{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true,"_kg_hide-input":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)\nimport matplotlib.pyplot as plt\n\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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"from tensorflow import keras","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# loading the saved weights and making predictions\n**the code below loads the weights of the model that I trained in this notebook https://www.kaggle.com/sarangbhatnagar/leaf-efficientnet-sub1\nand saved it using model.save() \nI downloaded those weights on computer and then imported them in this notebook from +Add data option.**"},{"metadata":{"trusted":true},"cell_type":"code","source":"model = keras.models.load_model(\"../input/submission8/EffNetB4_300_try8.h5\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds = []\nss = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')\n\nfor image in ss.image_id:\n    img = keras.preprocessing.image.load_img('../input/cassava-leaf-disease-classification/test_images/' + image)\n    img = keras.preprocessing.image.img_to_array(img)\n    img = 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': ss.image_id, 'label': preds})\nmy_submission.to_csv('submission.csv', index=False)\n\nss = pd.read_csv(\"../input/cassava-leaf-disease-classification/sample_submission.csv\")\npreds = []\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=pd.read_csv('../input/submission8/history_undersampling8.csv')\nhistory.head(23)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history['accuracy'])\nplt.plot(history['val_accuracy'])\nplt.legend(['Accuracy of training','accuracy of validation'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history['loss'])\nplt.plot(history['val_loss'])\nplt.legend(['Loss','Val_loss'])\nplt.show()","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}