{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"#  Lets identify digits from thousands of hand written 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"}}},{"cell_type":"markdown","source":"# Import all required libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport itertools\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport seaborn as sns\n%matplotlib inline\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.manifold import MDS, TSNE\n\nimport tensorflow as tf\nfrom tensorflow import keras\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau\nfrom keras import  backend as K\nfrom keras.optimizers import Adam ,RMSprop\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-04T20:15:52.710683Z","iopub.execute_input":"2021-08-04T20:15:52.711083Z","iopub.status.idle":"2021-08-04T20:15:52.745655Z","shell.execute_reply.started":"2021-08-04T20:15:52.711048Z","shell.execute_reply":"2021-08-04T20:15:52.744577Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Importing train and test data","metadata":{}},{"cell_type":"code","source":"train =  pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\nprint(train.shape)\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:15:52.746993Z","iopub.execute_input":"2021-08-04T20:15:52.747344Z","iopub.status.idle":"2021-08-04T20:15:56.631051Z","shell.execute_reply.started":"2021-08-04T20:15:52.747316Z","shell.execute_reply":"2021-08-04T20:15:56.630050Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")\nprint(test.shape)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:15:56.633390Z","iopub.execute_input":"2021-08-04T20:15:56.633732Z","iopub.status.idle":"2021-08-04T20:15:58.625087Z","shell.execute_reply.started":"2021-08-04T20:15:56.633688Z","shell.execute_reply":"2021-08-04T20:15:58.624136Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Y_train = train[\"label\"]\nX_train = train.drop(labels = [\"label\"],axis = 1) \nX_train = X_train / 255.0\ntest = test / 255.0","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:15:58.627523Z","iopub.execute_input":"2021-08-04T20:15:58.627768Z","iopub.status.idle":"2021-08-04T20:16:00.170991Z","shell.execute_reply.started":"2021-08-04T20:15:58.627741Z","shell.execute_reply":"2021-08-04T20:16:00.169950Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = X_train.values.reshape(-1,28,28,1)\ntest = test.values.reshape(-1,28,28,1)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:00.172582Z","iopub.execute_input":"2021-08-04T20:16:00.172835Z","iopub.status.idle":"2021-08-04T20:16:00.178237Z","shell.execute_reply.started":"2021-08-04T20:16:00.172806Z","shell.execute_reply":"2021-08-04T20:16:00.177491Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Y_train = keras.utils.to_categorical(Y_train, 10)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:00.179324Z","iopub.execute_input":"2021-08-04T20:16:00.179659Z","iopub.status.idle":"2021-08-04T20:16:00.194487Z","shell.execute_reply.started":"2021-08-04T20:16:00.179632Z","shell.execute_reply":"2021-08-04T20:16:00.193627Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:00.195602Z","iopub.execute_input":"2021-08-04T20:16:00.196114Z","iopub.status.idle":"2021-08-04T20:16:01.166138Z","shell.execute_reply.started":"2021-08-04T20:16:00.196075Z","shell.execute_reply":"2021-08-04T20:16:01.165185Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def visualize_input(img, ax):\n    ax.imshow(img, cmap='gray')\n    width, height = img.shape\n    thresh = img.max()/2.5\n    for x in range(width):\n        for y in range(height):\n            ax.annotate(str(round(img[x][y],2)), xy=(y,x),\n                        horizontalalignment='center',\n                        verticalalignment='center',\n                        color='white' if img[x][y]<thresh else 'black')\n\nfig = plt.figure(figsize = (10,10)) \nax = fig.add_subplot(111)\nvisualize_input(X_train[27].reshape(28,28), ax)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:01.167363Z","iopub.execute_input":"2021-08-04T20:16:01.167664Z","iopub.status.idle":"2021-08-04T20:16:04.149762Z","shell.execute_reply.started":"2021-08-04T20:16:01.167634Z","shell.execute_reply":"2021-08-04T20:16:04.149018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Testing the data","metadata":{}},{"cell_type":"code","source":"batch_size = 86\nnum_classes = 10\nepochs = 10\ninput_shape = (28, 28, 1)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:04.151087Z","iopub.execute_input":"2021-08-04T20:16:04.151331Z","iopub.status.idle":"2021-08-04T20:16:04.155811Z","shell.execute_reply.started":"2021-08-04T20:16:04.151302Z","shell.execute_reply":"2021-08-04T20:16:04.155143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''#there are more 1 image samples\nsns.barplot(x = y_train.unique(), y=y_train.value_counts())\nplt.xlabel('Digits')\nplt.ylabel('Number of image samples')'''","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:04.156855Z","iopub.execute_input":"2021-08-04T20:16:04.157133Z","iopub.status.idle":"2021-08-04T20:16:04.178112Z","shell.execute_reply.started":"2021-08-04T20:16:04.157106Z","shell.execute_reply":"2021-08-04T20:16:04.177255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"batch_size = 86\nnum_classes = 10\nepochs = 10\ninput_shape = (28, 28, 1)\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3),activation='relu',kernel_initializer='he_normal',input_shape=input_shape))\nmodel.add(Conv2D(32, kernel_size=(3, 3),activation='relu',kernel_initializer='he_normal'))\nmodel.add(MaxPool2D((2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nmodel.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n                 activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\nmodel.add(Dropout(0.25))\n\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation = \"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation = \"softmax\"))\n\nmodel.compile(loss=keras.losses.categorical_crossentropy,\n              optimizer=keras.optimizers.RMSprop(),\n              metrics=['accuracy'])\n\nlearning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n                                            patience=3, \n                                            verbose=1, \n                                            factor=0.5, \n                                            min_lr=0.0001)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:04.179629Z","iopub.execute_input":"2021-08-04T20:16:04.179887Z","iopub.status.idle":"2021-08-04T20:16:04.351525Z","shell.execute_reply.started":"2021-08-04T20:16:04.179859Z","shell.execute_reply":"2021-08-04T20:16:04.350608Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datagen = ImageDataGenerator(\n        featurewise_center=False,  # set input mean to 0 over the dataset\n        samplewise_center=False,  # set each sample mean to 0\n        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n        samplewise_std_normalization=False,  # divide each input by its std\n        zca_whitening=False,  # apply ZCA whitening\n        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n        zoom_range = 0.1, # Randomly zoom image \n        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n        horizontal_flip=False,  # randomly flip images\n        vertical_flip=False)  # randomly flip images\n\ndatagen.fit(X_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:04.356359Z","iopub.execute_input":"2021-08-04T20:16:04.356620Z","iopub.status.idle":"2021-08-04T20:16:04.500502Z","shell.execute_reply.started":"2021-08-04T20:16:04.356594Z","shell.execute_reply":"2021-08-04T20:16:04.499671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mnist_test = pd.read_csv(\"../input/mnist-in-csv/mnist_test.csv\")\nmnist_train = pd.read_csv(\"../input/mnist-in-csv-train/mnist_train.csv\")","metadata":{"execution":{"iopub.status.busy":"2021-08-04T20:16:04.502542Z","iopub.execute_input":"2021-08-04T20:16:04.502889Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission = pd.read_csv(\"../input/digit-recognizer/sample_submission.csv\")\ntest = pd.read_csv(\"../input/digit-recognizer/test.csv\")\ntrain = pd.read_csv(\"../input/digit-recognizer/train.csv\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cols = test.columns","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test['dataset'] = 'test'","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['dataset'] = 'train'","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset = pd.concat([train.drop('label', axis=1), test]).reset_index()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mnist = pd.concat([mnist_train, mnist_test]).reset_index(drop=True)\nlabels = mnist['label'].values\nmnist.drop('label', axis=1, inplace=True)\nmnist.columns = cols","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"idx_mnist = mnist.sort_values(by=list(mnist.columns)).index\ndataset_from = dataset.sort_values(by=list(mnist.columns))['dataset'].values\noriginal_idx = dataset.sort_values(by=list(mnist.columns))['index'].values","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\ntest_images = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")\n\n#extract the label value from the training dataset, not required from test data as labels are not given for this test dataset\ntrain_label = np.array(train['label'])\ntrain_images = train.drop('label', axis=1)\n\n#training dataset is in 784 pixels which needs to be reshaped usign the re-shape function\ntrain_images_arr = np.array(train_images).reshape(42000,28,28)\ntest_images_arr = np.array(test_images).reshape(28000,28,28)\n\nplt.figure(figsize =(9,9))\nfor i in range(20):\n    plt.subplot(5,5,1+i)\n    plt.xticks([])\n    plt.yticks([])\n    plt.imshow(train_images_arr[i])\n    plt.xlabel(train_label[i])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Making the submission","metadata":{}},{"cell_type":"code","source":"for i in range(len(idx_mnist)):\n    if dataset_from[i] == 'test':\n        sample_submission.loc[original_idx[i], 'Label'] = labels[idx_mnist[i]]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission.to_csv('submission.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# If you like this notebook, please give an Upvote! Don't forget to check out my other notebooks too!\n\n* [ConnectX Baseline](https://www.kaggle.com/brendan45774/connectx-baseline)\n* [Countries Life Expectancy Animation](https://www.kaggle.com/brendan45774/countries-life-expectancy-animation)\n* [Data Visuals - Matplotlib](http://www.kaggle.com/brendan45774/data-visuals-matplotlib)\n* [Digit Recognizer Solution](http://www.kaggle.com/brendan45774/digit-recognizer-solution)\n* [Dictionary and Pandas Cheat sheet](https://www.kaggle.com/brendan45774/dictionary-and-pandas-cheat-sheet)\n* [EDA Tutorial Hollywood Movies](https://www.kaggle.com/brendan45774/eda-tutorial-hollywood-movies)\n* [Getting Started with Manifold Learning - Isomap](https://www.kaggle.com/brendan45774/getting-started-with-manifold-learning-isomap)\n* [Getting started with Matplotlib](http://www.kaggle.com/brendan45774/getting-started-with-matplotlib)\n* [Guide to Matplotlib Image](https://www.kaggle.com/brendan45774/guide-to-matplotlib-image)\n* [HOG features - Histogram of Oriented Gradients](https://www.kaggle.com/brendan45774/hog-features-histogram-of-oriented-gradients)\n* [How to get the lowest score](https://www.kaggle.com/brendan45774/how-to-get-the-lowest-score)\n* [House predict solution](http://www.kaggle.com/brendan45774/house-predict-solution)\n* [K-Means Clustering (Image Compression)](https://www.kaggle.com/brendan45774/k-means-clustering-image-compression)\n* [Kuzushiji-MNIST Panda](http://www.kaggle.com/brendan45774/kuzushiji-mnist-panda)\n* [Plotly Coronavirus (Covid-19)](https://www.kaggle.com/brendan45774/plotly-coronavirus-covid-19)\n* [Titanic Top Solution](http://www.kaggle.com/brendan45774/titanic-top-solution)\n* [Titanic Data Solution](http://www.kaggle.com/brendan45774/titanic-data-solution)\n* [Topic Modeling (LDA)](https://www.kaggle.com/brendan45774/topic-modeling-lda)\n* [Word Cloud - Analyzing Names](https://www.kaggle.com/brendan45774/word-cloud-analyzing-names)","metadata":{}}]}