{"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":"!pip install py7zr","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:56:54.965776Z","iopub.execute_input":"2022-08-24T15:56:54.966505Z","iopub.status.idle":"2022-08-24T15:57:08.810628Z","shell.execute_reply.started":"2022-08-24T15:56:54.966468Z","shell.execute_reply":"2022-08-24T15:57:08.809411Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport os\n\nimport itertools\nfrom py7zr import unpack_7zarchive\nimport shutil\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D,MaxPool2D,Dense,Flatten,Dropout,Input, AveragePooling2D, Activation,Conv2D, MaxPooling2D, BatchNormalization,Concatenate\nfrom tensorflow.keras.callbacks import EarlyStopping, TensorBoard\nfrom tensorflow.keras import regularizers, optimizers\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-08-24T15:56:54.932844Z","iopub.execute_input":"2022-08-24T15:56:54.933304Z","iopub.status.idle":"2022-08-24T15:56:54.963749Z","shell.execute_reply.started":"2022-08-24T15:56:54.933213Z","shell.execute_reply":"2022-08-24T15:56:54.962686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shutil.register_unpack_format('7zip',['.7z'],unpack_7zarchive)\nshutil.unpack_archive('../input/cifar-10/train.7z', '/kaggle/temp/')","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:57:08.812413Z","iopub.execute_input":"2022-08-24T15:57:08.812822Z","iopub.status.idle":"2022-08-24T15:57:08.963063Z","shell.execute_reply.started":"2022-08-24T15:57:08.812784Z","shell.execute_reply":"2022-08-24T15:57:08.958090Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_labels = pd.read_csv(\"../input/cifar-10/trainLabels.csv\", header=\"infer\")\nclasses = train_labels['label'].unique()\nprint(classes)","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:57:08.967129Z","iopub.execute_input":"2022-08-24T15:57:08.967587Z","iopub.status.idle":"2022-08-24T15:57:55.460568Z","shell.execute_reply.started":"2022-08-24T15:57:08.967540Z","shell.execute_reply":"2022-08-24T15:57:55.459572Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if not os.path.exists(\"/kaggle/temp/valid\"):\n    os.mkdir(\"/kaggle/temp/valid\")\n    \nparent_path_train = \"/kaggle/temp/train\"\nparent_path_valid = \"/kaggle/temp/valid\"\nparent_path_test = \"/kaggle/temp/test\"\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    path_valid = os.path.join(parent_path_valid,class1)\n    if not os.path.exists(path_valid):\n        os.mkdir(path_valid)\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<=0.8:\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_valid,row[\"label\"],id)\n        os.replace(source_path, target_path)","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:57:55.462130Z","iopub.execute_input":"2022-08-24T15:57:55.462508Z","iopub.status.idle":"2022-08-24T15:57:55.535107Z","shell.execute_reply.started":"2022-08-24T15:57:55.462469Z","shell.execute_reply":"2022-08-24T15:57:55.533817Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.layers import MaxPooling2D, Dropout\n\nmodel = Sequential()\nmodel.add(Conv2D(32, (3,3), padding='same', activation='relu', input_shape=(32,32,3)))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(64, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(128, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(128, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(512, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation='softmax'))\n","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:58:06.831919Z","iopub.execute_input":"2022-08-24T15:58:06.835789Z","iopub.status.idle":"2022-08-24T15:58:10.704623Z","shell.execute_reply.started":"2022-08-24T15:58:06.835732Z","shell.execute_reply":"2022-08-24T15:58:10.703300Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\ntrain_datagen = ImageDataGenerator(\n                             rotation_range=15,\n                             zoom_range=0.1,\n                             width_shift_range=0.1,\n                             height_shift_range=0.1,\n                             horizontal_flip=True,\n                             fill_mode='nearest',\n                             rescale=1./255)\nvalid_datagen = ImageDataGenerator()\n\ntrain_generator = train_datagen.flow_from_directory(directory='/kaggle/temp/train/', shuffle=True, target_size=(32,32),batch_size=128)\nvalid_generator = valid_datagen.flow_from_directory(directory='/kaggle/temp/valid/', shuffle=True, target_size=(32,32),batch_size=128)\n","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:58:10.706289Z","iopub.execute_input":"2022-08-24T15:58:10.706898Z","iopub.status.idle":"2022-08-24T15:58:13.117116Z","shell.execute_reply.started":"2022-08-24T15:58:10.706862Z","shell.execute_reply":"2022-08-24T15:58:13.115894Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(train_generator, epochs=60, validation_data=valid_generator,workers=8,use_multiprocessing=True,verbose=2)","metadata":{"execution":{"iopub.status.busy":"2022-08-24T15:58:13.125571Z","iopub.execute_input":"2022-08-24T15:58:13.127374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shutil.unpack_archive('/kaggle/input/cifar-10/test.7z','/kaggle/temp/test')\nshutil.unregister_unpack_format('7zip')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_datagen = ImageDataGenerator(rescale=1./255)\ntest_gen = test_datagen.flow_from_directory(directory='/kaggle/temp/test',target_size=(32,32),batch_size=64,class_mode=None,shuffle=False)\ntest_gen.reset()\npredictions_vecs = model.predict(test_gen)\npredictions_final = np.argmax(predictions_vecs, axis=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(type(train_generator.class_indices))\nprint(train_generator.class_indices)\n\nclasses = {value:key for (key,value) in train_generator.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[i]\n    ind=ind+1\n    \nfilenames_wo_ext = []\nfor fname in test_gen.filenames:\n    filenames_wo_ext.append(int(fname.split(sep=\"/\")[1].split(sep=\".\")[0])-1)\n\npredicted_classes_final = np.empty(shape=300000,dtype=np.dtype('U20'))\npredicted_classes_final[filenames_wo_ext]=predicted_classes","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Final_submit = pd.read_csv('../input/cifar-10/sampleSubmission.csv',header='infer')\nFinal_submit.info()\nFinal_submit['label'] = predicted_classes_final\nFinal_submit.to_csv('submission.csv',index=False)\nFinal_submit","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save('GPU_Rafaa_Model.h5')","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}