{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport numpy as np \nimport pandas as pd\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\nfrom keras.utils.np_utils import to_categorical\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom tensorflow.keras import layers, models\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"import pandas as pd\nDig_MNIST = 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":"x = train.drop('label', axis=1)\ny = train['label']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = to_categorical( y,num_classes=len(y.unique()))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = x/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x = x.values.reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(x[1][:,:,0])\nplt.title(train['label'][1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(filters = 32 ,kernel_size =(5,5),activation = 'relu',input_shape=(28,28,1)))\nmodel.add(MaxPooling2D(pool_size =(2, 2)))\nmodel.add(Dropout(0.3))\n\nmodel.add(Conv2D(filters = 64 ,kernel_size =(3,3),activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size =(2, 2)))\nmodel.add(Dropout(0.3))\n\n\nmodel.add(Conv2D(filters = 128 ,kernel_size =(3,3),activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size =(2, 2)))\nmodel.add(Dropout(0.3))\n          \nmodel.add(Flatten())\n          \nmodel.add(Dense(64,activation= 'relu'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(10, activation = \"softmax\"))\nmodel.summary()              \n          ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(x,y,epochs=10, batch_size=86,validation_split = 0.2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# test.drop('id',axis=1,inplace=True)\nx_test = test/255\nx_test = x_test.values.reshape(-1,28,28,1)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predicted = model.predict(x_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pos = 99\nplt.imshow(x_test[pos,:,:,0])\nprint(\"Predicted Value \" +str(np.argmax(predicted[pos])))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results = np.argmax(predicted,axis = 1)\nresults = pd.Series(results,name=\"label\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final=pd.DataFrame({\"id\": list(range(1,len(results)+1)),\"label\": results})\nfinal.to_csv(\"submission.csv\", index=False, header=True)","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}