{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":3362,"databundleVersionId":31148,"sourceType":"competition"}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:22.127748Z","iopub.execute_input":"2026-10-02T23:27:22.128703Z","iopub.status.idle":"2026-10-02T23:27:22.13341Z","shell.execute_reply.started":"2026-10-02T23:27:22.128672Z","shell.execute_reply":"2026-10-02T23:27:22.132277Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!python --version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:22.135412Z","iopub.execute_input":"2026-10-02T23:27:22.135832Z","iopub.status.idle":"2026-10-02T23:27:23.157341Z","shell.execute_reply.started":"2026-10-02T23:27:22.135784Z","shell.execute_reply":"2026-10-02T23:27:23.156403Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!uname --all","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:23.158902Z","iopub.execute_input":"2026-10-02T23:27:23.159255Z","iopub.status.idle":"2026-10-02T23:27:24.164775Z","shell.execute_reply.started":"2026-10-02T23:27:23.159217Z","shell.execute_reply":"2026-10-02T23:27:24.163576Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nprint(os.listdir(\"../input\"))\n\nimport zipfile\n\nwith zipfile.ZipFile(\"/kaggle/input/dogs-vs-cats/test1.zip\",\"r\") as z:\n    z.extractall(\".\")\n    \nwith zipfile.ZipFile(\"/kaggle/input/dogs-vs-cats/train.zip\",\"r\") as z:\n    z.extractall(\".\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:24.166351Z","iopub.execute_input":"2026-10-02T23:27:24.166804Z","iopub.status.idle":"2026-10-02T23:27:33.164749Z","shell.execute_reply.started":"2026-10-02T23:27:24.166775Z","shell.execute_reply":"2026-10-02T23:27:33.163926Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:33.166824Z","iopub.execute_input":"2026-10-02T23:27:33.167443Z","iopub.status.idle":"2026-10-02T23:27:34.188823Z","shell.execute_reply.started":"2026-10-02T23:27:33.167364Z","shell.execute_reply":"2026-10-02T23:27:34.187609Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls -l","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:34.190509Z","iopub.execute_input":"2026-10-02T23:27:34.190806Z","iopub.status.idle":"2026-10-02T23:27:35.236421Z","shell.execute_reply.started":"2026-10-02T23:27:34.19078Z","shell.execute_reply":"2026-10-02T23:27:35.235447Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls test1/ | wc -l","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:35.237983Z","iopub.execute_input":"2026-10-02T23:27:35.238286Z","iopub.status.idle":"2026-10-02T23:27:36.327725Z","shell.execute_reply.started":"2026-10-02T23:27:35.238261Z","shell.execute_reply":"2026-10-02T23:27:36.326684Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls train/ | wc -l","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:36.329196Z","iopub.execute_input":"2026-10-02T23:27:36.329621Z","iopub.status.idle":"2026-10-02T23:27:37.418305Z","shell.execute_reply.started":"2026-10-02T23:27:36.329593Z","shell.execute_reply":"2026-10-02T23:27:37.417075Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#ls train/","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:37.419961Z","iopub.execute_input":"2026-10-02T23:27:37.420373Z","iopub.status.idle":"2026-10-02T23:27:37.425241Z","shell.execute_reply.started":"2026-10-02T23:27:37.420334Z","shell.execute_reply":"2026-10-02T23:27:37.424433Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls -U train | head -n 10","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:37.426286Z","iopub.execute_input":"2026-10-02T23:27:37.4266Z","iopub.status.idle":"2026-10-02T23:27:38.517007Z","shell.execute_reply.started":"2026-10-02T23:27:37.426578Z","shell.execute_reply":"2026-10-02T23:27:38.515951Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls -U test1 | head -n 10","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:38.51851Z","iopub.execute_input":"2026-10-02T23:27:38.519615Z","iopub.status.idle":"2026-10-02T23:27:39.577033Z","shell.execute_reply.started":"2026-10-02T23:27:38.519574Z","shell.execute_reply":"2026-10-02T23:27:39.575978Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plot dog photos from the dogs vs cats dataset\nfrom matplotlib import pyplot\n# from matplotlib import pyplot as plt\nfrom matplotlib.image import imread\n# define location of dataset\nfolder = 'train/'\n# plot first few images\nfor i in range(9):\n\t# define subplot\n\tpyplot.subplot(330 + 1 + i)\n\t# define filename\n\tfilename = folder + 'dog.' + str(i) + '.jpg'\n\t# load image pixels\n\timage = imread(filename)\n\t# plot raw pixel data\n\tpyplot.imshow(image)\n# show the figure\npyplot.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:39.579039Z","iopub.execute_input":"2026-10-02T23:27:39.579326Z","iopub.status.idle":"2026-10-02T23:27:40.597935Z","shell.execute_reply.started":"2026-10-02T23:27:39.5793Z","shell.execute_reply":"2026-10-02T23:27:40.596933Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plot cat photos from the dogs vs cats dataset\nfrom matplotlib import pyplot\nfrom matplotlib.image import imread\n# define location of dataset\nfolder = 'train/'\n# plot first few images\nfor i in range(9):\n\t# define subplot\n\tpyplot.subplot(330 + 1 + i)\n\t# define filename\n\tfilename = folder + 'cat.' + str(i) + '.jpg'\n\t# load image pixels\n\timage = imread(filename)\n\t# plot raw pixel data\n\tpyplot.imshow(image)\n# show the figure\npyplot.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:40.599088Z","iopub.execute_input":"2026-10-02T23:27:40.59936Z","iopub.status.idle":"2026-10-02T23:27:41.557168Z","shell.execute_reply.started":"2026-10-02T23:27:40.599335Z","shell.execute_reply":"2026-10-02T23:27:41.556365Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!python --version","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:41.561791Z","iopub.execute_input":"2026-10-02T23:27:41.562139Z","iopub.status.idle":"2026-10-02T23:27:42.603015Z","shell.execute_reply.started":"2026-10-02T23:27:41.562114Z","shell.execute_reply":"2026-10-02T23:27:42.602113Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import tensorflow\nprint(tensorflow.__version__)\n\nimport keras\nprint(keras.__version__)\n\nimport tensorflow.keras\nprint(tensorflow.__version__)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:42.604419Z","iopub.execute_input":"2026-10-02T23:27:42.604697Z","iopub.status.idle":"2026-10-02T23:27:42.610675Z","shell.execute_reply.started":"2026-10-02T23:27:42.604672Z","shell.execute_reply":"2026-10-02T23:27:42.609861Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# load dogs vs cats dataset, reshape and save to a new file\nfrom os import listdir\nfrom numpy import asarray\nfrom numpy import save\nfrom keras.preprocessing.image import load_img\nfrom keras.preprocessing.image import img_to_array","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:42.611767Z","iopub.execute_input":"2026-10-02T23:27:42.612071Z","iopub.status.idle":"2026-10-02T23:27:42.624209Z","shell.execute_reply.started":"2026-10-02T23:27:42.612049Z","shell.execute_reply":"2026-10-02T23:27:42.623434Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# define location of dataset\nfolder = 'train/'\nphotos, labels = list(), list()\n# enumerate files in the directory\nfor file in listdir(folder):\n\t# determine class\n\toutput = 0.0\n\tif file.startswith('dog'):\n\t\toutput = 1.0\n\t# load image\n\tphoto = load_img(folder + file, target_size=(200, 200))\n\t# convert to numpy array\n\tphoto = img_to_array(photo)\n\t# store\n\tphotos.append(photo)\n\tlabels.append(output)\n# convert to a numpy arrays\nphotos = asarray(photos)\nlabels = asarray(labels)\nprint(photos.shape, labels.shape)\n# save the reshaped photos\nsave('dogs_vs_cats_photos.npy', photos)\nsave('dogs_vs_cats_labels.npy', labels)","metadata":{"execution":{"iopub.status.busy":"2024-01-27T03:36:15.416611Z","iopub.execute_input":"2024-01-27T03:36:15.417128Z","iopub.status.idle":"2024-01-27T03:36:38.886396Z","shell.execute_reply.started":"2024-01-27T03:36:15.417101Z","shell.execute_reply":"2024-01-27T03:36:38.883149Z"}}},{"cell_type":"markdown","source":"# load and confirm the shape\nfrom numpy import load\nphotos = load('dogs_vs_cats_photos.npy')\nlabels = load('dogs_vs_cats_labels.npy')","metadata":{"execution":{"iopub.status.busy":"2024-01-27T03:36:38.88711Z","iopub.status.idle":"2024-01-27T03:36:38.887458Z","shell.execute_reply.started":"2024-01-27T03:36:38.887281Z","shell.execute_reply":"2024-01-27T03:36:38.887296Z"}}},{"cell_type":"code","source":"# organize dataset into a useful structure\nfrom os import makedirs\nfrom os import listdir\nfrom shutil import copyfile\nfrom random import seed\nfrom random import random","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:42.625629Z","iopub.execute_input":"2026-10-02T23:27:42.626399Z","iopub.status.idle":"2026-10-02T23:27:42.631853Z","shell.execute_reply.started":"2026-10-02T23:27:42.626341Z","shell.execute_reply":"2026-10-02T23:27:42.630835Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# create directories\ndataset_home = 'dataset_dogs_vs_cats/'\nsubdirs = ['train/', 'test/'] #test==>validation\nfor subdir in subdirs:\n    # create label subdirectories\n    labeldirs = ['dogs/', 'cats/']\n    for labldir in labeldirs:\n        newdir = dataset_home + subdir + labldir       \n        makedirs(newdir, exist_ok=True)\n        print(newdir)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:42.633166Z","iopub.execute_input":"2026-10-02T23:27:42.633549Z","iopub.status.idle":"2026-10-02T23:27:42.644602Z","shell.execute_reply.started":"2026-10-02T23:27:42.633516Z","shell.execute_reply":"2026-10-02T23:27:42.643596Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls -l","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:42.645767Z","iopub.execute_input":"2026-10-02T23:27:42.64655Z","iopub.status.idle":"2026-10-02T23:27:43.667874Z","shell.execute_reply.started":"2026-10-02T23:27:42.646526Z","shell.execute_reply":"2026-10-02T23:27:43.666936Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls -l dataset_dogs_vs_cats/train","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:43.669284Z","iopub.execute_input":"2026-10-02T23:27:43.669628Z","iopub.status.idle":"2026-10-02T23:27:44.688202Z","shell.execute_reply.started":"2026-10-02T23:27:43.669599Z","shell.execute_reply":"2026-10-02T23:27:44.687185Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls -l dataset_dogs_vs_cats/test #validation","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:44.689702Z","iopub.execute_input":"2026-10-02T23:27:44.690012Z","iopub.status.idle":"2026-10-02T23:27:45.709634Z","shell.execute_reply.started":"2026-10-02T23:27:44.689986Z","shell.execute_reply":"2026-10-02T23:27:45.70867Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# seed random number generator\nseed(1337)\n# define ratio of pictures to use for validation\nval_ratio = 0.25 #changable\n# copy training dataset images into subdirectories\nsrc_directory = 'train/'\n\nfor file in listdir(src_directory):\n\tsrc = src_directory + '/' + file\n\tdst_dir = 'train/'\n\tif random() < val_ratio:\n\t\tdst_dir = 'test/'\n\tif file.startswith('cat'):\n\t\tdst = dataset_home + dst_dir + 'cats/'  + file\n\t\tcopyfile(src, dst)\n\telif file.startswith('dog'):\n\t\tdst = dataset_home + dst_dir + 'dogs/'  + file\n\t\tcopyfile(src, dst)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:45.711008Z","iopub.execute_input":"2026-10-02T23:27:45.711758Z","iopub.status.idle":"2026-10-02T23:27:48.822437Z","shell.execute_reply.started":"2026-10-02T23:27:45.711729Z","shell.execute_reply":"2026-10-02T23:27:48.821278Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(src)\nprint(dst)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:48.823721Z","iopub.execute_input":"2026-10-02T23:27:48.82473Z","iopub.status.idle":"2026-10-02T23:27:48.830061Z","shell.execute_reply.started":"2026-10-02T23:27:48.824703Z","shell.execute_reply":"2026-10-02T23:27:48.829078Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!ls -l dataset_dogs_vs_cats/train/dogs | wc -l\n!ls -l dataset_dogs_vs_cats/train/cats | wc -l","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:48.831129Z","iopub.execute_input":"2026-10-02T23:27:48.831353Z","iopub.status.idle":"2026-10-02T23:27:51.056409Z","shell.execute_reply.started":"2026-10-02T23:27:48.831333Z","shell.execute_reply":"2026-10-02T23:27:51.055124Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#validation set\n!ls -l dataset_dogs_vs_cats/test/dogs | wc -l\n!ls -l dataset_dogs_vs_cats/test/cats | wc -l","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:51.05832Z","iopub.execute_input":"2026-10-02T23:27:51.058736Z","iopub.status.idle":"2026-10-02T23:27:53.230328Z","shell.execute_reply.started":"2026-10-02T23:27:51.058694Z","shell.execute_reply":"2026-10-02T23:27:53.229312Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip -q install livelossplot==0.5.5","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:27:53.231923Z","iopub.execute_input":"2026-10-02T23:27:53.232455Z","iopub.status.idle":"2026-10-02T23:28:01.521833Z","shell.execute_reply.started":"2026-10-02T23:27:53.232411Z","shell.execute_reply":"2026-10-02T23:28:01.520482Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from livelossplot import PlotLossesKeras","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.523498Z","iopub.execute_input":"2026-10-02T23:28:01.524187Z","iopub.status.idle":"2026-10-02T23:28:01.529461Z","shell.execute_reply.started":"2026-10-02T23:28:01.524158Z","shell.execute_reply":"2026-10-02T23:28:01.528542Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Prepare Final Dataset","metadata":{}},{"cell_type":"code","source":"# https://keras.io/api/applications/","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.530662Z","iopub.execute_input":"2026-10-02T23:28:01.530962Z","iopub.status.idle":"2026-10-02T23:28:01.542852Z","shell.execute_reply.started":"2026-10-02T23:28:01.530932Z","shell.execute_reply":"2026-10-02T23:28:01.541925Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# baseline model for the dogs vs cats dataset\nimport sys\nfrom matplotlib import pyplot\nfrom keras.utils import to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.optimizers import SGD\nfrom keras.preprocessing.image import ImageDataGenerator","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.543935Z","iopub.execute_input":"2026-10-02T23:28:01.544299Z","iopub.status.idle":"2026-10-02T23:28:01.556042Z","shell.execute_reply.started":"2026-10-02T23:28:01.544278Z","shell.execute_reply":"2026-10-02T23:28:01.555203Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# define cnn model\ndef define_model_1():\n\tmodel = Sequential()\n\tmodel.add(Conv2D(32, (3, 3), activation='relu',\n                     kernel_initializer='he_uniform',\n                     padding='same',\n                     input_shape=(200, 200, 3))\n             )\n\tmodel.add(MaxPooling2D((2, 2)))\n\tmodel.add(Flatten())\n\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n\tmodel.add(Dense(1, activation='sigmoid'))\n\t# compile model\n\topt = SGD(lr=0.001, momentum=0.9)\n\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n\treturn model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.557283Z","iopub.execute_input":"2026-10-02T23:28:01.55765Z","iopub.status.idle":"2026-10-02T23:28:01.568512Z","shell.execute_reply.started":"2026-10-02T23:28:01.557616Z","shell.execute_reply":"2026-10-02T23:28:01.567714Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model_1 = define_model_1()\nprint(model_1.summary())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.569613Z","iopub.execute_input":"2026-10-02T23:28:01.569907Z","iopub.status.idle":"2026-10-02T23:28:01.643573Z","shell.execute_reply.started":"2026-10-02T23:28:01.569878Z","shell.execute_reply":"2026-10-02T23:28:01.642807Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plot diagnostic learning curves\ndef summarize_diagnostics(history):\n    # plot loss\n    pyplot.subplot(211)\n    pyplot.title('Cross Entropy Loss')\n    pyplot.plot(history.history['loss'], color='blue', label='train')\n    pyplot.plot(history.history['val_loss'], color='orange', label='test')\n    # plot accuracy\n    pyplot.subplot(212)\n    pyplot.title('Classification Accuracy')\n    pyplot.plot(history.history['accuracy'], color='blue', label='train')\n    pyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n    pyplot.show()\n    \n    # save plot to file\n    filename = sys.argv[0].split('/')[-1]\n    pyplot.savefig(filename + '_plot.png') \n    pyplot.close()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.64444Z","iopub.execute_input":"2026-10-02T23:28:01.644641Z","iopub.status.idle":"2026-10-02T23:28:01.651211Z","shell.execute_reply.started":"2026-10-02T23:28:01.644622Z","shell.execute_reply":"2026-10-02T23:28:01.65042Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(32,\n                 (3, 3),\n                 activation='relu',\n                 input_shape=(200, 200, 3)\n                )\n         )\n\nmodel.add(MaxPooling2D((2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(128, activation='relu'))\n\nmodel.add(Dense(2, activation='softmax'))\n\nprint(model.summary())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.652424Z","iopub.execute_input":"2026-10-02T23:28:01.653287Z","iopub.status.idle":"2026-10-02T23:28:01.718986Z","shell.execute_reply.started":"2026-10-02T23:28:01.653262Z","shell.execute_reply":"2026-10-02T23:28:01.718047Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# The Functional API\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense\n\n# explicitly define the input tensor topology\nmy_inputs = Input(shape=(200, 200, 3))\n\n# Construct the forward pass of the computational graph\nx1 = Conv2D(32, (3, 3), activation='relu')(my_inputs)\n\nx2 = MaxPooling2D((2, 2))(x1)\n\nx3 = Flatten()(x2)\n\nx5 = Dense(256, activation='relu')(x3)\n\nx4 = Dense(128, activation='relu')(x5)\n\n# Define the output tensor with the target objective function representation\nmy_outputs = Dense(2, activation='softmax')(x4)\n\n# Instantiate the formal model architecture\nmodel = Model(inputs=my_inputs, outputs=my_outputs)\n\nprint(model.summary())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.719967Z","iopub.execute_input":"2026-10-02T23:28:01.720276Z","iopub.status.idle":"2026-10-02T23:28:01.784844Z","shell.execute_reply.started":"2026-10-02T23:28:01.720255Z","shell.execute_reply":"2026-10-02T23:28:01.78418Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# The Functional API - new cell\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense\n\n# explicitly define the input tensor topology\nmy_inputs = Input(shape=(200, 200, 3))\n\n# Construct the forward pass of the computational graph\nx1 = Conv2D(32, (3, 3), activation='relu')(my_inputs)\n\nx1 = Conv2D(64, (3, 3), activation='relu')(x1)\n\nx2 = MaxPooling2D((2, 2))(x1)\n\nx3 = Flatten()(x2)\n\nx5 = Dense(256, activation='relu')(x3)\n\nx4 = Dense(128, activation='relu')(x5)\n\n# Define the output tensor with the target objective function representation\nmy_outputs = Dense(2, activation='softmax')(x4)\n\n# Instantiate the formal model architecture\nmodel = Model(inputs=my_inputs, outputs=my_outputs)\n\nprint(model.summary())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.786059Z","iopub.execute_input":"2026-10-02T23:28:01.786775Z","iopub.status.idle":"2026-10-02T23:28:01.874474Z","shell.execute_reply.started":"2026-10-02T23:28:01.786743Z","shell.execute_reply":"2026-10-02T23:28:01.873652Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"datagen = ImageDataGenerator(rescale=1.0/255.0)\ntype(datagen)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.87545Z","iopub.execute_input":"2026-10-02T23:28:01.876233Z","iopub.status.idle":"2026-10-02T23:28:01.881553Z","shell.execute_reply.started":"2026-10-02T23:28:01.876208Z","shell.execute_reply":"2026-10-02T23:28:01.880723Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ls dataset_dogs_vs_cats/train/","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:01.882492Z","iopub.execute_input":"2026-10-02T23:28:01.882855Z","iopub.status.idle":"2026-10-02T23:28:02.888352Z","shell.execute_reply.started":"2026-10-02T23:28:01.882806Z","shell.execute_reply":"2026-10-02T23:28:02.887136Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"IMAGE_SIZE = 224 #for vgg16\nBATCH_SIZE = 64\nEPOCHS = 30","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:02.890023Z","iopub.execute_input":"2026-10-02T23:28:02.890415Z","iopub.status.idle":"2026-10-02T23:28:02.895141Z","shell.execute_reply.started":"2026-10-02T23:28:02.890359Z","shell.execute_reply":"2026-10-02T23:28:02.894344Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Use another dataset\n\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\n# 1. Instantiate the generator with the validation_split ratio\ndatagen = ImageDataGenerator(\n    rescale=1.0/255.0,\n    horizontal_flip=True,\n    validation_split=0.2  # Defined here at initialization\n)\n\ntrain_path = '/kaggle/input/datasets/kasikrit/308-456-dogs-vs-cats-dataset/dataset_dogs_vs_cats/train'\n\n# 2. Extract the training subset (80% of data)\ntrain_it = datagen.flow_from_directory(\n    train_path,\n    class_mode='binary',\n    batch_size=BATCH_SIZE,\n    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n    subset='training',     # Explicitly request the training partition\n    shuffle=True           # Stochastic sampling is standard for training\n)\n\n# 3. Extract the validation subset (20% of data)\nval_it = datagen.flow_from_directory(\n    train_path,\n    class_mode='binary',\n    batch_size=BATCH_SIZE,\n    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n    subset='validation',   # Explicitly request the validation partition\n    shuffle=False          # Disable shuffle to maintain index alignment for metrics\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:02.89649Z","iopub.execute_input":"2026-10-02T23:28:02.8968Z","iopub.status.idle":"2026-10-02T23:28:08.62875Z","shell.execute_reply.started":"2026-10-02T23:28:02.89677Z","shell.execute_reply":"2026-10-02T23:28:08.628038Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# =============================================================================\n# Dogs vs Cats — data pipeline with a switchable stratified 10% sample\n#\n# Paste each BLOCK into its own notebook cell, in order.\n# To switch corpora, edit exactly one line: DATA_MODE in BLOCK 0.\n# =============================================================================\n\n\n# =============================================================================\n# BLOCK 0 — CONFIG\n# =============================================================================\nimport os\nimport json\nimport math\nimport random\nimport shutil\nimport time\n\nimport numpy as np\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nDATA_MODE       = 'full'   # 'full' | 'sample'   <- the only line you edit\nSAMPLE_FRACTION = 0.15       # ignored when DATA_MODE == 'full'\nVAL_SPLIT       = 0.2\nSEED            = 42\n\nBATCH_SIZE = 32              # keep your existing values\nIMAGE_SIZE = 224\n\nFULL_TRAIN_PATH = ('/kaggle/input/datasets/kasikrit/308-456-dogs-vs-cats-dataset'\n                   '/dataset_dogs_vs_cats/train')\nSAMPLE_ROOT     = '/kaggle/working/sample_train'\nMANIFEST_NAME   = '.sample_manifest.json'\n\nassert DATA_MODE in ('full', 'sample'), f\"DATA_MODE must be 'full' or 'sample', got {DATA_MODE!r}\"\n\n# sorted() matches the alphabetical ordering flow_from_directory uses internally,\n# so passing this as `classes=` locks the mapping to {'cats': 0, 'dogs': 1}.\nCLASS_NAMES = sorted(d for d in os.listdir(FULL_TRAIN_PATH)\n                     if os.path.isdir(os.path.join(FULL_TRAIN_PATH, d)))\n\nprint(f'DATA_MODE      : {DATA_MODE}')\nprint(f'classes        : {CLASS_NAMES}')\nprint(f'seed           : {SEED}')\n\n\n# =============================================================================\n# BLOCK 1 — BUILD THE STRATIFIED SUBSET TREE   (runs only when DATA_MODE == 'sample')\n#\n# Per-class sampling IS the stratification: taking round(N_c * fraction) files from\n# each class directory preserves the original class proportions by construction.\n# The tree is made of symlinks, so it costs no disk and takes seconds. It must live\n# under /kaggle/working because /kaggle/input is read-only.\n# =============================================================================\nIMG_EXT = ('.jpg', '.jpeg', '.png', '.bmp', '.ppm', '.tif', '.tiff', '.gif')\n\n\ndef list_class_files(root, cls):\n    \"\"\"Sorted image filenames for one class, so the listing order is not FS-dependent.\"\"\"\n    d = os.path.join(root, cls)\n    return sorted(f for f in os.listdir(d) if f.lower().endswith(IMG_EXT))\n\n\ndef build_stratified_sample(src_root, dst_root, classes, fraction, seed):\n    manifest_path = os.path.join(dst_root, MANIFEST_NAME)\n    wanted = {'source': src_root, 'fraction': fraction, 'seed': seed, 'classes': list(classes)}\n\n    if os.path.isdir(dst_root):\n        # Safety guard: only ever delete a tree this function created.\n        if not os.path.exists(manifest_path):\n            raise RuntimeError(\n                f'{dst_root} exists but has no {MANIFEST_NAME}; refusing to delete it. '\n                'Point SAMPLE_ROOT somewhere else or remove that directory yourself.')\n        with open(manifest_path) as fh:\n            old = json.load(fh)\n        if all(old.get(k) == v for k, v in wanted.items()):\n            print(f'reusing existing sample at {dst_root}')\n            for cls, c in old['counts'].items():\n                print(f\"  {cls:<10} {c['sampled']:>6} / {c['available']:>6}\")\n            return dst_root\n        print(f'config changed -> rebuilding {dst_root}')\n        shutil.rmtree(dst_root)\n\n    rng = random.Random(seed)          # one stream, so the whole draw is reproducible\n    counts, symlinked = {}, True\n\n    for cls in classes:\n        files = list_class_files(src_root, cls)\n        n = max(1, round(len(files) * fraction))\n        picked = rng.sample(files, n)\n\n        outdir = os.path.join(dst_root, cls)\n        os.makedirs(outdir, exist_ok=True)\n        for fname in picked:\n            # abspath matters: a relative symlink target is resolved relative to the\n            # LINK's directory, not the cwd, which would silently produce dead links.\n            src = os.path.abspath(os.path.join(src_root, cls, fname))\n            dst = os.path.join(outdir, fname)\n            try:\n                os.symlink(src, dst)\n            except (OSError, NotImplementedError, AttributeError):\n                shutil.copy2(src, dst)  # filesystem without symlink support\n                symlinked = False\n\n        counts[cls] = {'sampled': n, 'available': len(files)}\n        print(f'  {cls:<10} {n:>6} / {len(files):>6}  ({n / len(files):.1%})')\n\n    manifest = dict(wanted,\n                    counts=counts,\n                    symlinked=symlinked,\n                    created=time.strftime('%Y-%m-%d %H:%M:%S'))\n    with open(manifest_path, 'w') as fh:\n        json.dump(manifest, fh, indent=2)\n\n    print(f'built {dst_root}  (symlinked={symlinked})')\n    return dst_root\n\n\nif DATA_MODE == 'sample':\n    build_stratified_sample(FULL_TRAIN_PATH, SAMPLE_ROOT,\n                            CLASS_NAMES, SAMPLE_FRACTION, SEED)\nelse:\n    print('DATA_MODE=full -> skipping sample build')\n\n\n# =============================================================================\n# BLOCK 2 — SELECT THE ACTIVE CORPUS\n# Everything downstream reads TRAIN_DIR and is identical between the two modes.\n# =============================================================================\nTRAIN_DIR = SAMPLE_ROOT if DATA_MODE == 'sample' else FULL_TRAIN_PATH\nprint(f'TRAIN_DIR = {TRAIN_DIR}')\n\n\n# =============================================================================\n# BLOCK 3 — TWO GENERATORS\n#\n# Augmentation on train only. The original code shared one generator, so validation\n# images were randomly flipped every epoch: val metrics wobbled for no modelling\n# reason and the confusion matrix was computed on augmented inputs.\n#\n# Both generators MUST carry the same validation_split. flow_from_directory\n# partitions by slicing the sorted filename list *within each class directory*, so\n# the 80/20 split is itself stratified, is deterministic, and is unaffected by\n# `seed` — two separate instances therefore yield identical, non-overlapping\n# partitions. BLOCK 5 asserts this rather than trusting it.\n# =============================================================================\ntrain_datagen = ImageDataGenerator(\n    rescale=1.0 / 255.0,\n    horizontal_flip=True,\n    validation_split=VAL_SPLIT,\n)\n\nval_datagen = ImageDataGenerator(\n    rescale=1.0 / 255.0,          # no augmentation\n    validation_split=VAL_SPLIT,\n)\n\n\n# =============================================================================\n# BLOCK 4 — THE TWO ITERATORS\n# =============================================================================\ntrain_it = train_datagen.flow_from_directory(\n    TRAIN_DIR,\n    classes=CLASS_NAMES,          # lock the label mapping explicitly\n    class_mode='binary',\n    batch_size=BATCH_SIZE,\n    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n    subset='training',\n    shuffle=True,\n    seed=SEED,\n)\n\nval_it = val_datagen.flow_from_directory(\n    TRAIN_DIR,\n    classes=CLASS_NAMES,\n    class_mode='binary',\n    batch_size=BATCH_SIZE,\n    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n    subset='validation',\n    shuffle=False,                # keep index alignment for the confusion matrix\n)\n\n\n# =============================================================================\n# BLOCK 5 — VERIFICATION (print + assert)\n# =============================================================================\ndef class_counts(it):\n    n = np.bincount(it.classes, minlength=len(CLASS_NAMES))\n    return {cls: int(c) for cls, c in zip(CLASS_NAMES, n)}\n\n\nfull_total   = sum(len(list_class_files(FULL_TRAIN_PATH, c)) for c in CLASS_NAMES)\nactive_total = sum(len(list_class_files(TRAIN_DIR, c)) for c in CLASS_NAMES)\n\nprint(f'class_indices  : {train_it.class_indices}')\nprint(f'full train set : {full_total}')\nprint(f'active corpus  : {active_total}  ({active_total / full_total:.1%} of full)')\nprint(f'train          : {train_it.samples:>6}  {class_counts(train_it)}')\nprint(f'val            : {val_it.samples:>6}  {class_counts(val_it)}')\n\nassert train_it.class_indices == {c: i for i, c in enumerate(CLASS_NAMES)}\nassert val_it.class_indices == train_it.class_indices\nassert not (set(train_it.filenames) & set(val_it.filenames)), 'train/val leakage'\nassert train_it.samples + val_it.samples == active_total, 'iterators lost files'\n\n# class balance must survive the sampling\nfor cls, c in class_counts(train_it).items():\n    share = c / train_it.samples\n    assert abs(share - 1 / len(CLASS_NAMES)) < 0.05, f'{cls} is imbalanced in train: {share:.1%}'\n\nprint('all checks passed')\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:08.62988Z","iopub.execute_input":"2026-10-02T23:28:08.630167Z","iopub.status.idle":"2026-10-02T23:28:09.159238Z","shell.execute_reply.started":"2026-10-02T23:28:08.630144Z","shell.execute_reply":"2026-10-02T23:28:09.158445Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(train_it), len(train_it.classes), train_it.classes[:10]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:09.167048Z","iopub.execute_input":"2026-10-02T23:28:09.16743Z","iopub.status.idle":"2026-10-02T23:28:09.174361Z","shell.execute_reply.started":"2026-10-02T23:28:09.167364Z","shell.execute_reply":"2026-10-02T23:28:09.173458Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(val_it), len(val_it.classes), val_it.classes[-10:]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:09.175534Z","iopub.execute_input":"2026-10-02T23:28:09.175846Z","iopub.status.idle":"2026-10-02T23:28:09.189369Z","shell.execute_reply.started":"2026-10-02T23:28:09.175814Z","shell.execute_reply":"2026-10-02T23:28:09.18845Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# fit model\n# model_1.reset_states()\n# history_1 = model_1.fit(\n#     train_it,\n#     steps_per_epoch=len(train_it),\n#     validation_data=val_it,\n#     validation_steps=len(val_it),\n#     epochs=EPOCHS,\n#     verbose=2,\n#     callbacks = [PlotLossesKeras()]\n#     )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:09.19053Z","iopub.execute_input":"2026-10-02T23:28:09.19087Z","iopub.status.idle":"2026-10-02T23:28:09.201544Z","shell.execute_reply.started":"2026-10-02T23:28:09.190847Z","shell.execute_reply":"2026-10-02T23:28:09.200679Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# learning curves\n# summarize_diagnostics(history_1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:09.202629Z","iopub.execute_input":"2026-10-02T23:28:09.202936Z","iopub.status.idle":"2026-10-02T23:28:09.21483Z","shell.execute_reply.started":"2026-10-02T23:28:09.202889Z","shell.execute_reply":"2026-10-02T23:28:09.213856Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# evaluate model\n# _, acc = model_1.evaluate(val_it, steps=len(val_it), verbose=1)\n# print('> %.3f' % (acc * 100.0))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:09.216158Z","iopub.execute_input":"2026-10-02T23:28:09.216518Z","iopub.status.idle":"2026-10-02T23:28:09.229192Z","shell.execute_reply.started":"2026-10-02T23:28:09.216484Z","shell.execute_reply":"2026-10-02T23:28:09.228441Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Create test datagenerator from 308-456 Dogs vs Cats ","metadata":{}},{"cell_type":"code","source":"ls /kaggle/input/datasets/kasikrit/308-456-dogs-vs-cats-dataset/dataset_dogs_vs_cats/test","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:09.230414Z","iopub.execute_input":"2026-10-02T23:28:09.231562Z","iopub.status.idle":"2026-10-02T23:28:10.269057Z","shell.execute_reply.started":"2026-10-02T23:28:09.231536Z","shell.execute_reply":"2026-10-02T23:28:10.26815Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport pandas as pd\nfrom glob import glob\n\n# Define the image folder path\ntest_path = '/kaggle/input/datasets/kasikrit/308-456-dogs-vs-cats-dataset/dataset_dogs_vs_cats/test'\n\nprint(test_path)\n# # List all .jpg files in the directory (adjust the pattern if needed)\n# image_files = glob(os.path.join(image_folder, '*.jpg'))\n\n# # Check if images were found\n# if not image_files:\n#     print(\"No images found. Please verify the directory and file extensions.\")\n    \n# # Create a DataFrame with the file paths\n# test_df = pd.DataFrame({'filename': image_files})\n\n# If your use-case doesn't require labels (for example, during testing), you can leave out the label column.\n# Otherwise, if you need a dummy label, you can add one:\n# df['class'] = 'dummy_label'\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.270602Z","iopub.execute_input":"2026-10-02T23:28:10.270919Z","iopub.status.idle":"2026-10-02T23:28:10.276896Z","shell.execute_reply.started":"2026-10-02T23:28:10.270892Z","shell.execute_reply":"2026-10-02T23:28:10.275842Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# test_df.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.27816Z","iopub.execute_input":"2026-10-02T23:28:10.279896Z","iopub.status.idle":"2026-10-02T23:28:10.289884Z","shell.execute_reply.started":"2026-10-02T23:28:10.279855Z","shell.execute_reply":"2026-10-02T23:28:10.289121Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# image_files[:5], type(image_files), len(image_files)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.290751Z","iopub.execute_input":"2026-10-02T23:28:10.29104Z","iopub.status.idle":"2026-10-02T23:28:10.301101Z","shell.execute_reply.started":"2026-10-02T23:28:10.291009Z","shell.execute_reply":"2026-10-02T23:28:10.300271Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# test_df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.302158Z","iopub.execute_input":"2026-10-02T23:28:10.302551Z","iopub.status.idle":"2026-10-02T23:28:10.308545Z","shell.execute_reply.started":"2026-10-02T23:28:10.302499Z","shell.execute_reply":"2026-10-02T23:28:10.307762Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# test_df.loc[1]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.309504Z","iopub.execute_input":"2026-10-02T23:28:10.310235Z","iopub.status.idle":"2026-10-02T23:28:10.318536Z","shell.execute_reply.started":"2026-10-02T23:28:10.310212Z","shell.execute_reply":"2026-10-02T23:28:10.31787Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# test_df.sample(5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.319734Z","iopub.execute_input":"2026-10-02T23:28:10.319933Z","iopub.status.idle":"2026-10-02T23:28:10.331175Z","shell.execute_reply.started":"2026-10-02T23:28:10.319915Z","shell.execute_reply":"2026-10-02T23:28:10.330414Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# # Create test data generator\n# from tensorflow.keras.preprocessing.image import ImageDataGenerator\n\n# # Initialize your ImageDataGenerator (modify parameters as needed)\n# datagen_test = ImageDataGenerator(rescale=1./255,\n#                                  vertical_flip=True)\n\n# # Create the data generator using the DataFrame\n# test_it = datagen_test.flow_from_dataframe(\n#     dataframe=test_df,\n#     x_col='filename',\n#     y_col=None,            # No labels for testing, so set to None\n#     target_size=IMAGE_SIZE,\n#     batch_size=BATCH_SIZE,\n#     class_mode=None,       # Since there are no labels\n#     # shuffle=True,         # Usually testing data should not be shuffled\n#     shuffle=False,\n# )\n\n# type(test_it)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.332257Z","iopub.execute_input":"2026-10-02T23:28:10.332584Z","iopub.status.idle":"2026-10-02T23:28:10.33983Z","shell.execute_reply.started":"2026-10-02T23:28:10.332555Z","shell.execute_reply":"2026-10-02T23:28:10.338921Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"IMAGE_SIZE, BATCH_SIZE","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.34074Z","iopub.execute_input":"2026-10-02T23:28:10.340986Z","iopub.status.idle":"2026-10-02T23:28:10.359238Z","shell.execute_reply.started":"2026-10-02T23:28:10.340965Z","shell.execute_reply":"2026-10-02T23:28:10.358109Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 1. Instantiate the generator with the validation_split ratio\ntest_datagen = ImageDataGenerator(\n    rescale=1.0/255.0,\n    horizontal_flip=True\n)\n\ntest_it = test_datagen.flow_from_directory(\n    test_path,\n    class_mode='binary',\n    batch_size=BATCH_SIZE,\n    target_size=(IMAGE_SIZE, IMAGE_SIZE)\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:10.360466Z","iopub.execute_input":"2026-10-02T23:28:10.360939Z","iopub.status.idle":"2026-10-02T23:28:21.099606Z","shell.execute_reply.started":"2026-10-02T23:28:10.360904Z","shell.execute_reply":"2026-10-02T23:28:21.098825Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plot_exmaple(images, labels):\n    # Assuming images shape is (64, height, width, channels)\n    fig, axes = plt.subplots(8, 8, figsize=(16, 16))\n    axes = axes.flatten()  # Flatten the 8x8 grid into a 1D array for easy iteration\n\n    for i, ax in enumerate(axes):\n        if i < len(images):\n            ax.imshow(images[i])\n            ax.set_title(str(labels[i]))  # Fixed: set_title instead of title\n            # ax.axis(\"off\")  # Turn off axis ticks/labels for clarity\n            ax.axis(\"on\")\n        else:\n            ax.axis(\"off\")  # Hide unused subplots if images < 64\n\n    plt.tight_layout()\n    plt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:21.100782Z","iopub.execute_input":"2026-10-02T23:28:21.101581Z","iopub.status.idle":"2026-10-02T23:28:21.107053Z","shell.execute_reply.started":"2026-10-02T23:28:21.101548Z","shell.execute_reply":"2026-10-02T23:28:21.106077Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_images, train_labels = train_it.__next__()\nprint(train_images.shape, len(train_labels))\nplot_exmaple(train_images, train_labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:21.108421Z","iopub.execute_input":"2026-10-02T23:28:21.109216Z","iopub.status.idle":"2026-10-02T23:28:26.920285Z","shell.execute_reply.started":"2026-10-02T23:28:21.109193Z","shell.execute_reply":"2026-10-02T23:28:26.919266Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"val_images, val_labels = val_it.__next__()\nval_images.shape\nplot_exmaple(val_images, val_labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:26.921428Z","iopub.execute_input":"2026-10-02T23:28:26.921775Z","iopub.status.idle":"2026-10-02T23:28:32.236673Z","shell.execute_reply.started":"2026-10-02T23:28:26.921752Z","shell.execute_reply":"2026-10-02T23:28:32.235697Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def plot_exmaple_test(images, indices):\n    # Assuming images shape is (64, height, width, channels)\n    fig, axes = plt.subplots(8, 8, figsize=(16, 16))\n    axes = axes.flatten()  # Flatten the 8x8 grid into a 1D array for easy iteration\n\n    for i, ax in enumerate(axes):\n        if i < len(images):\n            ax.imshow(images[i])\n            ax.set_title(str(indices[i]))  # ตั้งค่า title ด้วย index จาก list/array indices\n            ax.axis(\"off\")  # Turn off axis ticks/labels for clarity\n        else:\n            ax.axis(\"off\")  # Hide unused subplots if images < 64\n\n    plt.tight_layout()\n    plt.show()","metadata":{"trusted":true,"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2026-10-02T23:28:32.237872Z","iopub.execute_input":"2026-10-02T23:28:32.238209Z","iopub.status.idle":"2026-10-02T23:28:32.245409Z","shell.execute_reply.started":"2026-10-02T23:28:32.238169Z","shell.execute_reply":"2026-10-02T23:28:32.244434Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_images, test_labels = test_it.__next__()  # หรือ next(test_it)\nprint(f\"{test_images.shape=}, {test_labels.shape}\")\n\n# สร้าง list เลขดัชนีตามจำนวนภาพที่มี\nindices = list(range(len(test_images)))\n\nplot_exmaple(test_images, test_labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:32.246988Z","iopub.execute_input":"2026-10-02T23:28:32.24775Z","iopub.status.idle":"2026-10-02T23:28:38.113406Z","shell.execute_reply.started":"2026-10-02T23:28:32.247722Z","shell.execute_reply":"2026-10-02T23:28:38.11223Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# import numpy as np\n\n\n# # 1. สุ่ม index มา 10 ตัวจากชุดข้อมูล\n# sample_indices = np.random.choice(len(test_images), size=10, replace=False)\n# sample_images = test_images[sample_indices]\n\n# # 2. Predict \n# predictions = model_1.predict(sample_images)\n# predicted_classes = np.argmax(predictions, axis=1)  # ดึง class ที่ได้คะแนนสูงสุด\n\n# # 3. แสดงผล 10 ภาพ (ปรับเป็น grid 2x5)\n# fig, axes = plt.subplots(2, 5, figsize=(15, 6))\n# axes = axes.flatten()\n\n# for i, ax in enumerate(axes):\n#     ax.imshow(sample_images[i])\n#     # แสดง Index เดิม และ Class ที่ทายได้\n#     ax.set_title(f\"Idx: {sample_indices[i]}\\nPred: {predicted_classes[i]}\")\n#     ax.axis(\"off\")\n\n# plt.tight_layout()\n# plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:38.114644Z","iopub.execute_input":"2026-10-02T23:28:38.114938Z","iopub.status.idle":"2026-10-02T23:28:38.119266Z","shell.execute_reply.started":"2026-10-02T23:28:38.114913Z","shell.execute_reply":"2026-10-02T23:28:38.118343Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 2026-09-13\n# Transfer Learning","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:38.120549Z","iopub.execute_input":"2026-10-02T23:28:38.120847Z","iopub.status.idle":"2026-10-02T23:28:38.139217Z","shell.execute_reply.started":"2026-10-02T23:28:38.120824Z","shell.execute_reply":"2026-10-02T23:28:38.138329Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"id(train_it), id(val_it), id(test_it)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:38.140104Z","iopub.execute_input":"2026-10-02T23:28:38.140363Z","iopub.status.idle":"2026-10-02T23:28:38.153361Z","shell.execute_reply.started":"2026-10-02T23:28:38.140342Z","shell.execute_reply":"2026-10-02T23:28:38.152668Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import sys\nfrom matplotlib import pyplot\nfrom keras.utils import to_categorical\nfrom keras.applications.vgg16 import VGG16\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D, Dropout \nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import ImageDataGenerator\n\n# Hyperparameter => changable\nIMAGE_SIZE = 224\nBATCH_SIZE = 64\nEPOCHS = 20\n\n# define cnn model\ndef define_model_vgg16():\n    # 1. โหลด Base Model\n    base_model_vgg = VGG16(include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n    \n    # 2. ปลดล็อค Base Model ให้เทรนได้ (Fine-Tuning)\n    base_model_vgg.trainable = True\n    \n    # แช่แข็งเลเยอร์ตั้งแต่แรก แต่ \"เปิด\" ไว้เฉพาะ 4 เลเยอร์สุดท้าย (Block 5)\n    for layer in base_model_vgg.layers[:-4]:\n        layer.trainable = False\n        \n    # 3. สร้างชั้น Classifier ใหม่\n    x = GlobalAveragePooling2D()(base_model_vgg.output)\n    x = Dropout(0.5)(x) # กัน Overfitting\n    output_vgg = Dense(1, activation='sigmoid')(x)\n    \n    # 4. ประกอบร่างโมเดล\n    model = Model(inputs=base_model_vgg.inputs, outputs=output_vgg)\n    \n    # 5. Compile ใช้ Learning Rate ต่ำมาก (0.00005) เพื่อไม่ให้น้ำหนักเดิมพัง\n    opt = Adam(learning_rate=0.00005)\n    model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n    return model\n\nmodel_vgg16 = define_model_vgg16()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:38.154543Z","iopub.execute_input":"2026-10-02T23:28:38.154746Z","iopub.status.idle":"2026-10-02T23:28:39.144975Z","shell.execute_reply.started":"2026-10-02T23:28:38.154726Z","shell.execute_reply":"2026-10-02T23:28:39.143758Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load model\nmodel_vgg16_top = VGG16(include_top=True,\n                        input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)\n                       )\nmodel_vgg16_top.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:39.148042Z","iopub.execute_input":"2026-10-02T23:28:39.148284Z","iopub.status.idle":"2026-10-02T23:28:43.918794Z","shell.execute_reply.started":"2026-10-02T23:28:39.148262Z","shell.execute_reply":"2026-10-02T23:28:43.916327Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load model\nmodel_16_feature_extractor = VGG16(include_top=False,\n                                   input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)\n                                  )\nmodel_16_feature_extractor.summary() #feature extractor as a backbone","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:43.919986Z","iopub.execute_input":"2026-10-02T23:28:43.920345Z","iopub.status.idle":"2026-10-02T23:28:44.277737Z","shell.execute_reply.started":"2026-10-02T23:28:43.920322Z","shell.execute_reply":"2026-10-02T23:28:44.274824Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for index, layer in enumerate(model_16_feature_extractor.layers):\n\t\tprint(index, \n              layer.name,\n              layer.trainable,\n              layer.output.shape\n        )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.278947Z","iopub.execute_input":"2026-10-02T23:28:44.279542Z","iopub.status.idle":"2026-10-02T23:28:44.285021Z","shell.execute_reply.started":"2026-10-02T23:28:44.279516Z","shell.execute_reply":"2026-10-02T23:28:44.283989Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for index, layer in enumerate(model_16_feature_extractor.layers):\n    if index <= 14:\n        layer.trainable = False\n\nfor index, layer in enumerate(model_16_feature_extractor.layers):\n    print(index, layer.name, layer.trainable)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.286226Z","iopub.execute_input":"2026-10-02T23:28:44.286754Z","iopub.status.idle":"2026-10-02T23:28:44.294696Z","shell.execute_reply.started":"2026-10-02T23:28:44.28673Z","shell.execute_reply":"2026-10-02T23:28:44.293824Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model_16_feature_extractor.layers[-1].output","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.297002Z","iopub.execute_input":"2026-10-02T23:28:44.297331Z","iopub.status.idle":"2026-10-02T23:28:44.309297Z","shell.execute_reply.started":"2026-10-02T23:28:44.297308Z","shell.execute_reply":"2026-10-02T23:28:44.308419Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Use functional API\n#flat1 = Flatten()(model_16_feature_extractor.layers[-1].output)\n#print(flat1.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.31055Z","iopub.execute_input":"2026-10-02T23:28:44.310837Z","iopub.status.idle":"2026-10-02T23:28:44.320958Z","shell.execute_reply.started":"2026-10-02T23:28:44.310808Z","shell.execute_reply":"2026-10-02T23:28:44.320261Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#layer1 = Dense(256, activation='relu', kernel_initializer='he_uniform')(flat1)\n#print(layer1.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.321767Z","iopub.execute_input":"2026-10-02T23:28:44.322087Z","iopub.status.idle":"2026-10-02T23:28:44.332586Z","shell.execute_reply.started":"2026-10-02T23:28:44.322065Z","shell.execute_reply":"2026-10-02T23:28:44.331823Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#layer2 = Dense(128, activation='relu', kernel_initializer='he_uniform')(layer1)\n#print(layer2.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.333829Z","iopub.execute_input":"2026-10-02T23:28:44.33415Z","iopub.status.idle":"2026-10-02T23:28:44.341217Z","shell.execute_reply.started":"2026-10-02T23:28:44.334128Z","shell.execute_reply":"2026-10-02T23:28:44.340059Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#output1 = Dense(2, activation='softmax')(layer2)\n#print(f\"{output1.shape=}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.342444Z","iopub.execute_input":"2026-10-02T23:28:44.342834Z","iopub.status.idle":"2026-10-02T23:28:44.353062Z","shell.execute_reply.started":"2026-10-02T23:28:44.34281Z","shell.execute_reply":"2026-10-02T23:28:44.352147Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#output2 = Dense(1, activation='sigmoid')(layer2)\n#print(output2.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.354162Z","iopub.execute_input":"2026-10-02T23:28:44.354512Z","iopub.status.idle":"2026-10-02T23:28:44.364856Z","shell.execute_reply.started":"2026-10-02T23:28:44.354489Z","shell.execute_reply":"2026-10-02T23:28:44.363916Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# define new model to be trained\n#model_vgg16 = Model(inputs=model_16_feature_extractor.inputs, outputs=output2)\n# compile model\n#opt = SGD(lr=0.001, momentum=0.9)\n\n#model_vgg16.compile(optimizer=opt,\n #                   loss='binary_crossentropy',\n #                   metrics=['accuracy']\n  #                 )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.365792Z","iopub.execute_input":"2026-10-02T23:28:44.3661Z","iopub.status.idle":"2026-10-02T23:28:44.375199Z","shell.execute_reply.started":"2026-10-02T23:28:44.366078Z","shell.execute_reply":"2026-10-02T23:28:44.37443Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#for index, layer in enumerate(model_vgg16.layers):\n #   print(index, layer.name, layer.trainable)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.376277Z","iopub.execute_input":"2026-10-02T23:28:44.376598Z","iopub.status.idle":"2026-10-02T23:28:44.387859Z","shell.execute_reply.started":"2026-10-02T23:28:44.376569Z","shell.execute_reply":"2026-10-02T23:28:44.386833Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#print(model_vgg16.summary())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.388976Z","iopub.execute_input":"2026-10-02T23:28:44.389283Z","iopub.status.idle":"2026-10-02T23:28:44.443094Z","shell.execute_reply.started":"2026-10-02T23:28:44.389254Z","shell.execute_reply":"2026-10-02T23:28:44.442196Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"id(train_it), id(val_it)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.444226Z","iopub.execute_input":"2026-10-02T23:28:44.444607Z","iopub.status.idle":"2026-10-02T23:28:44.453582Z","shell.execute_reply.started":"2026-10-02T23:28:44.444574Z","shell.execute_reply":"2026-10-02T23:28:44.452569Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# del ตัวแปร #ลบตัวแปรออกจากหน่วยความจำ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.45458Z","iopub.execute_input":"2026-10-02T23:28:44.456215Z","iopub.status.idle":"2026-10-02T23:28:44.464366Z","shell.execute_reply.started":"2026-10-02T23:28:44.456192Z","shell.execute_reply":"2026-10-02T23:28:44.463509Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# model_vgg16 = define_model_vgg16()\n# print(model_vgg16)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.465449Z","iopub.execute_input":"2026-10-02T23:28:44.465779Z","iopub.status.idle":"2026-10-02T23:28:44.475124Z","shell.execute_reply.started":"2026-10-02T23:28:44.465757Z","shell.execute_reply":"2026-10-02T23:28:44.474091Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# fit model\nimport tensorflow as tf\n\n# 1. Verify that TensorFlow can communicate with the GPU hardware\nphysical_devices = tf.config.list_physical_devices('GPU')\nprint(f\"Hardware status: {len(physical_devices)} GPU(s) detected.\")\n\nif len(physical_devices) > 0:\n    # Optional but highly recommended: Prevent TF from pre-allocating 100% of VRAM\n    try:\n        tf.config.experimental.set_memory_growth(physical_devices[0], True)\n    except Exception as e:\n        print(f\"Memory growth configuration failed: {e}\")\n\n    # 2. Explicitly bind the execution to the primary GPU\n    with tf.device('/GPU:0'):\n        \n        print(f\"Model ID: {id(model_vgg16)}\")\n        \n        # Note: reset_states() is strictly for stateful RNNs (LSTMs/GRUs). \n        # For a feed-forward CNN like VGG16, this method has no effect and can be removed.\n        \n        model_vgg16_history = model_vgg16.fit(\n            train_it, \n            steps_per_epoch=len(train_it),\n            validation_data=val_it,\n            validation_steps=len(val_it),\n            epochs=20, \n            verbose=1,\n            callbacks=[PlotLossesKeras()]\n        )\nelse:\n    print(\"WARNING: No GPU detected. Execution will proceed on the CPU, which will be significantly slower.\")\n    # Fallback to standard execution\n    model_vgg16_history = model_vgg16.fit(\n        train_it, \n        steps_per_epoch=len(train_it),\n        validation_data=val_it,\n        validation_steps=len(val_it),\n        epochs=2, \n        verbose=1,\n        callbacks=[PlotLossesKeras()]\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-02T23:28:44.476142Z","iopub.execute_input":"2026-10-02T23:28:44.477902Z","iopub.status.idle":"2026-10-03T00:02:33.060169Z","shell.execute_reply.started":"2026-10-02T23:28:44.477868Z","shell.execute_reply":"2026-10-03T00:02:33.059425Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# learning curves\nsummarize_diagnostics(model_vgg16_history)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:02:33.061507Z","iopub.execute_input":"2026-10-03T00:02:33.062074Z","iopub.status.idle":"2026-10-03T00:02:33.350776Z","shell.execute_reply.started":"2026-10-03T00:02:33.062049Z","shell.execute_reply":"2026-10-03T00:02:33.35012Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# evaluate model\n_, acc = model_vgg16.evaluate(val_it, steps=len(val_it), verbose=1)\nprint('Val: > %.3f' % (acc * 100.0))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:02:33.351857Z","iopub.execute_input":"2026-10-03T00:02:33.352208Z","iopub.status.idle":"2026-10-03T00:02:50.209329Z","shell.execute_reply.started":"2026-10-03T00:02:33.352181Z","shell.execute_reply":"2026-10-03T00:02:50.208614Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# evaluate model for the test set\n_, test_acc = model_vgg16.evaluate(test_it, steps=len(test_it), verbose=1)\nprint('Test: > %.3f' % (test_acc * 100.0))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:02:50.210579Z","iopub.execute_input":"2026-10-03T00:02:50.21093Z","iopub.status.idle":"2026-10-03T00:04:38.777994Z","shell.execute_reply.started":"2026-10-03T00:02:50.210898Z","shell.execute_reply":"2026-10-03T00:04:38.777233Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.metrics import classification_report\n\n# 1. Reset the generator state to ensure alignment from index 0\ntest_it.reset()\n\n# 2. Extract the ordered ground truth labels from the generator\ny_true = test_it.classes\nprint(y_true.shape)\n\n# 3. Execute the forward pass over the entire test dataset\n# Calculate exact steps to prevent infinite looping or partial evaluation\nsteps = np.ceil(test_it.samples / test_it.batch_size)\nprobabilities = model_vgg16.predict(test_it, steps=steps, verbose=1).squeeze()\nprint(probabilities.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:04:38.779039Z","iopub.execute_input":"2026-10-03T00:04:38.779685Z","iopub.status.idle":"2026-10-03T00:05:07.910204Z","shell.execute_reply.started":"2026-10-03T00:04:38.77966Z","shell.execute_reply":"2026-10-03T00:05:07.909176Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"probabilities[:20]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:05:07.911468Z","iopub.execute_input":"2026-10-03T00:05:07.911815Z","iopub.status.idle":"2026-10-03T00:05:07.922605Z","shell.execute_reply.started":"2026-10-03T00:05:07.911783Z","shell.execute_reply":"2026-10-03T00:05:07.921768Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"(probabilities[0] > 0.5).astype(int)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:05:07.923676Z","iopub.execute_input":"2026-10-03T00:05:07.923994Z","iopub.status.idle":"2026-10-03T00:05:07.932803Z","shell.execute_reply.started":"2026-10-03T00:05:07.923972Z","shell.execute_reply":"2026-10-03T00:05:07.9321Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 4. Discretize continuous logistic probabilities into binary class predictions\ny_pred = (probabilities > 0.5).astype(int)\nprint(y_pred.shape)\n\n# 5. Extract dynamic class labels mapping (e.g., {'cats': 0, 'dogs': 1})\n# This ensures the report correctly maps index 0 and 1 to your string labels\ntarget_names = list(test_it.class_indices.keys())\n\n# 6. Compute and output the classification report\nreport = classification_report(y_true, y_pred, target_names=target_names)\nprint(report)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:05:07.933685Z","iopub.execute_input":"2026-10-03T00:05:07.933944Z","iopub.status.idle":"2026-10-03T00:05:07.972414Z","shell.execute_reply.started":"2026-10-03T00:05:07.933899Z","shell.execute_reply":"2026-10-03T00:05:07.971666Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y_true[:10], y_pred[:10]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:05:07.973282Z","iopub.execute_input":"2026-10-03T00:05:07.973883Z","iopub.status.idle":"2026-10-03T00:05:07.979804Z","shell.execute_reply.started":"2026-10-03T00:05:07.973861Z","shell.execute_reply":"2026-10-03T00:05:07.978687Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Predict\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 1. Aggregate sufficient samples from the iterator\nimages_list = []\nlabels_list = []\n\n# Fetch 2 batches (2 * 64 = 128 samples) to ensure we have pool > 100\nfor _ in range(4):\n    batch_images, batch_labels = next(test_it)\n    images_list.append(batch_images)\n    labels_list.append(batch_labels)\n\n# Concatenate into a single tensor\nall_images = np.concatenate(images_list, axis=0)\nall_labels = np.concatenate(labels_list, axis=0)\n\n# 2. Stochastically sample 100 unique indices\nnp.random.seed(42) # For reproducibility\nrandom_indices = np.random.choice(all_images.shape[0], size=100, replace=False)\n\nsample_images = all_images[random_indices]\nsample_labels = all_labels[random_indices]\n\n# 3. Execute the forward pass\n# Squeeze is used to convert shape (100, 1) to (100,)\nprobabilities = model_vgg16.predict(sample_images).squeeze()\n\n# 4. Thresholding (Assuming standard mapping: 0 = Cat, 1 = Dog)\npredictions = (probabilities > 0.5).astype(int)\nclass_mapping = {0: 'Cat', 1: 'Dog'}\n\nprint(\"Done.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:05:07.981012Z","iopub.execute_input":"2026-10-03T00:05:07.981356Z","iopub.status.idle":"2026-10-03T00:05:10.22573Z","shell.execute_reply.started":"2026-10-03T00:05:07.981325Z","shell.execute_reply":"2026-10-03T00:05:10.224893Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Initialize a 10x10 matrix visualization\nfig, axes = plt.subplots(10, 10, figsize=(20, 22))\naxes = axes.flatten()\n\nfor i in range(100):\n    ax = axes[i]\n    \n    # Isolate image tensor. \n    # NOTE: If your VGG16 preprocessor applies mean-centering (caffe style) \n    # instead of standard [0,1] scaling, you must reverse it here for correct plt.imshow() rendering.\n    # We assume standard [0, 1] or [0, 255] spatial tensors below.\n    img = sample_images[i]\n    if img.max() > 1.0 or img.min() < 0:\n        # Simple normalization for display purposes\n        img = (img - img.min()) / (img.max() - img.min())\n        \n    ax.imshow(img)\n    ax.axis('off')\n    \n    # Extract structural labels\n    gt_class = class_mapping[int(sample_labels[i])]\n    pred_class = class_mapping[predictions[i]]\n    prob = probabilities[i]\n    \n    # Apply conditional color coding (Green for True Positives/Negatives, Red for False Positives/Negatives)\n    text_color = 'darkgreen' if gt_class == pred_class else 'darkred'\n    \n    title_str = f\"GT: {gt_class}\\nPred: {pred_class}\\nProb: {prob:.3f}\"\n    ax.set_title(title_str, color=text_color, fontsize=9, pad=4)\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:05:10.226913Z","iopub.execute_input":"2026-10-03T00:05:10.227286Z","iopub.status.idle":"2026-10-03T00:05:18.355782Z","shell.execute_reply.started":"2026-10-03T00:05:10.227262Z","shell.execute_reply":"2026-10-03T00:05:18.354075Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# InceptionV3","metadata":{}},{"cell_type":"code","source":"from keras.applications.vgg19 import VGG19\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D, Dropout\nfrom keras.optimizers import Adam\nfrom livelossplot import PlotLossesKeras\n\n# 1. โหลด Base Model\nbase_model_vgg19 = VGG19(include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\n\n# 2. ปลดล็อค Base Model (Fine-Tuning เลเยอร์ท้ายๆ เหมือน VGG16)\nbase_model_vgg19.trainable = True\nfor layer in base_model_vgg19.layers[:-4]:\n    layer.trainable = False\n        \n# 3. สร้าง Classifier\nx = GlobalAveragePooling2D()(base_model_vgg19.output)\nx = Dropout(0.5)(x) \noutput_vgg19 = Dense(1, activation='sigmoid')(x)\n    \nmodel_vgg19 = Model(inputs=base_model_vgg19.inputs, outputs=output_vgg19)\n\n# 4. Compile (ใช้ Learning Rate 0.00005)\nmodel_vgg19.compile(optimizer=Adam(learning_rate=0.00005), loss='binary_crossentropy', metrics=['accuracy'])\n\nprint(\"กำลัง Train VGG19...\")\nhistory_vgg19 = model_vgg19.fit(\n    train_it, steps_per_epoch=len(train_it),\n    validation_data=val_it, validation_steps=len(val_it),\n    epochs=10, callbacks=[PlotLossesKeras()]\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:30:39.827736Z","iopub.execute_input":"2026-10-03T00:30:39.828069Z","iopub.status.idle":"2026-10-03T00:50:03.30215Z","shell.execute_reply.started":"2026-10-03T00:30:39.828045Z","shell.execute_reply":"2026-10-03T00:50:03.30144Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#model_inceptionv3 = define_model_inceptionv3()\n#print(id(model_inceptionv3))\n# model_inceptionv3.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T01:10:22.289117Z","iopub.execute_input":"2026-10-03T01:10:22.289812Z","iopub.status.idle":"2026-10-03T01:10:22.293716Z","shell.execute_reply.started":"2026-10-03T01:10:22.289779Z","shell.execute_reply":"2026-10-03T01:10:22.29276Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nfrom tensorflow.keras import backend as K\n\ntrainable_params = int(np.sum([K.count_params(w) for w in model_inceptionv3.trainable_weights]))\nprint(trainable_params)\n","metadata":{"trusted":true,"scrolled":true,"execution":{"iopub.status.busy":"2026-10-03T01:10:25.329419Z","iopub.execute_input":"2026-10-03T01:10:25.330237Z","iopub.status.idle":"2026-10-03T01:10:25.360707Z","shell.execute_reply.started":"2026-10-03T01:10:25.330207Z","shell.execute_reply":"2026-10-03T01:10:25.359321Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tensorflow.keras import backend as K\n\nfor w in model_inceptionv3.trainable_weights:\n    print(w.name, K.count_params(w))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.595827Z","iopub.status.idle":"2026-10-03T00:24:43.596098Z","shell.execute_reply.started":"2026-10-03T00:24:43.595967Z","shell.execute_reply":"2026-10-03T00:24:43.59598Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# fit model\nmodel_inceptionv3.reset_states()\nhistory_inceptionv3 = model_inceptionv3.fit(\n    train_it, \n    steps_per_epoch=len(train_it),\n    validation_data=val_it,\n    validation_steps=len(val_it),\n    epochs=3, verbose=1,\n    callbacks = [PlotLossesKeras()]\n    )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.597687Z","iopub.status.idle":"2026-10-03T00:24:43.5981Z","shell.execute_reply.started":"2026-10-03T00:24:43.597888Z","shell.execute_reply":"2026-10-03T00:24:43.597909Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plot_images_with_labels(images, labels, train_log, set_label, save=False):\n    \"\"\"\n    Plots a grid of images with their corresponding labels and saves the figure.\n    \n    Args:\n    - images (list or np.array): List of images to be displayed.\n    - labels (np.array): Array of one-hot encoded labels.\n    - train_log (str): The directory or log name to save the plot.\n    - set_label (str): The specific set label (e.g., \"train\", \"validation\", etc.).\n    - config (dict): Configuration dictionary containing image dimensions.\n    \n    Returns:\n    - None: Displays and saves the plot.\n    \"\"\"\n    # Convert one-hot encoded labels to class indices\n    class_indices = labels\n\n    # Create subplots\n    fig, axes = plt.subplots(3, 4, figsize=(14, 10), dpi=120)\n    axes = axes.flatten()\n\n    for img, label_idx, ax in zip(images, class_indices, axes):\n        # Undo any preprocessing (like rescaling for pre-trained models)\n        if np.max(img) == 1:\n            img = img * 255\n\n        # Display the image\n        img_shape = (224, 224, 3)\n        ax.imshow(img.astype(\"uint8\"))\n        # ax.imshow(img)\n\n        # Set the title to the corresponding class label\n        ax.set_title(f\"Label: {label_idx}\", fontsize=18)\n        ax.axis('on')\n\n    # Set the overall title for the figure\n    plot_title = f\"{train_log}-{set_label}\"\n    plt.suptitle(plot_title, fontsize=18)\n\n    # Show the plot\n    plt.show()\n\n    # Save the figure\n    if save:\n        plot_file = os.path.join(train_log, f\"{plot_title}.png\")\n        fig.savefig(plot_file, dpi=120)\n        print(f'Saved {plot_file}')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.599739Z","iopub.status.idle":"2026-10-03T00:24:43.600155Z","shell.execute_reply.started":"2026-10-03T00:24:43.599938Z","shell.execute_reply":"2026-10-03T00:24:43.599958Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# plot_images_with_labels(images, labels, \"Dog-Cat\", \"Training\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.601305Z","iopub.status.idle":"2026-10-03T00:24:43.601768Z","shell.execute_reply.started":"2026-10-03T00:24:43.601544Z","shell.execute_reply":"2026-10-03T00:24:43.601565Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# for index, layer in enumerate(model.layers):\n#     print(index, layer.output.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.603495Z","iopub.status.idle":"2026-10-03T00:24:43.603906Z","shell.execute_reply.started":"2026-10-03T00:24:43.603693Z","shell.execute_reply":"2026-10-03T00:24:43.603713Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# # mark loaded layers as not trainable\n# for index, layer in enumerate(model.layers[:15]):\n#     print(index, layer.name, layer.output_shape)\n#     layer.trainable = False\n    \n# for index, layer in enumerate(model.layers[15:]):\n#     print(index, layer.name, layer.output_shape)\n#     layer.trainable = True","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.604976Z","iopub.status.idle":"2026-10-03T00:24:43.605409Z","shell.execute_reply.started":"2026-10-03T00:24:43.605169Z","shell.execute_reply":"2026-10-03T00:24:43.605189Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# evaluate model\n_, acc = model_inceptionv3.evaluate(test_it, steps=len(test_it), verbose=1)\nprint('> %.3f' % (acc * 100.0))\n# learning curves\n# summarize_diagnostics(history)\n\n# best_model = 'vgg16_model.hdf5'\n# my_model.save(best_model)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.607121Z","iopub.status.idle":"2026-10-03T00:24:43.607485Z","shell.execute_reply.started":"2026-10-03T00:24:43.607295Z","shell.execute_reply":"2026-10-03T00:24:43.60731Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"images, labels = test_it.__next__()\nimages.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.60859Z","iopub.status.idle":"2026-10-03T00:24:43.609041Z","shell.execute_reply.started":"2026-10-03T00:24:43.608806Z","shell.execute_reply":"2026-10-03T00:24:43.608829Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 5 first img: 1, 0, 1, 1, 0\nimages_to_predict = images[:5]\nimages_to_predict.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.610706Z","iopub.status.idle":"2026-10-03T00:24:43.610967Z","shell.execute_reply.started":"2026-10-03T00:24:43.610842Z","shell.execute_reply":"2026-10-03T00:24:43.610855Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"probs = model_inceptionv3.predict(images_to_predict, verbose=1)\nprobs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.612248Z","iopub.status.idle":"2026-10-03T00:24:43.612682Z","shell.execute_reply.started":"2026-10-03T00:24:43.612465Z","shell.execute_reply":"2026-10-03T00:24:43.612486Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Make Prediction","metadata":{}},{"cell_type":"code","source":"from keras.applications.densenet import DenseNet121\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D, Dropout\nfrom keras.optimizers import Adam\nfrom livelossplot import PlotLossesKeras\n\n# 1. โหลด Base Model และ \"แช่แข็ง 100%\" (ตัวนี้เก่งมาก แช่แข็งก็ทะลุ 97% ครับ)\nbase_model_dense = DenseNet121(include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))\nbase_model_dense.trainable = False\n\n# 2. สร้าง Classifier\nx = GlobalAveragePooling2D()(base_model_dense.output)\nx = Dropout(0.4)(x)\noutput_dense = Dense(1, activation='sigmoid')(x)\n    \nmodel_dense = Model(inputs=base_model_dense.inputs, outputs=output_dense)\n\n# 3. Compile (แช่แข็งไว้ เลยใช้ Learning rate ปกติได้เลย)\nmodel_dense.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n\nprint(\"กำลัง Train DenseNet121...\")\nhistory_dense = model_dense.fit(\n    train_it, steps_per_epoch=len(train_it),\n    validation_data=val_it, validation_steps=len(val_it),\n    epochs=10, callbacks=[PlotLossesKeras()]\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.614195Z","iopub.status.idle":"2026-10-03T00:24:43.614644Z","shell.execute_reply.started":"2026-10-03T00:24:43.61442Z","shell.execute_reply":"2026-10-03T00:24:43.614441Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.metrics import classification_report\n\n# เลือกโมเดลที่จะใช้ประเมิน\nbest_model = model_eff # หรือโมเดลอื่นที่คุณคิดว่าดีที่สุด\n\n# ต้อง reset เสมอก่อนการทำนายเพื่อให้ลำดับถูกต้อง\ntest_it.reset()\ny_true = test_it.classes\n\n# ทำนาย\nprint(\"กำลังประเมินบน Test Set...\")\nsteps = np.ceil(test_it.samples / test_it.batch_size)\nprobabilities = best_model.predict(test_it, steps=steps, verbose=1).squeeze()\ny_pred = (probabilities > 0.5).astype(int)\n\ntarget_names = list(test_it.class_indices.keys())\nreport = classification_report(y_true, y_pred, target_names=target_names, digits=4)\n\nprint(\"\\n=== Classification Report ===\")\nprint(report)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-10-03T00:24:43.615927Z","iopub.status.idle":"2026-10-03T00:24:43.616352Z","shell.execute_reply.started":"2026-10-03T00:24:43.616125Z","shell.execute_reply":"2026-10-03T00:24:43.616146Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}