ConvolutionalModel_Project_multiple_inputs final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests  \n",
    "import seaborn as sn\n",
    "import os, shutil\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, BatchNormalization,Concatenate,Input\n",
    "\n",
    "import pickle\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from tensorflow.keras import models, layers, optimizers\n",
    "from keras.preprocessing import image\n",
    "import keras_metrics\n",
    "\n",
    "from spicy import ndimage\n",
    "import imageio\n",
    "\n",
    "from tensorflow.keras.utils import Sequence, to_categorical\n",
    "\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow.keras.optimizers import RMSprop\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"fitzpatrick17k.csv\")\n",
    "data = df[df['url'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get unique labels\n",
    "labels = data['label'].unique()\n",
    "dw_dataset_dir = 'C:/Users/liita/OneDrive/Documentos/Nova IMS/Deep learning/Proyecto/Descarga Imagenes'\n",
    "\n",
    "for label in labels:\n",
    "    os.makedirs(os.path.join(dw_dataset_dir, label), exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Images corrupted image files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Images corrupted image files, unsupported image formats, or incorrect file paths \n",
    "\n",
    "# dirs = [dw_dataset_dir]\n",
    "# for dir in dirs:\n",
    "\n",
    "#     for label in labels:\n",
    "#         label_dir = os.path.join(dir, label)\n",
    "#         for fname in os.listdir(label_dir):\n",
    "#             try:\n",
    "#                 img = cv2.imread(os.path.join(label_dir, fname))\n",
    "#                 if img is None:\n",
    "#                     print('File {} is not a valid image file'.format(fname), label)\n",
    "#                     #delete the corrupted image file\n",
    "#                     os.remove(os.path.join(label_dir, fname))\n",
    "\n",
    "#             except:\n",
    "#                 print('File {} is not a valid image file'.format(fname), label)\n",
    "#                 os.remove(os.path.join(label_dir, fname))\n",
    "#                 dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# with open('dataset.csv', 'w') as file:\n",
    "#     # Loop through each directory and file\n",
    "#     for folder_name in os.listdir(dw_dataset_dir):\n",
    "#         folder_path = os.path.join(dw_dataset_dir, folder_name)\n",
    "#         if os.path.isdir(folder_path):\n",
    "#             for image_name in os.listdir(folder_path):\n",
    "#                 image_path = os.path.join(folder_path, image_name)\n",
    "#                 with Image.open(image_path) as img:\n",
    "#                     # Resize the image if necessary (no grayscale conversion)\n",
    "#                     img = img.resize((50, 50))  # Resize to 28x28 pixels\n",
    "                    \n",
    "#                     # Convert image to a numpy array (keep RGB channels)\n",
    "#                     img_array = np.array(img, dtype=np.uint8)\n",
    "                    \n",
    "#                     # Flatten the image array\n",
    "#                     flat_img_array = img_array.flatten()\n",
    "                    \n",
    "#                     # Convert array to string and write to file, include label\n",
    "#                     line = ','.join(map(str, flat_img_array)) + ',' + folder_name + '\\n'\n",
    "#                     file.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liita\\AppData\\Local\\Temp\\ipykernel_3484\\1871293629.py:2: DtypeWarning: Columns (2500) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data = pd.read_csv('dataset.csv', header=None)\n"
     ]
    }
   ],
   "source": [
    "# Read the CSV file\n",
    "data = pd.read_csv('dataset.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_data = data.iloc[0, :-1]  \n",
    "image_array = np.array(image_data, dtype=np.uint8).reshape(50, 50, 3)\n",
    "\n",
    "\n",
    "plt.imshow(image_array)\n",
    "plt.axis('off')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 9915\n",
      "Validation set size: 3305\n",
      "Test set size: 3306\n"
     ]
    }
   ],
   "source": [
    "X = data.iloc[:, :-1]  # Features (pixel values)\n",
    "y = data.iloc[:, -1]   # Labels\n",
    "\n",
    "# Split the data into training and temporary (test + validation) sets\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "\n",
    "print(\"Training set size:\", X_train.shape[0])\n",
    "print(\"Validation set size:\", X_val.shape[0])\n",
    "print(\"Test set size:\", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\series.py:1022: RuntimeWarning: invalid value encountered in cast\n",
      "  arr = np.asarray(values, dtype=dtype)\n"
     ]
    }
   ],
   "source": [
    "def rgb_to_grayscale(rgb_values):\n",
    "    # Reshape row to 50x50x3 array\n",
    "    rgb_array = np.array(rgb_values, dtype=np.uint8).reshape(50, 50, 3)\n",
    "    # --Calculate grayscale values using the formula. The formula aligns with the Rec. 709 standard (also used in HDTV formats)\n",
    "    grayscale_array = 0.2989 * rgb_array[:,:,0] + 0.5870 * rgb_array[:,:,1] + 0.1140 * rgb_array[:,:,2]\n",
    "    return np.round(grayscale_array).astype(np.uint8).flatten()\n",
    "\n",
    "\n",
    "X_train = X_train.apply(pd.to_numeric, errors='coerce')\n",
    "grayscale_images = X_train.apply(lambda x: rgb_to_grayscale(x), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = X_train.iloc[11]\n",
    "img =  np.array(img, dtype=np.uint8).reshape(50, 50, 3)\n",
    "\n",
    "img_in = rgb_to_grayscale(img)\n",
    "image_reshaped = np.array(img_in, dtype=np.uint8).reshape(50, 50)\n",
    "\n",
    "# Plot the image\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(131); plt.imshow(img)\n",
    "plt.subplot(132); plt.imshow(image_reshaped, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram Equalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogram_eq(img, bines):\n",
    "\n",
    "    img = np.array(img, dtype=np.uint8).reshape(50, 50, 3)\n",
    "\n",
    "    #count the number of pixels\n",
    "    n = img.size\n",
    "\n",
    "    #Get tje gary level histogram\n",
    "    r = img.flatten() #0=black 255=white\n",
    "    histogram, bins = np.histogram(r, bines, [0,bines])\n",
    "\n",
    "    Pf = histogram/n\n",
    "\n",
    "    #Get the cumulative distribution function\n",
    "    cdf = np.cumsum(Pf)\n",
    "    T = (bines-1)*cdf\n",
    "    Trounding = np.round(T).astype('uint8')\n",
    "\n",
    "    #Apply the transformation\n",
    "    img_eq = Trounding[r].reshape(img.shape)\n",
    "\n",
    "    return img_eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = X_train.iloc[11]\n",
    "img =  np.array(img, dtype=np.uint8).reshape(50, 50, 3)\n",
    "\n",
    "img_in = histogram_eq(img,250)\n",
    "image_reshaped = np.array(img_in, dtype=np.uint8).reshape(50, 50,3)\n",
    "\n",
    "# Plot the image\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(131); plt.imshow(img)\n",
    "plt.subplot(132); plt.imshow(image_reshaped, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thresholding based segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def thresholding(f, L):\n",
    "    f_tr = np.zeros(f.shape).astype('uint8')  # Initialize to zeros (background)\n",
    "    f_tr[f <= L] = 255  \n",
    "    return f_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Threshold 100')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = X_train.iloc[11]\n",
    "img =  np.array(img, dtype=np.uint8).reshape(50, 50, 3)\n",
    "\n",
    "img_in = histogram_eq(img,250)\n",
    "\n",
    "\n",
    "f = np.array(img_in, dtype=np.uint8).reshape(50, 50,3)\n",
    "\n",
    "f = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "#mean_v= np.mean(f)\n",
    "hist,_=np.histogram(f, bins=256, range=(0,256))\n",
    "\n",
    "\n",
    "t_thres6= thresholding(f, 60)\n",
    "hist_t6, _ = np.histogram(t_thres6*256, bins = 256 , range=(0,256))\n",
    "\n",
    "t_thres100= thresholding(f, 100)\n",
    "hist_t100, _ = np.histogram(t_thres100*256, bins = 256 , range=(0,256))\n",
    "\n",
    "# Plot the image\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(131); plt.imshow(f, cmap='gray'); plt.axis ('off')\n",
    "plt.subplot(132); plt.imshow(t_thres6, cmap='gray'); plt.axis ('off');plt.title('Threshold 150')\n",
    "plt.subplot(133); plt.imshow(t_thres100, cmap='gray'); plt.axis ('off');plt.title('Threshold 100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Otsu thresholding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Otsu thresholding\n",
    "def otsu_threshold(img):\n",
    "    max_L = 256\n",
    "    hist_t, bin_edges = np.histogram(img, bins=max_L, range=(0, max_L))\n",
    "    M = np.product(img.shape)\n",
    "    best_threshold = 0\n",
    "    min_var = float('inf')\n",
    "\n",
    "    for L in range(1, max_L - 1):\n",
    "        w_a = np.sum(hist_t[:L]) / M\n",
    "        w_b = np.sum(hist_t[L:]) / M\n",
    "        sig_a = np.var(img[img < bin_edges[L]])\n",
    "        sig_b = np.var(img[img >= bin_edges[L]])\n",
    "\n",
    "        within_class_variance = w_a * sig_a + w_b * sig_b\n",
    "        if within_class_variance < min_var:\n",
    "            min_var = within_class_variance\n",
    "            best_threshold = L\n",
    "\n",
    "    img_t = thresholding(img, best_threshold)\n",
    "    return img_t, best_threshold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rgb_to_grayscale(rgb_values):\n",
    "    rgb_array = np.array(rgb_values, dtype=np.uint8).reshape(50, 50, 3)\n",
    "    grayscale_array = 0.2989 * rgb_array[:,:,0] + 0.5870 * rgb_array[:,:,1] + 0.1140 * rgb_array[:,:,2]\n",
    "    return np.round(grayscale_array).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(X):\n",
    "    X = X.apply(pd.to_numeric, errors='coerce')\n",
    "    grayscale_image = rgb_to_grayscale(X)  \n",
    "    otsu_image, threshold = otsu_threshold(grayscale_image)\n",
    "\n",
    "    eq_image = histogram_eq(img, 256)    \n",
    "\n",
    "    return otsu_image, grayscale_image, eq_image, X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liita\\AppData\\Local\\Temp\\ipykernel_3484\\1903017961.py:4: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
      "  otsu_image, threshold = otsu_threshold(grayscale_image)\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3787: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = X_train.iloc[13]\n",
    "img_in = pipeline(img)[0]\n",
    "\n",
    "# Plot the image\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(121); plt.imshow(np.array(img, dtype=np.uint8).reshape(50, 50, 3))\n",
    "plt.subplot(122); plt.imshow(img_in, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Augment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 180\n",
    "\n",
    "resize_and_rescale = Sequential([\n",
    "  layers.Resizing(IMG_SIZE, IMG_SIZE),\n",
    "  layers.Rescaling(1./255)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_augmentation = Sequential([\n",
    "  layers.RandomFlip(\"horizontal_and_vertical\"),\n",
    "  layers.RandomRotation(0.4),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply pipeline to all datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(df):\n",
    "    df = df.apply(pd.to_numeric, errors='coerce')\n",
    "    \n",
    "    def process_row(row):\n",
    "        grayscale_image = rgb_to_grayscale(row)  \n",
    "        otsu_image, _ = otsu_threshold(grayscale_image)\n",
    "        eq_image = histogram_eq(row, 256)\n",
    "        return pd.Series({ 'grayscale_image': grayscale_image, 'otsu_image': otsu_image, 'eq_image': eq_image})\n",
    "\n",
    "    \n",
    "    processed_df = df.apply(process_row, axis=1) # To each row\n",
    "\n",
    "    return processed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liita\\AppData\\Local\\Temp\\ipykernel_3484\\453918214.py:6: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
      "  otsu_image, _ = otsu_threshold(grayscale_image)\n",
      "C:\\Users\\liita\\AppData\\Local\\Temp\\ipykernel_3484\\453918214.py:6: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
      "  otsu_image, _ = otsu_threshold(grayscale_image)\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3787: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\series.py:1022: RuntimeWarning: invalid value encountered in cast\n",
      "  arr = np.asarray(values, dtype=dtype)\n",
      "C:\\Users\\liita\\AppData\\Local\\Temp\\ipykernel_3484\\453918214.py:6: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
      "  otsu_image, _ = otsu_threshold(grayscale_image)\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3787: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\series.py:1022: RuntimeWarning: invalid value encountered in cast\n",
      "  arr = np.asarray(values, dtype=dtype)\n"
     ]
    }
   ],
   "source": [
    "results_train =  pipeline(X_train)\n",
    "results_val =  pipeline(X_val)\n",
    "results_test =  pipeline(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datasets \n",
    "grayscale_train =  pd.DataFrame([x.flatten() for x in results_train['grayscale_image']])\n",
    "otsu_train = pd.DataFrame([x.flatten() for x in results_train['otsu_image']])\n",
    "eq_train = pd.DataFrame([x.flatten() for x in results_train['eq_image']])\n",
    "\n",
    "grayscale_val = pd.DataFrame([x.flatten() for x in results_val['grayscale_image']])\n",
    "otsu_val = pd.DataFrame([x.flatten() for x in results_val['otsu_image']])\n",
    "eq_val =  pd.DataFrame([x.flatten() for x in results_val['eq_image']])\n",
    "\n",
    "grayscale_test =  pd.DataFrame([x.flatten() for x in results_test['grayscale_image']])\n",
    "otsu_test =  pd.DataFrame([x.flatten() for x in results_test['otsu_image']])\n",
    "eq_test = pd.DataFrame([x.flatten() for x in results_test['eq_image']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image input branch 1\n",
    "image_input = Input(shape=(50, 50, 1))\n",
    "x1 = resize_and_rescale\n",
    "x1 =  data_augmentation\n",
    "x1 = Conv2D(32, (3, 3), activation='relu')(image_input)\n",
    "x1 = MaxPooling2D((2, 2))(x1)\n",
    "# x1 = Conv2D(64, (3, 3), activation='sigmoid')(x1)\n",
    "# x1 = MaxPooling2D((2, 2))(x1)\n",
    "x1 = Conv2D(128, (3, 3), activation='relu')(x1)\n",
    "x1 = MaxPooling2D((2, 2))(x1)\n",
    "x1 = Conv2D(128, (3, 3), activation='relu')(x1)\n",
    "x1 = MaxPooling2D((2, 2))(x1)\n",
    "x1 = Flatten()(x1)\n",
    "\n",
    "# Image input branch 2\n",
    "image_input2 = Input(shape=(50, 50, 1))\n",
    "x2 = resize_and_rescale\n",
    "x2 =  data_augmentation\n",
    "x2 = Conv2D(32, (3, 3), activation='relu')(image_input2)\n",
    "x2 = MaxPooling2D((2, 2))(x2)\n",
    "# x2 = Conv2D(64, (3, 3), activation='sigmoid')(x2)\n",
    "# x2 = MaxPooling2D((2, 2))(x2)\n",
    "x2 = Conv2D(128, (3, 3), activation='relu')(x2)\n",
    "x2 = MaxPooling2D((2, 2))(x2)\n",
    "x2 = Conv2D(128, (3, 3), activation='relu')(x2)\n",
    "x2 = MaxPooling2D((2, 2))(x2)\n",
    "x2 = Flatten()(x2)\n",
    "\n",
    "# Image input branch 3\n",
    "image_input3 = Input(shape=(50, 50, 3))\n",
    "x3 = resize_and_rescale\n",
    "x3 =  data_augmentation\n",
    "x3 = Conv2D(32, (3, 3), activation='relu')(image_input2)\n",
    "x3 = MaxPooling2D((2, 2))(x3)\n",
    "# x3 = Conv2D(64, (3, 3), activation='sigmoid')(x3)\n",
    "# x3 = MaxPooling2D((2, 2))(x3)\n",
    "x3 = Conv2D(128, (3, 3), activation='relu')(x3)\n",
    "x3 = MaxPooling2D((2, 2))(x3)\n",
    "x3 = Conv2D(128, (3, 3), activation='relu')(x3)\n",
    "x3 = MaxPooling2D((2, 2))(x3)\n",
    "x3 = Flatten()(x3)\n",
    "\n",
    "\n",
    "# Concatenate \n",
    "combined = Concatenate()([x1, x2,x3])\n",
    "\n",
    "# Fully connected layers\n",
    "x = Dense(512, activation='relu')(combined)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "output = Dense(115, activation='softmax')(x)  \n",
    "\n",
    "\n",
    "model = Model(inputs=[image_input, image_input2,image_input3], outputs=output)\n",
    "\n",
    "model.compile(optimizer= RMSprop(),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print model summary\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_grayscale = grayscale_train.astype('float32')\n",
    "X_train_otsu = otsu_train.astype('float32')\n",
    "X_train_eq = eq_train.astype('float32')\n",
    "\n",
    "X_val_grayscale = grayscale_val.astype('float32')\n",
    "X_val_otsu = otsu_val.astype('float32')\n",
    "X_val_eq = eq_val.astype('float32')\n",
    "\n",
    "X_test_grayscale = grayscale_test.astype('float32')\n",
    "X_test_otsu = otsu_test.astype('float32')\n",
    "X_test_eq = eq_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Label Encoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "\n",
    "\n",
    "num_classes = len(label_encoder.classes_)\n",
    "y_train_one_hot = to_categorical(y_train_encoded, num_classes=num_classes)\n",
    "\n",
    "\n",
    "y_val_encoded = label_encoder.transform(y_val)\n",
    "y_val_one_hot = to_categorical(y_val_encoded, num_classes=num_classes)\n",
    "\n",
    "y_test_encoded = label_encoder.transform(y_test)\n",
    "y_test_one_hot = to_categorical(y_test_encoded, num_classes=num_classes)\n",
    "\n",
    "\n",
    "X_train_grayscale = np.reshape(X_train_grayscale, (-1, 50, 50, 1))\n",
    "X_val_grayscale = np.reshape(X_val_grayscale, (-1, 50, 50, 1))\n",
    "X_test_grayscale = np.reshape(X_test_grayscale, (-1, 50, 50, 1))\n",
    "\n",
    "X_train_otsu = np.reshape(X_train_otsu, (-1, 50, 50, 1))\n",
    "X_val_otsu = np.reshape(X_val_otsu, (-1, 50, 50, 1))\n",
    "X_test_otsu = np.reshape(X_test_otsu, (-1, 50, 50, 1))\n",
    "\n",
    "X_train_eq = np.reshape(X_train_eq, (-1, 50, 50, 3))\n",
    "X_val_eq = np.reshape(X_val_eq, (-1, 50, 50, 3))\n",
    "X_test_eq = np.reshape(X_test_eq, (-1, 50, 50, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 2s/step - accuracy: 0.0228 - loss: 5.2979 - val_accuracy: 0.0148 - val_loss: 9.8150\n",
      "Epoch 2/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 2s/step - accuracy: 0.0388 - loss: 4.7052 - val_accuracy: 0.0260 - val_loss: 8.4973\n",
      "Epoch 3/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 1s/step - accuracy: 0.0551 - loss: 4.5058 - val_accuracy: 0.0191 - val_loss: 5.2617\n",
      "Epoch 4/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 2s/step - accuracy: 0.0727 - loss: 4.3055 - val_accuracy: 0.0194 - val_loss: 5.6353\n",
      "Epoch 5/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 2s/step - accuracy: 0.0864 - loss: 4.1377 - val_accuracy: 0.0297 - val_loss: 5.2532\n",
      "Epoch 6/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 1s/step - accuracy: 0.1142 - loss: 3.9343 - val_accuracy: 0.0266 - val_loss: 5.0697\n",
      "Epoch 7/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.1416 - loss: 3.7578 - val_accuracy: 0.0342 - val_loss: 5.1815\n",
      "Epoch 8/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.1960 - loss: 3.4635 - val_accuracy: 0.0436 - val_loss: 4.8745\n",
      "Epoch 9/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.2663 - loss: 3.1032 - val_accuracy: 0.0629 - val_loss: 4.7777\n",
      "Epoch 10/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.3717 - loss: 2.6274 - val_accuracy: 0.0687 - val_loss: 4.6060\n",
      "Epoch 11/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 2s/step - accuracy: 0.5014 - loss: 2.1468 - val_accuracy: 0.0766 - val_loss: 4.7174\n",
      "Epoch 12/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 2s/step - accuracy: 0.6228 - loss: 1.6830 - val_accuracy: 0.0808 - val_loss: 4.7336\n",
      "Epoch 13/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.7432 - loss: 1.2227 - val_accuracy: 0.0726 - val_loss: 5.1391\n",
      "Epoch 14/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.7947 - loss: 0.9687 - val_accuracy: 0.0880 - val_loss: 4.8095\n",
      "Epoch 15/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 2s/step - accuracy: 0.8719 - loss: 0.6510 - val_accuracy: 0.0859 - val_loss: 5.0229\n",
      "Epoch 16/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 1s/step - accuracy: 0.9020 - loss: 0.4912 - val_accuracy: 0.0766 - val_loss: 5.6990\n",
      "Epoch 17/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 932ms/step - accuracy: 0.9314 - loss: 0.3741 - val_accuracy: 0.0720 - val_loss: 6.2008\n",
      "Epoch 18/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 931ms/step - accuracy: 0.9358 - loss: 0.3036 - val_accuracy: 0.0947 - val_loss: 5.8907\n",
      "Epoch 19/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 928ms/step - accuracy: 0.9549 - loss: 0.2386 - val_accuracy: 0.0890 - val_loss: 6.0061\n",
      "Epoch 20/20\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 928ms/step - accuracy: 0.9683 - loss: 0.1778 - val_accuracy: 0.1005 - val_loss: 6.3187\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    x=[X_train_grayscale, X_train_otsu, X_train_eq],  # Input data for each branch\n",
    "    y=y_train_one_hot ,  # Target labels\n",
    "    epochs=20,\n",
    "    batch_size=200,\n",
    "    validation_data=([X_val_grayscale, X_val_otsu, X_val_eq], y_val_one_hot)  # Validation data for each branch\n",
    "    ,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Save the model\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(121)\n",
    "plt.plot(history.history['loss'], label='train loss')\n",
    "plt.plot(history.history['val_loss'], label='validation loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['accuracy'], label='train accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 214 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000029FC2197060> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 214 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000029FC2197060> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 51ms/step\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "\n",
    "test_predictions = model.predict([X_test_grayscale, X_test_otsu, X_test_eq])\n",
    "predicted_classes = np.argmax(test_predictions, axis=1)\n",
    "\n",
    "true_classes = np.argmax(y_test_one_hot, axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.09528130671506352\n",
      "F1 Score: 0.08685410464559425\n",
      "F1 macroScore: 0.07928348919055205\n",
      "Precision: 0.1068728561224557\n",
      "Recall: 0.09528130671506352\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\liita\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: 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, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calcular la precisión\n",
    "accuracy = accuracy_score(true_classes, predicted_classes)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "#Print F1 score\n",
    "from sklearn.metrics import f1_score\n",
    "f1 = f1_score(true_classes, predicted_classes, average='weighted')\n",
    "print(\"F1 Score:\", f1)\n",
    "\n",
    "f1 = f1_score(true_classes, predicted_classes, average='macro')\n",
    "print(\"F1 macroScore:\", f1)\n",
    "\n",
    "#print precision and recall\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "precision = precision_score(true_classes, predicted_classes, average='weighted')\n",
    "recall = recall_score(true_classes, predicted_classes, average='weighted')\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grad-CAM Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "\n",
    "# Display\n",
    "from IPython.display import Image, display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_builder = keras.applications.xception.Xception\n",
    "img_size = (299, 299)\n",
    "preprocess_input = keras.applications.xception.preprocess_input\n",
    "decode_predictions = keras.applications.xception.decode_predictions\n",
    "\n",
    "last_conv_layer_name = \"block14_sepconv2_act\"\n",
    "\n",
    "# The local path to our target image\n",
    "img_path =\"C:/Users/liita/OneDrive/Documentos/Nova IMS/Deep learning/Proyecto/Descarga Imagenes/porphyria/img9150.jpg\" #Mal clasificado de uno que no clasifica bien\n",
    "\n",
    "display(Image(img_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_img_array(img_path, size):\n",
    "    img = keras.utils.load_img(img_path, target_size=size)\n",
    "    array = keras.utils.img_to_array(img)\n",
    "    array = np.expand_dims(array, axis=0)\n",
    "    return array\n",
    "\n",
    "\n",
    "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n",
    "    grad_model = keras.models.Model(\n",
    "        model.inputs, [model.get_layer(last_conv_layer_name).output, model.output]\n",
    "    )\n",
    "    with tf.GradientTape() as tape:\n",
    "        last_conv_layer_output, preds = grad_model(img_array)\n",
    "        if pred_index is None:\n",
    "            pred_index = tf.argmax(preds[0])\n",
    "        class_channel = preds[:, pred_index]\n",
    "\n",
    "    grads = tape.gradient(class_channel, last_conv_layer_output)\n",
    "\n",
    "    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n",
    "\n",
    "\n",
    "    last_conv_layer_output = last_conv_layer_output[0]\n",
    "    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]\n",
    "    heatmap = tf.squeeze(heatmap)\n",
    "\n",
    "    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n",
    "    return heatmap.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
      "Predicted: [('n04357314', 'sunscreen', 4.983275)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "img_array = preprocess_input(get_img_array(img_path, size=img_size))\n",
    "\n",
    "model = model_builder(weights=\"imagenet\")\n",
    "\n",
    "model.layers[-1].activation = None\n",
    "\n",
    "preds = model.predict(img_array)\n",
    "print(\"Predicted:\", decode_predictions(preds, top=1)[0])\n",
    "\n",
    "heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)\n",
    "\n",
    "plt.matshow(heatmap)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def save_and_display_gradcam(img_path, heatmap, cam_path=\"cam.jpg\", alpha=0.4):\n",
    "    img = keras.utils.load_img(img_path)\n",
    "    img = keras.utils.img_to_array(img)\n",
    "    heatmap = np.uint8(255 * heatmap)\n",
    "\n",
    "    jet = mpl.colormaps[\"jet\"]\n",
    "\n",
    "    jet_colors = jet(np.arange(256))[:, :3]\n",
    "    jet_heatmap = jet_colors[heatmap]\n",
    "\n",
    "    jet_heatmap = keras.utils.array_to_img(jet_heatmap)\n",
    "    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
    "    jet_heatmap = keras.utils.img_to_array(jet_heatmap)\n",
    "\n",
    "    superimposed_img = jet_heatmap * alpha + img\n",
    "    superimposed_img = keras.utils.array_to_img(superimposed_img)\n",
    "\n",
    "    superimposed_img.save(cam_path)\n",
    "\n",
    "    display(Image(cam_path))\n",
    "\n",
    "\n",
    "save_and_display_gradcam(img_path, heatmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import MobileNetV2\n",
    "from keras.layers import Dense, GlobalAveragePooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(115, activation='relu')(x)\n",
    "predictions = Dense(num_classes, activation='softmax')(x)  # num_classes \n",
    "\n",
    "\n",
    "model2 = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "model2.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 3s/step - accuracy: 0.1291 - loss: 3.8821 - val_accuracy: 0.0829 - val_loss: 4.1967\n",
      "Epoch 2/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 3s/step - accuracy: 0.1290 - loss: 3.8451 - val_accuracy: 0.0832 - val_loss: 4.2122\n",
      "Epoch 3/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 3s/step - accuracy: 0.1316 - loss: 3.8173 - val_accuracy: 0.0865 - val_loss: 4.1688\n",
      "Epoch 4/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m180s\u001b[0m 4s/step - accuracy: 0.1385 - loss: 3.7498 - val_accuracy: 0.0856 - val_loss: 4.1525\n",
      "Epoch 5/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 3s/step - accuracy: 0.1510 - loss: 3.7071 - val_accuracy: 0.0844 - val_loss: 4.1830\n",
      "Epoch 6/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.1509 - loss: 3.6779 - val_accuracy: 0.0920 - val_loss: 4.1939\n",
      "Epoch 7/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.1621 - loss: 3.6343 - val_accuracy: 0.0908 - val_loss: 4.1680\n",
      "Epoch 8/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 3s/step - accuracy: 0.1760 - loss: 3.5973 - val_accuracy: 0.0884 - val_loss: 4.1552\n",
      "Epoch 9/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.1855 - loss: 3.5452 - val_accuracy: 0.0932 - val_loss: 4.1702\n",
      "Epoch 10/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.1837 - loss: 3.4874 - val_accuracy: 0.0896 - val_loss: 4.1668\n",
      "Epoch 11/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 3s/step - accuracy: 0.1927 - loss: 3.4793 - val_accuracy: 0.1011 - val_loss: 4.1519\n",
      "Epoch 12/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.2021 - loss: 3.4256 - val_accuracy: 0.0962 - val_loss: 4.1648\n",
      "Epoch 13/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.2038 - loss: 3.3967 - val_accuracy: 0.1068 - val_loss: 4.1448\n",
      "Epoch 14/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 3s/step - accuracy: 0.2173 - loss: 3.3551 - val_accuracy: 0.0998 - val_loss: 4.1327\n",
      "Epoch 15/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.2219 - loss: 3.3265 - val_accuracy: 0.0953 - val_loss: 4.1733\n",
      "Epoch 16/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.2265 - loss: 3.2877 - val_accuracy: 0.1026 - val_loss: 4.1821\n",
      "Epoch 17/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.2294 - loss: 3.2587 - val_accuracy: 0.0962 - val_loss: 4.1775\n",
      "Epoch 18/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 3s/step - accuracy: 0.2377 - loss: 3.2398 - val_accuracy: 0.1056 - val_loss: 4.1965\n",
      "Epoch 19/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 3s/step - accuracy: 0.2518 - loss: 3.1994 - val_accuracy: 0.1083 - val_loss: 4.2012\n",
      "Epoch 20/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m180s\u001b[0m 4s/step - accuracy: 0.2580 - loss: 3.1282 - val_accuracy: 0.1026 - val_loss: 4.2208\n",
      "Epoch 21/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 4s/step - accuracy: 0.2544 - loss: 3.1307 - val_accuracy: 0.1029 - val_loss: 4.2450\n",
      "Epoch 22/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 4s/step - accuracy: 0.2664 - loss: 3.0868 - val_accuracy: 0.1020 - val_loss: 4.2352\n",
      "Epoch 23/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 4s/step - accuracy: 0.2791 - loss: 3.0452 - val_accuracy: 0.1005 - val_loss: 4.2021\n",
      "Epoch 24/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 4s/step - accuracy: 0.2801 - loss: 3.0313 - val_accuracy: 0.1077 - val_loss: 4.1859\n",
      "Epoch 25/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 4s/step - accuracy: 0.2782 - loss: 3.0123 - val_accuracy: 0.1053 - val_loss: 4.2318\n",
      "Epoch 26/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 4s/step - accuracy: 0.2933 - loss: 2.9715 - val_accuracy: 0.0983 - val_loss: 4.2566\n",
      "Epoch 27/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 4s/step - accuracy: 0.2994 - loss: 2.9393 - val_accuracy: 0.0944 - val_loss: 4.2498\n",
      "Epoch 28/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 4s/step - accuracy: 0.3075 - loss: 2.9140 - val_accuracy: 0.1044 - val_loss: 4.2565\n",
      "Epoch 29/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 4s/step - accuracy: 0.3154 - loss: 2.8547 - val_accuracy: 0.1011 - val_loss: 4.2645\n",
      "Epoch 30/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 4s/step - accuracy: 0.3142 - loss: 2.8664 - val_accuracy: 0.1044 - val_loss: 4.2850\n",
      "Epoch 31/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 4s/step - accuracy: 0.3202 - loss: 2.8139 - val_accuracy: 0.1056 - val_loss: 4.2958\n",
      "Epoch 32/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 4s/step - accuracy: 0.3256 - loss: 2.7900 - val_accuracy: 0.1098 - val_loss: 4.2420\n",
      "Epoch 33/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.3374 - loss: 2.7672 - val_accuracy: 0.1101 - val_loss: 4.2720\n",
      "Epoch 34/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.3428 - loss: 2.7444 - val_accuracy: 0.1092 - val_loss: 4.2994\n",
      "Epoch 35/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 3s/step - accuracy: 0.3492 - loss: 2.7194 - val_accuracy: 0.1089 - val_loss: 4.3232\n",
      "Epoch 36/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.3482 - loss: 2.6778 - val_accuracy: 0.1050 - val_loss: 4.3443\n",
      "Epoch 37/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 3s/step - accuracy: 0.3553 - loss: 2.6672 - val_accuracy: 0.1047 - val_loss: 4.3372\n",
      "Epoch 38/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 3s/step - accuracy: 0.3622 - loss: 2.6416 - val_accuracy: 0.0974 - val_loss: 4.3881\n",
      "Epoch 39/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.3636 - loss: 2.6220 - val_accuracy: 0.1113 - val_loss: 4.3340\n",
      "Epoch 40/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.3775 - loss: 2.5785 - val_accuracy: 0.1050 - val_loss: 4.3934\n",
      "Epoch 41/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m176s\u001b[0m 4s/step - accuracy: 0.3767 - loss: 2.5617 - val_accuracy: 0.1044 - val_loss: 4.4203\n",
      "Epoch 42/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.3784 - loss: 2.5707 - val_accuracy: 0.1053 - val_loss: 4.4556\n",
      "Epoch 43/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 3s/step - accuracy: 0.3926 - loss: 2.4976 - val_accuracy: 0.1080 - val_loss: 4.4391\n",
      "Epoch 44/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 3s/step - accuracy: 0.3917 - loss: 2.5056 - val_accuracy: 0.1005 - val_loss: 4.5297\n",
      "Epoch 45/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 3s/step - accuracy: 0.4004 - loss: 2.4769 - val_accuracy: 0.1044 - val_loss: 4.4326\n",
      "Epoch 46/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 3s/step - accuracy: 0.4036 - loss: 2.4363 - val_accuracy: 0.1077 - val_loss: 4.4076\n",
      "Epoch 47/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.4270 - loss: 2.4074 - val_accuracy: 0.1095 - val_loss: 4.4422\n",
      "Epoch 48/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.4198 - loss: 2.4037 - val_accuracy: 0.1005 - val_loss: 4.4785\n",
      "Epoch 49/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4226 - loss: 2.3543 - val_accuracy: 0.1098 - val_loss: 4.4793\n",
      "Epoch 50/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4337 - loss: 2.3287 - val_accuracy: 0.1110 - val_loss: 4.5063\n",
      "Epoch 51/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4333 - loss: 2.3167 - val_accuracy: 0.1068 - val_loss: 4.6134\n",
      "Epoch 52/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.4368 - loss: 2.3218 - val_accuracy: 0.1041 - val_loss: 4.6134\n",
      "Epoch 53/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4407 - loss: 2.2885 - val_accuracy: 0.1020 - val_loss: 4.6187\n",
      "Epoch 54/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 3s/step - accuracy: 0.4496 - loss: 2.2703 - val_accuracy: 0.1032 - val_loss: 4.6837\n",
      "Epoch 55/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.4579 - loss: 2.2430 - val_accuracy: 0.1068 - val_loss: 4.6241\n",
      "Epoch 56/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4616 - loss: 2.2203 - val_accuracy: 0.1104 - val_loss: 4.6905\n",
      "Epoch 57/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4587 - loss: 2.1958 - val_accuracy: 0.1110 - val_loss: 4.5838\n",
      "Epoch 58/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 3s/step - accuracy: 0.4724 - loss: 2.1724 - val_accuracy: 0.1074 - val_loss: 4.5882\n",
      "Epoch 59/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 3s/step - accuracy: 0.4697 - loss: 2.1738 - val_accuracy: 0.1123 - val_loss: 4.6879\n",
      "Epoch 60/60\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 3s/step - accuracy: 0.4842 - loss: 2.1322 - val_accuracy: 0.1032 - val_loss: 4.6942\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x29fe5f98ef0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess_images(images):\n",
    "    # Resize images to the expected size \n",
    "    return tf.image.resize(images, [224, 224])\n",
    "\n",
    "X_train_eq_resized = preprocess_images(X_train_eq)\n",
    "X_val_eq_resized = preprocess_images(X_val_eq)\n",
    "\n",
    "\n",
    "model2.fit(X_train_eq_resized, y_train_one_hot, epochs=60, batch_size=200,\n",
    "          validation_data=(X_val_eq_resized, y_val_one_hot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_builder = keras.applications.xception.Xception\n",
    "img_size = (299, 299)\n",
    "preprocess_input = keras.applications.xception.preprocess_input\n",
    "decode_predictions = keras.applications.xception.decode_predictions\n",
    "\n",
    "last_conv_layer_name = \"block14_sepconv2_act\"\n",
    "\n",
    "# The local path to our target image\n",
    "#img_path =\"C:/Users/liita/OneDrive/Documentos/Nova IMS/Deep learning/Proyecto/Descarga Imagenes/acne/img3434.jpg\"\n",
    "#img_path =\"C:/Users/liita/OneDrive/Documentos/Nova IMS/Deep learning/Proyecto/Descarga Imagenes/basal cell carcinoma/img2769.jpg\"\n",
    "\n",
    "img_path =\"C:/Users/liita/OneDrive/Documentos/Nova IMS/Deep learning/Proyecto/Descarga Imagenes/pediculosis lids/img12858.jpg\"\n",
    "\n",
    "display(Image(img_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 213 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000029FC2196B60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n",
      "Predicted: [('n01770081', 'harvestman', 7.0720444)]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "img_array = preprocess_input(get_img_array(img_path, size=img_size))\n",
    "\n",
    "\n",
    "model = model_builder(weights=\"imagenet\")\n",
    "\n",
    "\n",
    "model.layers[-1].activation = None\n",
    "\n",
    "\n",
    "preds = model.predict(img_array)\n",
    "print(\"Predicted:\", decode_predictions(preds, top=1)[0])\n",
    "\n",
    "\n",
    "heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)\n",
    "\n",
    "plt.matshow(heatmap)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_and_display_gradcam(img_path, heatmap)"
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}