{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# Example submission notebook for the midterm-project in the\n# Brown Data Science DATA2040 Deep Learning course. \n# Please visit dsi.brown.edu for information on the Data Sciene Masters Program.\n# The web page for this course is data2040.github.io \n# Authors: Kaiwen Yang, Dan Potter\n\n# 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\n# import os\n# for 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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Make sure to save the model you trained to /kaggle/working! \n\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\n\n## Load model. Use \"load_weights\" if you only save your model weights.\n## model = keras.models.load_model(\"path/to/your/model\")\n\npreds = []\nsample_sub = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/sample_submission.csv')\n\nfor image in sample_sub.image_id:\n    img = keras.preprocessing.image.load_img('/kaggle/input/cassava-leaf-disease-classification/test_images/' + image)\n    #\n    # Preprocess image here (rescale, etc. - you might need to use parameters you determined during training)\n    #\n    \n    # Now apply your model and save your prediction:\n    ## prediction = model.predict(img)\n    ## preds.append(np.argmax(prediction))\n    \n    # Blind-Monkey Model\n    # This is horrible possible baseline model.  You can improve it by\n    # putting all of p's mass on the most commonly occuring class.\n    # Question: if you set p to the actual class label distribution, on average,  \n    # will you get the same result, a better result or a worse result? \n    preds.append(np.random.choice(5, p=[.2, .2, .2, .2, .2])) \n\nmy_submission = pd.DataFrame({'image_id': sample_sub.image_id, 'label': preds})\nmy_submission.to_csv('/kaggle/working/submission.csv', index=False)","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}