model.ipynb

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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-11 08:33:39.326618: 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"
     ]
    }
   ],
   "source": [
    "from tensorflow.compat.v1 import ConfigProto,InteractiveSession\n",
    "config=ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction=0.5\n",
    "config.gpu_options.allow_growth=True\n",
    "session=InteractiveSession(config=config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2070 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set=ImageDataGenerator(shear_range=0.2,zoom_range=0.2,horizontal_flip=True,vertical_flip=True,)\n",
    "training_data=training_set.flow_from_directory(r'/Users/macbook/PycharmProjects/ISA_project/Dataset',\n",
    "                                              target_size=(224,224),\n",
    "                                              class_mode='categorical',\n",
    "                                              batch_size=16)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 0, '1': 1}\n"
     ]
    }
   ],
   "source": [
    "print(training_data.class_indices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "#first set\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3),padding='same',activation='relu',input_shape=(224,224,3)))\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3),padding='same',activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPool2D((2,2),padding='same'))\n",
    "\n",
    "\n",
    "#second set\n",
    "\n",
    "\n",
    "#fifth set\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.Dense(4096,activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.Dense(1,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.SGD(),loss=tf.keras.losses.mae,metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "history=model.fit(x=training_data,epochs=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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