{"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# 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/kannada-mnist/Kannada_MNIST_datataset_paper/Kannada_MNIST_npz/Kannada_MNIST/y_kannada_MNIST_train.npz'):\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 math\nimport numpy as np\nimport pandas as pd\nimport h5py\nimport skimage\nimport matplotlib.pyplot as plt\nimport scipy\nfrom PIL import Image\nfrom scipy import ndimage\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\nfrom keras.preprocessing.image import ImageDataGenerator\n# import tensorflow as tf\n# from tensorflow.python.framework import ops\n# from cnn_utils import *\n\n%matplotlib inline\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"num_px = 28\nmy_image = \"1.jpg\"   \nfname = \"\" + my_image\nimage = np.array(plt.imread(fname))\nplt.imshow(image)\nprint(image.shape)\ncompressed_image = skimage.transform.resize(image, output_shape=(num_px,num_px))\n# plt.imshow(compressed_image)\nmy_image = skimage.transform.resize(image, output_shape=(num_px,num_px)).reshape((1, num_px*num_px*3)).T","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')\nsample_sub=pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')\nprint(\"Train set shape = \" +str(train.shape))\nprint(\"Test set shape = \" +str(test.shape))\n# print(\"Dif set shape = \" +str(Dig_MNIST.shape))\ntrain.head(10)\n# test.head(3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head(3)\ntest=test.drop('id',axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y=train.label.value_counts()\nsns.barplot(y.index,y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train=train.drop('label',axis=1)\n# X_test=test\n# X_train.head(5)\nY_train=train.label\nX_train=X_train/255\ntest=test/255\nprint(test.shape)\nprint(X_train.shape)\nX_train=X_train.values.reshape(-1,28,28,1)\ntest=test.values.reshape(-1,28,28,1)\nY_train=to_categorical(Y_train)\nprint(X_train.shape)\nprint(Y_train.shape)\n\nX_train,X_test,y_train,y_test=train_test_split(X_train,Y_train,random_state=42,test_size=0.15)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# y_train.head\nplt.imshow(X_train[6][:,:,0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from __future__ import print_function\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\nbatch_size = 128\nnum_classes = 10\nepochs = 6\n\n# input image dimensions\nimg_rows, img_cols = 28, 28\ninput_shape = (img_rows, img_cols, 1)\n\n# y_train = keras.utils.to_categorical(y_train, num_classes)\n# y_test = keras.utils.to_categorical(y_test, num_classes)\n\nmodel = Sequential()\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=keras.losses.categorical_crossentropy,\n              optimizer=keras.optimizers.Adadelta(),\n              metrics=['accuracy'])\n\nmodel.fit(X_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          verbose=1,\n          validation_data=(X_test, y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"score = model.evaluate(X_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\ntest=pd.read_csv('../input/Kannada-MNIST/test.csv')\ntest_id=test.id\n\ntest=test.drop('id',axis=1)\ntest=test/255\ntest=test.values.reshape(-1,28,28,1)\nprint(test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pre=model.predict(test)     ##making prediction\ny_pre=np.argmax(y_pre,axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_sub['label']=y_pre\nsample_sub.to_csv('submission.csv',index=False)\nsample_sub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(test[3][:,:,0])","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}