cnn_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "035f97b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf \n",
    "from PIL import Image\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "\n",
    "data = []\n",
    "labels = []\n",
    "classes = 43\n",
    "cur_path = os.getcwd()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cdb2cdd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retrieving the images and their labels \n",
    "for i in range(classes):\n",
    "    path = os.path.join(cur_path,'train',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",
    "            #sim = Image.fromarray(image)\n",
    "            data.append(image)\n",
    "            labels.append(i)\n",
    "        except:\n",
    "            print(\"Error loading image\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65c49e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting lists into numpy arrays\n",
    "data = np.array(data)\n",
    "labels = np.array(labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1663a736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39209, 30, 30, 3) (39209,)\n",
      "(31367, 30, 30, 3) (7842, 30, 30, 3) (31367,) (7842,)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape, labels.shape)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "y_train = to_categorical(y_train, 43)\n",
    "y_test = to_categorical(y_test, 43)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf3d6476",
   "metadata": {},
   "outputs": [],
   "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(Conv2D(filters=32, kernel_size=(5,5), activation='relu'))\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(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",
    "\n",
    "#Compilation of the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f68b9416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 1.9443 - accuracy: 0.5004 - val_loss: 0.5552 - val_accuracy: 0.8579\n",
      "Epoch 2/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 0.7741 - accuracy: 0.7702 - val_loss: 0.3399 - val_accuracy: 0.9044\n",
      "Epoch 3/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 0.5338 - accuracy: 0.8388 - val_loss: 0.1958 - val_accuracy: 0.9475\n",
      "Epoch 4/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 0.4092 - accuracy: 0.8795 - val_loss: 0.1559 - val_accuracy: 0.9563\n",
      "Epoch 5/15\n",
      "981/981 [==============================] - 37s 37ms/step - loss: 0.3333 - accuracy: 0.9042 - val_loss: 0.1633 - val_accuracy: 0.9513\n",
      "Epoch 6/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 0.3218 - accuracy: 0.9060 - val_loss: 0.1567 - val_accuracy: 0.9540\n",
      "Epoch 7/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 0.2946 - accuracy: 0.9155 - val_loss: 0.1089 - val_accuracy: 0.9665\n",
      "Epoch 8/15\n",
      "981/981 [==============================] - 36s 37ms/step - loss: 0.2653 - accuracy: 0.9256 - val_loss: 0.0702 - val_accuracy: 0.9814\n",
      "Epoch 9/15\n",
      "981/981 [==============================] - 35s 36ms/step - loss: 0.2570 - accuracy: 0.9287 - val_loss: 0.0636 - val_accuracy: 0.9819\n",
      "Epoch 10/15\n",
      "981/981 [==============================] - 36s 36ms/step - loss: 0.2587 - accuracy: 0.9279 - val_loss: 0.0933 - val_accuracy: 0.9717\n",
      "Epoch 11/15\n",
      "981/981 [==============================] - 35s 36ms/step - loss: 0.2627 - accuracy: 0.9288 - val_loss: 0.0681 - val_accuracy: 0.9805\n",
      "Epoch 12/15\n",
      "981/981 [==============================] - 35s 36ms/step - loss: 0.2486 - accuracy: 0.9339 - val_loss: 0.0517 - val_accuracy: 0.9851\n",
      "Epoch 13/15\n",
      "981/981 [==============================] - 35s 36ms/step - loss: 0.2263 - accuracy: 0.9391 - val_loss: 0.1707 - val_accuracy: 0.9541\n",
      "Epoch 14/15\n",
      "981/981 [==============================] - 36s 36ms/step - loss: 0.2614 - accuracy: 0.9289 - val_loss: 0.0559 - val_accuracy: 0.9834\n",
      "Epoch 15/15\n",
      "981/981 [==============================] - 36s 36ms/step - loss: 0.2244 - accuracy: 0.9399 - val_loss: 0.0621 - val_accuracy: 0.9829\n"
     ]
    }
   ],
   "source": [
    "epochs = 15\n",
    "history = model.fit(X_train, y_train, batch_size=32, epochs=epochs, validation_data=(X_test, y_test))\n",
    "model.save(\"my_model.h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b3a9b8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aaba0de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9423594615993666\n"
     ]
    }
   ],
   "source": [
    "#testing accuracy on test dataset\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_test = pd.read_csv('Test.csv')\n",
    "\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(img)\n",
    "    image = image.resize((30,30))\n",
    "    data.append(np.array(image))\n",
    "\n",
    "X_test=np.array(data)\n",
    "\n",
    "#pred = model.predict_classes(X_test)\n",
    "pred = np.argmax(model.predict(X_test), axis=-1)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(labels, pred))\n",
    "\n",
    "#model.save(‘traffic_classifier.h5’)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97b10cde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.10 64-bit",
   "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": "3.9.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "faccd6973bf53781eb68e615b0b0cd6e633cafd9c6e224548883e732ccb27332"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}