{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom keras.preprocessing.image import load_img, array_to_img\nfrom keras.utils.np_utils import to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\ntrain = pd.read_csv(\"../input/digit-recognizer/train.csv\")\ntest = pd.read_csv(\"../input/digit-recognizer/test.csv\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_lbl = train['label']\ntrain_data = train.drop(['label'],axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = train_data.astype('float32')\ntest = test.astype('float32')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data /= 255\ntest /= 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = train_data.to_numpy()\ntest = test.to_numpy()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_lbl = to_categorical(train_lbl,10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Dense (900,activation='relu',input_shape = (784,)))\nmodel.add(Dense(900,activation='relu'))\nmodel.add(Dense(10,activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam', loss ='categorical_crossentropy', metrics = ['accuracy'])\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist = model.fit(train_data,train_lbl,epochs=20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction = model.predict(test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"output = prediction.astype('int32')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"output2 = pd.DataFrame(output)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"output2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for col in output2:\n    output2[col] = output2[col].apply(lambda x : x * col)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_output = output2.sum(axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_output = pd.DataFrame(new_output)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_output = new_output[0].rename('Label')\nnew_output.index.name = 'ImageId'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_output.index += 1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_output","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nnew_output.to_csv('./Submission.csv')","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}