{"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 \nimport tensorflow as tf\nfrom tensorflow import keras\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":{"trusted":true},"cell_type":"code","source":"inputs = pd.read_csv('../input/digit-recognizer/train.csv')\nLabels = np.array(inputs['label'])\npixels = np.array(inputs)[:,1:]\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.Sequential([\n    keras.layers.Dense(2048,activation = tf.nn.relu),\n    keras.layers.Dense(1024, activation = tf.nn.relu),\n    keras.layers.Dense(512,activation = tf.nn.relu),\n    keras.layers.Dense(256, activation = tf.nn.relu),\n    keras.layers.Dense(128,activation = tf.nn.relu),\n    keras.layers.Dense(10,activation = tf.nn.softmax)\n])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.optimizers import RMSprop\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n              metrics = ['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(pixels[:40000,:], Labels[:40000], epochs = 30)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.evaluate(pixels[40000:42000,:], Labels[40000:42000])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_input_pd = pd.read_csv('../input/digit-recognizer/test.csv')\ntest_input = np.array(test_input_pd)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = []\ntest_mid_output = list(model.predict(test_input))\nfor i in (test_mid_output):\n    j = list(i)\n    output = j.index(max(j))\n    predictions.append(output)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"imageId = list(range(1,28001))\nout = pd.DataFrame({'ImageId': imageId, 'Label': predictions})\nout.to_csv('submission.csv', index=False)","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}