{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/Kannada-MNIST/train.csv\") \ntest = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = train[\"label\"]\ntrain.drop(\"label\",axis=1,inplace=True) \ntest.drop(\"id\",axis=1,inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = train.values\ntest = test.values\n\nx_train = x_train/255\ntest = test/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train.reshape(-1,28,28,1)\ntest = test.reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train = pd.get_dummies(y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nx_train, x_test, y_train, y_test = train_test_split(x_train,y_train,test_size=0.2,random_state=42,stratify=y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, AveragePooling2D, Flatten, Dropout,MaxPool2D,BatchNormalization\nfrom keras.preprocessing. image import ImageDataGenerator\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\nfrom keras.optimizers import RMSprop","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"model = Sequential()\n\nmodel.add(Conv2D(filters=6, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))\nmodel.add(AveragePooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=16, kernel_size=(5, 5), activation='relu'))\nmodel.add(AveragePooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(units=120, activation='relu'))\nmodel.add(Dense(units=84, activation='relu'))\nmodel.add(Dense(units=10, activation = 'softmax'))"},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1)))\nmodel.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.25))\n\n\nmodel.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\nmodel.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\nmodel.add(Dropout(0.25))\n\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation = \"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation = \"softmax\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n                                            patience=3, \n                                            verbose=1, \n                                            factor=0.5, \n                                            min_lr=0.00001)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"checkpoint=ModelCheckpoint('bestweights.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode=\"max\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['acc'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_gen = ImageDataGenerator(rotation_range=10, \n                               width_shift_range=0.1, \n                               height_shift_range=0.1, \n                               zoom_range=0.1) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history= model.fit_generator(train_gen.flow(x_train,y_train,batch_size=86),\n                             steps_per_epoch=x_train.shape[0]//86,\n                             validation_data=(x_test,y_test),\n                             validation_steps=x_test.shape[0]//86,\n                             epochs=30,callbacks=[checkpoint,learning_rate_reduction ])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"history = model.fit(x_train,y_train,epochs=20,batch_size=32,validation_data=(x_test,y_test),callbacks=[checkpoint])"},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history.history['acc'])\nplt.plot(history.history['val_acc'])\nplt.title('model accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\n# summarize history for loss\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.load_weights(\"bestweights.hdf5\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results = model.predict(test)\nresults = np.argmax(results,axis = 1)\nsubmission = pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')\nsubmission['label'] = results\nsubmission.to_csv('submission.csv', index=False)\nsubmission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}