{"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\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","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ndata.info()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = np.array(data)\n\ntrain = data[:,1:]\nlabel = data[:,0]\n\nprint(train.shape,label.shape)\nprint(label)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom sklearn.model_selection import train_test_split\n\nx_train, x_test, y_train, y_test = train_test_split(train, label, test_size=0.1, random_state=1)\nx_train = x_train.reshape(-1, 28, 28, 1)\nx_test = x_test.reshape(-1, 28, 28, 1)\nprint(x_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nrg = tf.keras.regularizers.l2(0.001)\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(170, 170, 1), kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.MaxPooling2D((1, 1), strides=2, padding='valid'),\n\n    tf.keras.layers.Conv2D(64, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(64, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.MaxPooling2D((1, 1), strides=2, padding='valid'),\n\n    tf.keras.layers.Conv2D(128, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(128, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.MaxPooling2D((1, 1), strides=2, padding='valid'),\n\n    tf.keras.layers.Conv2D(256, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(256, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.MaxPooling2D((1, 1), strides=1, padding='valid'),\n\n    tf.keras.layers.Conv2D(256, (3,3), padding='valid', activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n\n    tf.keras.layers.Flatten(),\n\n    tf.keras.layers.Dense(120, activation='relu', kernel_regularizer=rg),\n    tf.keras.layers.BatchNormalization(),\n\n    tf.keras.layers.Dense(10, activation='softmax', kernel_regularizer=rg)\n])\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"EPOCHS = 100\nhistory = model.fit(\n    x_train, y_train,\n    steps_per_epoch=70,\n    epochs=EPOCHS,\n    validation_data=(x_test,y_test),\n    validation_steps=70\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\ntest = np.array(test).reshape(-1,28,28,1)\nprint(test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"result = np.zeros(28000)\npredict = model.predict(test)\n\nfor i in range(28000):\n    a = max(predict[i,:])\n    for j in range(10):\n        if a == predict[i,j]:\n            result[i] = j\n            break\n    \n\nprint(result)    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from IPython.display import HTML\nimport base64\n\nImageId = np.arange(0,28000)\nLabel = result\n\ndata = {'ImageId':ImageId, 'Label':Label}\n\n\ndef create_download_link(df, title = \"Download CSV file\", filename = \"data.csv\"):  \n    csv = df.to_csv()\n    b64 = base64.b64encode(csv.encode())\n    payload = b64.decode()\n    html = '<a download=\"{filename}\" href=\"data:text/csv;base64,{payload}\" target=\"_blank\">{title}</a>'\n    html = html.format(payload=payload,title=title,filename=filename)\n    return HTML(html)\n\n# create a random sample dataframe\ndf = pd.DataFrame(data)\n\n# create a link to download the dataframe\ncreate_download_link(df)","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}