{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19848,"status":"ok","timestamp":1703909526254,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"OwvMZ2UAgEM1","outputId":"86da0ec4-9638-4fa5-9e76-4937c532fb3c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":493,"status":"ok","timestamp":1703909529529,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"xyvwdmv4gVK3","outputId":"4f1fff29-bfa1-416e-de4c-cf198e7271bb"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/omdena_ad\n"]}],"source":["cd /content/drive/MyDrive/omdena_ad"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6009,"status":"ok","timestamp":1703909538555,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"OexTQ7Zi-CZ-","outputId":"19b98685-3cf9-4c4a-cb3f-49fc5d49ef1a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting keras_tuner\n","  Downloading keras_tuner-1.4.6-py3-none-any.whl (128 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/128.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.9/128.9 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras_tuner) (2.15.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras_tuner) (23.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from keras_tuner) (2.31.0)\n","Collecting kt-legacy (from keras_tuner)\n","  Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->keras_tuner) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->keras_tuner) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->keras_tuner) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->keras_tuner) (2023.11.17)\n","Installing collected packages: kt-legacy, keras_tuner\n","Successfully installed keras_tuner-1.4.6 kt-legacy-1.0.5\n"]}],"source":["!pip install keras_tuner"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":537,"status":"ok","timestamp":1703912882966,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"02fbEU_rX1NY"},"outputs":[],"source":["import os\n","from distutils.dir_util import copy_tree, remove_tree\n","import cv2\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from skimage import io\n","import random\n","import os\n","from scipy.ndimage import rotate\n","import tensorflow\n","from tensorflow import keras\n","from keras.preprocessing.image import ImageDataGenerator\n","from sklearn.preprocessing import LabelEncoder\n","from keras.models import Sequential, Model\n","from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n","from keras.layers import Activation, Dropout, Flatten, Dense, Input, BatchNormalization, Dropout\n","from keras import backend as K\n","from keras.applications import ResNet50, VGG16\n","from keras.optimizers import Adam\n","from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n","from sklearn.decomposition import PCA\n","from keras.utils import to_categorical\n","import sklearn\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix, classification_report\n","import seaborn as sns\n","import kerastuner as kt\n","from kerastuner.tuners import RandomSearch, BayesianOptimization\n","from imblearn.over_sampling import SMOTE"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14795,"status":"ok","timestamp":1703777612320,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"Y-i7b0R-DgdX","outputId":"804e954b-dfd4-481d-cba1-dad2993e3660"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.11.17)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n","Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"]}],"source":["!pip install kaggle\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":73},"executionInfo":{"elapsed":10005,"status":"ok","timestamp":1703777634142,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"_gbf1Yt0D1Au","outputId":"dbc79075-f824-4456-93a4-2b67c8af4911"},"outputs":[{"data":{"text/html":["\n","     <input type=\"file\" id=\"files-6fcb704c-d988-4e33-9f5f-8005b3672423\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-6fcb704c-d988-4e33-9f5f-8005b3672423\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Saving kaggle.json to kaggle.json\n"]}],"source":["from google.colab import files\n","\n","uploaded = files.upload()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UE4VQ5RxD59u"},"outputs":[],"source":["!mkdir -p ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 ~/.kaggle/kaggle.json"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3133,"status":"ok","timestamp":1703777667984,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"PMA_v680D_Oq","outputId":"0c16ce9f-47e5-404a-cd24-48a191eb2558"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading alzheimers-dataset-4-class-of-images.zip to /content/drive/MyDrive/omdena_ad\n","100% 34.0M/34.1M [00:01<00:00, 39.8MB/s]\n","100% 34.1M/34.1M [00:01<00:00, 25.1MB/s]\n"]}],"source":["!kaggle datasets download -d tourist55/alzheimers-dataset-4-class-of-images"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50739,"status":"ok","timestamp":1703777802752,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"pjLBjQkgEDjV","outputId":"659cf23c-42c0-42c5-f299-0af770a6e6b5"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: omdena_kaggle/Alzheimer_s Dataset/train/MildDemented/mildDem207.jpg  \n","  inflating: omdena_kaggle/Alzheimer_s Dataset/train/MildDemented/mildDem208.jpg  \n","  inflating: omdena_kaggle/Alzheimer_s Dataset/train/MildDemented/mildDem209.jpg  \n","  inflating: omdena_kaggle/Alzheimer_s 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alzheimers-dataset-4-class-of-images.zip -d omdena_kaggle"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1703909553572,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"L8g4_yh2gX3D"},"outputs":[],"source":["train_data_dir = \"/content/drive/MyDrive/omdena_ad/omdena_kaggle/Alzheimer_s Dataset/train/\"\n","test_data_dir = \"/content/drive/MyDrive/omdena_ad/omdena_kaggle/Alzheimer_s Dataset/test/\"\n","all_dataset_dir =  \"/content/drive/MyDrive/omdena_ad/omdena_kaggle/Alzheimer_s Dataset/all_dataset\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kBXgpLP6zqY4"},"outputs":[],"source":["# combine train and test dataset to a single folder to split them later into train, test and val\n","# copy_tree(train_data_dir, all_dataset_dir)\n","# copy_tree(test_data_dir, all_dataset_dir)\n","# print(os.listdir(all_dataset_dir))"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":280,"status":"ok","timestamp":1703909571447,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"bb0gplz8gpWN"},"outputs":[],"source":["data_class = sorted(os.listdir(all_dataset_dir))"]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":4662,"status":"ok","timestamp":1703909593907,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"POAGLMIA1Gx_"},"outputs":[],"source":["for dir in data_class:\n","    if dir == 'MildDemented':\n","      dir_path_mild_dem = os.path.join(all_dataset_dir, dir)\n","      mild_demented = os.listdir(dir_path_mild_dem)\n","\n","    elif dir == 'ModerateDemented':\n","      dir_path_mod_dem = os.path.join(all_dataset_dir, dir)\n","      moderate_demented = os.listdir(dir_path_mod_dem)\n","\n","    elif dir == 'NonDemented':\n","      dir_path_non_dem = os.path.join(all_dataset_dir, dir)\n","      non_demented = os.listdir(dir_path_non_dem)\n","\n","    elif dir == 'VeryMildDemented':\n","      dir_path_v_m_dem = os.path.join(all_dataset_dir, dir)\n","      very_mild_demented = os.listdir(dir_path_v_m_dem)"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":391,"status":"ok","timestamp":1703909597059,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"MqYnm65C_ja4","outputId":"964ddee5-c111-4d99-c45d-3f80245a6616"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["896"]},"metadata":{},"execution_count":8}],"source":["len(os.listdir(dir_path_mild_dem))"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1703909598529,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"iew-CNArBmc0","outputId":"3fdc92e6-c346-4173-9993-a5c47d917014"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["64"]},"metadata":{},"execution_count":9}],"source":[" len(os.listdir(dir_path_mod_dem))"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1906,"status":"ok","timestamp":1703909602114,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"ZRU3X4kTDiCu","outputId":"c6a78b1e-fbc5-47f5-e8c7-ab0046420c25"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["3200"]},"metadata":{},"execution_count":10}],"source":["len(os.listdir(dir_path_non_dem))"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1703909603784,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"9B5ozpDKDorV","outputId":"27ff9ca1-27f9-41b1-c4e8-b9c8b7948298"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["2240"]},"metadata":{},"execution_count":11}],"source":["len(os.listdir(dir_path_v_m_dem))"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":322,"status":"ok","timestamp":1703912198320,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"I6WWKiEcSNsG","outputId":"39f56c1c-706b-47d7-f6d5-6ed30bc3e8cf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 6400 images belonging to 4 classes.\n"]}],"source":["train_datagen = ImageDataGenerator(\n","    rescale=1./255\n","    #validation_split=0.2\n",")\n","batch_size = 6500\n","\n","# Create a flow generator for training data\n","train_generator = train_datagen.flow_from_directory(\n","    all_dataset_dir,             # Path to the training data directory\n","    target_size=(176, 176), # Target size of the images (height, width)\n","    batch_size=batch_size,  # Size of the batches\n","    class_mode='categorical' # Type of labels (binary or categorical)\n","    #subset = 'training'\n",")"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":374920,"status":"ok","timestamp":1703912600161,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"x7_UqGVrgIhH"},"outputs":[],"source":["train_data, train_labels = train_generator.next()"]},{"cell_type":"code","source":["train_data, test_data, train_labels, test_labels = sklearn.model_selection.train_test_split(train_data, train_labels, test_size = 0.2, random_state=42)"],"metadata":{"id":"jIX2ea6eFPke","executionInfo":{"status":"ok","timestamp":1703912621578,"user_tz":300,"elapsed":819,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["train_data, val_data, train_labels, val_labels = sklearn.model_selection.train_test_split(train_data, train_labels, test_size = 0.2, random_state=42)"],"metadata":{"id":"TK5fhhytFSUl","executionInfo":{"status":"ok","timestamp":1703912626126,"user_tz":300,"elapsed":922,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["# image number for [mild, mod, non, very_mild] before SMOTE\n","train_image_num_before_smote = np.sum(train_labels, axis=0)\n","test_image_num_before_smote = np.sum(test_labels, axis=0)\n","val_image_num_before_smote = np.sum(val_labels, axis=0)\n","print(train_image_num_before_smote, test_image_num_before_smote, val_image_num_before_smote)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6nRnMBon9orn","executionInfo":{"status":"ok","timestamp":1703912634032,"user_tz":300,"elapsed":311,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}},"outputId":"eff9e517-3034-4f46-aad7-a667c2c51644"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["[ 584.   45. 2030. 1437.] [169.  12. 660. 439.] [143.   7. 510. 364.]\n"]}]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":345,"status":"ok","timestamp":1703912658331,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"-wMdrHDcou4u","outputId":"72f884ba-2811-4747-99f4-c0747dbbd158"},"outputs":[{"output_type":"stream","name":"stdout","text":["(4096, 176, 176, 3) (4096, 4) (1024, 176, 176, 3) (1024, 4)\n"]}],"source":["print(train_data.shape, train_labels.shape, val_data.shape, val_labels.shape)"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15878,"status":"ok","timestamp":1703912736262,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"aZ8OP_cvVmZ0","outputId":"4d1989ee-85ed-48f7-9be5-69be69ef122d"},"outputs":[{"output_type":"stream","name":"stdout","text":["(8120, 176, 176, 3) (8120, 4)\n"]}],"source":["#SMOTE to tackle class imbalance, but only to training data\n","sm = SMOTE(random_state=42)\n","train_data, train_labels = sm.fit_resample(train_data.reshape(-1, 176 * 176 * 3), train_labels)\n","train_data = train_data.reshape(-1, 176, 176, 3)\n","print(train_data.shape, train_labels.shape)"]},{"cell_type":"code","source":["# image number for [mild, mod, non, very_mild] after SMOTE\n","train_image_num_after_smote = np.sum(train_labels, axis=0)\n","test_image_num_after_smote = np.sum(test_labels, axis=0)\n","val_image_num_after_smote = np.sum(val_labels, axis=0)\n","print(train_image_num_after_smote, test_image_num_after_smote, val_image_num_after_smote)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"C8AMzQvS-MsX","executionInfo":{"status":"ok","timestamp":1703912744528,"user_tz":300,"elapsed":323,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}},"outputId":"00387da4-ff10-4fbd-ad59-749300c5fc9e"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[2030 2030 2030 2030] [169.  12. 660. 439.] [143.   7. 510. 364.]\n"]}]},{"cell_type":"markdown","metadata":{"id":"nPDOiqOG_WBA"},"source":["**KERAS TUNER TO BUILD A CUSTOM ARCHITECTURE**"]},{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":521,"status":"ok","timestamp":1703912892493,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"22HI4QcJLXPF"},"outputs":[],"source":["# Define the model builder function\n","def build_model(hp):\n","    model = keras.Sequential()\n","\n","    # Choose the number of convolutional layers\n","    num_conv_layers = hp.Int('num_conv_layers', min_value=1, max_value=15, step=1)\n","    current_shape = (176, 176, 3)\n","    # Add convolutional layers\n","    for i in range(num_conv_layers):\n","        filters=hp.Int(f'conv_{i}_filters', min_value=16, max_value=128, step=16)\n","        kernel_size=hp.Choice(f'conv_{i}_kernel', values=[3,5])\n","        kernel_size = (kernel_size, kernel_size)\n","        model.add(tensorflow.keras.layers.Conv2D(\n","            filters=filters,\n","            kernel_size=kernel_size,\n","            activation='relu',\n","            padding='same',\n","            input_shape=current_shape\n","        ))\n","\n","        # Check if the input size before MaxPooling2D is valid\n","        if current_shape[0] >= 2 and current_shape[1] >= 2:\n","            model.add(tensorflow.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n","            current_shape = (current_shape[0] // 2, current_shape[1] // 2, current_shape[2])\n","        else:\n","            # Skip this combination\n","            print(f\"Skipping combination due to invalid input size: {current_shape}, (2,2)\")\n","            continue\n","\n","        # BatchNormalization with tunable hyperparameter\n","        use_batchnorm = hp.Boolean(f'conv_{i}_use_batchnorm', default=True)\n","        if use_batchnorm:\n","            model.add(tensorflow.keras.layers.BatchNormalization())\n","\n","        # Dropout with tunable hyperparameter\n","        dropout_rate = hp.Float(f'conv_{i}_dropout_rate', min_value=0.0, max_value=0.7, step=0.1)\n","        model.add(tensorflow.keras.layers.Dropout(rate=dropout_rate))\n","\n","    model.add(tensorflow.keras.layers.Flatten())\n","\n","    # Choose the number of dense layers\n","    num_dense_layers = hp.Int('num_dense_layers', min_value=1, max_value=5, step=1)\n","\n","    # Add dense layers\n","    for i in range(num_dense_layers):\n","        model.add(tensorflow.keras.layers.Dense(\n","            units=hp.Int(f'dense_{i}_units', min_value=32, max_value=256, step=32),\n","            activation='relu'\n","        ))\n","\n","        # BatchNormalization with tunable hyperparameter\n","        use_batchnorm_dense = hp.Boolean(f'dense_{i}_use_batchnorm', default=True)\n","        if use_batchnorm_dense:\n","            model.add(tensorflow.keras.layers.BatchNormalization())\n","        # Dropout with tunable hyperparameter\n","        dropout_rate_dense = hp.Float(f'dense_{i}_dropout_rate', min_value=0.0, max_value=0.7, step=0.1)\n","        model.add(tensorflow.keras.layers.Dropout(rate=dropout_rate_dense))\n","    # Output layer\n","    model.add(tensorflow.keras.layers.Dense(4, activation='softmax'))\n","\n","    # Choose the learning rate\n","    learning_rate = hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log')\n","    # Compile the model\n","    model.compile(optimizer=keras.optimizers.Adam(learning_rate),\n","                  loss='categorical_crossentropy',\n","                  metrics=['accuracy'])\n","\n","    return model\n"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k86KUBi5GGs3","executionInfo":{"status":"ok","timestamp":1703915075349,"user_tz":300,"elapsed":2177236,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}},"outputId":"e951829b-f259-45eb-d9c7-89bbd9e4d9fa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Trial 20 Complete [00h 01m 44s]\n","val_accuracy: 0.35546875\n","\n","Best val_accuracy So Far: 0.951171875\n","Total elapsed time: 00h 36m 17s\n"]}],"source":["# Create the tuner\n","tuner = BayesianOptimization(\n","    build_model,\n","    objective='val_accuracy',\n","    max_trials=20,\n","    directory='tuner_0012',\n","    project_name='image_classification_tuning'\n",")\n","\n","# Search for the best hyperparameters\n","tuner.search(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))\n","\n","# Get the best model\n","best_model = tuner.get_best_models(num_models=1)[0]\n"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":556,"status":"ok","timestamp":1703915083315,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"toLLOcRPEFBg","outputId":"339492b4-1e83-441b-9888-00e9b34a4f4c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 176, 176, 64)      1792      \n","                                                                 \n"," max_pooling2d (MaxPooling2  (None, 88, 88, 64)        0         \n"," D)                                                              \n","                                                                 \n"," batch_normalization (Batch  (None, 88, 88, 64)        256       \n"," Normalization)                                                  \n","                                                                 \n"," dropout (Dropout)           (None, 88, 88, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 88, 88, 16)        25616     \n","                                                                 \n"," max_pooling2d_1 (MaxPoolin  (None, 44, 44, 16)        0         \n"," g2D)                                                            \n","                                                                 \n"," dropout_1 (Dropout)         (None, 44, 44, 16)        0         \n","                                                                 \n"," flatten (Flatten)           (None, 30976)             0         \n","                                                                 \n"," dense (Dense)               (None, 256)               7930112   \n","                                                                 \n"," batch_normalization_1 (Bat  (None, 256)               1024      \n"," chNormalization)                                                \n","                                                                 \n"," dropout_2 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 224)               57568     \n","                                                                 \n"," dropout_3 (Dropout)         (None, 224)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 64)                14400     \n","                                                                 \n"," batch_normalization_2 (Bat  (None, 64)                256       \n"," chNormalization)                                                \n","                                                                 \n"," dropout_4 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_3 (Dense)             (None, 4)                 260       \n","                                                                 \n","=================================================================\n","Total params: 8031284 (30.64 MB)\n","Trainable params: 8030516 (30.63 MB)\n","Non-trainable params: 768 (3.00 KB)\n","_________________________________________________________________\n"]}],"source":["best_model.summary()"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":528,"status":"ok","timestamp":1703915146057,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"7ZNyhyke7Ev5","outputId":"01b5d840-b8c9-4f39-8946-c2b3734f8c3c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'num_conv_layers': 2,\n"," 'conv_0_filters': 64,\n"," 'conv_0_kernel': 3,\n"," 'conv_0_use_batchnorm': True,\n"," 'conv_0_dropout_rate': 0.4,\n"," 'num_dense_layers': 3,\n"," 'dense_0_units': 256,\n"," 'dense_0_use_batchnorm': True,\n"," 'dense_0_dropout_rate': 0.0,\n"," 'learning_rate': 0.00023557488496216865,\n"," 'conv_1_filters': 16,\n"," 'conv_1_kernel': 5,\n"," 'conv_1_use_batchnorm': False,\n"," 'conv_1_dropout_rate': 0.1,\n"," 'conv_2_filters': 112,\n"," 'conv_2_kernel': 3,\n"," 'conv_2_use_batchnorm': True,\n"," 'conv_2_dropout_rate': 0.0,\n"," 'conv_3_filters': 128,\n"," 'conv_3_kernel': 3,\n"," 'conv_3_use_batchnorm': False,\n"," 'conv_3_dropout_rate': 0.2,\n"," 'conv_4_filters': 16,\n"," 'conv_4_kernel': 5,\n"," 'conv_4_use_batchnorm': True,\n"," 'conv_4_dropout_rate': 0.0,\n"," 'conv_5_filters': 32,\n"," 'conv_5_kernel': 3,\n"," 'conv_5_use_batchnorm': False,\n"," 'conv_5_dropout_rate': 0.2,\n"," 'conv_6_filters': 64,\n"," 'conv_6_kernel': 5,\n"," 'conv_6_use_batchnorm': False,\n"," 'conv_6_dropout_rate': 0.0,\n"," 'conv_7_filters': 32,\n"," 'conv_7_kernel': 3,\n"," 'conv_8_filters': 128,\n"," 'conv_8_kernel': 3,\n"," 'conv_9_filters': 80,\n"," 'conv_9_kernel': 3,\n"," 'conv_10_filters': 64,\n"," 'conv_10_kernel': 5,\n"," 'conv_11_filters': 32,\n"," 'conv_11_kernel': 3,\n"," 'dense_1_units': 224,\n"," 'dense_1_use_batchnorm': False,\n"," 'dense_1_dropout_rate': 0.2,\n"," 'dense_2_units': 64,\n"," 'dense_2_use_batchnorm': True,\n"," 'dense_2_dropout_rate': 0.1,\n"," 'dense_3_units': 32,\n"," 'dense_3_use_batchnorm': False,\n"," 'dense_3_dropout_rate': 0.30000000000000004,\n"," 'conv_12_filters': 48,\n"," 'conv_12_kernel': 5,\n"," 'conv_13_filters': 128,\n"," 'conv_13_kernel': 3}"]},"metadata":{},"execution_count":28}],"source":["#Get the best hyperparameters\n","best_hp = tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values\n","best_hp"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":543,"status":"ok","timestamp":1703915533494,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"KDPVXIsBQPNx","outputId":"d2e3f7c9-8305-4f03-c990-cff735690179"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d_2 (Conv2D)           (None, 176, 176, 64)      1792      \n","                                                                 \n"," max_pooling2d_2 (MaxPoolin  (None, 88, 88, 64)        0         \n"," g2D)                                                            \n","                                                                 \n"," batch_normalization_3 (Bat  (None, 88, 88, 64)        256       \n"," chNormalization)                                                \n","                                                                 \n"," dropout_5 (Dropout)         (None, 88, 88, 64)        0         \n","                                                                 \n"," conv2d_3 (Conv2D)           (None, 88, 88, 16)        25616     \n","                                                                 \n"," max_pooling2d_3 (MaxPoolin  (None, 44, 44, 16)        0         \n"," g2D)                                                            \n","                                                                 \n"," dropout_6 (Dropout)         (None, 44, 44, 16)        0         \n","                                                                 \n"," flatten_1 (Flatten)         (None, 30976)             0         \n","                                                                 \n"," dense_4 (Dense)             (None, 256)               7930112   \n","                                                                 \n"," batch_normalization_4 (Bat  (None, 256)               1024      \n"," chNormalization)                                                \n","                                                                 \n"," dense_5 (Dense)             (None, 224)               57568     \n","                                                                 \n"," dropout_7 (Dropout)         (None, 224)               0         \n","                                                                 \n"," dense_6 (Dense)             (None, 64)                14400     \n","                                                                 \n"," batch_normalization_5 (Bat  (None, 64)                256       \n"," chNormalization)                                                \n","                                                                 \n"," dropout_8 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_7 (Dense)             (None, 4)                 260       \n","                                                                 \n","=================================================================\n","Total params: 8031284 (30.64 MB)\n","Trainable params: 8030516 (30.63 MB)\n","Non-trainable params: 768 (3.00 KB)\n","_________________________________________________________________\n"]}],"source":["#Use the best hyperparameters obtained to construct the model\n","\n","# Best hyperparameter values generated\n","num_conv_layers = 2\n","conv_0_filters = 64\n","conv_0_kernel = 3\n","num_dense_layers = 3\n","dense_0_units = 256\n","dense_1_units = 224\n","dense_2_units = 64\n","learning_rate = 0.00023557488496216865\n","conv_1_filters = 16\n","conv_1_kernel = 5\n","\n","# Define the model\n","model = Sequential()\n","\n","# Add convolutional layers\n","for i in range(num_conv_layers):\n","    model.add(Conv2D(\n","        filters=eval(f'conv_{i}_filters'),\n","        kernel_size=eval(f'conv_{i}_kernel'),\n","        activation='relu',\n","        padding='same',\n","        input_shape=(176, 176, 3)\n","    ))\n","    model.add(MaxPooling2D(pool_size=(2, 2)))\n","    if i == 0:\n","      model.add(BatchNormalization())\n","      model.add(Dropout(0.4))  # Adding dropout for regularization\n","    elif i == 1:\n","      #model.add(BatchNormalization())\n","      model.add(Dropout(0.2))  # Adding dropout for regularization\n","\n","model.add(Flatten())\n","\n","# Add dense layers\n","for i in range(num_dense_layers):\n","    model.add(Dense(\n","        units=eval(f'dense_{i}_units'),\n","        activation='relu'\n","    ))\n","    if i == 0:\n","      model.add(BatchNormalization())\n","      #model.add(Dropout(0.5))  # Adding dropout for regularization\n","    elif i == 1:\n","      #model.add(BatchNormalization())\n","      model.add(Dropout(0.3))  # Adding dropout for regularization\n","    elif i == 2:\n","      model.add(BatchNormalization())\n","      model.add(Dropout(0.1))  # Adding dropout for regularization\n","\n","\n","# Output layer\n","model.add(Dense(4, activation='softmax'))\n","\n","# Choose the learning rate\n","opt = tensorflow.keras.optimizers.Adam(learning_rate)\n","# Compile the model\n","model.compile(optimizer=opt,\n","              loss='categorical_crossentropy',\n","              metrics=['accuracy'])\n","\n","# Print the model summary\n","model.summary()\n"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":568,"status":"ok","timestamp":1703915580810,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"0hjfMpjoRwxT","outputId":"656fe1bb-b8e5-4a43-929e-22cf3cb08963"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]}],"source":["# Save the model to an h5 file\n","model.save('model_1.h5')"]},{"cell_type":"code","execution_count":31,"metadata":{"executionInfo":{"elapsed":571,"status":"ok","timestamp":1703915589767,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"8p-EhK3YzEGn"},"outputs":[],"source":["model_checkpoint = ModelCheckpoint('best_model_custom_1.h5', save_best_only=True, save_weights_only=True, monitor='val_accuracy', mode='max', verbose=1)\n","#early_stopping = EarlyStopping(monitor='val_accuracy', patience=20, mode='max', verbose=1, restore_best_weights=True)\n","#reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, mode='min', verbose=1)\n","callbacks_list = [model_checkpoint] #, reduce_lr]"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":807752,"status":"ok","timestamp":1703916405387,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"CXH9pFiuZpY3","outputId":"d6ea19b6-ae5b-4b97-d67d-9689799db4f4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","508/508 [==============================] - ETA: 0s - loss: 0.8552 - accuracy: 0.6533\n","Epoch 1: val_accuracy improved from -inf to 0.45605, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 12s 18ms/step - loss: 0.8552 - accuracy: 0.6533 - val_loss: 1.4434 - val_accuracy: 0.4561\n","Epoch 2/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.4300 - accuracy: 0.8277\n","Epoch 2: val_accuracy improved from 0.45605 to 0.79883, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 8s 16ms/step - loss: 0.4296 - accuracy: 0.8280 - val_loss: 0.4938 - val_accuracy: 0.7988\n","Epoch 3/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.2739 - accuracy: 0.8990\n","Epoch 3: val_accuracy improved from 0.79883 to 0.89453, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 8s 16ms/step - loss: 0.2736 - accuracy: 0.8991 - val_loss: 0.2994 - val_accuracy: 0.8945\n","Epoch 4/100\n","508/508 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.9303\n","Epoch 4: val_accuracy did not improve from 0.89453\n","508/508 [==============================] - 8s 15ms/step - loss: 0.1971 - accuracy: 0.9303 - val_loss: 0.2866 - val_accuracy: 0.8916\n","Epoch 5/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.1339 - accuracy: 0.9543\n","Epoch 5: val_accuracy did not improve from 0.89453\n","508/508 [==============================] - 8s 15ms/step - loss: 0.1336 - accuracy: 0.9546 - val_loss: 0.2963 - val_accuracy: 0.8809\n","Epoch 6/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.1057 - accuracy: 0.9644\n","Epoch 6: val_accuracy did not improve from 0.89453\n","508/508 [==============================] - 8s 15ms/step - loss: 0.1062 - accuracy: 0.9642 - val_loss: 0.7117 - val_accuracy: 0.7354\n","Epoch 7/100\n","507/508 [============================>.] - ETA: 0s - loss: 0.1038 - accuracy: 0.9638\n","Epoch 7: val_accuracy improved from 0.89453 to 0.92969, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 9s 17ms/step - loss: 0.1038 - accuracy: 0.9638 - val_loss: 0.2183 - val_accuracy: 0.9297\n","Epoch 8/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0797 - accuracy: 0.9705\n","Epoch 8: val_accuracy improved from 0.92969 to 0.96973, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0795 - accuracy: 0.9706 - val_loss: 0.0914 - val_accuracy: 0.9697\n","Epoch 9/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0639 - accuracy: 0.9788\n","Epoch 9: val_accuracy did not improve from 0.96973\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0638 - accuracy: 0.9788 - val_loss: 0.1555 - val_accuracy: 0.9551\n","Epoch 10/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0596 - accuracy: 0.9786\n","Epoch 10: val_accuracy improved from 0.96973 to 0.97559, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 8s 17ms/step - loss: 0.0596 - accuracy: 0.9786 - val_loss: 0.0722 - val_accuracy: 0.9756\n","Epoch 11/100\n","507/508 [============================>.] - ETA: 0s - loss: 0.0532 - accuracy: 0.9818\n","Epoch 11: val_accuracy did not improve from 0.97559\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0532 - accuracy: 0.9818 - val_loss: 0.1027 - val_accuracy: 0.9658\n","Epoch 12/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0493 - accuracy: 0.9833\n","Epoch 12: val_accuracy improved from 0.97559 to 0.97754, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 9s 17ms/step - loss: 0.0495 - accuracy: 0.9831 - val_loss: 0.0830 - val_accuracy: 0.9775\n","Epoch 13/100\n","506/508 [============================>.] - ETA: 0s - loss: 0.0527 - accuracy: 0.9813\n","Epoch 13: val_accuracy did not improve from 0.97754\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0526 - accuracy: 0.9814 - val_loss: 0.1298 - val_accuracy: 0.9590\n","Epoch 14/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0420 - accuracy: 0.9854\n","Epoch 14: val_accuracy did not improve from 0.97754\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0424 - accuracy: 0.9852 - val_loss: 0.6759 - val_accuracy: 0.7627\n","Epoch 15/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0422 - accuracy: 0.9858\n","Epoch 15: val_accuracy did not improve from 0.97754\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0429 - accuracy: 0.9855 - val_loss: 0.1434 - val_accuracy: 0.9609\n","Epoch 16/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0389 - accuracy: 0.9860\n","Epoch 16: val_accuracy did not improve from 0.97754\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0392 - accuracy: 0.9860 - val_loss: 0.2269 - val_accuracy: 0.9385\n","Epoch 17/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0287 - accuracy: 0.9906\n","Epoch 17: val_accuracy improved from 0.97754 to 0.98145, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0287 - accuracy: 0.9906 - val_loss: 0.0740 - val_accuracy: 0.9814\n","Epoch 18/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0326 - accuracy: 0.9900\n","Epoch 18: val_accuracy did not improve from 0.98145\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0325 - accuracy: 0.9900 - val_loss: 0.0853 - val_accuracy: 0.9775\n","Epoch 19/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0371 - accuracy: 0.9874\n","Epoch 19: val_accuracy did not improve from 0.98145\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0370 - accuracy: 0.9874 - val_loss: 0.1262 - val_accuracy: 0.9619\n","Epoch 20/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0289 - accuracy: 0.9913\n","Epoch 20: val_accuracy improved from 0.98145 to 0.98828, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 9s 17ms/step - loss: 0.0288 - accuracy: 0.9914 - val_loss: 0.0615 - val_accuracy: 0.9883\n","Epoch 21/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0296 - accuracy: 0.9895\n","Epoch 21: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0296 - accuracy: 0.9895 - val_loss: 0.1218 - val_accuracy: 0.9697\n","Epoch 22/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0288 - accuracy: 0.9906\n","Epoch 22: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0288 - accuracy: 0.9906 - val_loss: 0.1934 - val_accuracy: 0.9355\n","Epoch 23/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0236 - accuracy: 0.9925\n","Epoch 23: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0236 - accuracy: 0.9925 - val_loss: 0.0709 - val_accuracy: 0.9727\n","Epoch 24/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0224 - accuracy: 0.9922\n","Epoch 24: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0225 - accuracy: 0.9921 - val_loss: 0.0890 - val_accuracy: 0.9766\n","Epoch 25/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0278 - accuracy: 0.9917\n","Epoch 25: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0276 - accuracy: 0.9917 - val_loss: 0.0889 - val_accuracy: 0.9736\n","Epoch 26/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 0.9911\n","Epoch 26: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0257 - accuracy: 0.9911 - val_loss: 0.1048 - val_accuracy: 0.9668\n","Epoch 27/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0204 - accuracy: 0.9937\n","Epoch 27: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0203 - accuracy: 0.9937 - val_loss: 0.0522 - val_accuracy: 0.9873\n","Epoch 28/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0221 - accuracy: 0.9928\n","Epoch 28: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0222 - accuracy: 0.9929 - val_loss: 0.0763 - val_accuracy: 0.9736\n","Epoch 29/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0206 - accuracy: 0.9935\n","Epoch 29: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0206 - accuracy: 0.9935 - val_loss: 0.1634 - val_accuracy: 0.9619\n","Epoch 30/100\n","507/508 [============================>.] - ETA: 0s - loss: 0.0239 - accuracy: 0.9922\n","Epoch 30: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0238 - accuracy: 0.9922 - val_loss: 0.0643 - val_accuracy: 0.9814\n","Epoch 31/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0191 - accuracy: 0.9942\n","Epoch 31: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0191 - accuracy: 0.9942 - val_loss: 0.0697 - val_accuracy: 0.9805\n","Epoch 32/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0311 - accuracy: 0.9900\n","Epoch 32: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0311 - accuracy: 0.9900 - val_loss: 0.1644 - val_accuracy: 0.9639\n","Epoch 33/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0266 - accuracy: 0.9917\n","Epoch 33: val_accuracy did not improve from 0.98828\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0265 - accuracy: 0.9917 - val_loss: 0.0638 - val_accuracy: 0.9844\n","Epoch 34/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0206 - accuracy: 0.9938\n","Epoch 34: val_accuracy improved from 0.98828 to 0.98926, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 9s 18ms/step - loss: 0.0206 - accuracy: 0.9938 - val_loss: 0.0456 - val_accuracy: 0.9893\n","Epoch 35/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0182 - accuracy: 0.9946\n","Epoch 35: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0182 - accuracy: 0.9946 - val_loss: 0.0746 - val_accuracy: 0.9844\n","Epoch 36/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0134 - accuracy: 0.9954\n","Epoch 36: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0134 - accuracy: 0.9953 - val_loss: 0.0518 - val_accuracy: 0.9824\n","Epoch 37/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0176 - accuracy: 0.9949\n","Epoch 37: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0179 - accuracy: 0.9948 - val_loss: 0.0963 - val_accuracy: 0.9727\n","Epoch 38/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0135 - accuracy: 0.9953\n","Epoch 38: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0135 - accuracy: 0.9953 - val_loss: 0.0603 - val_accuracy: 0.9883\n","Epoch 39/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0120 - accuracy: 0.9962\n","Epoch 39: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0120 - accuracy: 0.9962 - val_loss: 0.0903 - val_accuracy: 0.9863\n","Epoch 40/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0129 - accuracy: 0.9954\n","Epoch 40: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0130 - accuracy: 0.9953 - val_loss: 0.1329 - val_accuracy: 0.9697\n","Epoch 41/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0148 - accuracy: 0.9950\n","Epoch 41: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0147 - accuracy: 0.9951 - val_loss: 0.0655 - val_accuracy: 0.9834\n","Epoch 42/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0138 - accuracy: 0.9956\n","Epoch 42: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0138 - accuracy: 0.9956 - val_loss: 0.0973 - val_accuracy: 0.9775\n","Epoch 43/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0089 - accuracy: 0.9974\n","Epoch 43: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0089 - accuracy: 0.9974 - val_loss: 0.0445 - val_accuracy: 0.9873\n","Epoch 44/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0101 - accuracy: 0.9964\n","Epoch 44: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0101 - accuracy: 0.9964 - val_loss: 0.0451 - val_accuracy: 0.9883\n","Epoch 45/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0124 - accuracy: 0.9963\n","Epoch 45: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0124 - accuracy: 0.9963 - val_loss: 0.1064 - val_accuracy: 0.9814\n","Epoch 46/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0113 - accuracy: 0.9969\n","Epoch 46: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0113 - accuracy: 0.9969 - val_loss: 0.1678 - val_accuracy: 0.9795\n","Epoch 47/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0191 - accuracy: 0.9942\n","Epoch 47: val_accuracy did not improve from 0.98926\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0191 - accuracy: 0.9942 - val_loss: 0.1051 - val_accuracy: 0.9854\n","Epoch 48/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0084 - accuracy: 0.9980\n","Epoch 48: val_accuracy improved from 0.98926 to 0.99121, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 9s 17ms/step - loss: 0.0084 - accuracy: 0.9980 - val_loss: 0.0394 - val_accuracy: 0.9912\n","Epoch 49/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0044 - accuracy: 0.9991\n","Epoch 49: val_accuracy improved from 0.99121 to 0.99219, saving model to best_model_custom_1.h5\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.0481 - val_accuracy: 0.9922\n","Epoch 50/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0067 - accuracy: 0.9979\n","Epoch 50: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0066 - accuracy: 0.9979 - val_loss: 0.0466 - val_accuracy: 0.9902\n","Epoch 51/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0167 - accuracy: 0.9943\n","Epoch 51: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0167 - accuracy: 0.9943 - val_loss: 0.0609 - val_accuracy: 0.9844\n","Epoch 52/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0102 - accuracy: 0.9969\n","Epoch 52: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0102 - accuracy: 0.9969 - val_loss: 0.0529 - val_accuracy: 0.9863\n","Epoch 53/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0067 - accuracy: 0.9983\n","Epoch 53: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 0.0429 - val_accuracy: 0.9893\n","Epoch 54/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0085 - accuracy: 0.9972\n","Epoch 54: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0085 - accuracy: 0.9972 - val_loss: 0.1629 - val_accuracy: 0.9502\n","Epoch 55/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0154 - accuracy: 0.9949\n","Epoch 55: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0153 - accuracy: 0.9950 - val_loss: 0.0741 - val_accuracy: 0.9766\n","Epoch 56/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0092 - accuracy: 0.9967\n","Epoch 56: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0092 - accuracy: 0.9967 - val_loss: 0.0918 - val_accuracy: 0.9834\n","Epoch 57/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0073 - accuracy: 0.9981\n","Epoch 57: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0073 - accuracy: 0.9982 - val_loss: 0.0530 - val_accuracy: 0.9873\n","Epoch 58/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0081 - accuracy: 0.9977\n","Epoch 58: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.0459 - val_accuracy: 0.9902\n","Epoch 59/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0071 - accuracy: 0.9974\n","Epoch 59: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0072 - accuracy: 0.9974 - val_loss: 0.0841 - val_accuracy: 0.9736\n","Epoch 60/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0093 - accuracy: 0.9973\n","Epoch 60: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0093 - accuracy: 0.9973 - val_loss: 0.0578 - val_accuracy: 0.9873\n","Epoch 61/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0134 - accuracy: 0.9959\n","Epoch 61: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0134 - accuracy: 0.9959 - val_loss: 0.0835 - val_accuracy: 0.9795\n","Epoch 62/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0092 - accuracy: 0.9968\n","Epoch 62: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0092 - accuracy: 0.9968 - val_loss: 0.0528 - val_accuracy: 0.9873\n","Epoch 63/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0057 - accuracy: 0.9979\n","Epoch 63: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0061 - accuracy: 0.9978 - val_loss: 0.0916 - val_accuracy: 0.9854\n","Epoch 64/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0105 - accuracy: 0.9969\n","Epoch 64: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0105 - accuracy: 0.9969 - val_loss: 0.6551 - val_accuracy: 0.9150\n","Epoch 65/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0064 - accuracy: 0.9980\n","Epoch 65: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.0568 - val_accuracy: 0.9834\n","Epoch 66/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9988\n","Epoch 66: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 0.0941 - val_accuracy: 0.9873\n","Epoch 67/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0075 - accuracy: 0.9974\n","Epoch 67: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0075 - accuracy: 0.9974 - val_loss: 0.0456 - val_accuracy: 0.9893\n","Epoch 68/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0080 - accuracy: 0.9972\n","Epoch 68: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0080 - accuracy: 0.9972 - val_loss: 0.0852 - val_accuracy: 0.9814\n","Epoch 69/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0095 - accuracy: 0.9974\n","Epoch 69: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0095 - accuracy: 0.9974 - val_loss: 0.0706 - val_accuracy: 0.9854\n","Epoch 70/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9984\n","Epoch 70: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0039 - accuracy: 0.9984 - val_loss: 0.0617 - val_accuracy: 0.9883\n","Epoch 71/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0068 - accuracy: 0.9981\n","Epoch 71: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0068 - accuracy: 0.9982 - val_loss: 0.1428 - val_accuracy: 0.9805\n","Epoch 72/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0051 - accuracy: 0.9984\n","Epoch 72: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 0.2694 - val_accuracy: 0.9561\n","Epoch 73/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0098 - accuracy: 0.9968\n","Epoch 73: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0097 - accuracy: 0.9968 - val_loss: 0.0488 - val_accuracy: 0.9883\n","Epoch 74/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0060 - accuracy: 0.9978\n","Epoch 74: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0060 - accuracy: 0.9977 - val_loss: 0.0619 - val_accuracy: 0.9863\n","Epoch 75/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0057 - accuracy: 0.9979\n","Epoch 75: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0060 - accuracy: 0.9977 - val_loss: 0.0648 - val_accuracy: 0.9873\n","Epoch 76/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0041 - accuracy: 0.9988\n","Epoch 76: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.0602 - val_accuracy: 0.9863\n","Epoch 77/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0034 - accuracy: 0.9985\n","Epoch 77: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0034 - accuracy: 0.9985 - val_loss: 0.0490 - val_accuracy: 0.9883\n","Epoch 78/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0051 - accuracy: 0.9989\n","Epoch 78: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0051 - accuracy: 0.9989 - val_loss: 0.0863 - val_accuracy: 0.9854\n","Epoch 79/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0065 - accuracy: 0.9976\n","Epoch 79: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0068 - accuracy: 0.9975 - val_loss: 0.0427 - val_accuracy: 0.9902\n","Epoch 80/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0102 - accuracy: 0.9967\n","Epoch 80: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0106 - accuracy: 0.9966 - val_loss: 0.0826 - val_accuracy: 0.9834\n","Epoch 81/100\n","508/508 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9989\n","Epoch 81: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0643 - val_accuracy: 0.9883\n","Epoch 82/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0063 - accuracy: 0.9980\n","Epoch 82: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.0717 - val_accuracy: 0.9863\n","Epoch 83/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0068 - accuracy: 0.9981\n","Epoch 83: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0068 - accuracy: 0.9982 - val_loss: 0.0869 - val_accuracy: 0.9863\n","Epoch 84/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0047 - accuracy: 0.9989\n","Epoch 84: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0047 - accuracy: 0.9989 - val_loss: 0.0578 - val_accuracy: 0.9883\n","Epoch 85/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0037 - accuracy: 0.9990\n","Epoch 85: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.0460 - val_accuracy: 0.9863\n","Epoch 86/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0094 - accuracy: 0.9969\n","Epoch 86: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.0477 - val_accuracy: 0.9883\n","Epoch 87/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0080 - accuracy: 0.9970\n","Epoch 87: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0079 - accuracy: 0.9970 - val_loss: 0.0534 - val_accuracy: 0.9893\n","Epoch 88/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0050 - accuracy: 0.9986\n","Epoch 88: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 0.0677 - val_accuracy: 0.9854\n","Epoch 89/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0054 - accuracy: 0.9985\n","Epoch 89: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.0766 - val_accuracy: 0.9844\n","Epoch 90/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0049 - accuracy: 0.9983\n","Epoch 90: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0049 - accuracy: 0.9983 - val_loss: 0.0634 - val_accuracy: 0.9844\n","Epoch 91/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0053 - accuracy: 0.9985\n","Epoch 91: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.0398 - val_accuracy: 0.9922\n","Epoch 92/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0074 - accuracy: 0.9975\n","Epoch 92: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0073 - accuracy: 0.9975 - val_loss: 0.0454 - val_accuracy: 0.9902\n","Epoch 93/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0055 - accuracy: 0.9985\n","Epoch 93: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0055 - accuracy: 0.9985 - val_loss: 0.0350 - val_accuracy: 0.9922\n","Epoch 94/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0030 - accuracy: 0.9990\n","Epoch 94: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 0.0450 - val_accuracy: 0.9863\n","Epoch 95/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0077 - accuracy: 0.9970\n","Epoch 95: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0077 - accuracy: 0.9970 - val_loss: 0.0603 - val_accuracy: 0.9893\n","Epoch 96/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0042 - accuracy: 0.9988\n","Epoch 96: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0042 - accuracy: 0.9988 - val_loss: 0.1175 - val_accuracy: 0.9814\n","Epoch 97/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0037 - accuracy: 0.9990\n","Epoch 97: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.0483 - val_accuracy: 0.9873\n","Epoch 98/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0050 - accuracy: 0.9981\n","Epoch 98: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0052 - accuracy: 0.9982 - val_loss: 0.0539 - val_accuracy: 0.9873\n","Epoch 99/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0029 - accuracy: 0.9990\n","Epoch 99: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 16ms/step - loss: 0.0028 - accuracy: 0.9990 - val_loss: 0.0572 - val_accuracy: 0.9883\n","Epoch 100/100\n","505/508 [============================>.] - ETA: 0s - loss: 0.0022 - accuracy: 0.9993\n","Epoch 100: val_accuracy did not improve from 0.99219\n","508/508 [==============================] - 8s 15ms/step - loss: 0.0026 - accuracy: 0.9990 - val_loss: 0.1254 - val_accuracy: 0.9805\n"]}],"source":["history = model.fit(train_data, train_labels, epochs=100, validation_data=(val_data, val_labels), batch_size=16, callbacks=callbacks_list)"]},{"cell_type":"code","execution_count":42,"metadata":{"executionInfo":{"elapsed":3837,"status":"ok","timestamp":1703917228049,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"N6JRh9st-Ok9"},"outputs":[],"source":["model = tensorflow.keras.models.load_model('/content/drive/MyDrive/omdena_ad/model_1.h5')\n","model.load_weights('/content/drive/MyDrive/omdena_ad/best_model_custom_1.h5')"]},{"cell_type":"code","source":["plt.plot(history.history['loss'], label='Training Loss')\n","# Plot validation loss\n","plt.plot(history.history['val_loss'], label='Validation Loss')\n","plt.title('Training and Validation Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"TUMAW7Ywi5VO","executionInfo":{"status":"ok","timestamp":1703917230615,"user_tz":300,"elapsed":883,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}},"outputId":"dc97de18-7a4f-462d-c4d7-71357ce2d235"},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Plot training accuracy\n","plt.plot(history.history['accuracy'], label='Training Accuracy')\n","# Plot validation accuracy\n","plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","plt.title('Training and Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"WmIEECSDjGtb","executionInfo":{"status":"ok","timestamp":1703917233961,"user_tz":300,"elapsed":442,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"}},"outputId":"c573fb34-3773-47d5-ee5f-ceced9f01304"},"execution_count":44,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"h36OPz5VHMtq"},"source":["**PREDICTION ON THE VALIDATION DATA**"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2042,"status":"ok","timestamp":1703917240727,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"uL4gD_zFGo1p","outputId":"7b293bb5-068b-4e0b-cc95-f5010b93b485"},"outputs":[{"output_type":"stream","name":"stdout","text":["32/32 [==============================] - 0s 7ms/step\n"]}],"source":["pred_val = model.predict(val_data)"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":674,"status":"ok","timestamp":1703917243062,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"tk_0TLKUGwAd","outputId":"f927b274-e07c-4974-f111-194d8564300f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":46},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["predict_val = np.argmax(pred_val, axis=1)\n","true_val = np.argmax(val_labels, axis=1)\n","cm = confusion_matrix(predict_val, true_val)\n","sns.heatmap(cm, annot=True, fmt='d')"]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":557,"status":"ok","timestamp":1703917246208,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"NM5XpHy2Hklm","outputId":"18b793c1-d2b9-4388-f233-2dd17798e1a1"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.99      0.99      0.99       143\n","           1       1.00      1.00      1.00         7\n","           2       0.99      1.00      0.99       510\n","           3       1.00      0.98      0.99       364\n","\n","    accuracy                           0.99      1024\n","   macro avg       0.99      0.99      0.99      1024\n","weighted avg       0.99      0.99      0.99      1024\n","\n"]}],"source":["val_report = classification_report(true_val, predict_val)\n","print(val_report)"]},{"cell_type":"markdown","metadata":{"id":"zUWV3xUFHSUj"},"source":["**PREDICTION ON THE TEST DATA**"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1946,"status":"ok","timestamp":1703917258046,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"lRoUphDSHAqT","outputId":"d8766887-96f6-4e35-9119-9a5fd6aafbc9"},"outputs":[{"output_type":"stream","name":"stdout","text":["40/40 [==============================] - 1s 8ms/step - loss: 0.0603 - accuracy: 0.9852\n"]}],"source":["test_scores = model.evaluate(test_data, test_labels)"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1919,"status":"ok","timestamp":1703917263013,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"lAkAoDfbCUCt","outputId":"2ea392a8-deb5-45cc-9ce6-5ec2f8749925"},"outputs":[{"output_type":"stream","name":"stdout","text":["40/40 [==============================] - 0s 7ms/step\n"]}],"source":["pred_test = model.predict(test_data)"]},{"cell_type":"code","execution_count":50,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":871,"status":"ok","timestamp":1703917265261,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"Dy6JeHt4fGnB","outputId":"614e5a34-3ec7-4165-f999-6932e77d31b8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":50},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["predict_test = np.argmax(pred_test, axis=1)\n","true_test = np.argmax(test_labels, axis=1)\n","cm = confusion_matrix(predict_test, true_test)\n","sns.heatmap(cm, annot=True, fmt='d')"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":566,"status":"ok","timestamp":1703917270047,"user":{"displayName":"Alok Bhattarai","userId":"02407344521211545734"},"user_tz":300},"id":"ANDCiIvfH_xe","outputId":"cea7eed0-87b2-40a0-842c-18b23e71f142"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.95      0.98      0.97       169\n","           1       1.00      1.00      1.00        12\n","           2       0.99      1.00      0.99       660\n","           3       1.00      0.97      0.98       439\n","\n","    accuracy                           0.99      1280\n","   macro avg       0.98      0.99      0.98      1280\n","weighted avg       0.99      0.99      0.99      1280\n","\n"]}],"source":["test_report = classification_report(true_test, predict_test)\n","print(test_report)"]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[],"gpuType":"V100"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}