Train_with_MobileNetV2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ddb1071",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import AveragePooling2D\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.models import Model\n",
    "import tensorflow_hub as hub\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a7013b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train =ImageDataGenerator(rescale=1/255)\n",
    "validation =ImageDataGenerator(rescale=1/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a57475b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11180 images belonging to 5 classes.\n",
      "Found 1519 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "train_dataset =train.flow_from_directory(\"Dataset\\\\train\\\\\",\n",
    "                                         target_size=(224,224)\n",
    "                                         ,batch_size=32,\n",
    "                                        class_mode='categorical')\n",
    "Validation_dataset =validation.flow_from_directory(\"Dataset\\\\test\\\\\",\n",
    "                                         target_size=(224,224)\n",
    "                                         ,batch_size=32,\n",
    "                                        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a384953",
   "metadata": {},
   "outputs": [],
   "source": [
    "mobilenet_v2 = \"https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6de0776",
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "SavedModel file does not exist at: C:\\Users\\Super\\AppData\\Local\\Temp\\tfhub_modules\\145bb06ec3b59b08fb564ab752bd5aa222bfb50a\\{saved_model.pbtxt|saved_model.pb}",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m mobile_net_layers \u001b[38;5;241m=\u001b[39m \u001b[43mhub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mKerasLayer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmobilenet_v2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_shape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m224\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m224\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow_hub\\keras_layer.py:153\u001b[0m, in \u001b[0;36mKerasLayer.__init__\u001b[1;34m(self, handle, trainable, arguments, _sentinel, tags, signature, signature_outputs_as_dict, output_key, output_shape, load_options, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_shape \u001b[38;5;241m=\u001b[39m data_structures\u001b[38;5;241m.\u001b[39mNoDependency(\n\u001b[0;32m    150\u001b[0m       _convert_nest_to_shapes(output_shape))\n\u001b[0;32m    152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_load_options \u001b[38;5;241m=\u001b[39m load_options\n\u001b[1;32m--> 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_func \u001b[38;5;241m=\u001b[39m \u001b[43mload_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_training_argument \u001b[38;5;241m=\u001b[39m func_has_training_argument(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_func)\n\u001b[0;32m    155\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_hub_module_v1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_func, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_is_hub_module_v1\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow_hub\\keras_layer.py:449\u001b[0m, in \u001b[0;36mload_module\u001b[1;34m(handle, tags, load_options)\u001b[0m\n\u001b[0;32m    447\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:  \u001b[38;5;66;03m# Expected before TF2.4.\u001b[39;00m\n\u001b[0;32m    448\u001b[0m     set_load_options \u001b[38;5;241m=\u001b[39m load_options\n\u001b[1;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodule_v2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mset_load_options\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow_hub\\module_v2.py:106\u001b[0m, in \u001b[0;36mload\u001b[1;34m(handle, tags, options)\u001b[0m\n\u001b[0;32m    103\u001b[0m   obj \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mcompat\u001b[38;5;241m.\u001b[39mv1\u001b[38;5;241m.\u001b[39msaved_model\u001b[38;5;241m.\u001b[39mload_v2(\n\u001b[0;32m    104\u001b[0m       module_path, tags\u001b[38;5;241m=\u001b[39mtags, options\u001b[38;5;241m=\u001b[39moptions)\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 106\u001b[0m   obj \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mv1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msaved_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_v2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodule_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    107\u001b[0m obj\u001b[38;5;241m.\u001b[39m_is_hub_module_v1 \u001b[38;5;241m=\u001b[39m is_hub_module_v1  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m    108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\saved_model\\load.py:782\u001b[0m, in \u001b[0;36mload\u001b[1;34m(export_dir, tags, options)\u001b[0m\n\u001b[0;32m    780\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(export_dir, os\u001b[38;5;241m.\u001b[39mPathLike):\n\u001b[0;32m    781\u001b[0m   export_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(export_dir)\n\u001b[1;32m--> 782\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mload_partial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexport_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    783\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\saved_model\\load.py:887\u001b[0m, in \u001b[0;36mload_partial\u001b[1;34m(export_dir, filters, tags, options)\u001b[0m\n\u001b[0;32m    882\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tags \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tags, \u001b[38;5;28mset\u001b[39m):\n\u001b[0;32m    883\u001b[0m   \u001b[38;5;66;03m# Supports e.g. tags=SERVING and tags=[SERVING]. Sets aren't considered\u001b[39;00m\n\u001b[0;32m    884\u001b[0m   \u001b[38;5;66;03m# sequences for nest.flatten, so we put those through as-is.\u001b[39;00m\n\u001b[0;32m    885\u001b[0m   tags \u001b[38;5;241m=\u001b[39m nest\u001b[38;5;241m.\u001b[39mflatten(tags)\n\u001b[0;32m    886\u001b[0m saved_model_proto, debug_info \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 887\u001b[0m     \u001b[43mloader_impl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_saved_model_with_debug_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexport_dir\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(saved_model_proto\u001b[38;5;241m.\u001b[39mmeta_graphs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    890\u001b[0m     saved_model_proto\u001b[38;5;241m.\u001b[39mmeta_graphs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mHasField(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject_graph_def\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[0;32m    891\u001b[0m   metrics\u001b[38;5;241m.\u001b[39mIncrementReadApi(_LOAD_V2_LABEL)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py:57\u001b[0m, in \u001b[0;36mparse_saved_model_with_debug_info\u001b[1;34m(export_dir)\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_saved_model_with_debug_info\u001b[39m(export_dir):\n\u001b[0;32m     45\u001b[0m   \u001b[38;5;124;03m\"\"\"Reads the savedmodel as well as the graph debug info.\u001b[39;00m\n\u001b[0;32m     46\u001b[0m \n\u001b[0;32m     47\u001b[0m \u001b[38;5;124;03m  Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;124;03m    parsed. Missing graph debug info file is fine.\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;124;03m  \"\"\"\u001b[39;00m\n\u001b[1;32m---> 57\u001b[0m   saved_model \u001b[38;5;241m=\u001b[39m \u001b[43mparse_saved_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexport_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     59\u001b[0m   debug_info_path \u001b[38;5;241m=\u001b[39m file_io\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[0;32m     60\u001b[0m       saved_model_utils\u001b[38;5;241m.\u001b[39mget_debug_dir(export_dir),\n\u001b[0;32m     61\u001b[0m       constants\u001b[38;5;241m.\u001b[39mDEBUG_INFO_FILENAME_PB)\n\u001b[0;32m     62\u001b[0m   debug_info \u001b[38;5;241m=\u001b[39m graph_debug_info_pb2\u001b[38;5;241m.\u001b[39mGraphDebugInfo()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py:115\u001b[0m, in \u001b[0;36mparse_saved_model\u001b[1;34m(export_dir)\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot parse file \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_to_pbtxt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 115\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\n\u001b[0;32m    116\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSavedModel file does not exist at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexport_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    117\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m{{\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mSAVED_MODEL_FILENAME_PBTXT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m|\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    118\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mSAVED_MODEL_FILENAME_PB\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m}}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mOSError\u001b[0m: SavedModel file does not exist at: C:\\Users\\Super\\AppData\\Local\\Temp\\tfhub_modules\\145bb06ec3b59b08fb564ab752bd5aa222bfb50a\\{saved_model.pbtxt|saved_model.pb}"
     ]
    }
   ],
   "source": [
    "mobile_net_layers = hub.KerasLayer(mobilenet_v2, input_shape=(224,224,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0de6b829",
   "metadata": {},
   "outputs": [],
   "source": [
    "mobile_net_layers.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b4e2809",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "  [\n",
    "    layers.RandomFlip(\"horizontal\",\n",
    "                      input_shape=(220,\n",
    "                                  220,\n",
    "                                  3)),\n",
    "    layers.RandomRotation(0.1),\n",
    "    layers.RandomZoom(0.1),\n",
    "  ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "72300858",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural_net = tf.keras.Sequential([\n",
    "  \n",
    "    mobile_net_layers,\n",
    "                              \n",
    "                                 \n",
    "  tf.keras.layers.Dropout(0.3),\n",
    "  tf.keras.layers.Dense(5,activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "196bd179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " keras_layer (KerasLayer)    (None, 1280)              2257984   \n",
      "                                                                 \n",
      " sequential_1 (Sequential)   (None, 220, 220, 3)       0         \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 1280)              0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 5)                 6405      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,264,389\n",
      "Trainable params: 6,405\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "neural_net.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b8ddb99",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural_net.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.001),\n",
    "              loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ce2b5ce2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "350/350 [==============================] - 329s 932ms/step - loss: 0.3302 - accuracy: 0.8720 - val_loss: 1.4691 - val_accuracy: 0.6899\n",
      "Epoch 2/10\n",
      "350/350 [==============================] - 324s 927ms/step - loss: 0.1934 - accuracy: 0.9257 - val_loss: 1.4435 - val_accuracy: 0.7051\n",
      "Epoch 3/10\n",
      "350/350 [==============================] - 334s 954ms/step - loss: 0.1596 - accuracy: 0.9383 - val_loss: 1.3693 - val_accuracy: 0.7261\n",
      "Epoch 4/10\n",
      "350/350 [==============================] - 325s 927ms/step - loss: 0.1525 - accuracy: 0.9438 - val_loss: 1.5695 - val_accuracy: 0.7143\n",
      "Epoch 5/10\n",
      "350/350 [==============================] - 324s 926ms/step - loss: 0.1480 - accuracy: 0.9441 - val_loss: 1.7684 - val_accuracy: 0.7255\n",
      "Epoch 6/10\n",
      "350/350 [==============================] - 325s 930ms/step - loss: 0.1409 - accuracy: 0.9462 - val_loss: 1.8446 - val_accuracy: 0.7163\n",
      "Epoch 7/10\n",
      "350/350 [==============================] - 325s 928ms/step - loss: 0.1340 - accuracy: 0.9519 - val_loss: 1.7340 - val_accuracy: 0.7314\n",
      "Epoch 8/10\n",
      "350/350 [==============================] - 323s 923ms/step - loss: 0.1361 - accuracy: 0.9504 - val_loss: 1.9226 - val_accuracy: 0.7288\n",
      "Epoch 9/10\n",
      "350/350 [==============================] - 325s 930ms/step - loss: 0.1269 - accuracy: 0.9504 - val_loss: 1.8593 - val_accuracy: 0.7400\n",
      "Epoch 10/10\n",
      "350/350 [==============================] - 300s 858ms/step - loss: 0.1262 - accuracy: 0.9520 - val_loss: 1.6953 - val_accuracy: 0.7459\n"
     ]
    }
   ],
   "source": [
    "model_fit=neural_net.fit(\n",
    "    train_dataset,\n",
    "    validation_data=Validation_dataset,\n",
    "    epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eb7ad8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = model_fit.history['accuracy']\n",
    "val_acc = model_fit.history['val_accuracy']\n",
    "\n",
    "loss = model_fit.history['loss']\n",
    "val_loss = model_fit.history['val_loss']\n",
    "\n",
    "epochs_range = range(10)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57801311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] saving mask detector model...\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] saving mask detector model...\")\n",
    "neural_net.save(\"mask_MobileNet_detector.model\", save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aaa8f36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c191037",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}