{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport tensorflow.keras as keras\n\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras import layers","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"val = pd.read_csv(\"../input/Kannada-MNIST/Dig-MNIST.csv\")\nsample_submission = pd.read_csv(\"../input/Kannada-MNIST/sample_submission.csv\")\ntest = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")\ntrain = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del test['id']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = train.iloc[:,1:].values\ny = train.iloc[:,0].values\nx_val = val.iloc[:,1:].values\ny_val = val.iloc[:,0].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = X.reshape(X.shape[0], 28, 28, 1)\nx_val = x_val.reshape(x_val.shape[0], 28, 28, 1)\ntest = test.values.reshape(test.values.shape[0], 28, 28, 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = np.pad(X, ((0, 0), (2, 2), (2, 2), (0, 0)), 'constant')\nx_val = np.pad(x_val, ((0, 0), (2, 2), (2, 2), (0, 0)), 'constant')\ntest = np.pad(test, ((0, 0), (2, 2), (2, 2), (0, 0)), 'constant')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.utils import to_categorical","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = to_categorical(y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint\nfrom sklearn.metrics import accuracy_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"checkpoint = ModelCheckpoint(\"best_weights.h5\", monitor='val_accuracy', save_best_only=True, mode='max')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential([\n                    layers.Conv2D(6, (5, 5), strides=(1, 1), input_shape=(32, 32, 1), activation='relu'),\n                    layers.BatchNormalization(),\n                    layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),\n                    layers.Conv2D(16, (5, 5), strides=(1, 1), input_shape=(14, 14, 6), activation='relu'),\n                    layers.BatchNormalization(),\n                    layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),\n                    layers.Conv2D(120, (5, 5), strides=(1, 1), input_shape=(5, 5, 16), activation='relu'),\n                    layers.Flatten(),\n                    layers.Dense(84, activation='relu'),\n                    layers.BatchNormalization(),\n                    layers.Dropout(0.5),\n                    layers.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(optimizer=keras.optimizers.Adam(),\n              loss='categorical_crossentropy',\n              metrics=['accuracy']\n              )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(X / 255, y, batch_size=64, shuffle=True, validation_split=0.2, epochs=30, callbacks=[checkpoint])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.models import load_model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"bm = load_model(\"best_weights.h5\")\npred = bm.predict(x_val / 255, batch_size=16)\naccuracy_score(y_val, pred.argmax(axis=1))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred = bm.predict(test / 255, batch_size=16)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission['label'] = pred.argmax(axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission.to_csv(\"submission.csv\", index=False)","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":4}