{"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\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\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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# Test image folder directory\nTEST_DIR = '../input/cassava-leaf-disease-classification/test_images/'","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Loading the trained model\n* Training Notebok: https://www.kaggle.com/deepakat002/effficientnetb3-cassava\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# load the best saved model as a new model\nfrom keras.models import load_model\nmodel = load_model('../input/effficientnetb3-cassava/best_model.hdf5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#This is the image size on which our model was trained\nIMG_SIZE = 300\nsize = (IMG_SIZE,IMG_SIZE)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#loading images\nfrom PIL import Image\n\ntest_images = os.listdir(TEST_DIR)\npreds = []\nfor image in test_images:\n    img = Image.open(TEST_DIR + image)\n    img = img.resize(size)\n    img = np.expand_dims(img, axis=0)\n    preds.extend(model.predict(img).argmax(axis = 1))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Submission:"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Creating submission file\n\nsub = pd.DataFrame({'image_id': test_images, 'label': preds})\nprint(sub)\nsub.to_csv('submission.csv', index = False)","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":4}