Epoch24_ResNet50V2_6class .ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"QFLf1y4xqzGl"},"source":["# IMPORT LIBRARIES/DATASETS"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12898,"status":"ok","timestamp":1645335756000,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"AnIDvdTGhN1V","outputId":"188a213b-3caf-4472-a261-837d0ad9b199"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":595,"status":"ok","timestamp":1645335756019,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"AsRmVyYPPPjB","outputId":"6e4fed0b-23c3-4114-f2bf-ac433e9202cf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: jupyterthemes in /usr/local/lib/python3.7/dist-packages (0.20.0)\n","Requirement already satisfied: notebook>=5.6.0 in /usr/local/lib/python3.7/dist-packages (from jupyterthemes) (6.4.8)\n","Requirement already satisfied: lesscpy>=0.11.2 in /usr/local/lib/python3.7/dist-packages (from jupyterthemes) (0.15.0)\n","Requirement already satisfied: ipython>=5.4.1 in /usr/local/lib/python3.7/dist-packages (from jupyterthemes) (5.5.0)\n","Requirement already satisfied: matplotlib>=1.4.3 in /usr/local/lib/python3.7/dist-packages (from jupyterthemes) (3.2.2)\n","Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from jupyterthemes) (4.9.1)\n","Requirement already satisfied: 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/usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=5.6.0->jupyterthemes) (4.3.3)\n","Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat->notebook>=5.6.0->jupyterthemes) (21.4.0)\n","Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat->notebook>=5.6.0->jupyterthemes) (5.4.0)\n","Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat->notebook>=5.6.0->jupyterthemes) (0.18.1)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat->notebook>=5.6.0->jupyterthemes) (4.11.0)\n","Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema!=2.5.0,>=2.4->nbformat->notebook>=5.6.0->jupyterthemes) (3.7.0)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=5.6.0->jupyterthemes) (0.5.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=5.6.0->jupyterthemes) (21.3)\n"]}],"source":["#!pip install sklearn\n","#!pip install tensorflow\n","#!pip install tf-nightly\n","#!pip install -U efficientnet\n","!pip install jupyterthemes\n","\n","#import tensorflow as tf\n","#config = tf.compat.v1.ConfigProto()\n","#config.gpu_options.allow_growth = True\n","#sess = tf.compat.v1.Session(config=config)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":416,"status":"ok","timestamp":1645335756056,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"lf_7805PvGgZ","outputId":"801b7f10-c5bc-4642-fd76-8a2b6560a4f8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tensor Flow Version: 2.8.0\n","\n","Pandas 1.3.5\n","GPU is NOT AVAILABLE\n"]}],"source":["# import the necessary packages\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","import os\n","import matplotlib.pyplot as plt\n","import PIL\n","import seaborn as sns\n","\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","#from tensorflow.keras.applications.inception_v3 import InceptionV3\n","#from tensorflow.keras.applications.efficientnet import EfficientNetB7\n","#from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\n","from tensorflow.keras.applications.resnet_v2 import ResNet50V2\n","from tensorflow.keras.layers import *\n","from tensorflow.keras.models import Model, load_model\n","from tensorflow.keras.initializers import glorot_uniform\n","from tensorflow.keras.utils import plot_model\n","from IPython.display import display\n","from tensorflow.keras import backend as K\n","from tensorflow.keras.optimizers import SGD\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, LearningRateScheduler\n","from jupyterthemes import jtplot\n","jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False) \n","\n","# setting the style of the notebook to be monokai theme  \n","# this line of code is important to ensure that we are able to see the x and y axes clearly\n","# If you don't run this code line, you will notice that the xlabel and ylabel on any plot is black on black and it will be hard to see them. \n","\n","print(f\"Tensor Flow Version: {tf.__version__}\")\n","#print(f\"Keras Version: {tensorflow.keras.__version__}\")\n","print()\n","#print(f\"Python {sys.version}\")\n","print(f\"Pandas {pd.__version__}\")\n","gpu = len(tf.config.list_physical_devices('GPU'))>0\n","print(\"GPU is\", \"available\" if gpu else \"NOT AVAILABLE\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":316,"status":"ok","timestamp":1645335756061,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"GUmPRk_NvrXz","outputId":"00e6694f-9ff7-400f-cbed-d4c9324d4572"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Skripsi/Skins01\n"]}],"source":["%cd /content/drive/MyDrive/Skripsi/Skins01\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":255,"status":"ok","timestamp":1645335756076,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"9UzbaqcHgi4u","outputId":"2a25405c-ded0-46b2-cd2d-87f1f71d3e5c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['chickenpox', 'keratosis', 'dermatofibroma', 'eczema', 'scabies', 'psoriasis']"]},"metadata":{},"execution_count":5}],"source":["# List all folders under training\n","os.listdir('./training')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2701,"status":"ok","timestamp":1645335758611,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"LxKlRG7Pgi4u","outputId":"405e47a8-9739-47b7-f5c2-94d2a7f5d65d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of train images: 828 \n","Number of test images: 338\n"]}],"source":["# Check the number of images in training, validation and evaluation dataset\n","train = []\n","test = []\n","\n","# os.listdir returns the list of files in the folder, in this case image class names\n","for i in os.listdir('./training'):\n","    train.extend(os.listdir(os.path.join('training', i)))\n","    test.extend(os.listdir(os.path.join('evaluation', i))) \n","    \n","print('Number of train images: {} \\nNumber of test images: {}'.format(len(train),len(test)))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":610,"status":"ok","timestamp":1645335758622,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"rwODuv7-gi4v","outputId":"905b0be2-1a08-4d34-8e23-6193a0f3ca41"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['ISIC_0072907.jpg',\n"," 'ISIC_0072853.jpg',\n"," 'ISIC_0072852.jpg',\n"," 'ISIC_0072871.jpg',\n"," 'ISIC_0072897.jpg',\n"," 'ISIC_0072835.jpg',\n"," 'ISIC_0072839.jpg',\n"," 'ISIC_0072935.jpg',\n"," 'ISIC_0072877.jpg',\n"," 'ISIC_0072931.jpg',\n"," 'ISIC_0072933.jpg',\n"," 'ISIC_0072914.jpg',\n"," 'ISIC_0072851.jpg',\n"," 'ISIC_0072916.jpg',\n"," 'ISIC_0072842.jpg',\n"," 'ISIC_0072880.jpg',\n"," 'ISIC_0072869.jpg',\n"," 'ISIC_0073002.jpg',\n"," 'ISIC_0073059.jpg',\n"," 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'ISIC_0016040.jpg',\n"," 'ISIC_0016042.jpg',\n"," 'ISIC_0016048.jpg',\n"," 'ISIC_0016049.jpg',\n"," 'ISIC_0016050.jpg',\n"," 'ISIC_0016056.jpg',\n"," 'ISIC_0016057.jpg',\n"," 'ISIC_0016058.jpg',\n"," 'ISIC_0016059.jpg',\n"," 'ISIC_0016063.jpg',\n"," 'ISIC_0016064.jpg',\n"," 'ISIC_0016065.jpg',\n"," 'ISIC_0016069.jpg']"]},"metadata":{},"execution_count":7}],"source":["train"]},{"cell_type":"markdown","metadata":{"id":"WiV9-QvCrA3I"},"source":["# TASK #3: PERFORM DATA EXPLORATION AND DATA VISUALIZATION"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":486,"status":"ok","timestamp":1645335758636,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"6k7JAlpCgi4x","outputId":"3344acfe-7c0b-47d8-9c68-4000fca6011d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['ISIC_3409440.jpg',\n"," 'ISIC_2985753.jpg',\n"," 'ISIC_2488468.jpg',\n"," 'ISIC_3047981.jpg',\n"," 'ISIC_3417390.jpg',\n"," 'ISIC_3522554.jpg',\n"," 'ISIC_4342416.jpg',\n"," 'ISIC_3698568.jpg',\n"," 'ISIC_3610783.jpg',\n"," 'ISIC_3577410.jpg',\n"," 'ISIC_4354857.jpg',\n"," 'ISIC_4354896.jpeg',\n"," 'ISIC_5136612.jpg',\n"," 'ISIC_4891233.jpg',\n"," 'ISIC_5177353.jpg',\n"," 'ISIC_5215191.jpeg',\n"," 'ISIC_5278307.jpeg',\n"," 'ISIC_5399828.jpg',\n"," 'ISIC_5402119.jpg',\n"," 'ISIC_5407240.jpeg',\n"," 'ISIC_5469457.jpg',\n"," 'ISIC_5576241.jpg',\n"," 'ISIC_5573611.jpg',\n"," 'ISIC_5634573.jpg',\n"," 'ISIC_5787278.jpg',\n"," 'ISIC_5829749.jpg',\n"," 'ISIC_5722465.jpg',\n"," 'ISIC_5958409.jpeg',\n"," 'ISIC_6098213.jpg',\n"," 'ISIC_6413890.jpg',\n"," 'ISIC_6416127.jpg',\n"," 'ISIC_6446825.jpg',\n"," 'ISIC_6433084.jpg',\n"," 'ISIC_6555039.jpg',\n"," 'ISIC_6511141.jpeg',\n"," 'ISIC_6594555.jpeg',\n"," 'ISIC_6596403.jpg',\n"," 'ISIC_6810183.jpg',\n"," 'ISIC_6893147.jpg',\n"," 'ISIC_6931024.jpg',\n"," 'ISIC_6709150.jpg',\n"," 'ISIC_7194695.jpg',\n"," 'ISIC_7282441.jpg',\n"," 'ISIC_7050773.jpeg',\n"," 'ISIC_7833008.jpg',\n"," 'ISIC_7866488.jpg',\n"," 'ISIC_8022913.jpeg',\n"," 'ISIC_7918608.jpg',\n"," 'ISIC_8085467.jpeg',\n"," 'ISIC_8201028.jpg',\n"," 'ISIC_8501248.jpg',\n"," 'ISIC_8340426.jpg',\n"," 'ISIC_8754972.jpg',\n"," 'ISIC_8725993.jpg',\n"," 'ISIC_8828670.jpeg',\n"," 'ISIC_8814735.jpg',\n"," 'ISIC_8851901.jpg',\n"," 'ISIC_9221004.jpg',\n"," 'ISIC_9318322.jpg',\n"," 'ISIC_9424172.jpg',\n"," 'ISIC_9525930.jpg',\n"," 'ISIC_9631590.jpg',\n"," 'ISIC_9756079.jpg',\n"," 'ISIC_4761090.jpg',\n"," 'ISIC_4891204.jpg',\n"," 'ISIC_4419720.jpg',\n"," 'ISIC_4896088.jpg',\n"," 'ISIC_5238910.jpg',\n"," 'ISIC_4939497.jpg',\n"," 'ISIC_5573695.jpg',\n"," 'ISIC_5346936.jpg',\n"," 'ISIC_5605916.jpg',\n"," 'ISIC_5958657.jpg',\n"," 'ISIC_5997266.jpg',\n"," 'ISIC_6019539.jpg',\n"," 'ISIC_6051431.jpg',\n"," 'ISIC_6129538.jpg',\n"," 'ISIC_6427046.jpg',\n"," 'ISIC_7165819.jpg',\n"," 'ISIC_6974497.jpg',\n"," 'ISIC_7323249.jpg',\n"," 'ISIC_7200676.jpg',\n"," 'ISIC_7408392.jpg',\n"," 'ISIC_7560157.jpg',\n"," 'ISIC_7574101.jpg',\n"," 'ISIC_7575644.jpg',\n"," 'ISIC_7867320.jpg',\n"," 'ISIC_7785457.jpg',\n"," 'ISIC_8172419.jpg',\n"," 'ISIC_8357133.jpg',\n"," 'ISIC_8894528.jpg',\n"," 'ISIC_9006160.jpg',\n"," 'ISIC_9274776.jpg',\n"," 'ISIC_9360610.jpg',\n"," 'ISIC_9728805.jpg',\n"," 'ISIC_9496738.jpg',\n"," 'ISIC_9540352.jpg',\n"," 'ISIC_9711252.jpg',\n"," 'ISIC_9492000.jpg',\n"," 'ISIC_9811367.jpg',\n"," 'ISIC_0013370.jpg',\n"," 'ISIC_0013364.jpg',\n"," 'ISIC_0013360.jpg',\n"," 'ISIC_0013371.jpg',\n"," 'ISIC_0013393.jpg',\n"," 'ISIC_0013397.jpg',\n"," 'ISIC_0013400.jpg',\n"," 'ISIC_0013409.jpg',\n"," 'ISIC_0013421.jpg',\n"," 'ISIC_0013443.jpg',\n"," 'ISIC_0013458.jpg',\n"," 'ISIC_0013465.jpg',\n"," 'ISIC_0013474.jpg',\n"," 'ISIC_0013493.jpg',\n"," 'ISIC_0013580.jpg',\n"," 'ISIC_0013584.jpg',\n"," 'ISIC_0013585.jpg',\n"," 'ISIC_0013621.jpg',\n"," 'ISIC_0013625.jpg',\n"," 'ISIC_0013632.jpg',\n"," 'ISIC_0013635.jpg',\n"," 'ISIC_0013637.jpg',\n"," 'ISIC_0013667.jpg',\n"," 'ISIC_0013673.jpg',\n"," 'ISIC_0013680.jpg',\n"," 'ISIC_0013682.jpg',\n"," 'ISIC_0013684.jpg',\n"," 'ISIC_0013685.jpg',\n"," 'ISIC_0013691.jpg',\n"," 'ISIC_0013687.jpg',\n"," 'ISIC_0013706.jpg',\n"," 'ISIC_0013707.jpg',\n"," 'ISIC_0013708.jpg',\n"," 'ISIC_0013709.jpg',\n"," 'ISIC_0013725.jpg',\n"," 'ISIC_0013731.jpg',\n"," 'ISIC_0013742.jpg',\n"," 'ISIC_0013764.jpg']"]},"metadata":{},"execution_count":8}],"source":["train_class = os.listdir(os.path.join('training','keratosis'))\n","train_class"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"19-Ae11OUMY_l-vLOMtdUXbJwxDuRL0us"},"executionInfo":{"elapsed":39107,"status":"ok","timestamp":1645335797626,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"8bw_N3GC2GPi","outputId":"7ed8ba5b-d57d-4394-eec8-ced59f61e012"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["# Visualize images di the dataset\n","\n","fig, axs = plt.subplots(6, 5, figsize = (32,32))\n","count = 0\n","# for every class in the dataset\n","for i in os.listdir('./training'):\n","  # get the list of all images that belong to a particular class\n","  train_class = os.listdir(os.path.join('training', i))\n","  \n","  # plot 5 images per class\n","  for j in range(5):\n","    img = os.path.join('training', i, train_class[j])\n","    axs[count][j].title.set_text(i)\n","    axs[count][j].imshow(PIL.Image.open(img))  \n","  count += 1\n","\n","fig.tight_layout()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":173,"status":"ok","timestamp":1645335797633,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"BgmZEvwgIzqc","outputId":"2c01286f-c04e-4409-9641-2ce1b86883cf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of images in chickenpox=149\n","\n","Number of images in keratosis=138\n","\n","Number of images in dermatofibroma=62\n","\n","Number of images in eczema=172\n","\n","Number of images in scabies=164\n","\n","Number of images in psoriasis=143\n","\n"]}],"source":["# check jumlah image di tiap kelas\n","\n","No_images_per_class = []\n","Class_name = []\n","for i in os.listdir('./training'):\n","  Class_name.append(i)\n","  train_class = os.listdir(os.path.join('training',i))\n","  print('Number of images in {}={}\\n'.format(i,len(train_class)))\n","  No_images_per_class.append(len(train_class))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":144,"status":"ok","timestamp":1645335797641,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"5QHgjJizypbk","outputId":"8e92c502-78fb-4001-92bf-39e9e1ee0fb4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of images in eczema=52\n","\n","Number of images in keratosis=59\n","\n","Number of images in psoriasis=60\n","\n","Number of images in dermatofibroma=27\n","\n","Number of images in chickenpox=78\n","\n","Number of images in scabies=62\n","\n"]}],"source":["No_images_per_class = []\n","Class_name = []\n","for i in os.listdir('./evaluation'):\n","  Class_name.append(i)\n","  test_class = os.listdir(os.path.join('evaluation',i))\n","  print('Number of images in {}={}\\n'.format(i,len(test_class)))\n","  No_images_per_class.append(len(test_class))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":176,"status":"ok","timestamp":1645335797684,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"6qdqrpxZgi40","outputId":"956f0979-1554-43a3-882e-b2b27b993212"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[52, 59, 60, 27, 78, 62]"]},"metadata":{},"execution_count":12}],"source":["No_images_per_class"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":362},"executionInfo":{"elapsed":199,"status":"ok","timestamp":1645335797717,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"5Bimf3qJN0bk","outputId":"36da6a0f-9f57-4586-aa18-a778db023bbc","scrolled":true},"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x360 with 1 Axes>"]},"metadata":{}}],"source":["# visualize the number of images in each class in the training dataset\n","\n","fig = plt.figure(figsize=(6,5))\n","plt.bar(Class_name, No_images_per_class, color = sns.color_palette(\"cubehelix\",len(Class_name)))\n","fig.tight_layout()"]},{"cell_type":"markdown","metadata":{"id":"TRCi25WyuZ-T"},"source":["# PERFORM IMAGE AUGMENTATION AND CREATE DATA GENERATOR"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7phFxj7xuUP4"},"outputs":[],"source":["# Untuk mentraining data generator, gunakan normalisasi, zooming range, horizontal flip, brightness range\n","train_datagen = ImageDataGenerator(\n","                rescale = 1./255,\n","                zoom_range = 0.2,\n","                horizontal_flip = True,\n","                vertical_flip=True,\n","                brightness_range=None)\n","\n","\n","test_datagen = ImageDataGenerator(\n","    rescale=1./255,\n","    width_shift_range=0.2, height_shift_range=0.2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":204,"status":"ok","timestamp":1645335797730,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"0gD1o9WpyOwy","outputId":"243c3eac-63c2-40d0-dfb3-eefc97d92f72"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 828 images belonging to 6 classes.\n"]}],"source":["# Create data generator for training, validation and test dataset.\n","train_generator = train_datagen.flow_from_directory(\n","        'training',\n","        target_size=(256, 256),\n","        batch_size=40,\n","        class_mode='categorical')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":198,"status":"ok","timestamp":1645335797734,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"PIXh92jygi42","outputId":"b9bd2032-d167-455f-accf-0fbed2803c24","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 338 images belonging to 6 classes.\n"]}],"source":["\n","# Create data generator for training, validation and test dataset.\n","test_generator = test_datagen.flow_from_directory(\n","        'evaluation',\n","        target_size=(256, 256),\n","        batch_size=40,\n","        class_mode='categorical')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":195,"status":"ok","timestamp":1645335797740,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"q-MWBeZqgi42","outputId":"ae330e4a-1877-4c23-a679-a2f8f25a2965"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<keras.preprocessing.image.DirectoryIterator at 0x7fef6aa90310>"]},"metadata":{},"execution_count":17}],"source":["train_generator"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":194,"status":"ok","timestamp":1645335797747,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"rHSDePtpknwi","outputId":"981671ca-808b-4375-959e-a0ce22182dec"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<keras.preprocessing.image.DirectoryIterator at 0x7fef528b3990>"]},"metadata":{},"execution_count":18}],"source":["test_generator"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O33BWPY_ejd8"},"outputs":[],"source":[""]},{"cell_type":"markdown","metadata":{"id":"ser8TL4hx0d1"},"source":["# BUILD DEEP LEARNING MODEL USING PRE-TRAINED InceptionResnet\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13969,"status":"ok","timestamp":1645335811544,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"DuDXHzHwz3dR","outputId":"4df800fb-23a6-4344-f54f-d3dc249f8307","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n","94674944/94668760 [==============================] - 0s 0us/step\n","94683136/94668760 [==============================] - 0s 0us/step\n"]}],"source":["# load model inception resnetv2 \n","basemodel = ResNet50V2(weights = 'imagenet', include_top = False, input_tensor=Input(shape = (256,256,3)))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10470,"status":"ok","timestamp":1645335811549,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"1HLFCO1H05C2","outputId":"8f28f527-07d0-42dd-d4a3-fe20d87beae9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"resnet50v2\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," conv1_pad (ZeroPadding2D)      (None, 262, 262, 3)  0           ['input_1[0][0]']                \n","                                                                                                  \n"," conv1_conv (Conv2D)            (None, 128, 128, 64  9472        ['conv1_pad[0][0]']              \n","                                )                                                                 \n","                                                                                                  \n"," pool1_pad (ZeroPadding2D)      (None, 130, 130, 64  0           ['conv1_conv[0][0]']             \n","                                )                                                                 \n","                                                                                                  \n"," pool1_pool (MaxPooling2D)      (None, 64, 64, 64)   0           ['pool1_pad[0][0]']              \n","                                                                                                  \n"," conv2_block1_preact_bn (BatchN  (None, 64, 64, 64)  256         ['pool1_pool[0][0]']             \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2_block1_preact_relu (Acti  (None, 64, 64, 64)  0           ['conv2_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv2_block1_1_conv (Conv2D)   (None, 64, 64, 64)   4096        ['conv2_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block1_1_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_1_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block1_2_pad (ZeroPaddin  (None, 66, 66, 64)  0           ['conv2_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv2_block1_2_conv (Conv2D)   (None, 64, 64, 64)   36864       ['conv2_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv2_block1_2_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_2_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block1_0_conv (Conv2D)   (None, 64, 64, 256)  16640       ['conv2_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block1_3_conv (Conv2D)   (None, 64, 64, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block1_out (Add)         (None, 64, 64, 256)  0           ['conv2_block1_0_conv[0][0]',    \n","                                                                  'conv2_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv2_block2_preact_bn (BatchN  (None, 64, 64, 256)  1024       ['conv2_block1_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2_block2_preact_relu (Acti  (None, 64, 64, 256)  0          ['conv2_block2_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv2_block2_1_conv (Conv2D)   (None, 64, 64, 64)   16384       ['conv2_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block2_1_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_1_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block2_2_pad (ZeroPaddin  (None, 66, 66, 64)  0           ['conv2_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv2_block2_2_conv (Conv2D)   (None, 64, 64, 64)   36864       ['conv2_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv2_block2_2_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_2_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block2_3_conv (Conv2D)   (None, 64, 64, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block2_out (Add)         (None, 64, 64, 256)  0           ['conv2_block1_out[0][0]',       \n","                                                                  'conv2_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv2_block3_preact_bn (BatchN  (None, 64, 64, 256)  1024       ['conv2_block2_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2_block3_preact_relu (Acti  (None, 64, 64, 256)  0          ['conv2_block3_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv2_block3_1_conv (Conv2D)   (None, 64, 64, 64)   16384       ['conv2_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block3_1_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_1_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block3_2_pad (ZeroPaddin  (None, 66, 66, 64)  0           ['conv2_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv2_block3_2_conv (Conv2D)   (None, 32, 32, 64)   36864       ['conv2_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv2_block3_2_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_2_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," max_pooling2d (MaxPooling2D)   (None, 32, 32, 256)  0           ['conv2_block2_out[0][0]']       \n","                                                                                                  \n"," conv2_block3_3_conv (Conv2D)   (None, 32, 32, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block3_out (Add)         (None, 32, 32, 256)  0           ['max_pooling2d[0][0]',          \n","                                                                  'conv2_block3_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block1_preact_bn (BatchN  (None, 32, 32, 256)  1024       ['conv2_block3_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block1_preact_relu (Acti  (None, 32, 32, 256)  0          ['conv3_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block1_1_conv (Conv2D)   (None, 32, 32, 128)  32768       ['conv3_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block1_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block1_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block1_2_conv (Conv2D)   (None, 32, 32, 128)  147456      ['conv3_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block1_2_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_2_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block1_0_conv (Conv2D)   (None, 32, 32, 512)  131584      ['conv3_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block1_3_conv (Conv2D)   (None, 32, 32, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block1_out (Add)         (None, 32, 32, 512)  0           ['conv3_block1_0_conv[0][0]',    \n","                                                                  'conv3_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block2_preact_bn (BatchN  (None, 32, 32, 512)  2048       ['conv3_block1_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block2_preact_relu (Acti  (None, 32, 32, 512)  0          ['conv3_block2_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block2_1_conv (Conv2D)   (None, 32, 32, 128)  65536       ['conv3_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block2_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block2_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block2_2_conv (Conv2D)   (None, 32, 32, 128)  147456      ['conv3_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block2_2_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_2_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block2_3_conv (Conv2D)   (None, 32, 32, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block2_out (Add)         (None, 32, 32, 512)  0           ['conv3_block1_out[0][0]',       \n","                                                                  'conv3_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block3_preact_bn (BatchN  (None, 32, 32, 512)  2048       ['conv3_block2_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block3_preact_relu (Acti  (None, 32, 32, 512)  0          ['conv3_block3_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block3_1_conv (Conv2D)   (None, 32, 32, 128)  65536       ['conv3_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block3_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block3_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block3_2_conv (Conv2D)   (None, 32, 32, 128)  147456      ['conv3_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block3_2_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_2_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block3_3_conv (Conv2D)   (None, 32, 32, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block3_out (Add)         (None, 32, 32, 512)  0           ['conv3_block2_out[0][0]',       \n","                                                                  'conv3_block3_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block4_preact_bn (BatchN  (None, 32, 32, 512)  2048       ['conv3_block3_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block4_preact_relu (Acti  (None, 32, 32, 512)  0          ['conv3_block4_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block4_1_conv (Conv2D)   (None, 32, 32, 128)  65536       ['conv3_block4_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block4_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block4_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block4_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block4_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block4_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block4_2_conv (Conv2D)   (None, 16, 16, 128)  147456      ['conv3_block4_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block4_2_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block4_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_2_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block4_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 512)  0          ['conv3_block3_out[0][0]']       \n","                                                                                                  \n"," conv3_block4_3_conv (Conv2D)   (None, 16, 16, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block4_out (Add)         (None, 16, 16, 512)  0           ['max_pooling2d_1[0][0]',        \n","                                                                  'conv3_block4_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block1_preact_bn (BatchN  (None, 16, 16, 512)  2048       ['conv3_block4_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv4_block1_preact_relu (Acti  (None, 16, 16, 512)  0          ['conv4_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv4_block1_1_conv (Conv2D)   (None, 16, 16, 256)  131072      ['conv4_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block1_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block1_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block1_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block1_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block1_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block1_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block1_0_conv (Conv2D)   (None, 16, 16, 1024  525312      ['conv4_block1_preact_relu[0][0]'\n","                                )                                ]                                \n","                                                                                                  \n"," conv4_block1_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block1_out (Add)         (None, 16, 16, 1024  0           ['conv4_block1_0_conv[0][0]',    \n","                                )                                 'conv4_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block2_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block1_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block2_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block2_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block2_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block2_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block2_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block2_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block2_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block2_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block2_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block2_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block2_out (Add)         (None, 16, 16, 1024  0           ['conv4_block1_out[0][0]',       \n","                                )                                 'conv4_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block3_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block2_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block3_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block3_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block3_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block3_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block3_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block3_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block3_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block3_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block3_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block3_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block3_out (Add)         (None, 16, 16, 1024  0           ['conv4_block2_out[0][0]',       \n","                                )                                 'conv4_block3_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block4_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block3_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block4_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block4_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block4_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block4_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block4_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block4_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block4_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block4_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block4_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block4_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block4_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block4_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block4_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block4_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block4_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block4_out (Add)         (None, 16, 16, 1024  0           ['conv4_block3_out[0][0]',       \n","                                )                                 'conv4_block4_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block5_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block4_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block5_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block5_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block5_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block5_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block5_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block5_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block5_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block5_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block5_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block5_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block5_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block5_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block5_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block5_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block5_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block5_out (Add)         (None, 16, 16, 1024  0           ['conv4_block4_out[0][0]',       \n","                                )                                 'conv4_block5_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block6_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block5_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block6_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block6_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block6_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block6_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block6_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block6_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block6_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block6_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block6_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block6_2_conv (Conv2D)   (None, 8, 8, 256)    589824      ['conv4_block6_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block6_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block6_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block6_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block6_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," max_pooling2d_2 (MaxPooling2D)  (None, 8, 8, 1024)  0           ['conv4_block5_out[0][0]']       \n","                                                                                                  \n"," conv4_block6_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block6_2_relu[0][0]']    \n","                                                                                                  \n"," conv4_block6_out (Add)         (None, 8, 8, 1024)   0           ['max_pooling2d_2[0][0]',        \n","                                                                  'conv4_block6_3_conv[0][0]']    \n","                                                                                                  \n"," conv5_block1_preact_bn (BatchN  (None, 8, 8, 1024)  4096        ['conv4_block6_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv5_block1_preact_relu (Acti  (None, 8, 8, 1024)  0           ['conv5_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv5_block1_1_conv (Conv2D)   (None, 8, 8, 512)    524288      ['conv5_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block1_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block1_2_pad (ZeroPaddin  (None, 10, 10, 512)  0          ['conv5_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv5_block1_2_conv (Conv2D)   (None, 8, 8, 512)    2359296     ['conv5_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv5_block1_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block1_0_conv (Conv2D)   (None, 8, 8, 2048)   2099200     ['conv5_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block1_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block1_out (Add)         (None, 8, 8, 2048)   0           ['conv5_block1_0_conv[0][0]',    \n","                                                                  'conv5_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv5_block2_preact_bn (BatchN  (None, 8, 8, 2048)  8192        ['conv5_block1_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv5_block2_preact_relu (Acti  (None, 8, 8, 2048)  0           ['conv5_block2_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv5_block2_1_conv (Conv2D)   (None, 8, 8, 512)    1048576     ['conv5_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block2_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block2_2_pad (ZeroPaddin  (None, 10, 10, 512)  0          ['conv5_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv5_block2_2_conv (Conv2D)   (None, 8, 8, 512)    2359296     ['conv5_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv5_block2_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block2_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block2_out (Add)         (None, 8, 8, 2048)   0           ['conv5_block1_out[0][0]',       \n","                                                                  'conv5_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv5_block3_preact_bn (BatchN  (None, 8, 8, 2048)  8192        ['conv5_block2_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv5_block3_preact_relu (Acti  (None, 8, 8, 2048)  0           ['conv5_block3_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv5_block3_1_conv (Conv2D)   (None, 8, 8, 512)    1048576     ['conv5_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block3_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block3_2_pad (ZeroPaddin  (None, 10, 10, 512)  0          ['conv5_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv5_block3_2_conv (Conv2D)   (None, 8, 8, 512)    2359296     ['conv5_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv5_block3_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block3_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block3_out (Add)         (None, 8, 8, 2048)   0           ['conv5_block2_out[0][0]',       \n","                                                                  'conv5_block3_3_conv[0][0]']    \n","                                                                                                  \n"," post_bn (BatchNormalization)   (None, 8, 8, 2048)   8192        ['conv5_block3_out[0][0]']       \n","                                                                                                  \n"," post_relu (Activation)         (None, 8, 8, 2048)   0           ['post_bn[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 23,564,800\n","Trainable params: 23,519,360\n","Non-trainable params: 45,440\n","__________________________________________________________________________________________________\n"]}],"source":["# print model summary\n","basemodel.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EN_NR37Z07Z-"},"outputs":[],"source":["# Freeze the basemodel weights (supaya weight tdk berubah saat training)\n","basemodel.trainable = False"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"utpCBuch5K5j"},"outputs":[],"source":["# Add classification head to the model\n","headmodel = basemodel.output\n","headmodel = GlobalAveragePooling2D(name ='global_average_pool')(headmodel)\n","headmodel = Flatten(name= 'flatten')(headmodel)\n","headmodel = Dense(256, activation = 'relu', name = 'dense_1')(headmodel)\n","headmodel = Dropout(0.3)(headmodel)\n","headmodel = Dense(128, activation = 'relu', name = 'dense_2')(headmodel)\n","headmodel = Dropout(0.3)(headmodel)\n","headmodel = Dense(6, activation = 'softmax', name = 'dense_3')(headmodel)\n","\n","model = Model(inputs = basemodel.input, outputs = headmodel)"]},{"cell_type":"markdown","metadata":{"id":"mv5gK4Yjztgt"},"source":["# COMPILE AND TRAIN DEEP LEARNING MODEL"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"L02vuv875Uv7"},"outputs":[],"source":["# compile modelnya\n","\n","model.compile(loss = 'categorical_crossentropy', \n","              optimizer = SGD(learning_rate =0.01, momentum =0.9),\n","              metrics=['accuracy'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wNwcURek5YnQ"},"outputs":[],"source":["earlystopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=15)\n","\n","checkpointer = ModelCheckpoint(filepath=\"weights.hdf5\", verbose=1, save_best_only=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2442901,"status":"ok","timestamp":1645338254273,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"UWlcUKekAcza","outputId":"885cd822-2a23-4c62-8ee3-a9e1ddbc4e61"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/18\n","20/20 [==============================] - ETA: 0s - loss: 1.5438 - accuracy: 0.4124\n","Epoch 1: val_loss improved from inf to 1.34540, saving model to weights.hdf5\n","20/20 [==============================] - 268s 14s/step - loss: 1.5438 - accuracy: 0.4124 - val_loss: 1.3454 - val_accuracy: 0.4556\n","Epoch 2/18\n","20/20 [==============================] - ETA: 0s - loss: 1.1643 - accuracy: 0.5584\n","Epoch 2: val_loss improved from 1.34540 to 1.32974, saving model to weights.hdf5\n","20/20 [==============================] - 128s 7s/step - loss: 1.1643 - accuracy: 0.5584 - val_loss: 1.3297 - val_accuracy: 0.4142\n","Epoch 3/18\n","20/20 [==============================] - ETA: 0s - loss: 1.0252 - accuracy: 0.5825\n","Epoch 3: val_loss improved from 1.32974 to 1.24306, saving model to weights.hdf5\n","20/20 [==============================] - 129s 7s/step - loss: 1.0252 - accuracy: 0.5825 - val_loss: 1.2431 - val_accuracy: 0.5059\n","Epoch 4/18\n","20/20 [==============================] - ETA: 0s - loss: 0.9039 - accuracy: 0.6332\n","Epoch 4: val_loss improved from 1.24306 to 1.24221, saving model to weights.hdf5\n","20/20 [==============================] - 128s 7s/step - loss: 0.9039 - accuracy: 0.6332 - val_loss: 1.2422 - val_accuracy: 0.5385\n","Epoch 5/18\n","20/20 [==============================] - ETA: 0s - loss: 0.8801 - accuracy: 0.6548\n","Epoch 5: val_loss improved from 1.24221 to 1.22882, saving model to weights.hdf5\n","20/20 [==============================] - 128s 7s/step - loss: 0.8801 - accuracy: 0.6548 - val_loss: 1.2288 - val_accuracy: 0.5089\n","Epoch 6/18\n","20/20 [==============================] - ETA: 0s - loss: 0.7850 - accuracy: 0.6967\n","Epoch 6: val_loss improved from 1.22882 to 1.12641, saving model to weights.hdf5\n","20/20 [==============================] - 128s 7s/step - loss: 0.7850 - accuracy: 0.6967 - val_loss: 1.1264 - val_accuracy: 0.5828\n","Epoch 7/18\n","20/20 [==============================] - ETA: 0s - loss: 0.8144 - accuracy: 0.6929\n","Epoch 7: val_loss did not improve from 1.12641\n","20/20 [==============================] - 127s 6s/step - loss: 0.8144 - accuracy: 0.6929 - val_loss: 1.2319 - val_accuracy: 0.5385\n","Epoch 8/18\n","20/20 [==============================] - ETA: 0s - loss: 0.7595 - accuracy: 0.6942\n","Epoch 8: val_loss did not improve from 1.12641\n","20/20 [==============================] - 126s 6s/step - loss: 0.7595 - accuracy: 0.6942 - val_loss: 1.2167 - val_accuracy: 0.5059\n","Epoch 9/18\n","20/20 [==============================] - ETA: 0s - loss: 0.7219 - accuracy: 0.7208\n","Epoch 9: val_loss did not improve from 1.12641\n","20/20 [==============================] - 127s 6s/step - loss: 0.7219 - accuracy: 0.7208 - val_loss: 1.1846 - val_accuracy: 0.5473\n","Epoch 10/18\n","20/20 [==============================] - ETA: 0s - loss: 0.6373 - accuracy: 0.7538\n","Epoch 10: val_loss improved from 1.12641 to 1.02735, saving model to weights.hdf5\n","20/20 [==============================] - 128s 7s/step - loss: 0.6373 - accuracy: 0.7538 - val_loss: 1.0273 - val_accuracy: 0.5740\n","Epoch 11/18\n","20/20 [==============================] - ETA: 0s - loss: 0.7202 - accuracy: 0.7297\n","Epoch 11: val_loss did not improve from 1.02735\n","20/20 [==============================] - 126s 6s/step - loss: 0.7202 - accuracy: 0.7297 - val_loss: 1.2486 - val_accuracy: 0.5503\n","Epoch 12/18\n","20/20 [==============================] - ETA: 0s - loss: 0.6798 - accuracy: 0.7335\n","Epoch 12: val_loss did not improve from 1.02735\n","20/20 [==============================] - 127s 6s/step - loss: 0.6798 - accuracy: 0.7335 - val_loss: 1.2447 - val_accuracy: 0.5325\n","Epoch 13/18\n","20/20 [==============================] - ETA: 0s - loss: 0.6352 - accuracy: 0.7551\n","Epoch 13: val_loss did not improve from 1.02735\n","20/20 [==============================] - 129s 6s/step - loss: 0.6352 - accuracy: 0.7551 - val_loss: 1.1364 - val_accuracy: 0.5710\n","Epoch 14/18\n","20/20 [==============================] - ETA: 0s - loss: 0.6328 - accuracy: 0.7551\n","Epoch 14: val_loss did not improve from 1.02735\n","20/20 [==============================] - 129s 7s/step - loss: 0.6328 - accuracy: 0.7551 - val_loss: 1.2225 - val_accuracy: 0.5148\n","Epoch 15/18\n","20/20 [==============================] - ETA: 0s - loss: 0.5912 - accuracy: 0.7638\n","Epoch 15: val_loss did not improve from 1.02735\n","20/20 [==============================] - 129s 7s/step - loss: 0.5912 - accuracy: 0.7638 - val_loss: 1.1051 - val_accuracy: 0.5858\n","Epoch 16/18\n","20/20 [==============================] - ETA: 0s - loss: 0.5509 - accuracy: 0.7766\n","Epoch 16: val_loss did not improve from 1.02735\n","20/20 [==============================] - 129s 7s/step - loss: 0.5509 - accuracy: 0.7766 - val_loss: 1.1689 - val_accuracy: 0.5917\n","Epoch 17/18\n","20/20 [==============================] - ETA: 0s - loss: 0.5776 - accuracy: 0.7792\n","Epoch 17: val_loss did not improve from 1.02735\n","20/20 [==============================] - 129s 7s/step - loss: 0.5776 - accuracy: 0.7792 - val_loss: 1.0776 - val_accuracy: 0.5858\n","Epoch 18/18\n","20/20 [==============================] - ETA: 0s - loss: 0.5540 - accuracy: 0.7855\n","Epoch 18: val_loss did not improve from 1.02735\n","20/20 [==============================] - 128s 7s/step - loss: 0.5540 - accuracy: 0.7855 - val_loss: 1.1621 - val_accuracy: 0.5651\n"]}],"source":["history = model.fit(train_generator, \n","                    steps_per_epoch= train_generator.n // 40, epochs = 18, \n","                    validation_data= test_generator, \n","                    validation_steps= len(test_generator), \n","                    callbacks=[checkpointer, earlystopping])\n"]},{"cell_type":"markdown","metadata":{"id":"5XsIk6XQz3fH"},"source":["# FINE TUNE THE TRAINED MODEL"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f8Vg55sU6N7s"},"outputs":[],"source":["# unfreeze the weights di base model\n","basemodel.trainable = True"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6047,"status":"ok","timestamp":1645338254283,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"RijFgtp_H3w2","outputId":"2ce4ac0d-777b-4e9a-dd95-c0e4e6d3c3c1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," conv1_pad (ZeroPadding2D)      (None, 262, 262, 3)  0           ['input_1[0][0]']                \n","                                                                                                  \n"," conv1_conv (Conv2D)            (None, 128, 128, 64  9472        ['conv1_pad[0][0]']              \n","                                )                                                                 \n","                                                                                                  \n"," pool1_pad (ZeroPadding2D)      (None, 130, 130, 64  0           ['conv1_conv[0][0]']             \n","                                )                                                                 \n","                                                                                                  \n"," pool1_pool (MaxPooling2D)      (None, 64, 64, 64)   0           ['pool1_pad[0][0]']              \n","                                                                                                  \n"," conv2_block1_preact_bn (BatchN  (None, 64, 64, 64)  256         ['pool1_pool[0][0]']             \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2_block1_preact_relu (Acti  (None, 64, 64, 64)  0           ['conv2_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv2_block1_1_conv (Conv2D)   (None, 64, 64, 64)   4096        ['conv2_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block1_1_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_1_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block1_2_pad (ZeroPaddin  (None, 66, 66, 64)  0           ['conv2_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv2_block1_2_conv (Conv2D)   (None, 64, 64, 64)   36864       ['conv2_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv2_block1_2_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block1_2_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block1_0_conv (Conv2D)   (None, 64, 64, 256)  16640       ['conv2_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block1_3_conv (Conv2D)   (None, 64, 64, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block1_out (Add)         (None, 64, 64, 256)  0           ['conv2_block1_0_conv[0][0]',    \n","                                                                  'conv2_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv2_block2_preact_bn (BatchN  (None, 64, 64, 256)  1024       ['conv2_block1_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2_block2_preact_relu (Acti  (None, 64, 64, 256)  0          ['conv2_block2_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv2_block2_1_conv (Conv2D)   (None, 64, 64, 64)   16384       ['conv2_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block2_1_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_1_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block2_2_pad (ZeroPaddin  (None, 66, 66, 64)  0           ['conv2_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv2_block2_2_conv (Conv2D)   (None, 64, 64, 64)   36864       ['conv2_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv2_block2_2_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block2_2_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block2_3_conv (Conv2D)   (None, 64, 64, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block2_out (Add)         (None, 64, 64, 256)  0           ['conv2_block1_out[0][0]',       \n","                                                                  'conv2_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv2_block3_preact_bn (BatchN  (None, 64, 64, 256)  1024       ['conv2_block2_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2_block3_preact_relu (Acti  (None, 64, 64, 256)  0          ['conv2_block3_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv2_block3_1_conv (Conv2D)   (None, 64, 64, 64)   16384       ['conv2_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2_block3_1_bn (BatchNormal  (None, 64, 64, 64)  256         ['conv2_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_1_relu (Activatio  (None, 64, 64, 64)  0           ['conv2_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv2_block3_2_pad (ZeroPaddin  (None, 66, 66, 64)  0           ['conv2_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv2_block3_2_conv (Conv2D)   (None, 32, 32, 64)   36864       ['conv2_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv2_block3_2_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv2_block3_2_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," max_pooling2d (MaxPooling2D)   (None, 32, 32, 256)  0           ['conv2_block2_out[0][0]']       \n","                                                                                                  \n"," conv2_block3_3_conv (Conv2D)   (None, 32, 32, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv2_block3_out (Add)         (None, 32, 32, 256)  0           ['max_pooling2d[0][0]',          \n","                                                                  'conv2_block3_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block1_preact_bn (BatchN  (None, 32, 32, 256)  1024       ['conv2_block3_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block1_preact_relu (Acti  (None, 32, 32, 256)  0          ['conv3_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block1_1_conv (Conv2D)   (None, 32, 32, 128)  32768       ['conv3_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block1_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block1_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block1_2_conv (Conv2D)   (None, 32, 32, 128)  147456      ['conv3_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block1_2_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block1_2_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block1_0_conv (Conv2D)   (None, 32, 32, 512)  131584      ['conv3_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block1_3_conv (Conv2D)   (None, 32, 32, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block1_out (Add)         (None, 32, 32, 512)  0           ['conv3_block1_0_conv[0][0]',    \n","                                                                  'conv3_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block2_preact_bn (BatchN  (None, 32, 32, 512)  2048       ['conv3_block1_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block2_preact_relu (Acti  (None, 32, 32, 512)  0          ['conv3_block2_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block2_1_conv (Conv2D)   (None, 32, 32, 128)  65536       ['conv3_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block2_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block2_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block2_2_conv (Conv2D)   (None, 32, 32, 128)  147456      ['conv3_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block2_2_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block2_2_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block2_3_conv (Conv2D)   (None, 32, 32, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block2_out (Add)         (None, 32, 32, 512)  0           ['conv3_block1_out[0][0]',       \n","                                                                  'conv3_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block3_preact_bn (BatchN  (None, 32, 32, 512)  2048       ['conv3_block2_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block3_preact_relu (Acti  (None, 32, 32, 512)  0          ['conv3_block3_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block3_1_conv (Conv2D)   (None, 32, 32, 128)  65536       ['conv3_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block3_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block3_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block3_2_conv (Conv2D)   (None, 32, 32, 128)  147456      ['conv3_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block3_2_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block3_2_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block3_3_conv (Conv2D)   (None, 32, 32, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block3_out (Add)         (None, 32, 32, 512)  0           ['conv3_block2_out[0][0]',       \n","                                                                  'conv3_block3_3_conv[0][0]']    \n","                                                                                                  \n"," conv3_block4_preact_bn (BatchN  (None, 32, 32, 512)  2048       ['conv3_block3_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv3_block4_preact_relu (Acti  (None, 32, 32, 512)  0          ['conv3_block4_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv3_block4_1_conv (Conv2D)   (None, 32, 32, 128)  65536       ['conv3_block4_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv3_block4_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv3_block4_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv3_block4_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv3_block4_2_pad (ZeroPaddin  (None, 34, 34, 128)  0          ['conv3_block4_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv3_block4_2_conv (Conv2D)   (None, 16, 16, 128)  147456      ['conv3_block4_2_pad[0][0]']     \n","                                                                                                  \n"," conv3_block4_2_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block4_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv3_block4_2_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block4_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 512)  0          ['conv3_block3_out[0][0]']       \n","                                                                                                  \n"," conv3_block4_3_conv (Conv2D)   (None, 16, 16, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n","                                                                                                  \n"," conv3_block4_out (Add)         (None, 16, 16, 512)  0           ['max_pooling2d_1[0][0]',        \n","                                                                  'conv3_block4_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block1_preact_bn (BatchN  (None, 16, 16, 512)  2048       ['conv3_block4_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv4_block1_preact_relu (Acti  (None, 16, 16, 512)  0          ['conv4_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv4_block1_1_conv (Conv2D)   (None, 16, 16, 256)  131072      ['conv4_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block1_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block1_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block1_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block1_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block1_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block1_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block1_0_conv (Conv2D)   (None, 16, 16, 1024  525312      ['conv4_block1_preact_relu[0][0]'\n","                                )                                ]                                \n","                                                                                                  \n"," conv4_block1_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block1_out (Add)         (None, 16, 16, 1024  0           ['conv4_block1_0_conv[0][0]',    \n","                                )                                 'conv4_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block2_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block1_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block2_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block2_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block2_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block2_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block2_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block2_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block2_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block2_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block2_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block2_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block2_out (Add)         (None, 16, 16, 1024  0           ['conv4_block1_out[0][0]',       \n","                                )                                 'conv4_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block3_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block2_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block3_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block3_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block3_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block3_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block3_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block3_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block3_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block3_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block3_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block3_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block3_out (Add)         (None, 16, 16, 1024  0           ['conv4_block2_out[0][0]',       \n","                                )                                 'conv4_block3_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block4_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block3_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block4_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block4_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block4_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block4_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block4_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block4_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block4_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block4_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block4_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block4_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block4_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block4_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block4_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block4_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block4_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block4_out (Add)         (None, 16, 16, 1024  0           ['conv4_block3_out[0][0]',       \n","                                )                                 'conv4_block4_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block5_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block4_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block5_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block5_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block5_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block5_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block5_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block5_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block5_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block5_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block5_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block5_2_conv (Conv2D)   (None, 16, 16, 256)  589824      ['conv4_block5_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block5_2_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block5_2_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block5_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block5_3_conv (Conv2D)   (None, 16, 16, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," conv4_block5_out (Add)         (None, 16, 16, 1024  0           ['conv4_block4_out[0][0]',       \n","                                )                                 'conv4_block5_3_conv[0][0]']    \n","                                                                                                  \n"," conv4_block6_preact_bn (BatchN  (None, 16, 16, 1024  4096       ['conv4_block5_out[0][0]']       \n"," ormalization)                  )                                                                 \n","                                                                                                  \n"," conv4_block6_preact_relu (Acti  (None, 16, 16, 1024  0          ['conv4_block6_preact_bn[0][0]'] \n"," vation)                        )                                                                 \n","                                                                                                  \n"," conv4_block6_1_conv (Conv2D)   (None, 16, 16, 256)  262144      ['conv4_block6_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv4_block6_1_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block6_1_relu (Activatio  (None, 16, 16, 256)  0          ['conv4_block6_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv4_block6_2_pad (ZeroPaddin  (None, 18, 18, 256)  0          ['conv4_block6_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv4_block6_2_conv (Conv2D)   (None, 8, 8, 256)    589824      ['conv4_block6_2_pad[0][0]']     \n","                                                                                                  \n"," conv4_block6_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block6_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv4_block6_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block6_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," max_pooling2d_2 (MaxPooling2D)  (None, 8, 8, 1024)  0           ['conv4_block5_out[0][0]']       \n","                                                                                                  \n"," conv4_block6_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block6_2_relu[0][0]']    \n","                                                                                                  \n"," conv4_block6_out (Add)         (None, 8, 8, 1024)   0           ['max_pooling2d_2[0][0]',        \n","                                                                  'conv4_block6_3_conv[0][0]']    \n","                                                                                                  \n"," conv5_block1_preact_bn (BatchN  (None, 8, 8, 1024)  4096        ['conv4_block6_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv5_block1_preact_relu (Acti  (None, 8, 8, 1024)  0           ['conv5_block1_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv5_block1_1_conv (Conv2D)   (None, 8, 8, 512)    524288      ['conv5_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block1_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block1_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block1_2_pad (ZeroPaddin  (None, 10, 10, 512)  0          ['conv5_block1_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv5_block1_2_conv (Conv2D)   (None, 8, 8, 512)    2359296     ['conv5_block1_2_pad[0][0]']     \n","                                                                                                  \n"," conv5_block1_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block1_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block1_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block1_0_conv (Conv2D)   (None, 8, 8, 2048)   2099200     ['conv5_block1_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block1_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block1_out (Add)         (None, 8, 8, 2048)   0           ['conv5_block1_0_conv[0][0]',    \n","                                                                  'conv5_block1_3_conv[0][0]']    \n","                                                                                                  \n"," conv5_block2_preact_bn (BatchN  (None, 8, 8, 2048)  8192        ['conv5_block1_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv5_block2_preact_relu (Acti  (None, 8, 8, 2048)  0           ['conv5_block2_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv5_block2_1_conv (Conv2D)   (None, 8, 8, 512)    1048576     ['conv5_block2_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block2_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block2_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block2_2_pad (ZeroPaddin  (None, 10, 10, 512)  0          ['conv5_block2_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv5_block2_2_conv (Conv2D)   (None, 8, 8, 512)    2359296     ['conv5_block2_2_pad[0][0]']     \n","                                                                                                  \n"," conv5_block2_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block2_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block2_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block2_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block2_out (Add)         (None, 8, 8, 2048)   0           ['conv5_block1_out[0][0]',       \n","                                                                  'conv5_block2_3_conv[0][0]']    \n","                                                                                                  \n"," conv5_block3_preact_bn (BatchN  (None, 8, 8, 2048)  8192        ['conv5_block2_out[0][0]']       \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv5_block3_preact_relu (Acti  (None, 8, 8, 2048)  0           ['conv5_block3_preact_bn[0][0]'] \n"," vation)                                                                                          \n","                                                                                                  \n"," conv5_block3_1_conv (Conv2D)   (None, 8, 8, 512)    1048576     ['conv5_block3_preact_relu[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv5_block3_1_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_1_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block3_1_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block3_2_pad (ZeroPaddin  (None, 10, 10, 512)  0          ['conv5_block3_1_relu[0][0]']    \n"," g2D)                                                                                             \n","                                                                                                  \n"," conv5_block3_2_conv (Conv2D)   (None, 8, 8, 512)    2359296     ['conv5_block3_2_pad[0][0]']     \n","                                                                                                  \n"," conv5_block3_2_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," conv5_block3_2_relu (Activatio  (None, 8, 8, 512)   0           ['conv5_block3_2_bn[0][0]']      \n"," n)                                                                                               \n","                                                                                                  \n"," conv5_block3_3_conv (Conv2D)   (None, 8, 8, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n","                                                                                                  \n"," conv5_block3_out (Add)         (None, 8, 8, 2048)   0           ['conv5_block2_out[0][0]',       \n","                                                                  'conv5_block3_3_conv[0][0]']    \n","                                                                                                  \n"," post_bn (BatchNormalization)   (None, 8, 8, 2048)   8192        ['conv5_block3_out[0][0]']       \n","                                                                                                  \n"," post_relu (Activation)         (None, 8, 8, 2048)   0           ['post_bn[0][0]']                \n","                                                                                                  \n"," global_average_pool (GlobalAve  (None, 2048)        0           ['post_relu[0][0]']              \n"," ragePooling2D)                                                                                   \n","                                                                                                  \n"," flatten (Flatten)              (None, 2048)         0           ['global_average_pool[0][0]']    \n","                                                                                                  \n"," dense_1 (Dense)                (None, 256)          524544      ['flatten[0][0]']                \n","                                                                                                  \n"," dropout (Dropout)              (None, 256)          0           ['dense_1[0][0]']                \n","                                                                                                  \n"," dense_2 (Dense)                (None, 128)          32896       ['dropout[0][0]']                \n","                                                                                                  \n"," dropout_1 (Dropout)            (None, 128)          0           ['dense_2[0][0]']                \n","                                                                                                  \n"," dense_3 (Dense)                (None, 6)            774         ['dropout_1[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 24,123,014\n","Trainable params: 24,077,574\n","Non-trainable params: 45,440\n","__________________________________________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sXb_P5YbI1IA"},"outputs":[],"source":["earlystopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=15)\n","\n","# save the best model with lower validation loss\n","checkpointer = ModelCheckpoint(filepath=\"weights_fine.hdf5\", verbose=1, save_best_only=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JTIVBL27H7qM"},"outputs":[],"source":["# fine tune model dengan learning rate yang rendah\n","model.compile(loss = 'categorical_crossentropy',\n","              optimizer = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name='Adam'),\n","              metrics=['accuracy'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2612428,"status":"ok","timestamp":1645340866662,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"oYdts8FDgi45","outputId":"fe65d615-f113-4b41-8652-684a6db7d9e1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","20/20 [==============================] - ETA: 0s - loss: 0.8443 - accuracy: 0.6850\n","Epoch 1: val_loss improved from inf to 0.67525, saving model to weights_fine.hdf5\n","20/20 [==============================] - 151s 7s/step - loss: 0.8443 - accuracy: 0.6850 - val_loss: 0.6753 - val_accuracy: 0.7344\n","Epoch 2/20\n","20/20 [==============================] - ETA: 0s - loss: 0.5372 - accuracy: 0.7957\n","Epoch 2: val_loss did not improve from 0.67525\n","20/20 [==============================] - 128s 6s/step - loss: 0.5372 - accuracy: 0.7957 - val_loss: 0.7726 - val_accuracy: 0.7750\n","Epoch 3/20\n","20/20 [==============================] - ETA: 0s - loss: 0.3932 - accuracy: 0.8617\n","Epoch 3: val_loss improved from 0.67525 to 0.56838, saving model to weights_fine.hdf5\n","20/20 [==============================] - 133s 7s/step - loss: 0.3932 - accuracy: 0.8617 - val_loss: 0.5684 - val_accuracy: 0.8156\n","Epoch 4/20\n","20/20 [==============================] - ETA: 0s - loss: 0.2946 - accuracy: 0.8921\n","Epoch 4: val_loss did not improve from 0.56838\n","20/20 [==============================] - 129s 6s/step - loss: 0.2946 - accuracy: 0.8921 - val_loss: 0.5992 - val_accuracy: 0.8188\n","Epoch 5/20\n","20/20 [==============================] - ETA: 0s - loss: 0.2544 - accuracy: 0.9036\n","Epoch 5: val_loss improved from 0.56838 to 0.31167, saving model to weights_fine.hdf5\n","20/20 [==============================] - 132s 7s/step - loss: 0.2544 - accuracy: 0.9036 - val_loss: 0.3117 - val_accuracy: 0.8969\n","Epoch 6/20\n","20/20 [==============================] - ETA: 0s - loss: 0.2215 - accuracy: 0.9226\n","Epoch 6: val_loss improved from 0.31167 to 0.18823, saving model to weights_fine.hdf5\n","20/20 [==============================] - 130s 6s/step - loss: 0.2215 - accuracy: 0.9226 - val_loss: 0.1882 - val_accuracy: 0.9406\n","Epoch 7/20\n","20/20 [==============================] - ETA: 0s - loss: 0.2201 - accuracy: 0.9226\n","Epoch 7: val_loss did not improve from 0.18823\n","20/20 [==============================] - 130s 6s/step - loss: 0.2201 - accuracy: 0.9226 - val_loss: 0.2862 - val_accuracy: 0.8875\n","Epoch 8/20\n","20/20 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.9378\n","Epoch 8: val_loss did not improve from 0.18823\n","20/20 [==============================] - 128s 6s/step - loss: 0.1983 - accuracy: 0.9378 - val_loss: 0.6010 - val_accuracy: 0.8438\n","Epoch 9/20\n","20/20 [==============================] - ETA: 0s - loss: 0.1690 - accuracy: 0.9416\n","Epoch 9: val_loss did not improve from 0.18823\n","20/20 [==============================] - 128s 6s/step - loss: 0.1690 - accuracy: 0.9416 - val_loss: 0.2933 - val_accuracy: 0.8938\n","Epoch 10/20\n","20/20 [==============================] - ETA: 0s - loss: 0.1277 - accuracy: 0.9556\n","Epoch 10: val_loss did not improve from 0.18823\n","20/20 [==============================] - 127s 6s/step - loss: 0.1277 - accuracy: 0.9556 - val_loss: 0.2614 - val_accuracy: 0.9156\n","Epoch 11/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0912 - accuracy: 0.9708\n","Epoch 11: val_loss improved from 0.18823 to 0.17926, saving model to weights_fine.hdf5\n","20/20 [==============================] - 127s 6s/step - loss: 0.0912 - accuracy: 0.9708 - val_loss: 0.1793 - val_accuracy: 0.9281\n","Epoch 12/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0947 - accuracy: 0.9734\n","Epoch 12: val_loss improved from 0.17926 to 0.16719, saving model to weights_fine.hdf5\n","20/20 [==============================] - 131s 6s/step - loss: 0.0947 - accuracy: 0.9734 - val_loss: 0.1672 - val_accuracy: 0.9500\n","Epoch 13/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0889 - accuracy: 0.9734\n","Epoch 13: val_loss did not improve from 0.16719\n","20/20 [==============================] - 127s 6s/step - loss: 0.0889 - accuracy: 0.9734 - val_loss: 0.3148 - val_accuracy: 0.9281\n","Epoch 14/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0966 - accuracy: 0.9721\n","Epoch 14: val_loss did not improve from 0.16719\n","20/20 [==============================] - 130s 6s/step - loss: 0.0966 - accuracy: 0.9721 - val_loss: 0.2636 - val_accuracy: 0.9281\n","Epoch 15/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9721\n","Epoch 15: val_loss did not improve from 0.16719\n","20/20 [==============================] - 131s 7s/step - loss: 0.0875 - accuracy: 0.9721 - val_loss: 0.3079 - val_accuracy: 0.9187\n","Epoch 16/20\n","20/20 [==============================] - ETA: 0s - loss: 0.1036 - accuracy: 0.9645\n","Epoch 16: val_loss did not improve from 0.16719\n","20/20 [==============================] - 131s 7s/step - loss: 0.1036 - accuracy: 0.9645 - val_loss: 0.6599 - val_accuracy: 0.8375\n","Epoch 17/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0838 - accuracy: 0.9721\n","Epoch 17: val_loss did not improve from 0.16719\n","20/20 [==============================] - 130s 6s/step - loss: 0.0838 - accuracy: 0.9721 - val_loss: 0.4544 - val_accuracy: 0.8438\n","Epoch 18/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0634 - accuracy: 0.9848\n","Epoch 18: val_loss did not improve from 0.16719\n","20/20 [==============================] - 131s 6s/step - loss: 0.0634 - accuracy: 0.9848 - val_loss: 0.3310 - val_accuracy: 0.8844\n","Epoch 19/20\n","20/20 [==============================] - ETA: 0s - loss: 0.1031 - accuracy: 0.9670\n","Epoch 19: val_loss did not improve from 0.16719\n","20/20 [==============================] - 131s 7s/step - loss: 0.1031 - accuracy: 0.9670 - val_loss: 0.3087 - val_accuracy: 0.9219\n","Epoch 20/20\n","20/20 [==============================] - ETA: 0s - loss: 0.0618 - accuracy: 0.9784\n","Epoch 20: val_loss did not improve from 0.16719\n","20/20 [==============================] - 131s 7s/step - loss: 0.0618 - accuracy: 0.9784 - val_loss: 0.5609 - val_accuracy: 0.8375\n"]}],"source":["history = model.fit(train_generator, \n","                    steps_per_epoch= train_generator.n // 40,\n","                    epochs = 20,\n","                    validation_data= train_generator,\n","                    validation_steps= test_generator.n // 42,\n","                    callbacks=[checkpointer, earlystopping])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nCRYdnXu7DDJ"},"outputs":[],"source":["# Get helper_functions.py script from course GitHub\n","#!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py \n","\n","# Import helper functions we're going to use\n","from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":927},"executionInfo":{"elapsed":1282,"status":"ok","timestamp":1645340868597,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"PrMtqSiu5eeL","outputId":"16efc57f-26e9-4178-edbb-da7d722b6ce8"},"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 576x504 with 1 Axes>"]},"metadata":{}}],"source":["plot_loss_curves(history)"]},{"cell_type":"markdown","metadata":{"id":"od4ORdp50W8O"},"source":["#  ASSESS THE PERFORMANCE OF THE TRAINED MODEL"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kSXjYrUkgi45"},"outputs":[],"source":["# Load pretrained weight\n","model.load_weights('weights_fine.hdf5')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":79854,"status":"ok","timestamp":1645340953046,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"gg_8avbOBYAV","outputId":"1212a067-4881-4cda-b30e-9dbdd8157921"},"outputs":[{"output_type":"stream","name":"stdout","text":["20/20 [==============================] - 75s 4s/step - loss: 0.1488 - accuracy: 0.9463\n","Accuracy Training : [0.14875249564647675, 0.9462500214576721]\n"]}],"source":["evaluate = model.evaluate(train_generator, steps = train_generator.n // 40, verbose =1)\n","print('Accuracy Training : {}'.format(evaluate[0:4]))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":54287,"status":"ok","timestamp":1645341007270,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"-ox5UpdcIe1L","outputId":"b9a83537-93bc-4125-efea-d716295989ae"},"outputs":[{"output_type":"stream","name":"stdout","text":["7/7 [==============================] - 43s 6s/step - loss: 1.3200 - accuracy: 0.6679\n","Accuracy Test : 0.6678571701049805\n"]}],"source":["# Evaluate the performance of the model (Akurasi di testing data)\n","#evaluate = model.evaluate(test_generator,\n"," #                         steps = len(test_generator),\n"," #                         verbose =2)\n","evaluate = model.evaluate(test_generator, steps = test_generator.n // 45, verbose =1)\n","print('Accuracy Test : {}'.format(evaluate[1]))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GmXWmLQPOGCc"},"outputs":[],"source":["# assigning label names to the corresponding indexes\n","labels = {0: 'chickenpox', 1: 'dermatofibroma', 2: 'eczema' ,3: 'keratosis', 4: 'psoriasis', 5: 'scabies'} "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c3lTpyOygi46"},"outputs":[],"source":["from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n","# Training\n","prediction = []\n","original = []\n","image = []\n","count = 0\n","for i in os.listdir('./training'):\n","  for item in os.listdir(os.path.join('./training', i)):\n","    #code to open the image\n","    img= PIL.Image.open(os.path.join('./training', i , item))\n","    #resizing the image to (256,256)\n","    img = img.resize((256,256))\n","    #appending image to the image list\n","    image.append(img)\n","    #converting image to array\n","    img = np.asarray(img, dtype= np.float32)\n","    #normalizing the image\n","    img = img / 255\n","    #reshaping the image in to a 4D array\n","    img = img.reshape(1, 256, 256, -3)\n","    #making prediction of the model\n","    predict = model.predict(img)\n","    #getting the index corresponding to the highest value in the prediction\n","    predict = np.argmax(predict)\n","    #appending the predicted class to the list\n","    prediction.append(labels[predict])\n","    #appending original class to the list\n","    original.append(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":100,"status":"ok","timestamp":1645341190196,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"1XbmWMQcSM0b","outputId":"e9f005e5-926e-419a-8eaa-24840c041958"},"outputs":[{"output_type":"stream","name":"stdout","text":["                precision    recall  f1-score   support\n","\n","    chickenpox       1.00      0.91      0.95       149\n","dermatofibroma       0.87      0.94      0.90        62\n","        eczema       0.97      0.95      0.96       172\n","     keratosis       0.94      0.94      0.94       138\n","     psoriasis       0.94      0.99      0.97       143\n","       scabies       0.98      1.00      0.99       164\n","\n","      accuracy                           0.96       828\n","     macro avg       0.95      0.96      0.95       828\n","  weighted avg       0.96      0.96      0.96       828\n","\n"]}],"source":["print(classification_report(np.asarray(original), np.array(prediction)))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":769,"status":"ok","timestamp":1645341190928,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"HaXGkGP8OO98","outputId":"5d39b835-fcf9-47e3-afc1-a499f748e5a6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Confusion_matrix')"]},"metadata":{},"execution_count":39},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x1440 with 2 Axes>"]},"metadata":{}}],"source":["# plot confusion matrix\n","plt.figure(figsize=(20,20))\n","cm = confusion_matrix(np.asarray(original), np.asarray(prediction))\n","ax = plt.subplot()\n","sns.heatmap(cm, annot = True, ax = ax)\n","\n","ax.set_xlabel('Predicted')\n","ax.set_ylabel('Original')\n","ax.set_title('Confusion_matrix')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a1L_CoBoMiMC"},"outputs":[],"source":["\n","from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n","#Testing\n","prediction = []\n","original = []\n","image = []\n","count = 0\n","for i in os.listdir('./evaluation'):\n","  for item in os.listdir(os.path.join('./evaluation', i)):\n","    #code to open the image\n","    img= PIL.Image.open(os.path.join('./evaluation', i , item))\n","    #resizing the image to (256,256)\n","    img = img.resize((256,256))\n","    #appending image to the image list\n","    image.append(img)\n","    #converting image to array\n","    img = np.asarray(img, dtype= np.float32)\n","    #normalizing the image\n","    img = img / 255\n","    #reshaping the image in to a 4D array\n","    img = img.reshape(1, 256, 256, -3)\n","    #making prediction of the model\n","    predict = model.predict(img)\n","    #getting the index corresponding to the highest value in the prediction\n","    predict = np.argmax(predict)\n","    #appending the predicted class to the list\n","    prediction.append(labels[predict])\n","    #appending original class to the list\n","    original.append(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1JoCnhWo3wBsRO0cuE6VwF6NXO7CUSQbZ"},"executionInfo":{"elapsed":8153,"status":"ok","timestamp":1645341298595,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"IO7vb7qYc_6-","outputId":"09883bb4-406f-438d-930c-a8aa4b3d7322"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["import random\n","\n","fig=plt.figure(figsize = (120,120)) #ukuran gambar\n","for i in range(40):\n","    j = random.randint(30,len(image))\n","    fig.add_subplot(40,1,i+1)\n","    plt.xlabel(\"Prediction -\" + prediction[j] +\"   Original -\" + original[j])\n","    plt.imshow(image[j])\n","fig.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":206,"status":"ok","timestamp":1645341298610,"user":{"displayName":"Posma Sianturi","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"09140199415163760636"},"user_tz":-420},"id":"LFYQuqjZdB0S","outputId":"e973377f-fb86-4c3b-e7a0-595fc522a9f4"},"outputs":[{"output_type":"stream","name":"stdout","text":["                precision    recall  f1-score   support\n","\n","    chickenpox       0.84      0.67      0.74        78\n","dermatofibroma       0.50      0.74      0.60        27\n","        eczema       0.66      0.56      0.60        52\n","     keratosis       0.54      0.63      0.58        59\n","     psoriasis       0.70      0.85      0.77        60\n","       scabies       0.80      0.66      0.73        62\n","\n","      accuracy                           0.68       338\n","     macro avg       0.67      0.68      0.67       338\n","  weighted avg       0.70      0.68      0.68       338\n","\n"]}],"source":["print(classification_report(np.asarray(original), np.array(prediction)))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ly4o4FbFdPSZ"},"outputs":[],"source":["#val_accuracy=[]\n","#print('Mean:', np.mean(val_accuracy))\n","#print('Standard Deviation:', np.std(val_accuracy))\n"]}],"metadata":{"accelerator":"TPU","colab":{"collapsed_sections":[],"machine_shape":"hm","name":"Epoch24_ResNet50V2_6class .ipynb","provenance":[{"file_id":"1MJ6J1mRsmV8wC9fYXTPrtHr19K2djnCB","timestamp":1644428841549},{"file_id":"15KwJK53BtttRQB5_sr7XogCd67bq9fLE","timestamp":1644404623373},{"file_id":"1Z4G1yOfcL6eznoSGNsQx0KkvF4XWYles","timestamp":1644397665252},{"file_id":"1WIi_4VCms1dXaovUbeRqrihIlSdlcSyR","timestamp":1644218823323},{"file_id":"1_-zGLrD_ZzJvcey30dqKllAA8tDr6stO","timestamp":1644010690431},{"file_id":"13sadVSH-is71sgY0wL9sjZ097KPH_xUG","timestamp":1643992204049},{"file_id":"1hPJfQY2OlJF3d5qLPjJgK7YO31-yX6me","timestamp":1643964402744},{"file_id":"1x-xrPdNIbeJseinWSVP9w6GLuQkfaQR9","timestamp":1643959954828},{"file_id":"1BInDhuC-qfqrOXJXfkDUwpo0Xsn-sLP-","timestamp":1642694415834},{"file_id":"1wL5jPOMtJ-tjCy790zeNbO-D2O4Hoi7O","timestamp":1641685540346},{"file_id":"1dPhMNb8S9uM_0Ads8PnRJ16jjIXswB9m","timestamp":1641575861690},{"file_id":"1OucySfZw9WcwV_-BRdr-yg86c4YlEwq8","timestamp":1641131707191},{"file_id":"1BudeQ0T33PPJiNM88ZUGcgwtgJcsSmxZ","timestamp":1640748097641},{"file_id":"1rFmnEI1hvJ4UZphYSfoZiTaw7cGuj2iA","timestamp":1592631060630}]},"kernelspec":{"display_name":"Python 3","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":"3.8.8"}},"nbformat":4,"nbformat_minor":0}