{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom keras.utils import np_utils,to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Dropout,Conv2D,MaxPool2D,Flatten,BatchNormalization\nfrom keras.optimizers import Adam,Nadam\nfrom keras import regularizers\nfrom sklearn.model_selection import train_test_split\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom sklearn.utils import shuffle\nfrom keras.callbacks import ReduceLROnPlateau","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\ntest = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')\nsubmission = pd.read_csv('/kaggle/input/Kannada-MNIST/sample_submission.csv')\ndisplay(train.shape,test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Id = test['id']\nx_train, x_test, y_train, y_test = train_test_split(train.iloc[:, 1:], train.iloc[:, 0], test_size=0.05)\ndisplay(x_train.shape,y_train.shape,x_test.shape,y_test.shape)\nx_train, y_train = shuffle(x_train, y_train)\nx_train = x_train.values.reshape(-1,28,28,1)/255.0\nx_test = x_test.values.reshape(-1,28,28,1)/255.0\ny_train = np_utils.to_categorical(y_train,num_classes=10)\ny_test = np_utils.to_categorical(y_test,num_classes=10)\ntraining_set=np.concatenate((x_train,x_test),axis=0)\ntraining_labels=np.concatenate((y_train,y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model=Sequential()\nmodel.add(Conv2D(64,kernel_size=(3,3),input_shape=(28,28,1),activation='relu',padding=\"same\"))\nmodel.add(Conv2D(64,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(MaxPool2D((2,2)))\nmodel.add(Conv2D(128,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(Conv2D(128,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(MaxPool2D((2,2)))\nmodel.add(Conv2D(256,kernel_size=(3,3),activation='relu',padding=\"same\"))\nmodel.add(Conv2D(256,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(Conv2D(256,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(MaxPool2D((2,2)))\nmodel.add(Conv2D(512,kernel_size=(3,3),activation='relu',padding=\"same\"))\nmodel.add(Conv2D(512,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(Conv2D(512,kernel_size=(3,3),activation='relu',padding='same'))\nmodel.add(MaxPool2D((2,2)))\nmodel.add(Flatten())\nmodel.add(Dense(4096,activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(4096,activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10,activation='softmax'))\nmodel.compile(Adam(learning_rate = 0.0007),loss='categorical_crossentropy',metrics=['accuracy'])\nmodel.summary()\nlearning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience=3, verbose=1, factor=0.5, min_lr=0.00001)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(training_set,training_labels,validation_split=0.1,batch_size=128,epochs=50,callbacks=[learning_rate_reduction])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = test.drop(['id'],axis=1)\ntest = test.values.reshape(test.shape[0],28,28,1)/255.0\nFINAL_PREDS = model.predict_classes(test)\nsubmission = pd.DataFrame({ 'id': Id,'label': FINAL_PREDS })\nsubmission.to_csv(path_or_buf =\"submission.csv\", index=False)\nsubmission.head()","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":1}