{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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","execution":{"iopub.status.busy":"2022-03-29T15:37:39.280732Z","iopub.execute_input":"2022-03-29T15:37:39.281381Z","iopub.status.idle":"2022-03-29T15:37:39.313457Z","shell.execute_reply.started":"2022-03-29T15:37:39.281281Z","shell.execute_reply":"2022-03-29T15:37:39.31278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install py7zr","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:37:39.316263Z","iopub.execute_input":"2022-03-29T15:37:39.316916Z","iopub.status.idle":"2022-03-29T15:37:51.676206Z","shell.execute_reply.started":"2022-03-29T15:37:39.316877Z","shell.execute_reply":"2022-03-29T15:37:51.675391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:37:51.677727Z","iopub.execute_input":"2022-03-29T15:37:51.677994Z","iopub.status.idle":"2022-03-29T15:37:51.684833Z","shell.execute_reply.started":"2022-03-29T15:37:51.677956Z","shell.execute_reply":"2022-03-29T15:37:51.684077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from py7zr import unpack_7zarchive\nimport shutil\n\nshutil.register_unpack_format('7zip',['.7z'],unpack_7zarchive)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:37:51.687225Z","iopub.execute_input":"2022-03-29T15:37:51.687599Z","iopub.status.idle":"2022-03-29T15:37:51.929862Z","shell.execute_reply.started":"2022-03-29T15:37:51.687563Z","shell.execute_reply":"2022-03-29T15:37:51.929155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#unpack train images to temp directory\nshutil.unpack_archive('/kaggle/input/cifar-10/train.7z','/kaggle/temp/')","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:37:51.93089Z","iopub.execute_input":"2022-03-29T15:37:51.93115Z","iopub.status.idle":"2022-03-29T15:38:32.992755Z","shell.execute_reply.started":"2022-03-29T15:37:51.931114Z","shell.execute_reply":"2022-03-29T15:38:32.992Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_labels = pd.read_csv('/kaggle/input/cifar-10/trainLabels.csv', header= 'infer')\n\n#unique labels\nclasses = train_labels['label'].unique()\n\nprint(classes)\n\nimport os\n\nif not os.path.exists(\"/kaggle/temp/validation/\"):\n    os.mkdir(\"/kaggle/temp/validation/\")\n\nparent_path_train = \"/kaggle/temp/train/\"\nparent_path_validation = \"/kaggle/temp/validation/\"\nparent_path_test = \"/kaggle/temp/test/\"\n\n\nfor class1 in classes:\n    path_train = os.path.join(parent_path_train,class1)\n    if not os.path.exists(path_train):\n        os.mkdir(path_train)\n        \n    path_validation = os.path.join(parent_path_validation,class1)\n    if not os.path.exists(path_validation):\n        os.mkdir(path_validation)\n        \nfor (int_ind,row) in train_labels.iterrows():\n    id = str(row[\"id\"]) + \".png\"\n    source_path = os.path.join(parent_path_train,id)\n    \n    p = np.random.random()\n    if p <= 0.80:\n        target_path = os.path.join(parent_path_train,row[\"label\"],id)\n        os.replace(source_path,target_path)\n    else:\n        target_path = os.path.join(parent_path_validation,row[\"label\"],id)\n        os.replace(source_path,target_path)\n","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:38:32.994193Z","iopub.execute_input":"2022-03-29T15:38:32.994462Z","iopub.status.idle":"2022-03-29T15:38:37.684945Z","shell.execute_reply.started":"2022-03-29T15:38:32.994428Z","shell.execute_reply":"2022-03-29T15:38:37.684241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\n\nfrom keras import Sequential\nfrom keras.layers import Dense,Conv2D,MaxPooling2D,Activation,BatchNormalization,GlobalAveragePooling2D,Flatten\nfrom keras.utils.np_utils import to_categorical\nfrom keras.callbacks import ReduceLROnPlateau\n\nfrom keras.preprocessing.image import ImageDataGenerator\n","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:38:37.686274Z","iopub.execute_input":"2022-03-29T15:38:37.686792Z","iopub.status.idle":"2022-03-29T15:38:43.078725Z","shell.execute_reply.started":"2022-03-29T15:38:37.686722Z","shell.execute_reply":"2022-03-29T15:38:43.077505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def my_model():\n    model = Sequential()\n    model.add(Conv2D(filters=32,kernel_size=(3,3),strides=(1,1),padding='valid',activation=None,use_bias=False,input_shape=(32,32,3)))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    \n    model.add(Conv2D(filters=48, kernel_size=(3,3), strides=(1,1), padding='valid', activation=None, use_bias=False))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    \n    model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='valid', activation=None, use_bias=False))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    \n    model.add(Conv2D(filters=80, kernel_size=(3,3), strides=(1,1), padding='valid', activation=None, use_bias=False))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    \n    model.add(Conv2D(filters=96, kernel_size=(3,3), strides=(1,1), padding='valid', activation=None, use_bias=False))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n\n    model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='valid', activation=None, use_bias=False))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n\n    model.add(Flatten())\n    model.add(Dense(units=10))\n    model.add(BatchNormalization())\n    model.add(Activation('softmax'))\n    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n    return model\n","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:38:43.083842Z","iopub.execute_input":"2022-03-29T15:38:43.084087Z","iopub.status.idle":"2022-03-29T15:38:43.124783Z","shell.execute_reply.started":"2022-03-29T15:38:43.084054Z","shell.execute_reply":"2022-03-29T15:38:43.117487Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = my_model()","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:38:43.126176Z","iopub.execute_input":"2022-03-29T15:38:43.126427Z","iopub.status.idle":"2022-03-29T15:38:46.400446Z","shell.execute_reply.started":"2022-03-29T15:38:43.126393Z","shell.execute_reply":"2022-03-29T15:38:46.399576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_datagen = ImageDataGenerator(featurewise_center= False,\n                                  samplewise_center= False,\n                                  featurewise_std_normalization=False,\n                                  samplewise_std_normalization=False,\n                                  zca_whitening=False,\n                                  rotation_range=10,\n                                  zoom_range=0.1,\n                                  width_shift_range=0.1,\n                                  height_shift_range=0.1,\n                                  horizontal_flip=False,\n                                  vertical_flip=False,\n                                  rescale=1./255\n                                  )\n\ntrain_gen = train_datagen.flow_from_directory(directory='/kaggle/temp/train/',target_size=(32,32),batch_size=128)\nvalidation_datagen = ImageDataGenerator(rescale=1./255)\nvalid_gen = validation_datagen.flow_from_directory(directory='/kaggle/temp/validation/',target_size=(32,32),batch_size=128)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:38:46.403256Z","iopub.execute_input":"2022-03-29T15:38:46.403633Z","iopub.status.idle":"2022-03-29T15:38:49.088482Z","shell.execute_reply.started":"2022-03-29T15:38:46.403596Z","shell.execute_reply":"2022-03-29T15:38:49.08711Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(train_gen,epochs=75,validation_data=valid_gen,steps_per_epoch=train_gen.n//train_gen.batch_size,\n         validation_steps= valid_gen.n//valid_gen.batch_size,workers=8,use_multiprocessing=True)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T15:38:49.098108Z","iopub.execute_input":"2022-03-29T15:38:49.105098Z","iopub.status.idle":"2022-03-29T16:37:51.839821Z","shell.execute_reply.started":"2022-03-29T15:38:49.105051Z","shell.execute_reply":"2022-03-29T16:37:51.830905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shutil.unpack_archive('/kaggle/input/cifar-10/test.7z','/kaggle/temp/test')","metadata":{"execution":{"iopub.status.busy":"2022-03-29T16:37:51.843699Z","iopub.execute_input":"2022-03-29T16:37:51.844031Z","iopub.status.idle":"2022-03-29T16:52:04.044775Z","shell.execute_reply.started":"2022-03-29T16:37:51.843979Z","shell.execute_reply":"2022-03-29T16:52:04.043985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shutil.unregister_unpack_format('7zip')","metadata":{"execution":{"iopub.status.busy":"2022-03-29T16:52:04.046347Z","iopub.execute_input":"2022-03-29T16:52:04.046603Z","iopub.status.idle":"2022-03-29T16:52:04.051258Z","shell.execute_reply.started":"2022-03-29T16:52:04.04657Z","shell.execute_reply":"2022-03-29T16:52:04.05065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_datagen = ImageDataGenerator(rescale=1./255)\n\ntest_gen = test_datagen.flow_from_directory(directory='/kaggle/temp/test',target_size=(32,32),batch_size=64,class_mode=None,shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T17:45:13.010923Z","iopub.execute_input":"2022-03-29T17:45:13.0116Z","iopub.status.idle":"2022-03-29T17:45:19.565227Z","shell.execute_reply.started":"2022-03-29T17:45:13.011562Z","shell.execute_reply":"2022-03-29T17:45:19.564373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\nos.chdir('/kaggle/temp/test/test')\nprint(os.getcwd())","metadata":{"execution":{"iopub.status.busy":"2022-03-29T17:45:49.251555Z","iopub.execute_input":"2022-03-29T17:45:49.251837Z","iopub.status.idle":"2022-03-29T17:45:49.257292Z","shell.execute_reply.started":"2022-03-29T17:45:49.251805Z","shell.execute_reply":"2022-03-29T17:45:49.256153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_gen.filenames","metadata":{"execution":{"iopub.status.busy":"2022-03-29T17:46:54.020705Z","iopub.execute_input":"2022-03-29T17:46:54.021308Z","iopub.status.idle":"2022-03-29T17:46:54.046855Z","shell.execute_reply.started":"2022-03-29T17:46:54.021267Z","shell.execute_reply":"2022-03-29T17:46:54.04611Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions_final = model.predict(test_gen)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T17:41:17.311399Z","iopub.execute_input":"2022-03-29T17:41:17.311672Z","iopub.status.idle":"2022-03-29T17:42:37.043417Z","shell.execute_reply.started":"2022-03-29T17:41:17.311639Z","shell.execute_reply":"2022-03-29T17:42:37.042685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions_final[1]","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:09:40.300702Z","iopub.execute_input":"2022-03-29T18:09:40.300993Z","iopub.status.idle":"2022-03-29T18:09:40.307482Z","shell.execute_reply.started":"2022-03-29T18:09:40.300963Z","shell.execute_reply":"2022-03-29T18:09:40.306621Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(type(train_gen.class_indices))\nprint(train_gen.class_indices)\n\nclasses = {value:key for (key,value) in train_gen.class_indices.items()}\nprint(classes)\n\npredicted_classes=np.empty(shape=300000,dtype=np.dtype('U20'))\n\nind=0\nfor i in predictions_final.tolist():\n    predicted_classes[ind]=classes[np.argmax(i)]\n    ind=ind+1","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:10:54.98399Z","iopub.execute_input":"2022-03-29T18:10:54.984265Z","iopub.status.idle":"2022-03-29T18:10:57.879285Z","shell.execute_reply.started":"2022-03-29T18:10:54.984234Z","shell.execute_reply":"2022-03-29T18:10:57.878535Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predicted_classes[9]","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:05:23.799929Z","iopub.execute_input":"2022-03-29T18:05:23.800181Z","iopub.status.idle":"2022-03-29T18:05:23.805002Z","shell.execute_reply.started":"2022-03-29T18:05:23.800154Z","shell.execute_reply":"2022-03-29T18:05:23.804369Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"filenames_wo_ext = []\nfor fname in test_gen.filenames:\n    filenames_wo_ext.append(int(fname.split(sep=\"/\")[-1].split(sep=\".\")[0])-1)\n\n# print(filenames_wo_ext)\n# print(len(predicted_classes))\n\npredicted_classes_final = np.empty(shape=300000,dtype=np.dtype('U20'))\n# for i in range(0,300000) :\n#     predicted_classes_final[i]= predicted_classes[i]\npredicted_classes_final[filenames_wo_ext]=predicted_classes","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:11:04.084072Z","iopub.execute_input":"2022-03-29T18:11:04.084593Z","iopub.status.idle":"2022-03-29T18:11:04.382077Z","shell.execute_reply.started":"2022-03-29T18:11:04.084554Z","shell.execute_reply":"2022-03-29T18:11:04.38133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predicted_classes_final","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:11:08.533633Z","iopub.execute_input":"2022-03-29T18:11:08.534191Z","iopub.status.idle":"2022-03-29T18:11:08.539436Z","shell.execute_reply.started":"2022-03-29T18:11:08.53415Z","shell.execute_reply":"2022-03-29T18:11:08.538676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = pd.read_csv('/kaggle/input/cifar-10/sampleSubmission.csv',header='infer')\nsub.info()","metadata":{"execution":{"iopub.status.busy":"2022-03-29T17:38:19.879245Z","iopub.execute_input":"2022-03-29T17:38:19.879659Z","iopub.status.idle":"2022-03-29T17:38:19.973867Z","shell.execute_reply.started":"2022-03-29T17:38:19.879624Z","shell.execute_reply":"2022-03-29T17:38:19.97312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub['label'] = predicted_classes_final\nsub.to_csv('/kaggle/working/submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:13:04.255086Z","iopub.execute_input":"2022-03-29T18:13:04.25535Z","iopub.status.idle":"2022-03-29T18:13:04.770539Z","shell.execute_reply.started":"2022-03-29T18:13:04.255319Z","shell.execute_reply":"2022-03-29T18:13:04.769798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub.head(10)","metadata":{"execution":{"iopub.status.busy":"2022-03-29T18:11:37.814058Z","iopub.execute_input":"2022-03-29T18:11:37.814316Z","iopub.status.idle":"2022-03-29T18:11:37.823874Z","shell.execute_reply.started":"2022-03-29T18:11:37.814286Z","shell.execute_reply":"2022-03-29T18:11:37.823025Z"},"trusted":true},"execution_count":null,"outputs":[]}]}