MSID (EfficientNetB5_TrueFalse).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82d3c0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: split-folders in c:\\users\\rajcs\\anaconda3\\lib\\site-packages (0.5.1)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pyserde 0.12.2 does not provide the extra 'json'\n"
     ]
    }
   ],
   "source": [
    "!pip install split-folders matplotlib opencv-python spic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0949933f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import os\n",
    "import splitfolders\n",
    "\n",
    "# image processing\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3a1318f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Copying files: 5680 files [01:08, 83.48 files/s] \n"
     ]
    }
   ],
   "source": [
    "splitfolders.ratio(\"Downloads/aug_msid/aug_mkp copy\", output=\"Downloads/aug_msidDN121/aug_mkp copy/EFNetB5_TrueFalse\", seed=1337, ratio=(0.8, 0.1, 0.1), group_prefix=None, move=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2ab6122",
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(rescale=1.0/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9064bc46",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['Chickenpox','Measles','Monkeypox','Normal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "139628a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4543 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = datagen.flow_from_directory(\n",
    "    directory=\"Downloads/aug_msidDN121/aug_mkp copy/EFNetB5_TrueFalse/train\",\n",
    "    classes = class_names,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb1a4202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 565 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "valid_generator = datagen.flow_from_directory(\n",
    "    directory=\"Downloads/aug_msidDN121/aug_mkp copy/EFNetB5_TrueFalse/val\",\n",
    "    classes = class_names,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16772fbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 572 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = datagen.flow_from_directory(\n",
    "    directory=\"Downloads/aug_msidDN121/aug_mkp copy/EFNetB5_TrueFalse/test/\",\n",
    "    classes = class_names,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a184669f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "EFNetB5 = tf.keras.applications.efficientnet.EfficientNetB5(\n",
    "    include_top=False,\n",
    "    weights='imagenet',\n",
    "    input_shape=(224,224,3),\n",
    "    classes=4,\n",
    "    classifier_activation=\"softmax\"\n",
    ")\n",
    "#print(EFNetB5.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2906492",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_point = len(EFNetB5.layers) // 2\n",
    "for layer in (EFNetB5.layers)[:split_point]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4c1e491d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.python.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "EFNetB5_model = Sequential()\n",
    "EFNetB5_model.add(EFNetB5)\n",
    "EFNetB5_model.add(Flatten())\n",
    "EFNetB5_model.add(Dense(256, activation='relu'))\n",
    "EFNetB5_model.add(Dense(4, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f162806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " efficientnetb5 (Functional  (None, 7, 7, 2048)        28513527  \n",
      " )                                                               \n",
      "                                                                 \n",
      " module_wrapper (ModuleWrap  (None, 100352)            0         \n",
      " per)                                                            \n",
      "                                                                 \n",
      " module_wrapper_1 (ModuleWr  (None, 256)               25690368  \n",
      " apper)                                                          \n",
      "                                                                 \n",
      " module_wrapper_2 (ModuleWr  (None, 4)                 1028      \n",
      " apper)                                                          \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 54204923 (206.78 MB)\n",
      "Trainable params: 25691396 (98.00 MB)\n",
      "Non-trainable params: 28513527 (108.77 MB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "EFNetB5_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06d445cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "EFNetB5_model.compile(optimizer=Adam(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f72b6ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "142/142 [==============================] - 1399s 9s/step - loss: 1.9472 - accuracy: 0.2648 - val_loss: 1.6188 - val_accuracy: 0.2867\n",
      "Epoch 2/15\n",
      "142/142 [==============================] - 1286s 9s/step - loss: 1.6487 - accuracy: 0.3011 - val_loss: 1.5056 - val_accuracy: 0.3292\n",
      "Epoch 3/15\n",
      "142/142 [==============================] - 1279s 9s/step - loss: 1.5149 - accuracy: 0.3227 - val_loss: 1.9136 - val_accuracy: 0.3257\n",
      "Epoch 4/15\n",
      "142/142 [==============================] - 1266s 9s/step - loss: 1.6022 - accuracy: 0.3079 - val_loss: 1.8876 - val_accuracy: 0.2743\n",
      "Epoch 5/15\n",
      "142/142 [==============================] - 1266s 9s/step - loss: 1.5493 - accuracy: 0.3209 - val_loss: 1.9879 - val_accuracy: 0.2708\n",
      "Epoch 6/15\n",
      "142/142 [==============================] - 1270s 9s/step - loss: 1.4488 - accuracy: 0.3372 - val_loss: 1.3035 - val_accuracy: 0.3540\n",
      "Epoch 7/15\n",
      "142/142 [==============================] - 1282s 9s/step - loss: 1.4977 - accuracy: 0.3311 - val_loss: 1.3883 - val_accuracy: 0.3221\n",
      "Epoch 8/15\n",
      "142/142 [==============================] - 2313s 16s/step - loss: 1.4563 - accuracy: 0.3458 - val_loss: 1.4151 - val_accuracy: 0.3646\n",
      "Epoch 9/15\n",
      "142/142 [==============================] - 2871s 20s/step - loss: 1.3953 - accuracy: 0.3500 - val_loss: 1.4725 - val_accuracy: 0.3823\n",
      "Epoch 10/15\n",
      "142/142 [==============================] - 2056s 14s/step - loss: 1.4481 - accuracy: 0.3278 - val_loss: 1.2861 - val_accuracy: 0.3841\n",
      "Epoch 11/15\n",
      "142/142 [==============================] - 2054s 14s/step - loss: 1.4384 - accuracy: 0.3467 - val_loss: 1.9915 - val_accuracy: 0.3009\n",
      "Epoch 12/15\n",
      "142/142 [==============================] - 1855s 13s/step - loss: 1.4494 - accuracy: 0.3487 - val_loss: 1.4115 - val_accuracy: 0.3628\n",
      "Epoch 13/15\n",
      "142/142 [==============================] - 939s 7s/step - loss: 1.3803 - accuracy: 0.3482 - val_loss: 1.3793 - val_accuracy: 0.3522\n",
      "Epoch 14/15\n",
      "142/142 [==============================] - 1420s 10s/step - loss: 1.4292 - accuracy: 0.3418 - val_loss: 1.3627 - val_accuracy: 0.3044\n",
      "Epoch 15/15\n",
      "142/142 [==============================] - 1532s 11s/step - loss: 1.4089 - accuracy: 0.3555 - val_loss: 1.3117 - val_accuracy: 0.3540\n"
     ]
    }
   ],
   "source": [
    "epochs=15\n",
    "history = EFNetB5_model.fit(\n",
    "  train_generator,\n",
    "  validation_data=valid_generator,\n",
    "  epochs=epochs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0adf615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Accuracy vs. epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Loss vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93d35842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 219s 12s/step - loss: 1.3389 - accuracy: 0.3322\n",
      "The test loss is:  1.3389428853988647\n",
      "The best accuracy is:  0.3321678340435028\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the model\n",
    "test_loss, test_acc = EFNetB5_model.evaluate(test_generator)\n",
    "print(\"The test loss is: \", test_loss)\n",
    "print(\"The best accuracy is: \", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3110c512",
   "metadata": {},
   "outputs": [],
   "source": [
    "EFNetB5_model.save('my_EFNetB5_TF_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "528cdb7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 231s 12s/step\n",
      "Confusion Matrix:\n",
      "[[ 12   4 103  23]\n",
      " [ 19   4  99  21]\n",
      " [ 28   5  88  21]\n",
      " [ 14   8  98  25]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Chickenpox       0.16      0.08      0.11       142\n",
      "     Measles       0.19      0.03      0.05       143\n",
      "   Monkeypox       0.23      0.62      0.33       142\n",
      "      Normal       0.28      0.17      0.21       145\n",
      "\n",
      "    accuracy                           0.23       572\n",
      "   macro avg       0.21      0.23      0.18       572\n",
      "weighted avg       0.22      0.23      0.18       572\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# Make predictions on the test dataset\n",
    "predictions = EFNetB5_model.predict(test_generator)\n",
    "predicted_labels = np.argmax(predictions, axis=1)\n",
    "true_labels = test_generator.classes\n",
    "\n",
    "# Calculate the confusion matrix\n",
    "confusion = confusion_matrix(true_labels, predicted_labels)\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "report = classification_report(true_labels, predicted_labels, target_names=class_names)\n",
    "\n",
    "# Print confusion matrix and classification report\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(report)\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}