step 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e060fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "\n",
    "from keras import backend as K\n",
    "from keras.preprocessing.image import ImageDataGenerator,load_img, img_to_array\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Conv2D, MaxPooling2D,GlobalAveragePooling2D\n",
    "from keras.layers import Activation, Dropout, BatchNormalization, Flatten, Dense, AvgPool2D,MaxPool2D\n",
    "from keras.models import Sequential, Model\n",
    "from keras.applications.vgg16 import VGG16, preprocess_input\n",
    "from tensorflow.keras.optimizers import Adam, SGD, RMSprop\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65f9e7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_DIR = r\"F:\\data for final project sem3\\archive\\Coronahack-Chest-XRay-Dataset\\Coronahack-Chest-XRay-Dataset\\exp4\\train\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e60f739",
   "metadata": {},
   "source": [
    "## model 1 save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbf34e3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1600 images belonging to 2 classes.\n",
      "Found 400 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_W = 700\n",
    "IMG_H = 700\n",
    "CHANNELS = 3\n",
    "\n",
    "INPUT_SHAPE = (IMG_W, IMG_H, CHANNELS)\n",
    "NB_CLASSES = 2\n",
    "EPOCHS = 10\n",
    "BATCH_SIZE = 16\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "    #shear_range=0.2,\n",
    "    #zoom_range=0.2,\n",
    "    #horizontal_flip=True,\n",
    "    validation_split=0.2)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR,\n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    subset='training')\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR, \n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    shuffle= True,\n",
    "    subset='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa9853fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), input_shape=INPUT_SHAPE))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "\n",
    "model.add(Conv2D(64,(3,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(Conv2D(250,(3,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Conv2D(128,(3,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(AvgPool2D(2,2))\n",
    "model.add(Conv2D(64,(3,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(AvgPool2D(2,2))\n",
    "\n",
    "model.add(Conv2D(256,(2,2)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPool2D(2,2))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30186283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 71s 608ms/step - loss: 1.9417 - accuracy: 0.6156 - val_loss: 0.5125 - val_accuracy: 0.7925\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 60s 594ms/step - loss: 0.4485 - accuracy: 0.8425 - val_loss: 0.5453 - val_accuracy: 0.8000\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 60s 603ms/step - loss: 0.6667 - accuracy: 0.8675 - val_loss: 0.3231 - val_accuracy: 0.8875\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 61s 610ms/step - loss: 0.3947 - accuracy: 0.8888 - val_loss: 0.3546 - val_accuracy: 0.8625\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 60s 602ms/step - loss: 0.7455 - accuracy: 0.9031 - val_loss: 0.2801 - val_accuracy: 0.8975\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 59s 592ms/step - loss: 0.2944 - accuracy: 0.9038 - val_loss: 0.7983 - val_accuracy: 0.8150\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 60s 597ms/step - loss: 0.2056 - accuracy: 0.9269 - val_loss: 0.3998 - val_accuracy: 0.8900\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 60s 594ms/step - loss: 0.2391 - accuracy: 0.9244 - val_loss: 0.8190 - val_accuracy: 0.7675\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 60s 598ms/step - loss: 0.1495 - accuracy: 0.9469 - val_loss: 0.2707 - val_accuracy: 0.9050\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 59s 589ms/step - loss: 0.1577 - accuracy: 0.9463 - val_loss: 0.2796 - val_accuracy: 0.9200\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // BATCH_SIZE,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = validation_generator.samples // BATCH_SIZE, epochs = EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "637172fe",
   "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"
    },
    {
     "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": [
    "accuracy = history.history[\"accuracy\"]\n",
    "val_accuracy = history.history[\"val_accuracy\"]\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(accuracy) + 1)\n",
    "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e6ffdb",
   "metadata": {},
   "source": [
    "## Model 2 save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70c7c847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 images belonging to 2 classes.\n",
      "Found 120 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_W = 150\n",
    "IMG_H = 150\n",
    "CHANNELS = 3\n",
    "\n",
    "INPUT_SHAPE = (IMG_W, IMG_H, CHANNELS)\n",
    "NB_CLASSES = 2\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 8\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "    #shear_range=0.2,\n",
    "    #zoom_range=0.2,\n",
    "    #horizontal_flip=True,\n",
    "    validation_split=0.2)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR,\n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    subset='training')\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR, \n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    shuffle= True,\n",
    "    subset='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "48916f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=INPUT_SHAPE)\n",
    "\n",
    "x = inputs\n",
    "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(32, activation=\"sigmoid\")(x)\n",
    "\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "optimizer=\"rmsprop\",\n",
    "metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "492d15d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "60/60 [==============================] - 11s 162ms/step - loss: 0.7526 - accuracy: 0.5667 - val_loss: 0.6147 - val_accuracy: 0.6500\n",
      "Epoch 2/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.4653 - accuracy: 0.8188 - val_loss: 0.4699 - val_accuracy: 0.7833\n",
      "Epoch 3/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.3322 - accuracy: 0.8646 - val_loss: 0.2543 - val_accuracy: 0.8917\n",
      "Epoch 4/50\n",
      "60/60 [==============================] - 10s 163ms/step - loss: 0.2634 - accuracy: 0.9146 - val_loss: 0.3163 - val_accuracy: 0.8750\n",
      "Epoch 5/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.2830 - accuracy: 0.8896 - val_loss: 0.2727 - val_accuracy: 0.8833\n",
      "Epoch 6/50\n",
      "60/60 [==============================] - 10s 161ms/step - loss: 0.2380 - accuracy: 0.9042 - val_loss: 0.2877 - val_accuracy: 0.8917\n",
      "Epoch 7/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.1948 - accuracy: 0.9312 - val_loss: 0.5880 - val_accuracy: 0.7833\n",
      "Epoch 8/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.2400 - accuracy: 0.9208 - val_loss: 0.3164 - val_accuracy: 0.8833\n",
      "Epoch 9/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.2064 - accuracy: 0.9312 - val_loss: 0.2946 - val_accuracy: 0.9000\n",
      "Epoch 10/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.1847 - accuracy: 0.9333 - val_loss: 0.2332 - val_accuracy: 0.9083\n",
      "Epoch 11/50\n",
      "60/60 [==============================] - 12s 195ms/step - loss: 0.1536 - accuracy: 0.9563 - val_loss: 0.3062 - val_accuracy: 0.9000\n",
      "Epoch 12/50\n",
      "60/60 [==============================] - 11s 182ms/step - loss: 0.1719 - accuracy: 0.9396 - val_loss: 0.2283 - val_accuracy: 0.9250\n",
      "Epoch 13/50\n",
      "60/60 [==============================] - 12s 197ms/step - loss: 0.1228 - accuracy: 0.9625 - val_loss: 0.3758 - val_accuracy: 0.8917\n",
      "Epoch 14/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.1471 - accuracy: 0.9583 - val_loss: 0.2327 - val_accuracy: 0.9250\n",
      "Epoch 15/50\n",
      "60/60 [==============================] - 9s 152ms/step - loss: 0.1054 - accuracy: 0.9646 - val_loss: 0.3373 - val_accuracy: 0.9167\n",
      "Epoch 16/50\n",
      "60/60 [==============================] - 9s 154ms/step - loss: 0.0978 - accuracy: 0.9729 - val_loss: 0.2761 - val_accuracy: 0.9167\n",
      "Epoch 17/50\n",
      "60/60 [==============================] - 13s 225ms/step - loss: 0.1008 - accuracy: 0.9667 - val_loss: 0.3673 - val_accuracy: 0.8917\n",
      "Epoch 18/50\n",
      "60/60 [==============================] - 11s 191ms/step - loss: 0.0778 - accuracy: 0.9750 - val_loss: 0.2567 - val_accuracy: 0.9333\n",
      "Epoch 19/50\n",
      "60/60 [==============================] - 10s 172ms/step - loss: 0.0799 - accuracy: 0.9833 - val_loss: 0.3131 - val_accuracy: 0.9083\n",
      "Epoch 20/50\n",
      "60/60 [==============================] - 10s 173ms/step - loss: 0.0955 - accuracy: 0.9729 - val_loss: 0.2561 - val_accuracy: 0.8917\n",
      "Epoch 21/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0654 - accuracy: 0.9833 - val_loss: 0.3469 - val_accuracy: 0.9167\n",
      "Epoch 22/50\n",
      "60/60 [==============================] - 10s 161ms/step - loss: 0.0620 - accuracy: 0.9833 - val_loss: 0.5740 - val_accuracy: 0.8417\n",
      "Epoch 23/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0509 - accuracy: 0.9875 - val_loss: 0.4809 - val_accuracy: 0.8583\n",
      "Epoch 24/50\n",
      "60/60 [==============================] - 10s 161ms/step - loss: 0.0811 - accuracy: 0.9771 - val_loss: 0.2057 - val_accuracy: 0.9333\n",
      "Epoch 25/50\n",
      "60/60 [==============================] - 10s 166ms/step - loss: 0.0522 - accuracy: 0.9896 - val_loss: 0.3460 - val_accuracy: 0.9083\n",
      "Epoch 26/50\n",
      "60/60 [==============================] - 10s 171ms/step - loss: 0.0677 - accuracy: 0.9812 - val_loss: 0.3115 - val_accuracy: 0.9250\n",
      "Epoch 27/50\n",
      "60/60 [==============================] - 10s 157ms/step - loss: 0.0721 - accuracy: 0.9750 - val_loss: 0.2733 - val_accuracy: 0.9167\n",
      "Epoch 28/50\n",
      "60/60 [==============================] - 10s 158ms/step - loss: 0.0248 - accuracy: 0.9937 - val_loss: 0.3206 - val_accuracy: 0.9167\n",
      "Epoch 29/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.0505 - accuracy: 0.9854 - val_loss: 0.3108 - val_accuracy: 0.9250\n",
      "Epoch 30/50\n",
      "60/60 [==============================] - 10s 157ms/step - loss: 0.0839 - accuracy: 0.9792 - val_loss: 0.2263 - val_accuracy: 0.9500\n",
      "Epoch 31/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0351 - accuracy: 0.9917 - val_loss: 0.2899 - val_accuracy: 0.9250\n",
      "Epoch 32/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.0774 - accuracy: 0.9729 - val_loss: 0.2769 - val_accuracy: 0.9167\n",
      "Epoch 33/50\n",
      "60/60 [==============================] - 10s 158ms/step - loss: 0.0361 - accuracy: 0.9854 - val_loss: 0.5296 - val_accuracy: 0.8750\n",
      "Epoch 34/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0203 - accuracy: 0.9958 - val_loss: 0.5095 - val_accuracy: 0.8833\n",
      "Epoch 35/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.0702 - accuracy: 0.9812 - val_loss: 0.3645 - val_accuracy: 0.9167\n",
      "Epoch 36/50\n",
      "60/60 [==============================] - 10s 166ms/step - loss: 0.0856 - accuracy: 0.9854 - val_loss: 0.2928 - val_accuracy: 0.9167\n",
      "Epoch 37/50\n",
      "60/60 [==============================] - 10s 161ms/step - loss: 0.0517 - accuracy: 0.9917 - val_loss: 0.3943 - val_accuracy: 0.9167\n",
      "Epoch 38/50\n",
      "60/60 [==============================] - 10s 166ms/step - loss: 0.0244 - accuracy: 0.9958 - val_loss: 0.2939 - val_accuracy: 0.9417\n",
      "Epoch 39/50\n",
      "60/60 [==============================] - 10s 161ms/step - loss: 0.0257 - accuracy: 0.9958 - val_loss: 0.6512 - val_accuracy: 0.8750\n",
      "Epoch 40/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0279 - accuracy: 0.9896 - val_loss: 0.2326 - val_accuracy: 0.9333\n",
      "Epoch 41/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0716 - accuracy: 0.9833 - val_loss: 0.5204 - val_accuracy: 0.9000\n",
      "Epoch 42/50\n",
      "60/60 [==============================] - 10s 162ms/step - loss: 0.0245 - accuracy: 0.9958 - val_loss: 0.4745 - val_accuracy: 0.9083\n",
      "Epoch 43/50\n",
      "60/60 [==============================] - 10s 162ms/step - loss: 0.0318 - accuracy: 0.9917 - val_loss: 0.5465 - val_accuracy: 0.8667\n",
      "Epoch 44/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0337 - accuracy: 0.9937 - val_loss: 0.4362 - val_accuracy: 0.9167\n",
      "Epoch 45/50\n",
      "60/60 [==============================] - 10s 159ms/step - loss: 0.0130 - accuracy: 0.9979 - val_loss: 0.5675 - val_accuracy: 0.8750\n",
      "Epoch 46/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0469 - accuracy: 0.9917 - val_loss: 0.3739 - val_accuracy: 0.9083\n",
      "Epoch 47/50\n",
      "60/60 [==============================] - 10s 160ms/step - loss: 0.0160 - accuracy: 0.9979 - val_loss: 0.6487 - val_accuracy: 0.8750\n",
      "Epoch 48/50\n",
      "60/60 [==============================] - 10s 165ms/step - loss: 0.0865 - accuracy: 0.9875 - val_loss: 0.3790 - val_accuracy: 0.9250\n",
      "Epoch 49/50\n",
      "60/60 [==============================] - 10s 163ms/step - loss: 0.0139 - accuracy: 0.9979 - val_loss: 0.3728 - val_accuracy: 0.9333\n",
      "Epoch 50/50\n",
      "60/60 [==============================] - 9s 154ms/step - loss: 0.0133 - accuracy: 0.9979 - val_loss: 0.5515 - val_accuracy: 0.9083\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // BATCH_SIZE,\n",
    "    validation_data = validation_generator, \n",
    "    validation_steps = validation_generator.samples // BATCH_SIZE, epochs = EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "55e0a2c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: F:\\data for final project sem3\\results\\model 2\\assets\n"
     ]
    }
   ],
   "source": [
    "# model.save(r'F:\\data for final project sem3\\results\\model 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b3bdba4",
   "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"
    },
    {
     "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": [
    "accuracy = history.history[\"accuracy\"]\n",
    "val_accuracy = history.history[\"val_accuracy\"]\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(accuracy) + 1)\n",
    "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82462ea",
   "metadata": {},
   "source": [
    "## model 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9012f6db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 images belonging to 2 classes.\n",
      "Found 120 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_W = 150\n",
    "IMG_H = 150\n",
    "CHANNELS = 1\n",
    "\n",
    "INPUT_SHAPE = (IMG_W, IMG_H, CHANNELS)\n",
    "NB_CLASSES = 2\n",
    "EPOCHS = 20\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "    #shear_range=0.2,\n",
    "    #zoom_range=0.2,\n",
    "    #horizontal_flip=True,\n",
    "    validation_split=0.2)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR,\n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    subset='training')\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR, \n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    shuffle= True,\n",
    "    subset='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5389fc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(150, 150, 3))\n",
    "\n",
    "x = inputs\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(64, activation=\"sigmoid\")(x)\n",
    "\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "optimizer=\"rmsprop\",\n",
    "metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b7029e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "7/7 [==============================] - 14s 2s/step - loss: 1.5355 - accuracy: 0.5168 - val_loss: 1.5618 - val_accuracy: 0.5156\n",
      "Epoch 2/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 1.5973 - accuracy: 0.4888 - val_loss: 1.7781 - val_accuracy: 0.4062\n",
      "Epoch 3/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 1.4791 - accuracy: 0.4952 - val_loss: 1.4200 - val_accuracy: 0.5000\n",
      "Epoch 4/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 1.4264 - accuracy: 0.4832 - val_loss: 1.2209 - val_accuracy: 0.5469\n",
      "Epoch 5/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 1.3044 - accuracy: 0.5024 - val_loss: 1.1917 - val_accuracy: 0.5312\n",
      "Epoch 6/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 1.2589 - accuracy: 0.4904 - val_loss: 1.3995 - val_accuracy: 0.4062\n",
      "Epoch 7/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 1.1709 - accuracy: 0.4976 - val_loss: 1.2847 - val_accuracy: 0.4219\n",
      "Epoch 8/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 1.0937 - accuracy: 0.5048 - val_loss: 1.0362 - val_accuracy: 0.5156\n",
      "Epoch 9/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 1.0530 - accuracy: 0.4928 - val_loss: 0.9536 - val_accuracy: 0.5312\n",
      "Epoch 10/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 0.9800 - accuracy: 0.5000 - val_loss: 0.9972 - val_accuracy: 0.4688\n",
      "Epoch 11/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 0.9350 - accuracy: 0.4952 - val_loss: 0.8171 - val_accuracy: 0.5625\n",
      "Epoch 12/20\n",
      "7/7 [==============================] - 12s 2s/step - loss: 0.8764 - accuracy: 0.5048 - val_loss: 0.8190 - val_accuracy: 0.5312\n",
      "Epoch 13/20\n",
      "7/7 [==============================] - 10s 1s/step - loss: 0.8422 - accuracy: 0.4952 - val_loss: 0.7367 - val_accuracy: 0.5781\n",
      "Epoch 14/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 0.7910 - accuracy: 0.5120 - val_loss: 0.7307 - val_accuracy: 0.5625\n",
      "Epoch 15/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 0.7814 - accuracy: 0.4904 - val_loss: 0.7805 - val_accuracy: 0.4688\n",
      "Epoch 16/20\n",
      "7/7 [==============================] - 8s 1s/step - loss: 0.7481 - accuracy: 0.5000 - val_loss: 0.7546 - val_accuracy: 0.4688\n",
      "Epoch 17/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 0.7316 - accuracy: 0.4928 - val_loss: 0.7325 - val_accuracy: 0.4688\n",
      "Epoch 18/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 0.7103 - accuracy: 0.5096 - val_loss: 0.7245 - val_accuracy: 0.4531\n",
      "Epoch 19/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 0.7057 - accuracy: 0.4952 - val_loss: 0.7176 - val_accuracy: 0.4219\n",
      "Epoch 20/20\n",
      "7/7 [==============================] - 9s 1s/step - loss: 0.6984 - accuracy: 0.5000 - val_loss: 0.6913 - val_accuracy: 0.5312\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // BATCH_SIZE,\n",
    "    validation_data = validation_generator, \n",
    "    validation_steps = validation_generator.samples // BATCH_SIZE, epochs = EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9df563a6",
   "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"
    },
    {
     "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": [
    "accuracy = history.history[\"accuracy\"]\n",
    "val_accuracy = history.history[\"val_accuracy\"]\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(accuracy) + 1)\n",
    "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edb2ff2",
   "metadata": {},
   "source": [
    "## model 4 save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb1883ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1600 images belonging to 2 classes.\n",
      "Found 400 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "IMG_W = 150\n",
    "IMG_H = 150\n",
    "CHANNELS = 3\n",
    "\n",
    "INPUT_SHAPE = (IMG_W, IMG_H, CHANNELS)\n",
    "NB_CLASSES = 2\n",
    "EPOCHS = 15\n",
    "BATCH_SIZE = 16\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "    #shear_range=0.2,\n",
    "    #zoom_range=0.2,\n",
    "    #horizontal_flip=True,\n",
    "    validation_split=0.2)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR,\n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    subset='training')\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    DATASET_DIR, \n",
    "    target_size=(IMG_H, IMG_W),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    shuffle= True,\n",
    "    subset='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d60dbb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=INPUT_SHAPE)\n",
    "\n",
    "x = inputs\n",
    "x = layers.Conv2D(filters=8, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=16, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(8, activation=\"sigmoid\")(x)\n",
    "\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "optimizer=\"rmsprop\",\n",
    "metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4715aa16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "100/100 [==============================] - 32s 311ms/step - loss: 0.7030 - accuracy: 0.5638 - val_loss: 0.6907 - val_accuracy: 0.5525\n",
      "Epoch 2/15\n",
      "100/100 [==============================] - 30s 300ms/step - loss: 0.6260 - accuracy: 0.6475 - val_loss: 0.6676 - val_accuracy: 0.6625\n",
      "Epoch 3/15\n",
      "100/100 [==============================] - 30s 297ms/step - loss: 0.5180 - accuracy: 0.8069 - val_loss: 0.4512 - val_accuracy: 0.8125\n",
      "Epoch 4/15\n",
      "100/100 [==============================] - 30s 298ms/step - loss: 0.4554 - accuracy: 0.8656 - val_loss: 0.4183 - val_accuracy: 0.9025\n",
      "Epoch 5/15\n",
      "100/100 [==============================] - 30s 295ms/step - loss: 0.4028 - accuracy: 0.9119 - val_loss: 0.3871 - val_accuracy: 0.9150\n",
      "Epoch 6/15\n",
      "100/100 [==============================] - 35s 353ms/step - loss: 0.3759 - accuracy: 0.9181 - val_loss: 0.3568 - val_accuracy: 0.9225\n",
      "Epoch 7/15\n",
      "100/100 [==============================] - 31s 310ms/step - loss: 0.3526 - accuracy: 0.9187 - val_loss: 0.3361 - val_accuracy: 0.9450\n",
      "Epoch 8/15\n",
      "100/100 [==============================] - 30s 299ms/step - loss: 0.3384 - accuracy: 0.9269 - val_loss: 0.3148 - val_accuracy: 0.9425\n",
      "Epoch 9/15\n",
      "100/100 [==============================] - 32s 325ms/step - loss: 0.3066 - accuracy: 0.9431 - val_loss: 0.3537 - val_accuracy: 0.8850\n",
      "Epoch 10/15\n",
      "100/100 [==============================] - 30s 304ms/step - loss: 0.2793 - accuracy: 0.9531 - val_loss: 0.4232 - val_accuracy: 0.8000\n",
      "Epoch 11/15\n",
      "100/100 [==============================] - 30s 298ms/step - loss: 0.2787 - accuracy: 0.9463 - val_loss: 0.2619 - val_accuracy: 0.9525\n",
      "Epoch 12/15\n",
      "100/100 [==============================] - 30s 297ms/step - loss: 0.2639 - accuracy: 0.9563 - val_loss: 0.2756 - val_accuracy: 0.9350\n",
      "Epoch 13/15\n",
      "100/100 [==============================] - 30s 300ms/step - loss: 0.2366 - accuracy: 0.9588 - val_loss: 0.2406 - val_accuracy: 0.9550\n",
      "Epoch 14/15\n",
      "100/100 [==============================] - 30s 295ms/step - loss: 0.2322 - accuracy: 0.9575 - val_loss: 0.3418 - val_accuracy: 0.8675\n",
      "Epoch 15/15\n",
      "100/100 [==============================] - 30s 297ms/step - loss: 0.2076 - accuracy: 0.9681 - val_loss: 0.2212 - val_accuracy: 0.9525\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // BATCH_SIZE,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = validation_generator.samples // BATCH_SIZE, epochs = EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e2731cf",
   "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"
    },
    {
     "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": [
    "accuracy = history.history[\"accuracy\"]\n",
    "val_accuracy = history.history[\"val_accuracy\"]\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(accuracy) + 1)\n",
    "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c77d2880",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}