{"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":"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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom keras.utils.np_utils import to_categorical\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:12.662268Z","iopub.execute_input":"2022-10-04T15:22:12.663325Z","iopub.status.idle":"2022-10-04T15:22:12.672150Z","shell.execute_reply.started":"2022-10-04T15:22:12.663291Z","shell.execute_reply":"2022-10-04T15:22:12.670002Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(\"../input/digit-recognizer/test.csv\")\ntrain = pd.read_csv(\"../input/digit-recognizer/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:13.068207Z","iopub.execute_input":"2022-10-04T15:22:13.069095Z","iopub.status.idle":"2022-10-04T15:22:17.270951Z","shell.execute_reply.started":"2022-10-04T15:22:13.069050Z","shell.execute_reply":"2022-10-04T15:22:17.269565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:17.273677Z","iopub.execute_input":"2022-10-04T15:22:17.275215Z","iopub.status.idle":"2022-10-04T15:22:17.309605Z","shell.execute_reply.started":"2022-10-04T15:22:17.275166Z","shell.execute_reply":"2022-10-04T15:22:17.308049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train = train[\"label\"]\nx_train = train.drop(columns=[\"label\"])\nx_train","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:17.311855Z","iopub.execute_input":"2022-10-04T15:22:17.312722Z","iopub.status.idle":"2022-10-04T15:22:17.447091Z","shell.execute_reply.started":"2022-10-04T15:22:17.312649Z","shell.execute_reply":"2022-10-04T15:22:17.445549Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:17.450907Z","iopub.execute_input":"2022-10-04T15:22:17.451760Z","iopub.status.idle":"2022-10-04T15:22:17.463888Z","shell.execute_reply.started":"2022-10-04T15:22:17.451714Z","shell.execute_reply":"2022-10-04T15:22:17.462231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Normalization**","metadata":{}},{"cell_type":"code","source":"x_train = x_train/255.0\ntest = test/255.0","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:17.466185Z","iopub.execute_input":"2022-10-04T15:22:17.467311Z","iopub.status.idle":"2022-10-04T15:22:18.095909Z","shell.execute_reply.started":"2022-10-04T15:22:17.467265Z","shell.execute_reply":"2022-10-04T15:22:18.094233Z"},"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":"2022-10-04T15:22:18.098922Z","iopub.execute_input":"2022-10-04T15:22:18.099798Z","iopub.status.idle":"2022-10-04T15:22:18.107487Z","shell.execute_reply.started":"2022-10-04T15:22:18.099735Z","shell.execute_reply":"2022-10-04T15:22:18.106007Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"exmpl = plt.imshow(x_train[9][:,:,0])","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:18.109753Z","iopub.execute_input":"2022-10-04T15:22:18.110756Z","iopub.status.idle":"2022-10-04T15:22:18.358607Z","shell.execute_reply.started":"2022-10-04T15:22:18.110710Z","shell.execute_reply":"2022-10-04T15:22:18.357284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The numeric values can be misinterpreted by algorithms as having some sort of hierarchy/order in them. I will use another common alternative approach called 'One-Hot Encoding' to solve this issue.","metadata":{}},{"cell_type":"code","source":"y_train.unique()","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:18.360698Z","iopub.execute_input":"2022-10-04T15:22:18.361586Z","iopub.status.idle":"2022-10-04T15:22:18.372430Z","shell.execute_reply.started":"2022-10-04T15:22:18.361514Z","shell.execute_reply":"2022-10-04T15:22:18.370961Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes = len(np.unique(y_train,axis=0))\ny_train = to_categorical(y_train,classes)\ny_train","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:18.374604Z","iopub.execute_input":"2022-10-04T15:22:18.375482Z","iopub.status.idle":"2022-10-04T15:22:18.420283Z","shell.execute_reply.started":"2022-10-04T15:22:18.375430Z","shell.execute_reply":"2022-10-04T15:22:18.418195Z"},"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 = 0.2, random_state=42)\nprint(\"x_train shape: \",x_train.shape)\nprint(\"x_val shape: \",x_val.shape)\nprint(\"y_train shape: \",y_train.shape)\nprint(\"y_val shape: \",y_val.shape)","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:18.425742Z","iopub.execute_input":"2022-10-04T15:22:18.426508Z","iopub.status.idle":"2022-10-04T15:22:19.070878Z","shell.execute_reply.started":"2022-10-04T15:22:18.426464Z","shell.execute_reply":"2022-10-04T15:22:19.069280Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CNN Architecture**","metadata":{}},{"cell_type":"markdown","source":"![A-generic-CNN-Architecture.png](attachment:61f11b25-17a2-41f9-8fd9-a8ff2111f053.png)","metadata":{},"attachments":{"61f11b25-17a2-41f9-8fd9-a8ff2111f053.png":{"image/png":"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"}}},{"cell_type":"code","source":"input_shape = (28,28,1)\nfrom keras.backend import dropout\nmodel = Sequential()\nmodel.add(Conv2D(filters = 32,kernel_size=(5,5),padding='Same',activation='relu',input_shape=input_shape))\nmodel.add(Conv2D(filters = 32,kernel_size=(5,5),padding='Same',activation='relu',input_shape=input_shape))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(filters=64,kernel_size=(3,3),padding='Same',activation='relu'))\nmodel.add(Conv2D(filters=64,kernel_size=(3,3),padding='Same',activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Flatten())\nmodel.add(Dense(256,activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(classes,activation='softmax'))\n\noptimizer = tf.keras.optimizers.Nadam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name=\"Nadam\")\nmodel.compile(optimizer=optimizer, loss='categorical_crossentropy',metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:19.073159Z","iopub.execute_input":"2022-10-04T15:22:19.074197Z","iopub.status.idle":"2022-10-04T15:22:19.179757Z","shell.execute_reply.started":"2022-10-04T15:22:19.074142Z","shell.execute_reply":"2022-10-04T15:22:19.178426Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I used Nadam optimizer. Please check the link :https://wandb.ai/sauravm/Optimizers/reports/How-to-Compare-Keras-Optimizers-in-Tensorflow-for-Deep-Learning--VmlldzoxNjU1OTA4#summary","metadata":{}},{"cell_type":"code","source":"model.fit(x_train, y_train, batch_size=86, epochs=30, validation_data=(x_val, y_val))","metadata":{"execution":{"iopub.status.busy":"2022-10-04T15:22:19.182974Z","iopub.execute_input":"2022-10-04T15:22:19.183787Z","iopub.status.idle":"2022-10-04T15:24:09.728296Z","shell.execute_reply.started":"2022-10-04T15:22:19.183744Z","shell.execute_reply":"2022-10-04T15:24:09.726763Z"},"trusted":true},"execution_count":null,"outputs":[]}]}