{"cells":[{"metadata":{"_uuid":"e4a9f96c-5cd1-4367-b101-1b484e0d0072","_cell_guid":"b6ddcaa4-d44c-425e-bd72-74d6a5e86c03","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)\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.models import Sequential \nfrom keras.layers import Dense, Dropout\nfrom keras.utils import np_utils\nfrom sklearn.metrics import confusion_matrix\nfrom keras.datasets import mnist\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\n        \ndata_train = pd.read_csv('../input/digit-recognizer/train.csv')\ndata_test = pd.read_csv('../input/digit-recognizer/test.csv')\n        \n\ny_train = data_train.iloc[:,0]\nprint(y_train.shape)\nX_train = (data_train.iloc[:,1:].values).astype('float32')\nprint(X_train.shape)\n\n\n\nX_test = (data_test.iloc[:,0:].values).astype('float32')\n\n\ny_train = np_utils.to_categorical(y_train,10)\ny_train =(np.array(y_train))\n\n\n\n\nmodelo = Sequential()\nmodelo.add(Dense(units = 64,activation = 'relu',input_dim = 784 ))\nmodelo.add(Dropout(0.2))\nmodelo.add(Dense(units = 64,activation = 'relu'))\nmodelo.add(Dropout(0.2))\nmodelo.add(Dense(units = 64,activation = 'relu'))\nmodelo.add(Dropout(0.2))\nmodelo.add(Dense(units = 10, activation = 'softmax'))\n\nmodelo.summary()\nmodelo.compile(optimizer = 'adam', loss = 'categorical_crossentropy',metrics = ['accuracy'])\nhistorico = modelo.fit(X_train, y_train, epochs = 20, validation_split = 0.2)\n\nhistorico.history.keys()\n","execution_count":null,"outputs":[]}],"metadata":{"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":1}