Code/test_model_VGG19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8870f399",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "from tensorflow.keras.utils import load_img\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a2915f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Leemos el modelo guardado\n",
    "model = load_model('../../model/classification/vgg19_trainable_true_best_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "501c48e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_dir = '../../dataset/classification/PCB_Dataset_Split/test'\n",
    "size = (224, 224)\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adb21285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1716 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator(rescale = 1. / 255)\n",
    "test_generator = test_datagen.flow_from_directory(test_data_dir,\n",
    "                                                  target_size = size,\n",
    "                                                  keep_aspect_ratio = True,\n",
    "                                                  batch_size = batch_size,\n",
    "                                                  shuffle = False,\n",
    "                                                  class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62499652",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiru\\AppData\\Local\\Temp\\ipykernel_19588\\558093203.py:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  test_score = model.evaluate_generator(test_generator, batch_size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] accuracy: 99.32%\n",
      "[INFO] Loss:  0.020894860848784447\n"
     ]
    }
   ],
   "source": [
    "test_score = model.evaluate_generator(test_generator, batch_size)\n",
    "\n",
    "print(\"[INFO] accuracy: {:.2f}%\".format(test_score[1] * 100))\n",
    "print(\"[INFO] Loss: \",test_score[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3be33f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the confusion matrix. Set Normalize = True/False\n",
    "\n",
    "def plot_confusion_matrix(cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    \n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        cm = np.around(cm, decimals=2)\n",
    "        cm[np.isnan(cm)] = 0.0\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "    \n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')#Print the Target names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a252f5c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54/54 [==============================] - 32s 598ms/step\n",
      "Confusion Matrix\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Print the Target names\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools \n",
    "\n",
    "#shuffle=False\n",
    "\n",
    "target_names = []\n",
    "for key in test_generator.class_indices:\n",
    "    target_names.append(key)\n",
    "\n",
    "# print(target_names)#Confution Matrix\n",
    "Y_pred = model.predict(test_generator)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "print('Confusion Matrix')\n",
    "\n",
    "cm = confusion_matrix(test_generator.classes, y_pred)\n",
    "plot_confusion_matrix(cm, target_names, title='Confusion Matrix', normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfba2244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108/108 [==============================] - 10s 93ms/step\n",
      "Confusion Matrix\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Print the Target names\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools \n",
    "\n",
    "#shuffle=False\n",
    "\n",
    "target_names = []\n",
    "for key in test_generator.class_indices:\n",
    "    target_names.append(key)\n",
    "\n",
    "# print(target_names)#Confution Matrix\n",
    "Y_pred = model.predict(test_generator)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "print('Confusion Matrix')\n",
    "\n",
    "cm = confusion_matrix(test_generator.classes, y_pred)\n",
    "plot_confusion_matrix(cm, target_names, title='Confusion Matrix', normalize=False)\n",
    "\n",
    "#Print Classification Report\n",
    "#print('Classification Report')\n",
    "#print(classification_report(test_generator.classes, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49a4c65",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}