/uRkVvM1bPWct/7CaQeK7K7NKVUNdo11NxtW+LsKhK4chZ0v8zuimpVVFbB7FXJzPlxLw4Dt4+O454LuhDo62V3aUq5BR0jaOmOJlvLWmckwvB74aKnmswFaDWl5xTzwvKdLP41jfBAXx6a0JVrB3fA00PHD5RyJQ0Cd1BRCiseh5/nQPRg6wK00I52V3VKiQdzeHbZNjamHKN9iB+X94tiSr8oekUF66CyUi6gQeBOtn4GS+93dhXNhu6T7K7olIwxrNh2mI83HmTlzkwqHIbzIgKY0i+aKf2jiAvXjXGUaigaBO7maLK1rHVGEoy4D8Y/2WS7iqocKyzjqy2HWJqUxoZ9RzEG+saEMKVfFJf3iyIy2M/uEpVq1jQI3FEz6yqq7lBuCct+S2dJYjqb03IRgeFxYUzpH8WlvdsR6t+0Q02ppkiDwJ0d7yrygKmzoduldldUL8mZBSxNSmdpYjrJWYV4ewpju0YwpX80F/Voi7+PzjpSqi40CNxdza6ii54Cz+a1D7Exhq3peSxJTOPzpAwO5ZXg7+PJxT0juaJ/FOfHR+DtqZfFKHUqGgTq5K6imCFwzbxm01VUk8Nh+Hn/UZYmpfPl5gxyisoJ9fdmUp/2TOkXxdDYNnjodFSlTqJBoE7YuhiW3A8ens4L0JrurKK6KKtwsGZPJksS01mx9TDF5ZW0D/Fjct/2XNE/WqejKuWkQaBOlr3X2gEtIwkGz4QJz4KPv91VnbOisgq+3X6EpYlprNqVSXmloXN4AFP6W9codI4ItLtEpWyjQaB+r6IUvv8HrP0/CO8KV78F7fvZXVWDySlyTkdNTGf9vmyMgT7RIVzRP4rJfaNoF6LTUZV70SBQp7b3B/jsbijMgvH/BSPuB4+WNehaNR11aVI6v6Va01GHxbVhSr9oJvXR6ajKPWgQqNMrOgqfPwDbl0LcGOuK5JBou6tyiX1ZhSxNTGdJUhrJmdZ01DHxEUzpH8UF3dsS7Ne8ZlMpVVcaBOrMjIFf34OvHrGmll7+CvS60u6qXKZqOurSpHQ+T0onI7cELw9hYKfWjOsWwbiubenRPkgHmlWLoUGg6i57L3x6B6Rtgv43wqXPg2+Q3VW5lMNh2HTgGD/sOMLKnZlsy8gDIDLYl7FdIxjbtS2j48MJaaWtBdV8aRCo+qksh1X/Dav/F0I7wVVvQochdlfVaI7klbByVyardmayencmeSUVeHoIAzuGMq5bW8Z2jdBpqarZ0SBQZydlHXx6J+SlwdhH4Py/gqd7LelQUekg8WAOK3dmsnLXEbakWa2FiCBfxsRHMK5bBGPiIwjx19aCato0CNTZK8mFLx6CzR9Bh2Fw1RxoHWt3VbY5kl/Cj7uyWLUrkx93ZZJbXI6HwICOrRnXNYKx3SLoHRWiVzarJkeDQJ273z6GLx60BpUv+1/oe52154Ebq3QYEg/msGrnEVbuyuS31FwAwgN9GBNvhcKY+AhaB+j0VGU/DQLVMI6lwOI/woF10PtquOwlaBVqd1VNRlZBKat3Z7Jyp9VaOFZUjgj0iwm1ZiJ1a0vfaG0tKHvYEgQiMg+YDBwxxvSu5XUBXgEmAUXADGNMwpk+V4PAZo5KWPMyrHwOgtrD1H9D7Ci7q2pyKh2G31KtsYVVuzJJSs3BGGgT4MOY+PDjrYWwQF+7S1Vuwq4gGAMUAO+cIggmAfdjBcEw4BVjzLAzfa4GQRORugk+vR2O7oPzH4RxjzW7pa0b09HCspNaC9mFZYhA3+gQxnZry7huEfSLCcVTWwvKRWzrGhKRWGDZKYLg38BKY8xC5+OdwDhjTMbpPlODoAkpLYCvH4Vf34WoAXDVWxDexe6qmjyHw7AlPdeaibTzCIkHc3AYCPX3ZnSXcPrGhNCjfTA92gcTri0G1UCaahAsA543xqxxPv4OeMQY87vf8iJyJ3AnQMeOHQelpKS4rGZ1FrYthc//ZC1kN/E5GHiL2w8k10dOURk/7s5i1c5MftqTxaG8kuOvhQf60qN9kDMYgujeLpjzIgLx8WpZ60Ep1ztdEDSLSeHGmDnAHLBaBDaXo2rqOQViBsPiu6w1i3Z/A5e/CgFhdlfWLIT6+zCln7VUNljdSDsy8th+KJ/tGXnsOJTHf9bup6zCAYC3p3BeRCA9na2G7s6g0NaDOlt2BkEa0KHa4xjnc6o5Co6Cmz6D9a/Dd8/ArJEwdRacd6HdlTU7bQJ8GNklnJFdwo8/V1HpIDmrkO0ZeWzPyGfHoTx+2pvFp7+e+CujrQd1tuzsGroMuI8Tg8WvGmOGnukzdYygGcj4DT65HbJ2wvB7YfwT4K3r/7tCba2HXYcLam09VLUcurcLJiJIWw/uxq5ZQwuBcUA4cBh4EvAGMMbMdk4ffQ2YiDV99Nbaxgdq0iBoJsqL4ZsnrD2SI3tbG9+07WF3VW6hotLBvqxCtmXkscMZENsz8jicV3r8GG09uB+9oEzZZ9cKWHIPlOTBhH/A0Dt1INkm9W09dI0Molu7INoF++kCey2ABoGyV8ERWHIv7F4BXS6GK16HoEi7q1LUrfUQ5OdFt8ggurYLsv50BkQbXTqjWdEgUPYzBn55C1Y8Dj6BVhh0m2h3VeoUjhWWsetwPrsO57PzcD67DhWw83A+ucXlx48JD/SlW7tAKxicQdE1MohA32YxGdHtaBCopuPIDuuK5EObYfBMmPAs+PjbXZWqA2MMR/JL2XnIGRDOP3cdLqC4vPL4cdGhrejWrqrlYAXFeRGB+Hl72li90iBQTUtFKXz/D1j7fxDeFSb8EzqPAy/tamiOHA5D6rFiq+VQLSD2ZhZQXmn9fvEQiA0POKlrqWtkELFh/nh56gB1Y9AgUE1T8kpYfDfkp4NvMMRPgB6TrXEE30C7q1PnqLzSQUp2ITud3Uq7nAGxP7sQh/PXjo+nB+e1DaRbZOBJYxDRoa10ldYGpkGgmq7yEti3CrZ/Dju/hKJs8PSF8y6A7pOh2yS9QrmFKSmvZM+RgmrjD1b3UlpO8fFj/H08iY8MoktEINGtWxHTuhUxoa2Ibt2K9iGtdJrrWdAgUM1DZQUcXA/bl8GOZZB7EMQDOo2yQqH7ZRDa4cyfo5qlvJJydh8uOKl7KTmzkMP5JVT/NSUCkUF+xLS2giE6tBUxrf2r3W+l4xG10CBQzY8xkJFkBcL2zyFzh/V8+/5W91GPKRDRzdYSVeMoq3BwKLeE1GNFpOYUk3asmNRjxaTlFJGWU0xGTgkVjpN/j4UH+pwUEDHOkKgKiyA/91syXYNANX9Ze2DH51ZrIc353z8s3gqF7pdby2B7aHeBO6p0GA7nlZwIh+NB4QyNnOLjF81VCWnlfbz1YAWF//HHMa1bEdLKu8VdRKdBoFqWvHTY8YXVWti/BhwVEBRldR31mGx1JekmOcrJ4TBkFZZa4eAMiNRjRdXuF1NUVnnSewJ8PE8KiHYhfrQN8qVtsB8Rgb60Dfaljb9PsxrQ1iBQLVfxMdi13Oo+2vMdVBSDXyh0u9QaVzjvQr1OQZ2WMYacovLjLYrUmi2KY0XklVT87n2eHkJ4oA9tg/yICPK1giLIl4ggXyKC/Ggb7EtEoPW4KYxZaBAo91BWBHu/s7qPdn0FJbng7Q9dxlvdR10vgVahdlepmqGisgoy80vJzC/lyPE/SziSV0pmQSlH8qznswtLqe1XarCfF22D/Y4HhRUaJwIkwvk4uJWXy7qkNAiU+6kst7qNdiyzupHyM8DDC2LPd44rTIagdnZXqVqYikoHRwvLTgqLk8PjRICU1hi3APDx8jje9VQ9IKoCo2tkEB3anF0LV4NAuTeHA9ITYPtSq7VwdC8gEDPkRCiEnWd3lcqNGGPIL62wWhTVAqNmiyMzv5RjRSfWd/rj2M48dunZLeeuQaBUFWOsqajbl1mzkDKSrOfb9rSmpPa8wto3oYXNGFHNV2lFJVkFZWTml9LG34eOYdoi0CBQDSvngNV1tP1zSFkLGGtaak9nKLTrq6GgWgQNAqXqIv+wNaawbYk1vmAqoXWsFQg9r4CogRoKqtnSIFCqvgqzYecXVigkr7SuVQjpcKL7KGaIXsCmmhUNAqXORfEx2PkVbFtqTU+tLIOg9tDjcisUOo4AD/vniSt1OhoESjWUkjzrArZtn8Geb6GiBAIiToRCp9HgqTt0qaZHg0ApVygtgD3fWN1Hu5ZDeRG0amMtddHzSogbo5vtqCbjdEGg/3RR6mz5BkKvqdat6qrmbUtg62fw67vgFwLdLrNmIHW+ALz97K5YqVppECjVEHz8re6hHpdbm+0kr7RCYecXkPQ++ARBt4lW91GXi8C7ld0VK3WcBoFSDc3bz/ql320iVJTBvh9h+xLrIrbNH4N3AMRfbIVC/ATdllPZTscIlGoslRWQssZqKWz/HAozwcvPaiH0vNJaFM8v2O4qVQulg8VKNTWOSjiw3hkKS61F8Tx9oPM462rmsC4QHm/9qSumqgagQaBUU+ZwQOovVijsXgFHk62rmqv4h58IheoB0TpOZyWpOtMgUKo5qSyHY/shazdk74Hs3dZWndl7oPDIiePEE1p3cgZEvLWCani8dT+onS6HoU6i00eVak48va1f6OHxv3+tOAey954IiOw9VkjsW23tzlbFJ9AKhrD4aq2I86z7vkGN9lVU86BBoFRz0ioUYgZZt+ocDshPr9aK2GPdT/0FtnwCVGv5B7X/fTdTWBcI7aRXRbspl/5XF5GJwCuAJ/CWMeb5Gq/PAF4A0pxPvWaMecuVNSnVInl4QEiMdTvvgpNfKy+xxh1qdjNt+8xaR+n4Z3hDmzirFRHeBSJ7W/s0hHfVsYgWzmVBICKewOvAxUAq8IuILDXGbKtx6IfGmPtcVYdSbs/bDyJ7Wreaio46WxG7T7QisvdYg9YO585YHl4Q3g0ie518C2qv4xAthCtbBEOBPcaYZAAR+QC4AqgZBEopu/i3gY7DrFt1leVWIBzeCoe3wOFt1sY9mz86cUyr1idaDZG9nPe7g09A434Hdc5cGQTRwMFqj1OBYbUcd7WIjAF2AX8xxhyseYCI3AncCdCxY0cXlKqUOomnt7VlZ9se0OeaE88XH7NC4fBWOLLV+vPX96C80HmAQJvOzhZI7xOth9BY3b+hCbN7ZOhzYKExplRE/gi8DVxY8yBjzBxgDljTRxu3RKXUca1aQ+wo61bF4YCc/c7WQ7Xb9mUcH6T2DrBCparlENnLCotWre34FqoGVwZBGtCh2uMYTgwKA2CMya728C3gf1xYj1LKFTw8rFZAm87WontVygrhyA6ra+mIsxWxfSkkvH3imODoauMOzoAI62K1SFSjcWUQ/ALEi0gcVgBcD/yh+gEi0t4Yk+F8OAXY7sJ6lFKNySfg91NdjbGW06jZetj7vbUdKFhLbVQNTkd0heAYCI6CkGgIitLlvF3AZUFgjKkQkfuA5VjTR+cZY7aKyDPARmPMUuBPIjIFqACOAjNcVY9SqgkQsX6pB0dZK7BWqSiDrF0njz3sWwW/ffD7z/APdwZDjNWiOH4/6sRjL9/G+04tgC4xoZRqukoLIC8d8tKct3TITT35uZLc378vIMIZDNVaE8HRJ4LCDcNCl5hQSjVPvoFW91BE11MfczwsnAGRm3YiJI7tg/1roPRUYeEMh+pBEeIMi6Aot7mQToNAKdW81Sks8k+0InLTTg6Oo8mnCYu2J0Ki6srt4GgI6WA9HxgJHp6u+26NRINAKdXy+QZBRDfrdipVYVGz6yk3zbq4LnkllBWc/B4PL6vlUBUSIc7ACK722C+0yV+BrUGglFJw5rAwxhqPyE11BsRBKySqHh9cD1vTT8x+quITWK1F4WxN1Gxh2DwTSoNAKaXqQsRa/bVVKLTrXfsxjkooOFJLUKRafx7afPKeElWqxiuOtywatwtKg0AppRqKhycEt7duMbVO0LFWg813dkHVDIrsvZC8Csrya3yuswtq2J0w8v4GL1uDQCmlGpO334krsU+lqgsq19myyHMGRmA7l5SkQaCUUk2NX4h1i+zVKKfT5QCVUsrNaRAopZSb0yBQSik3p0GglFJuToNAKaXcnAaBUkq5OQ0CpZRycxoESinl5prdxjQikgmknOXbw4GsBiynudOfx8n053GC/ixO1hJ+Hp2MMRG1vdDsguBciMjGU+3Q447053Ey/XmcoD+Lk7X0n4d2DSmllJvTIFBKKTfnbkEwx+4Cmhj9eZxMfx4n6M/iZC365+FWYwRKKaV+z91aBEoppWrQIFBKKTfnNkEgIhNFZKeI7BGRR+2ux04i0kFEfhCRbSKyVUQesLsmu4mIp4j8KiLL7K7FbiISKiKLRGSHiGwXkRF212QXEfmL8+/IFhFZKCL27jLvIm4RBCLiCbwOXAr0BKaLSE97q7JVBfBXY0xPYDhwr5v/PAAeALbbXUQT8QrwtTGmO9APN/25iEg08CdgsDGmN+AJXG9vVa7hFkEADAX2GGOSjTFlwAfAFTbXZBtjTIYxJsF5Px/rL3q0vVXZR0RigMuAt+yuxW4iEgKMAeYCGGPKjDE5thZlLy+glYh4Af5Aus31uIS7BEE0cLDa41Tc+BdfdSISCwwANthcip3+Bfw/wGFzHU1BHJAJzHd2lb0lIgF2F2UHY0wa8CJwAMgAco0xK+ytyjXcJQhULUQkEPgE+LMxJs/ueuwgIpOBI8aYTXbX0kR4AQOBWcaYAUAh4JZjaiLSGqvnIA6IAgJE5EZ7q3INdwmCNKBDtccxzufcloh4Y4XAAmPMp3bXY6NRwBQR2Y/VZXihiLxnb0m2SgVSjTFVLcRFWMHgji4C9hljMo0x5cCnwEiba3IJdwmCX4B4EYkTER+sAZ+lNtdkGxERrD7g7caYl+yux07GmMeMMTHGmFis/y++N8a0yH/11YUx5hBwUES6OZ8aD2yzsSQ7HQCGi4i/8+/MeFrowLmX3QU0BmNMhYjcByzHGvmfZ4zZanNZdhoF3ARsFpFE53N/M8Z8aV9Jqgm5H1jg/EdTMnCrzfXYwhizQUQWAQlYM+1+pYUuNaFLTCillJtzl64hpZRSp6BBoJRSbk6DQCml3JwGgVJKuTkNAqWUcnMaBEq5mIiM01VNVVOmQaCUUm5Og0ApJxG5UUR+FpFEEfm3c4+CAhF52bkm/XciEuE8tr+IrBeR30RksXNdGkSki4h8KyJJIpIgIuc5Pz6w2hr/C5xXqiIizzv3hfhNRF606asrN6dBoBQgIj2AacAoY0x/oBK4AQgANhpjegGrgCedb3kHeMQY0xfYXO35BcDrxph+WOvSZDifHwD8GWs/jM7AKBEJA6YCvZyf86wrv6NSp6JBoJRlPDAI+MW57MZ4rF/YDuBD5zHvAaOda/aHGmNWOZ9/GxgjIkFAtDFmMYAxpsQYU+Q85mdjTKoxxgEkArFALlACzBWRq4CqY5VqVBoESlkEeNsY099562aMeaqW4852TZbSavcrAS9jTAXWpkmLgMnA12f52UqdEw0CpSzfAdeISFsAEWkjIp2w/o5c4zzmD8AaY0wucExEznc+fxOwyrnbW6qIXOn8DF8R8T/VCZ37QYQ4F/v7C9a2kEo1OrdYfVSpMzHGbBORx4EVIuIBlAP3Ym3MMtT52hGscQSAW4DZzl/01VfovAn4t4g84/yMa09z2iBgiXNDdAEebOCvpVSd6OqjSp2GiBQYYwLtrkMpV9KuIaWUcnPaIlBKKTenLQKllHJzGgRKKeXmNAiUUsrNaRAopZSb0yBQSik39/8B57dyh3qmbPsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 3s 8ms/step\n",
      "Confusion Matrix:\n",
      "[[  0  45   0 ...   0   0   0]\n",
      " [  0 625  65 ...   0   0   0]\n",
      " [  0   7 553 ...   0   0   0]\n",
      " ...\n",
      " [  0   0   0 ...  85   0   0]\n",
      " [  0   0   0 ...   0  39   3]\n",
      " [  0   0   0 ...   0   0  59]]\n",
      "Accuracy: 0.850673\n",
      "Precision: 0.818703\n",
      "Recall: 0.771105\n",
      "F1 score: 0.771558\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patel\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "model_4 = Sequential()\n",
    "model_4.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_4.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_4.add(Dropout(rate=0.50))\n",
    "model_4.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_4.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_4.add(Dropout(rate=0.50))\n",
    "model_4.add(Conv2D(filters=128, kernel_size=(1,1), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model_4.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model_4.add(Dropout(rate=0.25))\n",
    "model_4.add(Flatten())\n",
    "model_4.add(Dense(256, activation='relu'))\n",
    "model_4.add(Dropout(rate=0.5))\n",
    "model_4.add(Dense(43, activation='softmax'))\n",
    "model_4.summary()\n",
    "print(len(model_4.layers))\n",
    "\n",
    "\n",
    "# Compilation of the model_4\n",
    "model_4.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "epochs = 10\n",
    "history_4 = model_4.fit(X_train, y_train, batch_size=64, epochs=epochs, validation_data=(X_val, y_val))\n",
    "model_4.save('traffic_classifier_4.h5')\n",
    "\n",
    "# Plotting graphs for accuracy\n",
    "plt.figure(0)\n",
    "plt.plot(history_4.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history_4.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history_4.history['loss'], label='training loss')\n",
    "plt.plot(history_4.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Testing the model_4\n",
    "test_path = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "path = os.path.join(test_path)\n",
    "data = \"C:\\\\Users\\\\patel\\\\OneDrive\\\\download\\\\archive\\\\\"\n",
    "y_test = pd.read_csv(data +'Test.csv')\n",
    "labels = y_test[\"ClassId\"].values\n",
    "imgs = y_test[\"Path\"].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for img in imgs:\n",
    "    image = Image.open(path + '\\\\' + img)\n",
    "    image = image.resize((30,30))\n",
    "    data.append(np.array(image))\n",
    "\n",
    "X_test = np.array(data)\n",
    "\n",
    "# Normalizing test set\n",
    "ascolumns = X_test.reshape(-1, 3)\n",
    "t = scaler.transform(ascolumns)\n",
    "X_test = t.reshape(X_test.shape)\n",
    "\n",
    "# Predicting on test set\n",
    "pred = np.argmax(model_4.predict(X_test),axis=1)\n",
    "\n",
    "# Performance evaluation\n",
    "cm = confusion_matrix(labels, pred)\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(labels, pred)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(labels, pred, average='macro')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(labels, pred, average='macro')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(labels, pred, average='macro')\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a24aec9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
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