{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:41:40.536537Z",
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     "shell.execute_reply": "2023-04-17T09:41:49.138560Z",
     "shell.execute_reply.started": "2023-04-17T09:41:40.536503Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.vgg19 import VGG19\n",
    "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n",
    "from tensorflow.keras.applications.vgg19 import preprocess_input as vgg19_preprocess_input\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input as mobilenetv2_preprocess_input\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Input, Average, concatenate\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, matthews_corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:41:57.227573Z",
     "iopub.status.busy": "2023-04-17T09:41:57.226671Z",
     "iopub.status.idle": "2023-04-17T09:41:57.232218Z",
     "shell.execute_reply": "2023-04-17T09:41:57.231311Z",
     "shell.execute_reply.started": "2023-04-17T09:41:57.227527Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:42:04.651926Z",
     "iopub.status.busy": "2023-04-17T09:42:04.651342Z",
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     "shell.execute_reply": "2023-04-17T09:42:04.885924Z",
     "shell.execute_reply.started": "2023-04-17T09:42:04.651876Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = '/kaggle/input/coviddataset-70-30/CovidDataset_70_30'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:42:10.895108Z",
     "iopub.status.busy": "2023-04-17T09:42:10.894738Z",
     "iopub.status.idle": "2023-04-17T09:42:10.900686Z",
     "shell.execute_reply": "2023-04-17T09:42:10.899576Z",
     "shell.execute_reply.started": "2023-04-17T09:42:10.895076Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "input_shape = (224, 224, 3)\n",
    "num_classes = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:42:15.424140Z",
     "iopub.status.busy": "2023-04-17T09:42:15.423768Z",
     "iopub.status.idle": "2023-04-17T09:42:21.502085Z",
     "shell.execute_reply": "2023-04-17T09:42:21.500720Z",
     "shell.execute_reply.started": "2023-04-17T09:42:15.424110Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5272 images belonging to 2 classes.\n",
      "Found 1132 images belonging to 2 classes.\n",
      "Found 1130 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        os.path.join(data_dir, 'Train'),\n",
    "        target_size=input_shape[:2],\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "        os.path.join(data_dir, 'Test'),\n",
    "        target_size=input_shape[:2],\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')\n",
    "\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        os.path.join(data_dir, 'Validation'),\n",
    "        target_size=input_shape[:2],\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:42:26.625792Z",
     "iopub.status.busy": "2023-04-17T09:42:26.625206Z",
     "iopub.status.idle": "2023-04-17T09:42:30.568898Z",
     "shell.execute_reply": "2023-04-17T09:42:30.568063Z",
     "shell.execute_reply.started": "2023-04-17T09:42:26.625744Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "80134624/80134624 [==============================] - 0s 0us/step\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " vgg19 (Functional)          (None, 7, 7, 512)         20024384  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 500)               12544500  \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 500)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 300)               150300    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 300)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 602       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32,719,786\n",
      "Trainable params: 12,695,402\n",
      "Non-trainable params: 20,024,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg19 = VGG19(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "\n",
    "modelV19 = Sequential()\n",
    "modelV19.add(vgg19)\n",
    "modelV19.add(Flatten())\n",
    "modelV19.add(Dense(500, activation='relu'))\n",
    "modelV19.add(Dropout(0.5))\n",
    "modelV19.add(Dense(300, activation='relu'))\n",
    "modelV19.add(Dropout(0.5))\n",
    "modelV19.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "for layer in vgg19.layers:\n",
    "    layer.trainable = False\n",
    "modelV19.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:42:34.683488Z",
     "iopub.status.busy": "2023-04-17T09:42:34.683094Z",
     "iopub.status.idle": "2023-04-17T09:42:36.570959Z",
     "shell.execute_reply": "2023-04-17T09:42:36.570095Z",
     "shell.execute_reply.started": "2023-04-17T09:42:34.683453Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
      "9406464/9406464 [==============================] - 0s 0us/step\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n",
      " ional)                                                          \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 62720)             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 500)               31360500  \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 500)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 300)               150300    \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 300)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 2)                 602       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 33,769,386\n",
      "Trainable params: 31,511,402\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mobilenetv2 = MobileNetV2(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "\n",
    "modelM2 = Sequential()\n",
    "modelM2.add(mobilenetv2)\n",
    "modelM2.add(Flatten())\n",
    "modelM2.add(Dense(500, activation='relu'))\n",
    "modelM2.add(Dropout(0.5))\n",
    "modelM2.add(Dense(300, activation='relu'))\n",
    "modelM2.add(Dropout(0.5))\n",
    "modelM2.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "for layer in mobilenetv2.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "modelM2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:43:03.856636Z",
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     "shell.execute_reply.started": "2023-04-17T09:43:03.856598Z"
    }
   },
   "outputs": [],
   "source": [
    "opt = Adam(learning_rate=0.0001, beta_1=0.9)\n",
    "modelV19.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer=opt,\n",
    "    metrics=['accuracy'])\n",
    "\n",
    "modelM2.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer=opt,\n",
    "    metrics=['accuracy'])\n",
    "\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=10)\n",
    "filepath_weights_V19 = \"/kaggle/working/save_weights/best_weights_V19-{epoch:02d}-{val_accuracy:.4f}.hdf5\"\n",
    "filepath_weights_M2 = \"/kaggle/working/save_weights/best_weights_M2-{epoch:02d}-{val_accuracy:.4f}.hdf5\"\n",
    "checkpoint_V19 = ModelCheckpoint(filepath_weights_V19, monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n",
    "checkpoint_M2 = ModelCheckpoint(filepath_weights_M2, monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T09:43:08.944492Z",
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     "shell.execute_reply.started": "2023-04-17T09:43:08.944452Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.4981 - accuracy: 0.7737\n",
      "Epoch 1: val_accuracy improved from -inf to 0.81770, saving model to /kaggle/working/save_weights/best_weights_V19-01-0.8177.hdf5\n",
      "165/165 [==============================] - 122s 689ms/step - loss: 0.4981 - accuracy: 0.7737 - val_loss: 0.3935 - val_accuracy: 0.8177\n",
      "Epoch 2/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.3994 - accuracy: 0.8285\n",
      "Epoch 2: val_accuracy improved from 0.81770 to 0.84602, saving model to /kaggle/working/save_weights/best_weights_V19-02-0.8460.hdf5\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.3994 - accuracy: 0.8285 - val_loss: 0.3421 - val_accuracy: 0.8460\n",
      "Epoch 3/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.3577 - accuracy: 0.8530\n",
      "Epoch 3: val_accuracy improved from 0.84602 to 0.86549, saving model to /kaggle/working/save_weights/best_weights_V19-03-0.8655.hdf5\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.3577 - accuracy: 0.8530 - val_loss: 0.2909 - val_accuracy: 0.8655\n",
      "Epoch 4/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.3279 - accuracy: 0.8585\n",
      "Epoch 4: val_accuracy improved from 0.86549 to 0.89204, saving model to /kaggle/working/save_weights/best_weights_V19-04-0.8920.hdf5\n",
      "165/165 [==============================] - 78s 474ms/step - loss: 0.3279 - accuracy: 0.8585 - val_loss: 0.2563 - val_accuracy: 0.8920\n",
      "Epoch 5/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.3050 - accuracy: 0.8704\n",
      "Epoch 5: val_accuracy improved from 0.89204 to 0.89735, saving model to /kaggle/working/save_weights/best_weights_V19-05-0.8973.hdf5\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.3050 - accuracy: 0.8704 - val_loss: 0.2298 - val_accuracy: 0.8973\n",
      "Epoch 6/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2935 - accuracy: 0.8784\n",
      "Epoch 6: val_accuracy did not improve from 0.89735\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.2935 - accuracy: 0.8784 - val_loss: 0.2507 - val_accuracy: 0.8938\n",
      "Epoch 7/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2720 - accuracy: 0.8873\n",
      "Epoch 7: val_accuracy did not improve from 0.89735\n",
      "165/165 [==============================] - 77s 469ms/step - loss: 0.2720 - accuracy: 0.8873 - val_loss: 0.2506 - val_accuracy: 0.8920\n",
      "Epoch 8/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2631 - accuracy: 0.8930\n",
      "Epoch 8: val_accuracy improved from 0.89735 to 0.90354, saving model to /kaggle/working/save_weights/best_weights_V19-08-0.9035.hdf5\n",
      "165/165 [==============================] - 77s 465ms/step - loss: 0.2631 - accuracy: 0.8930 - val_loss: 0.2364 - val_accuracy: 0.9035\n",
      "Epoch 9/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2523 - accuracy: 0.8968\n",
      "Epoch 9: val_accuracy did not improve from 0.90354\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.2523 - accuracy: 0.8968 - val_loss: 0.2750 - val_accuracy: 0.8823\n",
      "Epoch 10/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2551 - accuracy: 0.8951\n",
      "Epoch 10: val_accuracy improved from 0.90354 to 0.92655, saving model to /kaggle/working/save_weights/best_weights_V19-10-0.9265.hdf5\n",
      "165/165 [==============================] - 78s 471ms/step - loss: 0.2551 - accuracy: 0.8951 - val_loss: 0.1999 - val_accuracy: 0.9265\n",
      "Epoch 11/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2376 - accuracy: 0.9004\n",
      "Epoch 11: val_accuracy did not improve from 0.92655\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.2376 - accuracy: 0.9004 - val_loss: 0.2115 - val_accuracy: 0.9142\n",
      "Epoch 12/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2364 - accuracy: 0.9021\n",
      "Epoch 12: val_accuracy improved from 0.92655 to 0.92832, saving model to /kaggle/working/save_weights/best_weights_V19-12-0.9283.hdf5\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.2364 - accuracy: 0.9021 - val_loss: 0.1833 - val_accuracy: 0.9283\n",
      "Epoch 13/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2310 - accuracy: 0.9040\n",
      "Epoch 13: val_accuracy improved from 0.92832 to 0.93097, saving model to /kaggle/working/save_weights/best_weights_V19-13-0.9310.hdf5\n",
      "165/165 [==============================] - 78s 470ms/step - loss: 0.2310 - accuracy: 0.9040 - val_loss: 0.1907 - val_accuracy: 0.9310\n",
      "Epoch 14/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2329 - accuracy: 0.9048\n",
      "Epoch 14: val_accuracy did not improve from 0.93097\n",
      "165/165 [==============================] - 77s 469ms/step - loss: 0.2329 - accuracy: 0.9048 - val_loss: 0.2064 - val_accuracy: 0.9106\n",
      "Epoch 15/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2410 - accuracy: 0.8995\n",
      "Epoch 15: val_accuracy did not improve from 0.93097\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.2410 - accuracy: 0.8995 - val_loss: 0.2110 - val_accuracy: 0.9124\n",
      "Epoch 16/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2055 - accuracy: 0.9194\n",
      "Epoch 16: val_accuracy improved from 0.93097 to 0.93363, saving model to /kaggle/working/save_weights/best_weights_V19-16-0.9336.hdf5\n",
      "165/165 [==============================] - 78s 474ms/step - loss: 0.2055 - accuracy: 0.9194 - val_loss: 0.1800 - val_accuracy: 0.9336\n",
      "Epoch 17/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2236 - accuracy: 0.9076\n",
      "Epoch 17: val_accuracy did not improve from 0.93363\n",
      "165/165 [==============================] - 78s 473ms/step - loss: 0.2236 - accuracy: 0.9076 - val_loss: 0.2173 - val_accuracy: 0.9142\n",
      "Epoch 18/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2086 - accuracy: 0.9154\n",
      "Epoch 18: val_accuracy did not improve from 0.93363\n",
      "165/165 [==============================] - 79s 476ms/step - loss: 0.2086 - accuracy: 0.9154 - val_loss: 0.1898 - val_accuracy: 0.9221\n",
      "Epoch 19/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2130 - accuracy: 0.9116\n",
      "Epoch 19: val_accuracy improved from 0.93363 to 0.93628, saving model to /kaggle/working/save_weights/best_weights_V19-19-0.9363.hdf5\n",
      "165/165 [==============================] - 78s 470ms/step - loss: 0.2130 - accuracy: 0.9116 - val_loss: 0.1750 - val_accuracy: 0.9363\n",
      "Epoch 20/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2066 - accuracy: 0.9122\n",
      "Epoch 20: val_accuracy did not improve from 0.93628\n",
      "165/165 [==============================] - 78s 471ms/step - loss: 0.2066 - accuracy: 0.9122 - val_loss: 0.1680 - val_accuracy: 0.9319\n",
      "Epoch 21/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2095 - accuracy: 0.9108\n",
      "Epoch 21: val_accuracy did not improve from 0.93628\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.2095 - accuracy: 0.9108 - val_loss: 0.1764 - val_accuracy: 0.9345\n",
      "Epoch 22/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2070 - accuracy: 0.9131\n",
      "Epoch 22: val_accuracy improved from 0.93628 to 0.94248, saving model to /kaggle/working/save_weights/best_weights_V19-22-0.9425.hdf5\n",
      "165/165 [==============================] - 79s 478ms/step - loss: 0.2070 - accuracy: 0.9131 - val_loss: 0.1622 - val_accuracy: 0.9425\n",
      "Epoch 23/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1878 - accuracy: 0.9249\n",
      "Epoch 23: val_accuracy did not improve from 0.94248\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.1878 - accuracy: 0.9249 - val_loss: 0.2345 - val_accuracy: 0.8965\n",
      "Epoch 24/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1973 - accuracy: 0.9198\n",
      "Epoch 24: val_accuracy improved from 0.94248 to 0.94336, saving model to /kaggle/working/save_weights/best_weights_V19-24-0.9434.hdf5\n",
      "165/165 [==============================] - 79s 479ms/step - loss: 0.1973 - accuracy: 0.9198 - val_loss: 0.1606 - val_accuracy: 0.9434\n",
      "Epoch 25/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.9186\n",
      "Epoch 25: val_accuracy improved from 0.94336 to 0.94425, saving model to /kaggle/working/save_weights/best_weights_V19-25-0.9442.hdf5\n",
      "165/165 [==============================] - 79s 476ms/step - loss: 0.2030 - accuracy: 0.9186 - val_loss: 0.1680 - val_accuracy: 0.9442\n",
      "Epoch 26/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1915 - accuracy: 0.9249\n",
      "Epoch 26: val_accuracy did not improve from 0.94425\n",
      "165/165 [==============================] - 79s 477ms/step - loss: 0.1915 - accuracy: 0.9249 - val_loss: 0.1573 - val_accuracy: 0.9442\n",
      "Epoch 27/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2000 - accuracy: 0.9143\n",
      "Epoch 27: val_accuracy did not improve from 0.94425\n",
      "165/165 [==============================] - 77s 469ms/step - loss: 0.2000 - accuracy: 0.9143 - val_loss: 0.1733 - val_accuracy: 0.9345\n",
      "Epoch 28/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2007 - accuracy: 0.9175\n",
      "Epoch 28: val_accuracy did not improve from 0.94425\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.2007 - accuracy: 0.9175 - val_loss: 0.1679 - val_accuracy: 0.9363\n",
      "Epoch 29/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1909 - accuracy: 0.9224\n",
      "Epoch 29: val_accuracy improved from 0.94425 to 0.94513, saving model to /kaggle/working/save_weights/best_weights_V19-29-0.9451.hdf5\n",
      "165/165 [==============================] - 77s 465ms/step - loss: 0.1909 - accuracy: 0.9224 - val_loss: 0.1532 - val_accuracy: 0.9451\n",
      "Epoch 30/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1893 - accuracy: 0.9182\n",
      "Epoch 30: val_accuracy did not improve from 0.94513\n",
      "165/165 [==============================] - 78s 474ms/step - loss: 0.1893 - accuracy: 0.9182 - val_loss: 0.1992 - val_accuracy: 0.9150\n",
      "Epoch 31/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1795 - accuracy: 0.9277\n",
      "Epoch 31: val_accuracy did not improve from 0.94513\n",
      "165/165 [==============================] - 77s 465ms/step - loss: 0.1795 - accuracy: 0.9277 - val_loss: 0.1774 - val_accuracy: 0.9327\n",
      "Epoch 32/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1902 - accuracy: 0.9245\n",
      "Epoch 32: val_accuracy did not improve from 0.94513\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1902 - accuracy: 0.9245 - val_loss: 0.1573 - val_accuracy: 0.9416\n",
      "Epoch 33/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1788 - accuracy: 0.9306\n",
      "Epoch 33: val_accuracy did not improve from 0.94513\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.1788 - accuracy: 0.9306 - val_loss: 0.1520 - val_accuracy: 0.9434\n",
      "Epoch 34/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1770 - accuracy: 0.9292\n",
      "Epoch 34: val_accuracy did not improve from 0.94513\n",
      "165/165 [==============================] - 76s 462ms/step - loss: 0.1770 - accuracy: 0.9292 - val_loss: 0.1670 - val_accuracy: 0.9398\n",
      "Epoch 35/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1737 - accuracy: 0.9289\n",
      "Epoch 35: val_accuracy improved from 0.94513 to 0.94690, saving model to /kaggle/working/save_weights/best_weights_V19-35-0.9469.hdf5\n",
      "165/165 [==============================] - 78s 472ms/step - loss: 0.1737 - accuracy: 0.9289 - val_loss: 0.1484 - val_accuracy: 0.9469\n",
      "Epoch 36/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1783 - accuracy: 0.9262\n",
      "Epoch 36: val_accuracy improved from 0.94690 to 0.94867, saving model to /kaggle/working/save_weights/best_weights_V19-36-0.9487.hdf5\n",
      "165/165 [==============================] - 79s 477ms/step - loss: 0.1783 - accuracy: 0.9262 - val_loss: 0.1384 - val_accuracy: 0.9487\n",
      "Epoch 37/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1757 - accuracy: 0.9306\n",
      "Epoch 37: val_accuracy did not improve from 0.94867\n",
      "165/165 [==============================] - 77s 468ms/step - loss: 0.1757 - accuracy: 0.9306 - val_loss: 0.1480 - val_accuracy: 0.9398\n",
      "Epoch 38/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1599 - accuracy: 0.9346\n",
      "Epoch 38: val_accuracy improved from 0.94867 to 0.95133, saving model to /kaggle/working/save_weights/best_weights_V19-38-0.9513.hdf5\n",
      "165/165 [==============================] - 79s 477ms/step - loss: 0.1599 - accuracy: 0.9346 - val_loss: 0.1348 - val_accuracy: 0.9513\n",
      "Epoch 39/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1819 - accuracy: 0.9272\n",
      "Epoch 39: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 78s 473ms/step - loss: 0.1819 - accuracy: 0.9272 - val_loss: 0.1424 - val_accuracy: 0.9496\n",
      "Epoch 40/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1825 - accuracy: 0.9285\n",
      "Epoch 40: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 468ms/step - loss: 0.1825 - accuracy: 0.9285 - val_loss: 0.1439 - val_accuracy: 0.9496\n",
      "Epoch 41/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1776 - accuracy: 0.9270\n",
      "Epoch 41: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1776 - accuracy: 0.9270 - val_loss: 0.1389 - val_accuracy: 0.9487\n",
      "Epoch 42/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1666 - accuracy: 0.9310\n",
      "Epoch 42: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 465ms/step - loss: 0.1666 - accuracy: 0.9310 - val_loss: 0.1393 - val_accuracy: 0.9513\n",
      "Epoch 43/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1683 - accuracy: 0.9308\n",
      "Epoch 43: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 468ms/step - loss: 0.1683 - accuracy: 0.9308 - val_loss: 0.1377 - val_accuracy: 0.9496\n",
      "Epoch 44/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1564 - accuracy: 0.9359\n",
      "Epoch 44: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 470ms/step - loss: 0.1564 - accuracy: 0.9359 - val_loss: 0.1318 - val_accuracy: 0.9504\n",
      "Epoch 45/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1758 - accuracy: 0.9255\n",
      "Epoch 45: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.1758 - accuracy: 0.9255 - val_loss: 0.1583 - val_accuracy: 0.9381\n",
      "Epoch 46/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1733 - accuracy: 0.9294\n",
      "Epoch 46: val_accuracy did not improve from 0.95133\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1733 - accuracy: 0.9294 - val_loss: 0.1413 - val_accuracy: 0.9469\n",
      "Epoch 47/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1686 - accuracy: 0.9283\n",
      "Epoch 47: val_accuracy improved from 0.95133 to 0.95664, saving model to /kaggle/working/save_weights/best_weights_V19-47-0.9566.hdf5\n",
      "165/165 [==============================] - 81s 490ms/step - loss: 0.1686 - accuracy: 0.9283 - val_loss: 0.1318 - val_accuracy: 0.9566\n",
      "Epoch 48/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1691 - accuracy: 0.9298\n",
      "Epoch 48: val_accuracy did not improve from 0.95664\n",
      "165/165 [==============================] - 78s 474ms/step - loss: 0.1691 - accuracy: 0.9298 - val_loss: 0.1539 - val_accuracy: 0.9469\n",
      "Epoch 49/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1621 - accuracy: 0.9365\n",
      "Epoch 49: val_accuracy did not improve from 0.95664\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.1621 - accuracy: 0.9365 - val_loss: 0.2028 - val_accuracy: 0.9230\n",
      "Epoch 50/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1589 - accuracy: 0.9361\n",
      "Epoch 50: val_accuracy did not improve from 0.95664\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1589 - accuracy: 0.9361 - val_loss: 0.1324 - val_accuracy: 0.9504\n"
     ]
    }
   ],
   "source": [
    "# Dont run again model is saved @ /kaggle/working/save_weights/best_weights_V19-47-0.9566.hdf5\n",
    "\n",
    "history_V19 = modelV19.fit(train_generator, epochs=50, validation_data=validation_generator, callbacks=[early_stop, checkpoint_V19])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T10:54:11.760570Z",
     "iopub.status.busy": "2023-04-17T10:54:11.759474Z",
     "iopub.status.idle": "2023-04-17T10:54:12.009812Z",
     "shell.execute_reply": "2023-04-17T10:54:12.008839Z",
     "shell.execute_reply.started": "2023-04-17T10:54:11.760531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_V19.history['accuracy'])\n",
    "plt.plot(history_V19.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T10:54:15.949718Z",
     "iopub.status.busy": "2023-04-17T10:54:15.949333Z",
     "iopub.status.idle": "2023-04-17T10:54:16.169299Z",
     "shell.execute_reply": "2023-04-17T10:54:16.168308Z",
     "shell.execute_reply.started": "2023-04-17T10:54:15.949687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_V19.history['loss'])\n",
    "plt.plot(history_V19.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T10:54:22.915426Z",
     "iopub.status.busy": "2023-04-17T10:54:22.914443Z",
     "iopub.status.idle": "2023-04-17T11:58:44.293631Z",
     "shell.execute_reply": "2023-04-17T11:58:44.292681Z",
     "shell.execute_reply.started": "2023-04-17T10:54:22.915379Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 1.0464 - accuracy: 0.8299\n",
      "Epoch 1: val_accuracy improved from -inf to 0.85487, saving model to /kaggle/working/save_weights/best_weights_M2-01-0.8549.hdf5\n",
      "165/165 [==============================] - 80s 471ms/step - loss: 1.0464 - accuracy: 0.8299 - val_loss: 0.3006 - val_accuracy: 0.8549\n",
      "Epoch 2/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.3333 - accuracy: 0.8668\n",
      "Epoch 2: val_accuracy improved from 0.85487 to 0.89558, saving model to /kaggle/working/save_weights/best_weights_M2-02-0.8956.hdf5\n",
      "165/165 [==============================] - 76s 463ms/step - loss: 0.3333 - accuracy: 0.8668 - val_loss: 0.2511 - val_accuracy: 0.8956\n",
      "Epoch 3/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2849 - accuracy: 0.8871\n",
      "Epoch 3: val_accuracy improved from 0.89558 to 0.90177, saving model to /kaggle/working/save_weights/best_weights_M2-03-0.9018.hdf5\n",
      "165/165 [==============================] - 76s 463ms/step - loss: 0.2849 - accuracy: 0.8871 - val_loss: 0.2244 - val_accuracy: 0.9018\n",
      "Epoch 4/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2578 - accuracy: 0.8919\n",
      "Epoch 4: val_accuracy improved from 0.90177 to 0.92301, saving model to /kaggle/working/save_weights/best_weights_M2-04-0.9230.hdf5\n",
      "165/165 [==============================] - 75s 457ms/step - loss: 0.2578 - accuracy: 0.8919 - val_loss: 0.2182 - val_accuracy: 0.9230\n",
      "Epoch 5/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.9050\n",
      "Epoch 5: val_accuracy did not improve from 0.92301\n",
      "165/165 [==============================] - 75s 452ms/step - loss: 0.2411 - accuracy: 0.9050 - val_loss: 0.2010 - val_accuracy: 0.9177\n",
      "Epoch 6/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2220 - accuracy: 0.9122\n",
      "Epoch 6: val_accuracy improved from 0.92301 to 0.93009, saving model to /kaggle/working/save_weights/best_weights_M2-06-0.9301.hdf5\n",
      "165/165 [==============================] - 76s 462ms/step - loss: 0.2220 - accuracy: 0.9122 - val_loss: 0.1793 - val_accuracy: 0.9301\n",
      "Epoch 7/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.9196\n",
      "Epoch 7: val_accuracy improved from 0.93009 to 0.93097, saving model to /kaggle/working/save_weights/best_weights_M2-07-0.9310.hdf5\n",
      "165/165 [==============================] - 77s 469ms/step - loss: 0.1983 - accuracy: 0.9196 - val_loss: 0.1863 - val_accuracy: 0.9310\n",
      "Epoch 8/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.2006 - accuracy: 0.9200\n",
      "Epoch 8: val_accuracy improved from 0.93097 to 0.93628, saving model to /kaggle/working/save_weights/best_weights_M2-08-0.9363.hdf5\n",
      "165/165 [==============================] - 79s 476ms/step - loss: 0.2006 - accuracy: 0.9200 - val_loss: 0.1627 - val_accuracy: 0.9363\n",
      "Epoch 9/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1884 - accuracy: 0.9224\n",
      "Epoch 9: val_accuracy did not improve from 0.93628\n",
      "165/165 [==============================] - 81s 491ms/step - loss: 0.1884 - accuracy: 0.9224 - val_loss: 0.1666 - val_accuracy: 0.9301\n",
      "Epoch 10/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1839 - accuracy: 0.9266\n",
      "Epoch 10: val_accuracy did not improve from 0.93628\n",
      "165/165 [==============================] - 82s 495ms/step - loss: 0.1839 - accuracy: 0.9266 - val_loss: 0.1919 - val_accuracy: 0.9221\n",
      "Epoch 11/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1753 - accuracy: 0.9275\n",
      "Epoch 11: val_accuracy improved from 0.93628 to 0.94336, saving model to /kaggle/working/save_weights/best_weights_M2-11-0.9434.hdf5\n",
      "165/165 [==============================] - 81s 488ms/step - loss: 0.1753 - accuracy: 0.9275 - val_loss: 0.1449 - val_accuracy: 0.9434\n",
      "Epoch 12/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9370\n",
      "Epoch 12: val_accuracy did not improve from 0.94336\n",
      "165/165 [==============================] - 78s 470ms/step - loss: 0.1562 - accuracy: 0.9370 - val_loss: 0.1762 - val_accuracy: 0.9230\n",
      "Epoch 13/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1532 - accuracy: 0.9397\n",
      "Epoch 13: val_accuracy did not improve from 0.94336\n",
      "165/165 [==============================] - 78s 473ms/step - loss: 0.1532 - accuracy: 0.9397 - val_loss: 0.1641 - val_accuracy: 0.9310\n",
      "Epoch 14/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1513 - accuracy: 0.9376\n",
      "Epoch 14: val_accuracy did not improve from 0.94336\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1513 - accuracy: 0.9376 - val_loss: 0.1376 - val_accuracy: 0.9425\n",
      "Epoch 15/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1467 - accuracy: 0.9414\n",
      "Epoch 15: val_accuracy did not improve from 0.94336\n",
      "165/165 [==============================] - 78s 471ms/step - loss: 0.1467 - accuracy: 0.9414 - val_loss: 0.1422 - val_accuracy: 0.9398\n",
      "Epoch 16/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1284 - accuracy: 0.9444\n",
      "Epoch 16: val_accuracy improved from 0.94336 to 0.94867, saving model to /kaggle/working/save_weights/best_weights_M2-16-0.9487.hdf5\n",
      "165/165 [==============================] - 79s 477ms/step - loss: 0.1284 - accuracy: 0.9444 - val_loss: 0.1343 - val_accuracy: 0.9487\n",
      "Epoch 17/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1436 - accuracy: 0.9446\n",
      "Epoch 17: val_accuracy did not improve from 0.94867\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1436 - accuracy: 0.9446 - val_loss: 0.1606 - val_accuracy: 0.9389\n",
      "Epoch 18/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1276 - accuracy: 0.9505\n",
      "Epoch 18: val_accuracy did not improve from 0.94867\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.1276 - accuracy: 0.9505 - val_loss: 0.1531 - val_accuracy: 0.9319\n",
      "Epoch 19/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1279 - accuracy: 0.9486\n",
      "Epoch 19: val_accuracy improved from 0.94867 to 0.95398, saving model to /kaggle/working/save_weights/best_weights_M2-19-0.9540.hdf5\n",
      "165/165 [==============================] - 78s 471ms/step - loss: 0.1279 - accuracy: 0.9486 - val_loss: 0.1328 - val_accuracy: 0.9540\n",
      "Epoch 20/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1180 - accuracy: 0.9535\n",
      "Epoch 20: val_accuracy did not improve from 0.95398\n",
      "165/165 [==============================] - 76s 462ms/step - loss: 0.1180 - accuracy: 0.9535 - val_loss: 0.1379 - val_accuracy: 0.9504\n",
      "Epoch 21/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1245 - accuracy: 0.9486\n",
      "Epoch 21: val_accuracy did not improve from 0.95398\n",
      "165/165 [==============================] - 75s 453ms/step - loss: 0.1245 - accuracy: 0.9486 - val_loss: 0.1291 - val_accuracy: 0.9504\n",
      "Epoch 22/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1168 - accuracy: 0.9499\n",
      "Epoch 22: val_accuracy did not improve from 0.95398\n",
      "165/165 [==============================] - 74s 446ms/step - loss: 0.1168 - accuracy: 0.9499 - val_loss: 0.1477 - val_accuracy: 0.9451\n",
      "Epoch 23/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1213 - accuracy: 0.9492\n",
      "Epoch 23: val_accuracy did not improve from 0.95398\n",
      "165/165 [==============================] - 75s 455ms/step - loss: 0.1213 - accuracy: 0.9492 - val_loss: 0.1319 - val_accuracy: 0.9487\n",
      "Epoch 24/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1174 - accuracy: 0.9543\n",
      "Epoch 24: val_accuracy did not improve from 0.95398\n",
      "165/165 [==============================] - 75s 452ms/step - loss: 0.1174 - accuracy: 0.9543 - val_loss: 0.1293 - val_accuracy: 0.9540\n",
      "Epoch 25/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1195 - accuracy: 0.9530\n",
      "Epoch 25: val_accuracy improved from 0.95398 to 0.95752, saving model to /kaggle/working/save_weights/best_weights_M2-25-0.9575.hdf5\n",
      "165/165 [==============================] - 80s 487ms/step - loss: 0.1195 - accuracy: 0.9530 - val_loss: 0.1078 - val_accuracy: 0.9575\n",
      "Epoch 26/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1060 - accuracy: 0.9571\n",
      "Epoch 26: val_accuracy did not improve from 0.95752\n",
      "165/165 [==============================] - 83s 499ms/step - loss: 0.1060 - accuracy: 0.9571 - val_loss: 0.1167 - val_accuracy: 0.9513\n",
      "Epoch 27/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1151 - accuracy: 0.9495\n",
      "Epoch 27: val_accuracy did not improve from 0.95752\n",
      "165/165 [==============================] - 80s 481ms/step - loss: 0.1151 - accuracy: 0.9495 - val_loss: 0.1306 - val_accuracy: 0.9478\n",
      "Epoch 28/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1236 - accuracy: 0.9533\n",
      "Epoch 28: val_accuracy improved from 0.95752 to 0.95841, saving model to /kaggle/working/save_weights/best_weights_M2-28-0.9584.hdf5\n",
      "165/165 [==============================] - 79s 478ms/step - loss: 0.1236 - accuracy: 0.9533 - val_loss: 0.1097 - val_accuracy: 0.9584\n",
      "Epoch 29/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.1053 - accuracy: 0.9577\n",
      "Epoch 29: val_accuracy improved from 0.95841 to 0.96018, saving model to /kaggle/working/save_weights/best_weights_M2-29-0.9602.hdf5\n",
      "165/165 [==============================] - 80s 485ms/step - loss: 0.1053 - accuracy: 0.9577 - val_loss: 0.1078 - val_accuracy: 0.9602\n",
      "Epoch 30/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0989 - accuracy: 0.9628\n",
      "Epoch 30: val_accuracy did not improve from 0.96018\n",
      "165/165 [==============================] - 77s 466ms/step - loss: 0.0989 - accuracy: 0.9628 - val_loss: 0.1144 - val_accuracy: 0.9558\n",
      "Epoch 31/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0910 - accuracy: 0.9643\n",
      "Epoch 31: val_accuracy improved from 0.96018 to 0.96195, saving model to /kaggle/working/save_weights/best_weights_M2-31-0.9619.hdf5\n",
      "165/165 [==============================] - 79s 476ms/step - loss: 0.0910 - accuracy: 0.9643 - val_loss: 0.1115 - val_accuracy: 0.9619\n",
      "Epoch 32/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0923 - accuracy: 0.9621\n",
      "Epoch 32: val_accuracy improved from 0.96195 to 0.96283, saving model to /kaggle/working/save_weights/best_weights_M2-32-0.9628.hdf5\n",
      "165/165 [==============================] - 78s 471ms/step - loss: 0.0923 - accuracy: 0.9621 - val_loss: 0.1043 - val_accuracy: 0.9628\n",
      "Epoch 33/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0979 - accuracy: 0.9626\n",
      "Epoch 33: val_accuracy did not improve from 0.96283\n",
      "165/165 [==============================] - 76s 460ms/step - loss: 0.0979 - accuracy: 0.9626 - val_loss: 0.1248 - val_accuracy: 0.9522\n",
      "Epoch 34/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0916 - accuracy: 0.9638\n",
      "Epoch 34: val_accuracy improved from 0.96283 to 0.96372, saving model to /kaggle/working/save_weights/best_weights_M2-34-0.9637.hdf5\n",
      "165/165 [==============================] - 78s 470ms/step - loss: 0.0916 - accuracy: 0.9638 - val_loss: 0.1029 - val_accuracy: 0.9637\n",
      "Epoch 35/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0775 - accuracy: 0.9672\n",
      "Epoch 35: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.0775 - accuracy: 0.9672 - val_loss: 0.1176 - val_accuracy: 0.9611\n",
      "Epoch 36/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9590\n",
      "Epoch 36: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 75s 456ms/step - loss: 0.0963 - accuracy: 0.9590 - val_loss: 0.1034 - val_accuracy: 0.9611\n",
      "Epoch 37/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0902 - accuracy: 0.9634\n",
      "Epoch 37: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 75s 452ms/step - loss: 0.0902 - accuracy: 0.9634 - val_loss: 0.1141 - val_accuracy: 0.9602\n",
      "Epoch 38/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0877 - accuracy: 0.9638\n",
      "Epoch 38: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 76s 460ms/step - loss: 0.0877 - accuracy: 0.9638 - val_loss: 0.1173 - val_accuracy: 0.9549\n",
      "Epoch 39/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0941 - accuracy: 0.9647\n",
      "Epoch 39: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 78s 475ms/step - loss: 0.0941 - accuracy: 0.9647 - val_loss: 0.1353 - val_accuracy: 0.9487\n",
      "Epoch 40/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0882 - accuracy: 0.9600\n",
      "Epoch 40: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 78s 470ms/step - loss: 0.0882 - accuracy: 0.9600 - val_loss: 0.1130 - val_accuracy: 0.9558\n",
      "Epoch 41/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0802 - accuracy: 0.9657\n",
      "Epoch 41: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 75s 453ms/step - loss: 0.0802 - accuracy: 0.9657 - val_loss: 0.1388 - val_accuracy: 0.9460\n",
      "Epoch 42/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0820 - accuracy: 0.9676\n",
      "Epoch 42: val_accuracy did not improve from 0.96372\n",
      "165/165 [==============================] - 74s 449ms/step - loss: 0.0820 - accuracy: 0.9676 - val_loss: 0.1037 - val_accuracy: 0.9628\n",
      "Epoch 43/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0816 - accuracy: 0.9685\n",
      "Epoch 43: val_accuracy improved from 0.96372 to 0.96549, saving model to /kaggle/working/save_weights/best_weights_M2-43-0.9655.hdf5\n",
      "165/165 [==============================] - 77s 464ms/step - loss: 0.0816 - accuracy: 0.9685 - val_loss: 0.0929 - val_accuracy: 0.9655\n",
      "Epoch 44/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0772 - accuracy: 0.9668\n",
      "Epoch 44: val_accuracy did not improve from 0.96549\n",
      "165/165 [==============================] - 75s 456ms/step - loss: 0.0772 - accuracy: 0.9668 - val_loss: 0.0905 - val_accuracy: 0.9637\n",
      "Epoch 45/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0791 - accuracy: 0.9674\n",
      "Epoch 45: val_accuracy did not improve from 0.96549\n",
      "165/165 [==============================] - 76s 457ms/step - loss: 0.0791 - accuracy: 0.9674 - val_loss: 0.1204 - val_accuracy: 0.9566\n",
      "Epoch 46/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0718 - accuracy: 0.9702\n",
      "Epoch 46: val_accuracy did not improve from 0.96549\n",
      "165/165 [==============================] - 74s 450ms/step - loss: 0.0718 - accuracy: 0.9702 - val_loss: 0.1118 - val_accuracy: 0.9584\n",
      "Epoch 47/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0804 - accuracy: 0.9678\n",
      "Epoch 47: val_accuracy did not improve from 0.96549\n",
      "165/165 [==============================] - 74s 450ms/step - loss: 0.0804 - accuracy: 0.9678 - val_loss: 0.0867 - val_accuracy: 0.9646\n",
      "Epoch 48/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0787 - accuracy: 0.9643\n",
      "Epoch 48: val_accuracy did not improve from 0.96549\n",
      "165/165 [==============================] - 75s 457ms/step - loss: 0.0787 - accuracy: 0.9643 - val_loss: 0.1032 - val_accuracy: 0.9637\n",
      "Epoch 49/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0783 - accuracy: 0.9674\n",
      "Epoch 49: val_accuracy improved from 0.96549 to 0.96814, saving model to /kaggle/working/save_weights/best_weights_M2-49-0.9681.hdf5\n",
      "165/165 [==============================] - 76s 462ms/step - loss: 0.0783 - accuracy: 0.9674 - val_loss: 0.1010 - val_accuracy: 0.9681\n",
      "Epoch 50/50\n",
      "165/165 [==============================] - ETA: 0s - loss: 0.0728 - accuracy: 0.9717\n",
      "Epoch 50: val_accuracy did not improve from 0.96814\n",
      "165/165 [==============================] - 77s 467ms/step - loss: 0.0728 - accuracy: 0.9717 - val_loss: 0.1030 - val_accuracy: 0.9619\n"
     ]
    }
   ],
   "source": [
    "# Dont run again model is saved @ /kaggle/working/save_weights/best_weights_M2-49-0.9681.hdf5\n",
    "history_M2 = modelM2.fit(train_generator, epochs=50, validation_data=validation_generator, callbacks=[early_stop, checkpoint_M2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T11:58:56.255321Z",
     "iopub.status.busy": "2023-04-17T11:58:56.254959Z",
     "iopub.status.idle": "2023-04-17T11:58:56.470983Z",
     "shell.execute_reply": "2023-04-17T11:58:56.469412Z",
     "shell.execute_reply.started": "2023-04-17T11:58:56.255288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_M2.history['accuracy'])\n",
    "plt.plot(history_M2.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T11:59:04.035960Z",
     "iopub.status.busy": "2023-04-17T11:59:04.034979Z",
     "iopub.status.idle": "2023-04-17T11:59:04.239444Z",
     "shell.execute_reply": "2023-04-17T11:59:04.238312Z",
     "shell.execute_reply.started": "2023-04-17T11:59:04.035921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_M2.history['loss'])\n",
    "plt.plot(history_M2.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T12:00:59.573944Z",
     "iopub.status.busy": "2023-04-17T12:00:59.573208Z",
     "iopub.status.idle": "2023-04-17T12:01:03.871274Z",
     "shell.execute_reply": "2023-04-17T12:01:03.870051Z",
     "shell.execute_reply.started": "2023-04-17T12:00:59.573904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Ensemble\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " VGG16 (Functional)             (None, 2)            32719786    ['input_3[0][0]']                \n",
      "                                                                                                  \n",
      " MobileNetV2 (Functional)       (None, 2)            33769386    ['input_3[0][0]']                \n",
      "                                                                                                  \n",
      " average (Average)              (None, 2)            0           ['VGG16[0][0]',                  \n",
      "                                                                  'MobileNetV2[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 66,489,172\n",
      "Trainable params: 0\n",
      "Non-trainable params: 66,489,172\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input, Average\n",
    "\n",
    "model_1 = load_model('/kaggle/working/save_weights/best_weights_V19-47-0.9566.hdf5')\n",
    "model_2 = load_model('/kaggle/working/save_weights/best_weights_M2-49-0.9681.hdf5')\n",
    "\n",
    "# Set layers in model_1 and model_2 to be not trainable\n",
    "for layer in model_1.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "for layer in model_2.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "    \n",
    "model_1 = Model(\n",
    "    inputs = model_1.inputs,\n",
    "    outputs = model_1.outputs,\n",
    "    name = \"VGG16\"\n",
    ")\n",
    "\n",
    "model_2 = Model(\n",
    "    inputs = model_2.inputs,\n",
    "    outputs = model_2.outputs,\n",
    "    name = \"MobileNetV2\"\n",
    ")\n",
    "\n",
    "models = [model_1,model_2]\n",
    "model_input = Input(shape=(224, 224, 3))\n",
    "model_outputs = [model(model_input) for model in models]\n",
    "\n",
    "ensemble_output = Average()(model_outputs)\n",
    "\n",
    "\n",
    "ensemble_model = Model(\n",
    "    inputs = model_input,\n",
    "    outputs = ensemble_output,\n",
    "    name = \"Ensemble\"\n",
    ")\n",
    "\n",
    "ensemble_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T12:07:00.234155Z",
     "iopub.status.busy": "2023-04-17T12:07:00.233683Z",
     "iopub.status.idle": "2023-04-17T12:07:00.250703Z",
     "shell.execute_reply": "2023-04-17T12:07:00.249691Z",
     "shell.execute_reply.started": "2023-04-17T12:07:00.234119Z"
    }
   },
   "outputs": [],
   "source": [
    "# only compile when there are trainable parameters\n",
    "ensemble_model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer=opt,\n",
    "    metrics=['accuracy'])\n",
    "\n",
    "filepath_weights_ensemble = \"/kaggle/working/save_weights/best_weights_ensemble-{epoch:02d}-{val_accuracy:.4f}.tf\"\n",
    "checkpoint_ensemble = ModelCheckpoint(filepath_weights_ensemble, monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only fit when there are trainable parameters\n",
    "history_ensemble = ensemble_model.fit(train_generator, epochs=50, validation_data=validation_generator, callbacks=[early_stop, checkpoint_ensemble])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history_ensemble.history['accuracy'])\n",
    "plt.plot(history_ensemble.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-16T20:45:19.559073Z",
     "iopub.status.busy": "2023-04-16T20:45:19.558228Z",
     "iopub.status.idle": "2023-04-16T20:45:19.791401Z",
     "shell.execute_reply": "2023-04-16T20:45:19.790386Z",
     "shell.execute_reply.started": "2023-04-16T20:45:19.559021Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot(history_ensemble.history['loss'])\n",
    "plt.plot(history_ensemble.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T12:14:59.727959Z",
     "iopub.status.busy": "2023-04-17T12:14:59.727593Z",
     "iopub.status.idle": "2023-04-17T12:15:21.928598Z",
     "shell.execute_reply": "2023-04-17T12:15:21.927416Z",
     "shell.execute_reply.started": "2023-04-17T12:14:59.727921Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - 15s 428ms/step - loss: 0.0769 - accuracy: 0.9770\n",
      "Test accuracy: 0.9770318269729614\n"
     ]
    }
   ],
   "source": [
    "ensemble_model.load_weights('/kaggle/working/save_weights/best_weights_ensemble-01-0.9752.tf')\n",
    "test_loss, test_acc = ensemble_model.evaluate(test_generator)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T12:20:24.753793Z",
     "iopub.status.busy": "2023-04-17T12:20:24.753072Z",
     "iopub.status.idle": "2023-04-17T12:20:30.445796Z",
     "shell.execute_reply": "2023-04-17T12:20:30.444541Z",
     "shell.execute_reply.started": "2023-04-17T12:20:24.753754Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - 6s 153ms/step\n",
      "One-hot encoded predicted labels:\n",
      "[[1.1008696e-11 1.0000000e+00]\n",
      " [1.7217180e-14 1.0000000e+00]\n",
      " [7.5608725e-08 9.9999994e-01]\n",
      " ...\n",
      " [9.9957764e-01 4.2237682e-04]\n",
      " [9.9999994e-01 5.3283134e-08]\n",
      " [9.9935752e-01 6.4250652e-04]]\n",
      "[1 1 1 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "y_pred = ensemble_model.predict(test_generator)\n",
    "print(\"One-hot encoded predicted labels:\")\n",
    "print(y_pred)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "print(y_pred_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T12:21:13.115475Z",
     "iopub.status.busy": "2023-04-17T12:21:13.114275Z"
    }
   },
   "outputs": [],
   "source": [
    "y_true_classes = np.argmax(test_generator.classes, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T12:16:41.235043Z",
     "iopub.status.busy": "2023-04-17T12:16:41.234321Z",
     "iopub.status.idle": "2023-04-17T12:16:51.671202Z",
     "shell.execute_reply": "2023-04-17T12:16:51.669596Z",
     "shell.execute_reply.started": "2023-04-17T12:16:41.235004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - 5s 149ms/step\n"
     ]
    },
    {
     "ename": "AxisError",
     "evalue": "axis 1 is out of bounds for array of dimension 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_23/2965731218.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Convert one-hot encoded labels to integer labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0my_true_onehot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0my_true_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true_onehot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36margmax\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36margmax\u001b[0;34m(a, axis, out)\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m     \"\"\"\n\u001b[0;32m-> 1195\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'argmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
     ]
    }
   ],
   "source": [
    "#ensemble_model.load_weights('/kaggle/working/save_weights/best_weights_ensemble-31-0.9690.tf')\n",
    "#y_pred = ensemble_model.predict(test_generator)\n",
    "#y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "#y_true_classes = test_generator.classes\n",
    "\n",
    "#try this \n",
    "# Make predictions on test data\n",
    "y_pred = ensemble_model.predict(test_generator)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "\n",
    "# Convert one-hot encoded labels to integer labels\n",
    "y_true_onehot = test_generator.classes\n",
    "y_true_classes = np.argmax(y_true_onehot, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-16T20:46:57.948345Z",
     "iopub.status.busy": "2023-04-16T20:46:57.947965Z",
     "iopub.status.idle": "2023-04-16T20:46:57.96196Z",
     "shell.execute_reply": "2023-04-16T20:46:57.960524Z",
     "shell.execute_reply.started": "2023-04-16T20:46:57.948309Z"
    }
   },
   "outputs": [],
   "source": [
    "print(\"Classification Report:\\n\",classification_report(y_true_classes, y_pred_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-16T20:47:05.00894Z",
     "iopub.status.busy": "2023-04-16T20:47:05.008561Z",
     "iopub.status.idle": "2023-04-16T20:47:05.027339Z",
     "shell.execute_reply": "2023-04-16T20:47:05.026235Z",
     "shell.execute_reply.started": "2023-04-16T20:47:05.008906Z"
    }
   },
   "outputs": [],
   "source": [
    "# Calculate confusion matrix\n",
    "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
    "\n",
    "# Extract TP, TN, FP, FN\n",
    "TP = cm[1, 1]\n",
    "TN = cm[0, 0]\n",
    "FP = cm[0, 1]\n",
    "FN = cm[1, 0]\n",
    "\n",
    "# Calculate specificity\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "# Calculate sensitivity\n",
    "sensitivity = TP / (TP + FN)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_true_classes, y_pred_classes)\n",
    "\n",
    "# Calculate precision\n",
    "precision = precision_score(y_true_classes, y_pred_classes)\n",
    "\n",
    "# Calculate false positive rate (FPR)\n",
    "FPR = FP / (FP + TN)\n",
    "\n",
    "# Calculate false negative rate (FNR)\n",
    "FNR = FN / (FN + TP)\n",
    "\n",
    "# Calculate negative predictive value (NPV)\n",
    "NPV = TN / (TN + FN)\n",
    "\n",
    "# Calculate false discovery rate (FDR)\n",
    "FDR = FP / (FP + TP)\n",
    "\n",
    "# Calculate F1 score\n",
    "f1_score = f1_score(y_true_classes, y_pred_classes)\n",
    "\n",
    "# Calculate Matthews correlation coefficient (MCC)\n",
    "mcc = matthews_corrcoef(y_true_classes, y_pred_classes)\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Evaluation Metrics -\\n\")\n",
    "print(\"Specificity: \", specificity)\n",
    "print(\"Sensitivity: \", sensitivity)\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"Precision: \", precision)\n",
    "print(\"FPR: \", FPR)\n",
    "print(\"FNR: \", FNR)\n",
    "print(\"NPV: \", NPV)\n",
    "print(\"FDR: \", FDR)\n",
    "print(\"F1 Score: \", f1_score)\n",
    "print(\"MCC: \", mcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-16T20:47:09.888885Z",
     "iopub.status.busy": "2023-04-16T20:47:09.887905Z",
     "iopub.status.idle": "2023-04-16T20:47:10.150365Z",
     "shell.execute_reply": "2023-04-16T20:47:10.148887Z",
     "shell.execute_reply.started": "2023-04-16T20:47:09.888846Z"
    }
   },
   "outputs": [],
   "source": [
    "print(ConfusionMatrixDisplay.from_predictions(y_true_classes, y_pred_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-16T20:47:18.289352Z",
     "iopub.status.busy": "2023-04-16T20:47:18.288971Z",
     "iopub.status.idle": "2023-04-16T20:47:18.504488Z",
     "shell.execute_reply": "2023-04-16T20:47:18.503453Z",
     "shell.execute_reply.started": "2023-04-16T20:47:18.289316Z"
    }
   },
   "outputs": [],
   "source": [
    "# Assuming you have binary classification with two classes, you can adjust accordingly for multi-class\n",
    "fpr, tpr, _ = roc_curve(test_generator.classes, y_pred[:, 1])  # Use y_pred[:, 1] for positive class predictions\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC curve for positive class\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-16T20:47:33.750456Z",
     "iopub.status.busy": "2023-04-16T20:47:33.749346Z",
     "iopub.status.idle": "2023-04-16T20:47:35.93496Z",
     "shell.execute_reply": "2023-04-16T20:47:35.934049Z",
     "shell.execute_reply.started": "2023-04-16T20:47:33.750376Z"
    }
   },
   "outputs": [],
   "source": [
    "test_images, test_labels = next(test_generator)\n",
    "test_pred = ensemble_model.predict(test_images)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(10,10))\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    # Plot image\n",
    "    ax.imshow(test_images[i])\n",
    "\n",
    "    # Set the title\n",
    "    if test_pred[i][0] > 0.5:\n",
    "        title = f'COVID ({test_pred[i][0]:.2f})'\n",
    "    else:\n",
    "        title = f'Non-COVID ({test_pred[i][0]:.2f})'\n",
    "    ax.set_title(title)\n",
    "\n",
    "    # Remove ticks from the plot\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  }
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