{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":62057,"databundleVersionId":6714922,"sourceType":"competition"}],"dockerImageVersionId":31041,"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\nimport os\nfor 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":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:30.730919Z","iopub.execute_input":"2025-06-15T09:02:30.73149Z","iopub.status.idle":"2025-06-15T09:02:47.661522Z","shell.execute_reply.started":"2025-06-15T09:02:30.731464Z","shell.execute_reply":"2025-06-15T09:02:47.660598Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train=pd.read_csv(\"/kaggle/input/applications-of-deep-learning-wustl-fall-2023/train.csv\")\ntest=pd.read_csv(\"/kaggle/input/applications-of-deep-learning-wustl-fall-2023/test.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:47.663632Z","iopub.execute_input":"2025-06-15T09:02:47.664085Z","iopub.status.idle":"2025-06-15T09:02:47.733997Z","shell.execute_reply.started":"2025-06-15T09:02:47.66406Z","shell.execute_reply":"2025-06-15T09:02:47.733297Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sample=pd.read_csv(\"/kaggle/input/applications-of-deep-learning-wustl-fall-2023/sample_submission.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:47.734783Z","iopub.execute_input":"2025-06-15T09:02:47.735017Z","iopub.status.idle":"2025-06-15T09:02:47.744924Z","shell.execute_reply.started":"2025-06-15T09:02:47.734988Z","shell.execute_reply":"2025-06-15T09:02:47.744363Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:47.746687Z","iopub.execute_input":"2025-06-15T09:02:47.746932Z","iopub.status.idle":"2025-06-15T09:02:47.769882Z","shell.execute_reply.started":"2025-06-15T09:02:47.746916Z","shell.execute_reply":"2025-06-15T09:02:47.769183Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:47.770735Z","iopub.execute_input":"2025-06-15T09:02:47.770994Z","iopub.status.idle":"2025-06-15T09:02:47.784336Z","shell.execute_reply.started":"2025-06-15T09:02:47.770969Z","shell.execute_reply":"2025-06-15T09:02:47.783023Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import seaborn as sns\nsns.countplot(train,x='glasses')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:47.785234Z","iopub.execute_input":"2025-06-15T09:02:47.78552Z","iopub.status.idle":"2025-06-15T09:02:49.364683Z","shell.execute_reply.started":"2025-06-15T09:02:47.785496Z","shell.execute_reply":"2025-06-15T09:02:49.364069Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train.shape, test.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:49.365473Z","iopub.execute_input":"2025-06-15T09:02:49.365867Z","iopub.status.idle":"2025-06-15T09:02:49.370783Z","shell.execute_reply.started":"2025-06-15T09:02:49.36584Z","shell.execute_reply":"2025-06-15T09:02:49.370126Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport cv2\nimport os\n\nimages_dir = '/kaggle/input/applications-of-deep-learning-wustl-fall-2023/'\ncsv_path = '/kaggle/input/applications-of-deep-learning-wustl-fall-2023/train.csv'\n\ndf = pd.read_csv(csv_path)\n\ndata = []\nlabels = []\n\n\nfor index, row in df.iterrows():\n    \n    img_path = os.path.join(images_dir, str(row['file']))\n   \n    image = cv2.imread(img_path)\n    image = cv2.resize(image, (128, 128))\n    \n    data.append(image)\n    \n    labels.append(row['glasses'])\n\nprint(f'Total images: {len(data)}')\nprint(f'Total labels: {len(labels)}')\n\ndata = data[:10000]\nlabels = labels[:10000]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:02:49.371496Z","iopub.execute_input":"2025-06-15T09:02:49.371701Z","iopub.status.idle":"2025-06-15T09:04:57.641727Z","shell.execute_reply.started":"2025-06-15T09:02:49.371684Z","shell.execute_reply":"2025-06-15T09:04:57.640832Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data_arr=np.array(data)\nlabel_arr=np.array(labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:04:57.642592Z","iopub.execute_input":"2025-06-15T09:04:57.64281Z","iopub.status.idle":"2025-06-15T09:04:57.821663Z","shell.execute_reply.started":"2025-06-15T09:04:57.642787Z","shell.execute_reply":"2025-06-15T09:04:57.821067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y=labels","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:04:57.82481Z","iopub.execute_input":"2025-06-15T09:04:57.825299Z","iopub.status.idle":"2025-06-15T09:04:57.828572Z","shell.execute_reply.started":"2025-06-15T09:04:57.825276Z","shell.execute_reply":"2025-06-15T09:04:57.827887Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x=data_arr/255","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:04:57.829291Z","iopub.execute_input":"2025-06-15T09:04:57.829566Z","iopub.status.idle":"2025-06-15T09:04:59.062266Z","shell.execute_reply.started":"2025-06-15T09:04:57.829541Z","shell.execute_reply":"2025-06-15T09:04:59.061636Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:04:59.062958Z","iopub.execute_input":"2025-06-15T09:04:59.0632Z","iopub.status.idle":"2025-06-15T09:04:59.07431Z","shell.execute_reply.started":"2025-06-15T09:04:59.063181Z","shell.execute_reply":"2025-06-15T09:04:59.073573Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:04:59.075059Z","iopub.execute_input":"2025-06-15T09:04:59.075265Z","iopub.status.idle":"2025-06-15T09:04:59.382026Z","shell.execute_reply.started":"2025-06-15T09:04:59.07525Z","shell.execute_reply":"2025-06-15T09:04:59.381403Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.2,random_state=13)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:04:59.382766Z","iopub.execute_input":"2025-06-15T09:04:59.383023Z","iopub.status.idle":"2025-06-15T09:05:00.499266Z","shell.execute_reply.started":"2025-06-15T09:04:59.383Z","shell.execute_reply":"2025-06-15T09:05:00.498659Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:05:00.50007Z","iopub.execute_input":"2025-06-15T09:05:00.500393Z","iopub.status.idle":"2025-06-15T09:05:09.044814Z","shell.execute_reply.started":"2025-06-15T09:05:00.500364Z","shell.execute_reply":"2025-06-15T09:05:09.043973Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.regularizers import l2\n\nl2_reg = 0.001  # biraz daha düşük L2 ile daha esnek öğrenme\n\nmodel = Sequential([\n    layers.Conv2D(32, kernel_size=3, input_shape=(128, 128, 3), activation='relu', padding='same',\n                  kernel_regularizer=l2(l2_reg)),\n    layers.BatchNormalization(),\n    layers.MaxPooling2D(pool_size=(2, 2)),\n    layers.Dropout(0.3),\n\n    layers.Conv2D(64, kernel_size=3, activation='relu', padding='same',\n                  kernel_regularizer=l2(l2_reg)),\n    layers.BatchNormalization(),\n    layers.MaxPooling2D(pool_size=(2, 2)),\n    layers.Dropout(0.3),\n\n    layers.Conv2D(128, kernel_size=3, activation='relu', padding='same',\n                  kernel_regularizer=l2(l2_reg)),\n    layers.BatchNormalization(),\n    layers.MaxPooling2D(pool_size=(2, 2)),\n    layers.Dropout(0.4),\n\n    layers.Flatten(),\n    layers.Dense(128, activation='relu', kernel_regularizer=l2(l2_reg)),\n    layers.BatchNormalization(),\n    layers.Dropout(0.5),\n\n    layers.Dense(1,activation=\"sigmoid\")\n])\n\noptimizer = Adam(learning_rate=0.0002)  # biraz daha yüksek öğrenme oranı\nmodel.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:05:09.045854Z","iopub.execute_input":"2025-06-15T09:05:09.046312Z","iopub.status.idle":"2025-06-15T09:05:11.877859Z","shell.execute_reply.started":"2025-06-15T09:05:09.046293Z","shell.execute_reply":"2025-06-15T09:05:11.877307Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y_train=pd.DataFrame(y_train)\ny_test=pd.DataFrame(y_test)\ny_train=y_train.values\ny_test=y_test.values","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:05:11.878545Z","iopub.execute_input":"2025-06-15T09:05:11.878731Z","iopub.status.idle":"2025-06-15T09:05:11.885267Z","shell.execute_reply.started":"2025-06-15T09:05:11.878717Z","shell.execute_reply":"2025-06-15T09:05:11.88441Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:05:11.886097Z","iopub.execute_input":"2025-06-15T09:05:11.88656Z","iopub.status.idle":"2025-06-15T09:05:11.917738Z","shell.execute_reply.started":"2025-06-15T09:05:11.886533Z","shell.execute_reply":"2025-06-15T09:05:11.917203Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"history = model.fit(\n    x_train, y_train,\n    epochs=50,  # artık 350’ye gerek yok\n    batch_size=64,  # 300 çok büyük, 64 veya 128 daha verimli öğrenir\n    validation_data=(x_test,y_test)\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:05:11.918413Z","iopub.execute_input":"2025-06-15T09:05:11.918615Z","iopub.status.idle":"2025-06-15T09:08:14.787688Z","shell.execute_reply.started":"2025-06-15T09:05:11.918599Z","shell.execute_reply":"2025-06-15T09:08:14.786711Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'r', label='Training accuracy')\nplt.plot(epochs, val_acc, 'b', label='Validation accuracy')\nplt.title('Training and validation accuracy')\nplt.legend(loc=0)\nplt.figure()\n\nplt.plot(epochs, loss, 'r', label='Training Loss')\nplt.plot(epochs, val_loss, 'b', label='Validation Loss')\nplt.title('Training and validation loss')\nplt.legend()\n\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:14.795542Z","iopub.execute_input":"2025-06-15T09:08:14.795772Z","iopub.status.idle":"2025-06-15T09:08:15.118237Z","shell.execute_reply.started":"2025-06-15T09:08:14.795755Z","shell.execute_reply":"2025-06-15T09:08:15.117571Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Conv2D, Dense, Flatten, Input, MaxPooling2D, Dropout,BatchNormalization, Reshape\nfrom tensorflow.keras.applications import VGG16,ResNet50\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:15.118938Z","iopub.execute_input":"2025-06-15T09:08:15.119226Z","iopub.status.idle":"2025-06-15T09:08:15.127372Z","shell.execute_reply.started":"2025-06-15T09:08:15.119205Z","shell.execute_reply":"2025-06-15T09:08:15.126697Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport cv2\nimport os\n\nimages_dir = '/kaggle/input/applications-of-deep-learning-wustl-fall-2023/'\ncsv_path = '/kaggle/input/applications-of-deep-learning-wustl-fall-2023/test.csv'\n\ntest_df = pd.read_csv(csv_path)\n\ntest_data = []\nlabels = []\n\n\nfor index, row in test_df.iterrows():\n    \n    img_path = os.path.join(images_dir, str(row['file']))\n   \n    image = cv2.imread(img_path)\n    image = cv2.resize(image, (128, 128))\n    \n    test_data.append(image)\n\nprint(f'Total images: {len(test_data)}')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:15.128294Z","iopub.execute_input":"2025-06-15T09:08:15.128862Z","iopub.status.idle":"2025-06-15T09:08:50.248311Z","shell.execute_reply.started":"2025-06-15T09:08:15.128844Z","shell.execute_reply":"2025-06-15T09:08:50.247504Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_array=np.array(test_data)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:50.249246Z","iopub.execute_input":"2025-06-15T09:08:50.249532Z","iopub.status.idle":"2025-06-15T09:08:50.359742Z","shell.execute_reply.started":"2025-06-15T09:08:50.249506Z","shell.execute_reply":"2025-06-15T09:08:50.359035Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x_test=test_array/255","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:50.360574Z","iopub.execute_input":"2025-06-15T09:08:50.360794Z","iopub.status.idle":"2025-06-15T09:08:51.20774Z","shell.execute_reply.started":"2025-06-15T09:08:50.360777Z","shell.execute_reply":"2025-06-15T09:08:51.20711Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x_test.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:51.208474Z","iopub.execute_input":"2025-06-15T09:08:51.208668Z","iopub.status.idle":"2025-06-15T09:08:51.213814Z","shell.execute_reply.started":"2025-06-15T09:08:51.208653Z","shell.execute_reply":"2025-06-15T09:08:51.213027Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pred=model.predict(x_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:51.214447Z","iopub.execute_input":"2025-06-15T09:08:51.214664Z","iopub.status.idle":"2025-06-15T09:08:57.848445Z","shell.execute_reply.started":"2025-06-15T09:08:51.214647Z","shell.execute_reply":"2025-06-15T09:08:57.847773Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pred","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.849431Z","iopub.execute_input":"2025-06-15T09:08:57.849637Z","iopub.status.idle":"2025-06-15T09:08:57.854872Z","shell.execute_reply.started":"2025-06-15T09:08:57.849622Z","shell.execute_reply":"2025-06-15T09:08:57.854338Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sample.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.858083Z","iopub.execute_input":"2025-06-15T09:08:57.858355Z","iopub.status.idle":"2025-06-15T09:08:57.873697Z","shell.execute_reply.started":"2025-06-15T09:08:57.858328Z","shell.execute_reply":"2025-06-15T09:08:57.873013Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission= pd.DataFrame({\n    \"id\": test_df[\"id\"],\n})","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.874323Z","iopub.execute_input":"2025-06-15T09:08:57.874534Z","iopub.status.idle":"2025-06-15T09:08:57.88839Z","shell.execute_reply.started":"2025-06-15T09:08:57.87451Z","shell.execute_reply":"2025-06-15T09:08:57.887677Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission[\"glasses\"]=np.round(pred,5) #virgülden sonra 5 hane olacak şekilde yuvarla","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.889234Z","iopub.execute_input":"2025-06-15T09:08:57.889462Z","iopub.status.idle":"2025-06-15T09:08:57.903107Z","shell.execute_reply.started":"2025-06-15T09:08:57.889444Z","shell.execute_reply":"2025-06-15T09:08:57.902618Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.903751Z","iopub.execute_input":"2025-06-15T09:08:57.903944Z","iopub.status.idle":"2025-06-15T09:08:57.922228Z","shell.execute_reply.started":"2025-06-15T09:08:57.903929Z","shell.execute_reply":"2025-06-15T09:08:57.921665Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission.to_csv(\"submission.csv\",index=False,float_format=\"%.1f\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.922793Z","iopub.execute_input":"2025-06-15T09:08:57.923Z","iopub.status.idle":"2025-06-15T09:08:57.951035Z","shell.execute_reply.started":"2025-06-15T09:08:57.922984Z","shell.execute_reply":"2025-06-15T09:08:57.950536Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.951685Z","iopub.execute_input":"2025-06-15T09:08:57.951932Z","iopub.status.idle":"2025-06-15T09:08:57.956779Z","shell.execute_reply.started":"2025-06-15T09:08:57.951908Z","shell.execute_reply":"2025-06-15T09:08:57.956072Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"base_model=VGG16(weights='imagenet',input_shape=(128,128,3),include_top=False)\n\nmodel=Sequential()\nmodel.add(base_model)\n#modeli freeze ediyoruz aşağıda\nfor layer in base_model.layers:\n    layer.trainable= False\n\nmodel.add(Flatten())\nmodel.add(Dense(1024,activation='relu'))\nmodel.add(Dense(1,activation='sigmoid'))\nfrom keras import metrics\n\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', \n                                                                      metrics.Precision(), \n                                                                      metrics.Recall(), \n                                                                      metrics.AUC()])\n\n\nhistory = model.fit(x_train, y_train, batch_size=32, epochs=15, \n                    validation_data=(x_test, y_test))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-15T09:08:57.957586Z","iopub.execute_input":"2025-06-15T09:08:57.958055Z","iopub.status.idle":"2025-06-15T09:09:16.875216Z","shell.execute_reply.started":"2025-06-15T09:08:57.958027Z","shell.execute_reply":"2025-06-15T09:09:16.874226Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'r', label='Training accuracy')\nplt.plot(epochs, val_acc, 'b', label='Validation accuracy')\nplt.title('Training and validation accuracy')\nplt.legend(loc=0)\nplt.figure()\n\nplt.plot(epochs, loss, 'r', label='Training Loss')\nplt.plot(epochs, val_loss, 'b', label='Validation Loss')\nplt.title('Training and validation 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