{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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)\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout\nfrom tensorflow.keras.layers import Flatten, Conv2D, MaxPooling2D\nfrom sklearn.model_selection import train_test_split\nimport os\nfrom tensorflow.keras.layers import BatchNormalization","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"print(os.listdir(\"../input/Kannada-MNIST/\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")\ndf_test = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(df_train.shape)\nprint(df_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = df_train.iloc[:,1:]\ny = df_train.iloc[:,0]\nX_test_actual = df_test.iloc[:,1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = X.to_numpy().reshape(len(X), 28, 28,1).astype('float32')\nX_test_actual = X_test_actual.to_numpy().reshape(len(X_test_actual), 28, 28, 1).astype('float32')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = X/255\nX_test_actual = X_test_actual/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"n_classes=10\ny = to_categorical(y, n_classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train,  y_test = train_test_split(X, y, test_size=0.3, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(32, 3, activation='relu', padding='same', input_shape=(28, 28, 1)))\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(2, 2))\nmodel.add(BatchNormalization())\n\nmodel.add(Conv2D(128, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(128, 3, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(2, 2))\nmodel.add(BatchNormalization())\n\nmodel.add(Conv2D(256, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(256, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(256, 3, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(2, 2))\nmodel.add(BatchNormalization())\n\nmodel.add(Conv2D(512, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(512, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(512, 3, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(2, 1)) # default stride is 2\nmodel.add(BatchNormalization())\n\nmodel.add(Conv2D(512, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(512, 3, activation='relu', padding='same'))\nmodel.add(Conv2D(512, 3, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(2, 1)) # default stride is 2\nmodel.add(BatchNormalization())\n\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))\n\nmodel.add(Dense(10, activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',\n             optimizer='nadam',\n             metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(X_train, \n                    y_train, \n                    batch_size=128, \n                    epochs=50,\n                    verbose=1,\n                    validation_data=(X_test, y_test)\n                   )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(1, len(loss) + 1)\n\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend()\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\n\nplt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc')\nplt.title('Training and validation accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.legend()\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_submission = pd.read_csv(\"../input/Kannada-MNIST/sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pre=model.predict(X_test_actual)     ##making prediction\ny_pre=np.argmax(y_pre,axis=1) ##changing the prediction intro labels","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_submission['label']=y_pre\ndata_submission.to_csv('submission.csv',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_submission.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}