{"cells":[{"metadata":{},"cell_type":"markdown","source":"# NOTEBOOK Kaggle Cassava Competion"},{"metadata":{},"cell_type":"markdown","source":"## I. Pré-traitement\n### a. Import des librairies"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport random\n\nimport tensorflow as tf\nimport tensorflow.keras as keras\nimport matplotlib.pyplot as plt\nimport cv2\nimport os\nfrom functools import partial","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds = []\norigin = \"../input/cassava-leaf-disease-classification\"\n# sample_sub = pd.read_csv(origin + '/train.csv')\nsample_sub = pd.read_csv(origin + '/sample_submission.csv')\nmodel = keras.models.load_model(\"../input/convnet-top/convnet_top\")\n\nfor image in sample_sub.image_id:\n    img = tf.keras.preprocessing.image.load_img(origin + '/test_images/' + image)\n    img = tf.keras.preprocessing.image.img_to_array(img)\n    img = tf.keras.preprocessing.image.smart_resize(img, (256, 256))\n    img = np.expand_dims(img, 0)\n    prediction = model.predict(img)\n    preds.append(np.argmax(prediction))\n\nmy_submission = pd.DataFrame({'image_id': sample_sub.image_id, 'label': preds})\nmy_submission.to_csv('submission.csv', index=False)\nmy_submission","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}