initial commit

.ipynb_checkpoints/Untitled-checkpoint.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Untitled1-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "labeled-leone",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv('vgsales.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abroad-medline",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/imageclassification_plants-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "italic-negative",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Convo2D, MaxPool2D, Flatten, Dropout\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.optimizers import Adam\n",
+    "\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "import cv2\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "labels = ['diseased', 'healthy']\n",
+    "img_size = 224\n",
+    "def get_data(data_dir):\n",
+    "    data = []\n",
+    "    for label in labels:\n",
+    "        path = os.path.join(data_dir,label)\n",
+    "        class_num = labels.index(label)\n",
+    "        for img in os.listdir(path):\n",
+    "            \n",
+    "            try:\n",
+    "                img_arr = cv2.imread(os.path.join(path, img))[...,::-1] #to convert BGR to RGB format\n",
+    "                resized_arr = cv2.resize(img_arr, (img_size, img_size)) #Reshaping images to preferred size\n",
+    "                \n",
+    "                data.append([resized_arr, class_num])\n",
+    "        except Exception as e:\n",
+    "            print(e)\n",
+    "            \n",
+    "    return np.array(data)\n",
+    "\n",
+    "train = get_data('')\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/test-checkpoint.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

readme.txt

+dataset cannot be pushed to sliit github as it exceeds the allowed data