{"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":"","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"import pandas as pd\nsample_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\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\ntr=train.drop([\"label\"], axis =1)\nX_orig = tr.to_numpy()\nY_orig = train.label.to_numpy().reshape(42000,1)\nX_test = test.to_numpy() /255\nX_train = X_orig.reshape(42000,28,28,1) /255\nY_train = tf.keras.utils.to_categorical(Y_orig, num_classes=10, dtype=\"float32\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Training \", X_orig.shape)\nprint(\"Lavels \", Y_orig.shape) \nprint(\"Training 2 \", X_train.shape)\nprint(\"Lavels 2 \", Y_train.shape) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train[75]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"input_shape=(28,28,1)\nmodel = tf.keras.models.Sequential()\nmodel.add(tf.keras.layers.Conv2D(32, kernel_size=(3, 3), input_shape=input_shape, activation=\"relu\"))\nmodel.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\nmodel.add(tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\nmodel.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\nmodel.add(tf.keras.layers.Flatten())\nmodel.add(tf.keras.layers.Dense(256, activation=\"relu\"))\nmodel.add(tf.keras.layers.Dense(256, activation=\"relu\"))\nmodel.add(tf.keras.layers.Dense(10, activation='softmax'))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss=tf.keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(), metrics=[\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist=model.fit(X_train, Y_train, batch_size=512, epochs=10, validation_split=0.2, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')\nPred = model.predict(X_test)\nPred.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = Pred.argmax(axis=1)\nImageID = np.arange(len(y_pred))+1\nOut = pd.DataFrame([ImageID,y_pred]).T\nOut.rename(columns = {0:'ImageId', 1:'Label'})\n#Out\nOut.to_csv('MNIST_Predictionv2.csv', header =  ['ImageId', 'Label' ], index = None)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist.history","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nval = hist.history[\"val_loss\"]\n\ntra = hist.history[\"loss\"]\ni = range(1, 1+len(tra))\nplt.plot(i, tra, \"bo\", label=\"train\")\nplt.plot(i, val, \"b\", label =\"val\")\nplt.legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}