{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ntrain = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\n#est = pd.read_csv(r'C:\\Users\\HP\\Desktop\\test.csv')\n\n#y_train = train.values[:,0]\n#y_test = train.values[0:5000,0]\n#x_test = test.values[:,1:]\n#x_train = train.values[: , 1:]\n\nx = train.values[:, 1:]\ny = train.values[:,0]\nx_train,x_test,y_train,y_test = train_test_split(x, y,test_size=0.2)\n\n\nx_train = x_train.reshape(48000 , 28 , 28)\nx_test = x_test.reshape(12000, 28 , 28)\ny_test = y_test\ny_train = y_train\n\nfrom keras.datasets import mnist\nfrom keras.utils import np_utils\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend as K\n\n\nbatch_size = 128\nepochs = 5\n\n# Lets store the number of rows and columns\n\nimg_rows = x_train[0].shape[0]\nimg_cols = x_train[1].shape[0]\n\n# Getting our date in the right 'shape' needed for Keras\n# We need to add a 4th dimenion to our date thereby changing our\n# Our original image shape of (60000,28,28) to (60000,28,28,1)\n\nx_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\nx_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n\n\n\n# store the shape of a single image \ninput_shape = (img_rows, img_cols, 1)\n\n# change our image type to float32 data type\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\n\n# Normalize our data by changing the range from (0 to 255) to (0 to 1)\nx_train /= 255\nx_test /= 255\n\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n\n# Now we one hot encode outputs\ny_train = np_utils.to_categorical(y_train )\ny_test = np_utils.to_categorical(y_test)\n\n# Let's count the number columns in our hot encoded matrix \n#print (\"Number of Classes: \" + str(y_test.shape[1]))\n\nnum_classes =10\nnum_pixels = x_train.shape[1] * x_train.shape[2]\n\n\n# create model\nmodel = Sequential()\n\nmodel.add(Conv2D(32, kernel_size=(3, 3),\n                 activation='relu',\n                 input_shape=input_shape))\nmodel.add(Conv2D(64, (3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation='softmax'))\n\nmodel.compile(loss = 'categorical_crossentropy',\n              optimizer = keras.optimizers.Adadelta(),\n              metrics = ['accuracy'])\n\nprint(model.summary())\n\nhistory = model.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          verbose=1,\n          validation_data=(x_test, y_test))\n\nscore = model.evaluate(x_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n","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}