{"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":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nfrom tensorflow import keras\nimport tensorflow as tf\nfrom keras import Sequential\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, Activation, BatchNormalization, Flatten\nfrom keras.utils.np_utils import to_categorical\nfrom keras.preprocessing.image import ImageDataGenerator","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-04-07T16:50:39.576703Z","iopub.execute_input":"2022-04-07T16:50:39.577094Z","iopub.status.idle":"2022-04-07T16:50:44.264473Z","shell.execute_reply.started":"2022-04-07T16:50:39.576989Z","shell.execute_reply":"2022-04-07T16:50:44.2637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:50:44.266118Z","iopub.execute_input":"2022-04-07T16:50:44.266376Z","iopub.status.idle":"2022-04-07T16:50:44.276491Z","shell.execute_reply.started":"2022-04-07T16:50:44.26633Z","shell.execute_reply":"2022-04-07T16:50:44.275717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install py7zr\n","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:50:44.277982Z","iopub.execute_input":"2022-04-07T16:50:44.278332Z","iopub.status.idle":"2022-04-07T16:50:55.61639Z","shell.execute_reply.started":"2022-04-07T16:50:44.278294Z","shell.execute_reply":"2022-04-07T16:50:55.615495Z"},"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":"2022-04-07T16:50:55.618951Z","iopub.execute_input":"2022-04-07T16:50:55.619528Z","iopub.status.idle":"2022-04-07T16:50:55.763027Z","shell.execute_reply.started":"2022-04-07T16:50:55.619482Z","shell.execute_reply":"2022-04-07T16:50:55.762382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shutil.unpack_archive('../input/cifar-10/train.7z', '/kaggle/temp/')","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:50:55.764341Z","iopub.execute_input":"2022-04-07T16:50:55.764621Z","iopub.status.idle":"2022-04-07T16:51:37.787523Z","shell.execute_reply.started":"2022-04-07T16:50:55.764582Z","shell.execute_reply":"2022-04-07T16:51:37.78673Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_labels = pd.read_csv(\"../input/cifar-10/trainLabels.csv\", header=\"infer\")\n\nclasses = train_labels['label'].unique()\nprint(classes)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:37.788806Z","iopub.execute_input":"2022-04-07T16:51:37.7891Z","iopub.status.idle":"2022-04-07T16:51:37.848485Z","shell.execute_reply.started":"2022-04-07T16:51:37.789047Z","shell.execute_reply":"2022-04-07T16:51:37.847693Z"},"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":"2022-04-07T16:51:37.851444Z","iopub.execute_input":"2022-04-07T16:51:37.851875Z","iopub.status.idle":"2022-04-07T16:51:42.649815Z","shell.execute_reply.started":"2022-04-07T16:51:37.851832Z","shell.execute_reply":"2022-04-07T16:51:42.649025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls /kaggle/temp/valid\n!ls /kaggle/temp/train","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:42.651274Z","iopub.execute_input":"2022-04-07T16:51:42.651534Z","iopub.status.idle":"2022-04-07T16:51:43.997457Z","shell.execute_reply.started":"2022-04-07T16:51:42.651497Z","shell.execute_reply":"2022-04-07T16:51:43.996556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def my_model3():\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=112, 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( Conv2D(filters=144, 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=160, 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=176, 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    \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","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:43.999283Z","iopub.execute_input":"2022-04-07T16:51:43.999613Z","iopub.status.idle":"2022-04-07T16:51:44.019121Z","shell.execute_reply.started":"2022-04-07T16:51:43.99957Z","shell.execute_reply":"2022-04-07T16:51:44.018208Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def my_model5():\n    model=Sequential()\n    \n    model.add( Conv2D(filters=32, kernel_size=(5,5), 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=64, kernel_size=(5,5), 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=(5,5), 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=(5,5), 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=160, kernel_size=(5,5), 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    \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","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:44.023023Z","iopub.execute_input":"2022-04-07T16:51:44.023384Z","iopub.status.idle":"2022-04-07T16:51:44.038188Z","shell.execute_reply.started":"2022-04-07T16:51:44.023338Z","shell.execute_reply":"2022-04-07T16:51:44.037131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def my_model7():\n    model=Sequential()\n    \n    model.add( Conv2D(filters=48, kernel_size=(7,7), 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=96, kernel_size=(7,7), 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=144, kernel_size=(7,7), 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=192, kernel_size=(7,7), 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    \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","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:44.039571Z","iopub.execute_input":"2022-04-07T16:51:44.040439Z","iopub.status.idle":"2022-04-07T16:51:44.052253Z","shell.execute_reply.started":"2022-04-07T16:51:44.040368Z","shell.execute_reply":"2022-04-07T16:51:44.051558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model3=my_model3()\nmodel5=my_model5()\nmodel7=my_model7()","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:44.053863Z","iopub.execute_input":"2022-04-07T16:51:44.054567Z","iopub.status.idle":"2022-04-07T16:51:46.755676Z","shell.execute_reply.started":"2022-04-07T16:51:44.054524Z","shell.execute_reply":"2022-04-07T16:51:46.754966Z"},"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)\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)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:46.759956Z","iopub.execute_input":"2022-04-07T16:51:46.761892Z","iopub.status.idle":"2022-04-07T16:51:48.95017Z","shell.execute_reply.started":"2022-04-07T16:51:46.761842Z","shell.execute_reply":"2022-04-07T16:51:48.949391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model3=my_model3()\nmodel5=my_model5()\nmodel7=my_model7()","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:48.95264Z","iopub.execute_input":"2022-04-07T16:51:48.954235Z","iopub.status.idle":"2022-04-07T16:51:49.654576Z","shell.execute_reply.started":"2022-04-07T16:51:48.954191Z","shell.execute_reply":"2022-04-07T16:51:49.653419Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.callbacks import ReduceLROnPlateau\nreduceLROnPlateau = ReduceLROnPlateau(monitor='val_acc', \n                                patience=3,\n                                verbose=1, \n                                factor=0.5,\n                                min_lr=0.00001)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:49.661178Z","iopub.execute_input":"2022-04-07T16:51:49.66468Z","iopub.status.idle":"2022-04-07T16:51:49.677716Z","shell.execute_reply.started":"2022-04-07T16:51:49.664534Z","shell.execute_reply":"2022-04-07T16:51:49.67677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model3.fit(train_generator,epochs=15, validation_data=valid_generator,steps_per_epoch=train_generator.n//train_generator.batch_size,\n         validation_steps= valid_generator.n//valid_generator.batch_size,workers=8,use_multiprocessing=True)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T16:51:49.683325Z","iopub.execute_input":"2022-04-07T16:51:49.6856Z","iopub.status.idle":"2022-04-07T17:00:58.242706Z","shell.execute_reply.started":"2022-04-07T16:51:49.685561Z","shell.execute_reply":"2022-04-07T17:00:58.24171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model5.fit(train_generator,epochs=15, validation_data=valid_generator,steps_per_epoch=train_generator.n//train_generator.batch_size,\n         validation_steps= valid_generator.n//valid_generator.batch_size,workers=8,use_multiprocessing=True)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:00:58.285496Z","iopub.execute_input":"2022-04-07T17:00:58.285849Z","iopub.status.idle":"2022-04-07T17:10:24.039854Z","shell.execute_reply.started":"2022-04-07T17:00:58.285794Z","shell.execute_reply":"2022-04-07T17:10:24.038998Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model7.fit(train_generator,epochs=15, validation_data=valid_generator,steps_per_epoch=train_generator.n//train_generator.batch_size,\n         validation_steps= valid_generator.n//valid_generator.batch_size,workers=8,use_multiprocessing=True)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:10:24.041782Z","iopub.execute_input":"2022-04-07T17:10:24.042093Z","iopub.status.idle":"2022-04-07T17:19:10.33923Z","shell.execute_reply.started":"2022-04-07T17:10:24.042037Z","shell.execute_reply":"2022-04-07T17:19:10.338159Z"},"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":"2022-04-07T17:19:10.342414Z","iopub.execute_input":"2022-04-07T17:19:10.347273Z","iopub.status.idle":"2022-04-07T17:33:09.104106Z","shell.execute_reply.started":"2022-04-07T17:19:10.347218Z","shell.execute_reply":"2022-04-07T17:33:09.103081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"shutil.unregister_unpack_format('7zip')","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:33:09.105759Z","iopub.execute_input":"2022-04-07T17:33:09.106175Z","iopub.status.idle":"2022-04-07T17:33:09.136744Z","shell.execute_reply.started":"2022-04-07T17:33:09.106136Z","shell.execute_reply":"2022-04-07T17:33:09.109497Z"},"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":"2022-04-07T17:33:09.139756Z","iopub.execute_input":"2022-04-07T17:33:09.158905Z","iopub.status.idle":"2022-04-07T17:33:16.712287Z","shell.execute_reply.started":"2022-04-07T17:33:09.158854Z","shell.execute_reply":"2022-04-07T17:33:16.711243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#mearge 3 models here\n# test_gen.reset()\n# predictions_vecs = model.predict(test_gen)\n\n# predictions_final = np.argmax(predictions_vecs, axis=1)\n\nprediction_vectors3=model3.predict(test_gen)\nprediction_vectors5=model5.predict(test_gen)\nprediction_vectors7=model7.predict(test_gen)\n\nprint(prediction_vectors3.shape)\nprint(prediction_vectors5.shape)\nprint(prediction_vectors7.shape)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:33:16.714019Z","iopub.execute_input":"2022-04-07T17:33:16.714531Z","iopub.status.idle":"2022-04-07T17:37:23.955529Z","shell.execute_reply.started":"2022-04-07T17:33:16.714492Z","shell.execute_reply":"2022-04-07T17:37:23.95473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"average_prediction_vectors=(prediction_vectors3+prediction_vectors5+prediction_vectors7)/3.\npredictions_final=np.argmax(average_prediction_vectors, axis=1)\n","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:37:23.956677Z","iopub.execute_input":"2022-04-07T17:37:23.958232Z","iopub.status.idle":"2022-04-07T17:37:23.980884Z","shell.execute_reply.started":"2022-04-07T17:37:23.958183Z","shell.execute_reply":"2022-04-07T17:37:23.980224Z"},"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":{"execution":{"iopub.status.busy":"2022-04-07T17:37:23.982231Z","iopub.execute_input":"2022-04-07T17:37:23.982488Z","iopub.status.idle":"2022-04-07T17:37:24.433642Z","shell.execute_reply.started":"2022-04-07T17:37:23.982451Z","shell.execute_reply":"2022-04-07T17:37:24.432877Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = pd.read_csv('../input/cifar-10/sampleSubmission.csv',header='infer')\nsub.info()\nsub","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:43:26.855756Z","iopub.execute_input":"2022-04-07T17:43:26.856026Z","iopub.status.idle":"2022-04-07T17:43:26.969359Z","shell.execute_reply.started":"2022-04-07T17:43:26.855996Z","shell.execute_reply":"2022-04-07T17:43:26.96853Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sub.drop()\nsub['label'] = predicted_classes_final\nsub","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:44:17.428378Z","iopub.execute_input":"2022-04-07T17:44:17.428973Z","iopub.status.idle":"2022-04-07T17:44:17.477965Z","shell.execute_reply.started":"2022-04-07T17:44:17.428931Z","shell.execute_reply":"2022-04-07T17:44:17.4772Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#Write updated dataframes as submission.csv\nsub.to_csv('19bce192_submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2022-04-07T17:44:36.400184Z","iopub.execute_input":"2022-04-07T17:44:36.400468Z","iopub.status.idle":"2022-04-07T17:44:36.868452Z","shell.execute_reply.started":"2022-04-07T17:44:36.400428Z","shell.execute_reply":"2022-04-07T17:44:36.867683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"sub['label'] = predicted_classes_final\nsub.to_csv('submission.csv',index=False)","metadata":{}}]}