evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 147 images belonging to 4 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 21:49:45.800652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 6s 171ms/step - loss: 0.7067 - accuracy: 0.9388\n",
      "Test Loss: 0.7067480087280273\n",
      "Test Accuracy: 0.9387755393981934\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 21:49:51.956021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 3s 170ms/step\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      " AppleBlackrot       0.41      0.41      0.41        49\n",
      "  AppleHealthy       0.25      0.23      0.24        26\n",
      "     AppleScab       0.36      0.34      0.35        35\n",
      "Cedarapplerust       0.17      0.19      0.18        37\n",
      "\n",
      "      accuracy                           0.31       147\n",
      "     macro avg       0.30      0.29      0.30       147\n",
      "  weighted avg       0.31      0.31      0.31       147\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from scikitplot.metrics import plot_confusion_matrix\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "\n",
    "# Load the trained model\n",
    "model = keras.models.load_model('Apple_Disease_Detection.h5')\n",
    "\n",
    "# Load the test dataset\n",
    "test_dir = './valid'\n",
    "IMG_SIZE = (256, 256)\n",
    "BATCH_SIZE = 8\n",
    "\n",
    "test_gen = ImageDataGenerator(rescale=1./255)\n",
    "test_set = test_gen.flow_from_directory(\n",
    "    test_dir,\n",
    "    class_mode='categorical',\n",
    "    target_size=IMG_SIZE,\n",
    "    batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(test_set)\n",
    "print('Test Loss:', loss)\n",
    "print('Test Accuracy:', accuracy)\n",
    "\n",
    "# Generate predictions\n",
    "predictions = model.predict(test_set)\n",
    "predicted_classes = np.argmax(predictions, axis=1)\n",
    "\n",
    "# True labels\n",
    "true_classes = test_set.classes\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(true_classes, predicted_classes)\n",
    "plot_confusion_matrix(true_classes, predicted_classes)\n",
    "\n",
    "# Classification report\n",
    "class_labels = test_set.class_indices.keys()\n",
    "report = classification_report(true_classes, predicted_classes, target_names=class_labels)\n",
    "print(report)\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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}