CNN_From_Scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a7661c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41c81b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path=\"/home/bibek/Desktop/Project_Sem_8/Projects/Data/train/\"\n",
    "val_path=\"/home/bibek/Desktop/Project_Sem_8/Projects/Data/test/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "635cb2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers import Dense,MaxPool2D,Conv2D,Dropout,Flatten\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d5bddba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-06 23:43:28.604843: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-05-06 23:43:28.669164: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:28.675109: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:28.675386: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:29.064224: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:29.064535: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:29.064758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:29.065005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2382 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1650 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth=True\n",
    "sess = tf.compat.v1.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3dbb73cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1726 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_gen=ImageDataGenerator(rescale=1./255,\n",
    "                                  horizontal_flip=True,\n",
    "                                  zoom_range=0.2,\n",
    "                                  shear_range=0.3,\n",
    "                                  width_shift_range=0.3,\n",
    "                                  height_shift_range=0.3,\n",
    "                                  rotation_range=15)\n",
    "training_set=train_data_gen.flow_from_directory(directory=train_path,\n",
    "                                                target_size=(224,224),\n",
    "                                                class_mode='binary',\n",
    "                                                batch_size=16,\n",
    "                                                shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae15b7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COVID19': 0, 'NORMAL': 1}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fa78ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 433 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_data_gen=ImageDataGenerator(rescale=1./255)\n",
    "testing_set=test_data_gen.flow_from_directory(directory=val_path,\n",
    "                                              target_size=(224,224),\n",
    "                                             shuffle=False,\n",
    "                                             batch_size=16,\n",
    "                                             class_mode='binary')\n",
    "                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74d61337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COVID19': 0, 'NORMAL': 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_set.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10070256",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import matplotlib.image as img\n",
    "path_covid=list(glob.glob(val_path+\"COVID19/\"+\"/*\"))\n",
    "path_normal=list(glob.glob(val_path+\"NORMAL/\"+\"/*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35900b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcba0123b20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Covid 19 Image\")\n",
    "covid_image_sample=img.imread(path_covid[0])\n",
    "plt.imshow(covid_image_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "062823c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesImage(54,36;334.8x217.44)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Normal Image\")\n",
    "normal_image_sample=img.imread(path_normal[0])\n",
    "print(plt.imshow(normal_image_sample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ac3bab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-06 23:43:30.769078: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.769413: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.769624: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.769982: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.770199: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.770406: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.770651: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.770866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-05-06 23:43:30.771052: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2382 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1650 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(Conv2D(filters=128,kernel_size=(3,3),activation='relu',input_shape=(224,224,3)))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu'))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Dropout(0.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ae655ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu'))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Dropout(0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20c5cf80",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(filters=128,kernel_size=(3,3),activation='relu'))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Dropout(0.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2147cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Flatten())\n",
    "# model.add(Dropout(0.1))\n",
    "model.add(Dense(2048,activation='relu'))\n",
    "model.add(Dense(1024,activation='relu'))\n",
    "model.add(Dense(256,activation='relu'))\n",
    "model.add(Dense(64,activation='relu'))\n",
    "model.add(Dense(16,activation='relu'))\n",
    "model.add(Dense(1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc3f95b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import SGD,RMSprop\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau\n",
    "\n",
    "checkpoint=ModelCheckpoint('final_model.h5',\n",
    "                            monitor='val_loss',\n",
    "                            mode='min',\n",
    "                          save_best_only=True)\n",
    "\n",
    "earlystopping=EarlyStopping(monitor='val_loss',\n",
    "                            min_delta=0.001,\n",
    "                            patience=3,\n",
    "                           restore_best_weights=True)\n",
    "\n",
    "# reduce_lr=ReduceLROnPlateau(monitor='val_loss',\n",
    "#                            factor=0.1,\n",
    "#                            patience=3,\n",
    "#                            verbose=1,\n",
    "#                            min_delta=0.001)\n",
    "\n",
    "callback=[checkpoint,earlystopping]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "495a0586",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import Adam\n",
    "# opt = SGD(lr=0.001)\n",
    "# opt = Adam(lr=0.003, beta_1=0.9, beta_2=0.999,\n",
    "#                      epsilon=0.1, decay=0.0)\n",
    "opt=Adam(lr=0.0001)\n",
    "model.compile(loss = \"binary_crossentropy\", optimizer = opt,metrics=['accuracy'])\n",
    "# model.compile(loss = \"binary_crossentropy\",\n",
    "#               optimizer = RMSprop(learning_rate=0.0001),\n",
    "#               metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3df01b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 222, 222, 128)     3584      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 111, 111, 128)    0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 109, 109, 64)      73792     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 54, 54, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 54, 54, 64)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 52, 52, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 26, 26, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 26, 26, 64)        0         \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 24, 24, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 12, 12, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 12, 12, 128)       0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 18432)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 2048)              37750784  \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1024)              2098176   \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 40,317,025\n",
      "Trainable params: 40,317,025\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "041b835d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-06 23:43:34.947561: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8202\n",
      "2022-05-06 23:43:35.979912: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.02GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:35.979938: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.02GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:36.093011: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 918.72MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:36.093042: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 918.72MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:37.124914: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 851.38MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:37.124944: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 851.38MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:37.124953: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:37.124961: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.29GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:37.855996: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.16GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
      "2022-05-06 23:43:37.856025: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.16GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108/108 [==============================] - ETA: 0s - loss: 0.4393 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-06 23:44:24.381877: W tensorflow/core/common_runtime/bfc_allocator.cc:343] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108/108 [==============================] - 53s 443ms/step - loss: 0.4393 - accuracy: 0.8007 - val_loss: 0.4831 - val_accuracy: 0.8499\n",
      "Epoch 2/100\n",
      "108/108 [==============================] - 46s 426ms/step - loss: 0.3262 - accuracy: 0.8714 - val_loss: 0.3820 - val_accuracy: 0.9145\n",
      "Epoch 3/100\n",
      "108/108 [==============================] - 49s 450ms/step - loss: 0.2827 - accuracy: 0.8917 - val_loss: 0.2836 - val_accuracy: 0.9538\n",
      "Epoch 4/100\n",
      "108/108 [==============================] - 47s 437ms/step - loss: 0.2623 - accuracy: 0.9050 - val_loss: 0.3246 - val_accuracy: 0.9538\n",
      "Epoch 5/100\n",
      "108/108 [==============================] - 51s 469ms/step - loss: 0.2694 - accuracy: 0.9027 - val_loss: 0.2649 - val_accuracy: 0.9376\n",
      "Epoch 6/100\n",
      "108/108 [==============================] - 50s 460ms/step - loss: 0.2537 - accuracy: 0.9073 - val_loss: 0.2776 - val_accuracy: 0.9630\n",
      "Epoch 7/100\n",
      "108/108 [==============================] - 52s 485ms/step - loss: 0.2578 - accuracy: 0.9119 - val_loss: 0.1967 - val_accuracy: 0.9677\n",
      "Epoch 8/100\n",
      "108/108 [==============================] - 51s 471ms/step - loss: 0.2420 - accuracy: 0.9143 - val_loss: 0.2080 - val_accuracy: 0.9607\n",
      "Epoch 9/100\n",
      "108/108 [==============================] - 52s 485ms/step - loss: 0.2478 - accuracy: 0.9079 - val_loss: 0.1892 - val_accuracy: 0.9492\n",
      "Epoch 10/100\n",
      "108/108 [==============================] - 52s 483ms/step - loss: 0.2127 - accuracy: 0.9253 - val_loss: 0.1837 - val_accuracy: 0.9423\n",
      "Epoch 11/100\n",
      "108/108 [==============================] - 53s 491ms/step - loss: 0.2204 - accuracy: 0.9166 - val_loss: 0.1383 - val_accuracy: 0.9469\n",
      "Epoch 12/100\n",
      "108/108 [==============================] - 53s 491ms/step - loss: 0.2010 - accuracy: 0.9247 - val_loss: 0.1223 - val_accuracy: 0.9654\n",
      "Epoch 13/100\n",
      "108/108 [==============================] - 52s 482ms/step - loss: 0.1805 - accuracy: 0.9316 - val_loss: 0.1304 - val_accuracy: 0.9607\n",
      "Epoch 14/100\n",
      "108/108 [==============================] - 53s 488ms/step - loss: 0.1660 - accuracy: 0.9363 - val_loss: 0.1454 - val_accuracy: 0.9515\n",
      "Epoch 15/100\n",
      "108/108 [==============================] - 56s 513ms/step - loss: 0.1476 - accuracy: 0.9490 - val_loss: 0.1004 - val_accuracy: 0.9746\n",
      "Epoch 16/100\n",
      "108/108 [==============================] - 54s 503ms/step - loss: 0.1226 - accuracy: 0.9490 - val_loss: 0.0714 - val_accuracy: 0.9792\n",
      "Epoch 17/100\n",
      "108/108 [==============================] - 53s 492ms/step - loss: 0.1388 - accuracy: 0.9473 - val_loss: 0.1932 - val_accuracy: 0.9376\n",
      "Epoch 18/100\n",
      "108/108 [==============================] - 53s 492ms/step - loss: 0.1217 - accuracy: 0.9554 - val_loss: 0.1263 - val_accuracy: 0.9584\n",
      "Epoch 19/100\n",
      "108/108 [==============================] - 52s 480ms/step - loss: 0.1261 - accuracy: 0.9537 - val_loss: 0.1090 - val_accuracy: 0.9723\n"
     ]
    }
   ],
   "source": [
    "hist=model.fit(training_set,\n",
    "                epochs= 100,\n",
    "                validation_data= testing_set,\n",
    "                callbacks=callback) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da109956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = hist.history\n",
    "h.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4aa6c761",
   "metadata": {},
   "outputs": [],
   "source": [
    "h = hist.history\n",
    "# h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c02faf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(1, 20)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs=range(1,len(h['accuracy'])+1)\n",
    "epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d55c674d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs=range(1,len(h['accuracy'])+1)\n",
    "plt.plot(epochs,h['accuracy'],label=\"Training Accuracy\")\n",
    "plt.plot(epochs,h['val_accuracy'], c = \"red\",label=\"Validation Accuracy\")\n",
    "plt.title(\"acc vs v-acc\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d637c2c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs,h['loss'],c='green',label=\"Training Loss\")\n",
    "plt.plot(epochs,h['val_loss'],c='red',label=\"Validation Loss\")\n",
    "plt.title(\"Training Loss vs Validation Loss\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ecdb905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.12718136608600616, 0.9536500573158264]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a32c7e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.07141951471567154, 0.9792147874832153]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(testing_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "002d93cd",
   "metadata": {},
   "source": [
    "### Image Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dae6f427",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from keras.models import load_model\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import os\n",
    "model=load_model('final_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "64e34808",
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Input : Takes in image path as input \n",
    "#   Output : Gives out Pre-Processed image\n",
    "def get_img_array(img_path):\n",
    "    path = img_path\n",
    "    img = image.load_img(path, target_size=(224,224,3))\n",
    "    img = image.img_to_array(img)/255\n",
    "    img = np.expand_dims(img , axis= 0 )\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf8780cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Final_Result(array_value):\n",
    "    if array_value < 0.50:\n",
    "        return \"Covid\"\n",
    "    else:\n",
    "        return \"Normal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f432c186",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_type = {0:'Covid',  1 : 'Normal'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "405f5017",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"/home/bibek/Desktop/Project_Sem_8/Projects/Data/train/NORMAL/NORMAL(100).jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1416eecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = get_img_array(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16cd1fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=np.argmax(model.predict(img))\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e1672c1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The given X-Ray image is of type = Covid\n",
      "The chances of image being Covid is : 99.96517896652222 percent\n"
     ]
    }
   ],
   "source": [
    "res = class_type[np.argmax(model.predict(img))]\n",
    "print(f\"The given X-Ray image is of type = {res}\")\n",
    "print(f\"The chances of image being Covid is : {model.predict(img)[0][0]*100} percent\")\n",
    "# print(f\"The chances of image being Normal is : {model.predict(img)[0][1]*100} percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "873bc709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.99958104]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"/home/bibek/Downloads/Small_Size_Data/dataset/normal/NORMAL2-IM-1277-0001-0001.jpeg\"\n",
    "img = get_img_array(path)\n",
    "model.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e99120af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.14836256]]\n",
      "Covid\n",
      "85.16374379396439\n"
     ]
    }
   ],
   "source": [
    "path=\"/home/bibek/Downloads/Small_Size_Data/dataset/covid/1-s2.0-S0929664620300449-gr2_lrg-c.jpg\"\n",
    "img = get_img_array(path)\n",
    "x=model.predict(img)\n",
    "print(x)\n",
    "print(Final_Result(x))\n",
    "print(100-model.predict(img)[0][0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bad1ed46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.99996877]]\n",
      "Normal\n",
      "99.99687671661377\n"
     ]
    }
   ],
   "source": [
    "path=\"/home/bibek/Downloads/Small_Size_Data/dataset/normal/NORMAL2-IM-1090-0001.jpeg\"\n",
    "img = get_img_array(path)\n",
    "x=model.predict(img)\n",
    "print(x)\n",
    "print(Final_Result(x))\n",
    "print(model.predict(img)[0][0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d477c4a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cc3bf99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f07af01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1ba184a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66feab47",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68f2c11e",
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d92df32",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98df834c",
   "metadata": {},
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   "source": []
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