binary image classification.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Keras\n",
      "  Downloading Keras-2.4.3-py2.py3-none-any.whl (36 kB)\n",
      "Requirement already satisfied: h5py in c:\\users\\saravanan\\appdata\\roaming\\python\\python38\\site-packages (from Keras) (2.10.0)\n",
      "Requirement already satisfied: pyyaml in c:\\users\\saravanan\\anaconda3\\lib\\site-packages (from Keras) (5.3.1)\n",
      "Requirement already satisfied: numpy>=1.9.1 in c:\\users\\saravanan\\anaconda3\\lib\\site-packages (from Keras) (1.18.5)\n",
      "Requirement already satisfied: scipy>=0.14 in c:\\users\\saravanan\\anaconda3\\lib\\site-packages (from Keras) (1.5.2)\n",
      "Requirement already satisfied: six in c:\\users\\saravanan\\anaconda3\\lib\\site-packages (from h5py->Keras) (1.15.0)\n",
      "Installing collected packages: Keras\n",
      "Successfully installed Keras-2.4.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Conv2D(32,(3,3),input_shape=(64,64,3),activation='relu'))#32 no of filtres  3*3 shape of filte 64*64 *3 color image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(MaxPooling2D(pool_size=(2,2)))#pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Conv2D(32,(3,3),activation='relu') )\n",
    "classifier.add(MaxPooling2D(pool_size=(2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Dense(units=128,activation='relu'))\n",
    "classifier.add(Dense(units=1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.compile(optimizer='adam',loss='binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen= ImageDataGenerator(rescale=1./255,\n",
    "                                 shear_range=0.2,\n",
    "                                 zoom_range=0.2,\n",
    "                                  horizontal_flip=True)\n",
    "test_datagen=ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set= train_datagen.flow_from_directory('C:\\Users\\pet_images',\n",
    "                                               target_size=(64,64),\n",
    "                                               batch_size=32,\n",
    "                                               class_mode='binary')\n",
    "test_set=test.datagen.flow_from_directory('C:\\Users\\pet_images',\n",
    "                                               target_size=(64,64),\n",
    "                                               batch_size=32,\n",
    "                                               class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.fit_generator(training_set,\n",
    "                        steps_per_epoch=8000,\n",
    "                        epoch=15,\n",
    "                        validation_data=test_set,\n",
    "                        validation_steps=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "test_image=image.load_img('path to load',target_size=64*64)\n",
    "test_image=image.img_to_array(test_image)\n",
    "test_image=np.expand_dims(test_image,axis=0)\n",
    "result=classifier.predict(test_image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "if result[0]<=0.5:\n",
    "    print(\"The image classified is cat\")\n",
    "else:\n",
    "    print(\"The image classified is dog\")"
   ]
  }
 ],
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