{"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 20GB 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":"\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\ntest_dataframe=pd.read_csv('../input/Kannada-MNIST/test.csv')\ntrain_dataframe=pd.read_csv('../input/Kannada-MNIST/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dataframe.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels=train_dataframe.pop('label')\ntrain_labels.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_input=train_dataframe\ntrain_input.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_input.isnull().any().describe()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_dataframe.pop(\"id\")\n\ntest_dataframe","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_input=train_input/255.0\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ntest_input = test_dataframe/255.0\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_input=train_input.values.reshape(-1,28,28,1)\ntest_dataframe=test_input.values.reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_labels.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_set,val_set,train_label,val_label=train_test_split(train_input,train_labels,test_size=0.1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.imshow(test_dataframe[53][:,:,0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\n\ndatagen=tf.keras.preprocessing.image.ImageDataGenerator(\n    rotation_range=5,\n    zoom_range = 0.2,\n    width_shift_range=0.2,\n    height_shift_range=0.2,\n    shear_range=0.2,\n    fill_mode=\"nearest\"\n    \n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"datagen.fit(train_set)\n\ntrain_datagen=datagen.flow(train_set,train_label,batch_size=400)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model=tf.keras.Sequential([\n    \n  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),\n  tf.keras.layers.MaxPooling2D(2, 2),\n  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n  tf.keras.layers.MaxPooling2D(2,2),\n  tf.keras.layers.Flatten(),\n  tf.keras.layers.Dense(512, activation='relu'),\n  tf.keras.layers.Dropout(0.5),\n  tf.keras.layers.Dense(128, activation='relu'),\n  tf.keras.layers.Dense(10, activation='softmax')\n    \n])","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='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=model.fit(train_set,train_label,epochs=20,validation_data=(val_set,val_label),steps_per_epoch=70)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"accu=history.history['accuracy']\nloss=history.history['loss']\nval_accu=history.history['val_accuracy']\nval_loss=history.history['val_loss']\nepoch_num=range(len(accu))\nplt.plot(epoch_num,accu,'r',label='accuracy')\nplt.plot(epoch_num,loss,'b',label='loss')\nplt.legend()\nplt.figure()\n\nplt.plot(epoch_num,val_accu,'r',label='val_accuracy')\nplt.plot(epoch_num,val_loss,'b',label='val_loss')\nplt.legend()\nplt.figure()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pre=model.predict(test_dataframe)     ##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":"sample_sub=pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')\n\nsample_sub['label']=y_pre\nsample_sub.to_csv('submission.csv',index=False)\n\nsample_sub.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":4}