{"cells":[{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data Acquisition"},{"metadata":{"trusted":true},"cell_type":"code","source":"train=pd.read_csv('../input/train.csv')\ntest=pd.read_csv('../input/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=train.iloc[:,1:].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y=train.iloc[:,0].values","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Preparing a Deep Learning Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV,train_test_split\nfrom sklearn.neural_network import MLPClassifier","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train,X_val,y_train,y_val=train_test_split(X,y,test_size=0.33,random_state=62)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Here we have used 512 neurons for our hidden layer.\nmlp=MLPClassifier([512],activation='relu',solver='adam',learning_rate_init=0.001,)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Training the model\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"mlp.fit(X_train,y_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Evaluation"},{"metadata":{"trusted":true},"cell_type":"code","source":"mlp.score(X_train,y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mlp.score(X_val,y_val)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Training the model on whole dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"mlp.fit(X,y)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Submission for Competition"},{"metadata":{"trusted":true},"cell_type":"code","source":"pred=mlp.predict(test.values).reshape(-1,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"output=np.concatenate((np.arange(1,test.shape[0]+1).reshape(-1,1),pred),axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission=pd.DataFrame(output,columns=['ImageId','Label'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.to_csv('Submission.csv',index=False)    #Generating submission file.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}