{"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_minor":4,"nbformat":4,"cells":[{"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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-10-17T10:43:20.499706Z","iopub.execute_input":"2022-10-17T10:43:20.500086Z","iopub.status.idle":"2022-10-17T10:43:20.509444Z","shell.execute_reply.started":"2022-10-17T10:43:20.500053Z","shell.execute_reply":"2022-10-17T10:43:20.508576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nfrom numpy import loadtxt\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.utils import to_categorical, plot_model","metadata":{"execution":{"iopub.status.busy":"2022-10-17T10:43:20.511647Z","iopub.execute_input":"2022-10-17T10:43:20.512331Z","iopub.status.idle":"2022-10-17T10:43:20.525979Z","shell.execute_reply.started":"2022-10-17T10:43:20.512294Z","shell.execute_reply":"2022-10-17T10:43:20.524892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\nX = dataset.iloc[:,1:]\ny = to_categorical(dataset.iloc[:,0])\ny","metadata":{"execution":{"iopub.status.busy":"2022-10-17T10:43:20.527498Z","iopub.execute_input":"2022-10-17T10:43:20.528087Z","iopub.status.idle":"2022-10-17T10:43:22.973147Z","shell.execute_reply.started":"2022-10-17T10:43:20.528051Z","shell.execute_reply":"2022-10-17T10:43:22.972013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Dense(12, input_shape=(784,), activation='relu'))\nmodel.add(Dense(8, activation='relu'))\nmodel.add(Dense(10, activation='sigmoid'))","metadata":{"execution":{"iopub.status.busy":"2022-10-17T10:43:22.975743Z","iopub.execute_input":"2022-10-17T10:43:22.976119Z","iopub.status.idle":"2022-10-17T10:43:23.015303Z","shell.execute_reply.started":"2022-10-17T10:43:22.976085Z","shell.execute_reply":"2022-10-17T10:43:23.013985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-10-17T10:43:23.016738Z","iopub.execute_input":"2022-10-17T10:43:23.017658Z","iopub.status.idle":"2022-10-17T10:43:23.028938Z","shell.execute_reply.started":"2022-10-17T10:43:23.017615Z","shell.execute_reply":"2022-10-17T10:43:23.027539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(X, y, epochs=150, batch_size=10)","metadata":{"execution":{"iopub.status.busy":"2022-10-17T10:43:23.030460Z","iopub.execute_input":"2022-10-17T10:43:23.031070Z"},"trusted":true},"execution_count":null,"outputs":[]}]}