{"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\nimport keras\nimport json\nimport pandas as pd\nimport tensorflow as tf\nimport sklearn\nimport matplotlib.pyplot as plt\nimport itertools\nimport numpy as np\nimport os, shutil, io, glob\nfrom datetime import datetime\nfrom sklearn.model_selection import train_test_split\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras import backend as K\nfrom collections import Counter\nfrom keras.models import model_from_json","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_images = glob.glob('../input/cassava-leaf-disease-classification/test_images/*.jpg')\ndf_test = pd.DataFrame(test_images, columns = ['path'])\n\nIMG_SIZE = 224\nSIZE = (IMG_SIZE,IMG_SIZE)\n\ntest_datagen = ImageDataGenerator()\n\ntest_gen= test_datagen.flow_from_dataframe(dataframe=df_test,\n                                                x_col=\"path\",\n                                                y_col=None,\n                                                batch_size=32,\n                                                seed=42,\n                                                shuffle=False,\n                                                class_mode=None,\n                                                target_size=SIZE) ## (height, width)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#json_file = open('../input/resnet/resnet.json', 'r')\n#loaded_model_json = json_file.read()\n#json_file.close()\n#model = model_from_json(loaded_model_json)\n#model.load_weights(\"../input/resnet/resnet.h5\")\nmodel = tf.keras.models.load_model('../input/stacked-model-v2-low-epsilon/stacked_models_v2')\n\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\npred_test = model.predict(test_gen,  verbose = True)\npred_test_labels = np.argmax(pred_test, axis = -1)\n\nfinal_submission = df_test\nfinal_submission['image_id'] = final_submission.path.str.split('/').str[-1]\nfinal_submission['label'] = pred_test_labels\n\nfinal_csv = final_submission[['image_id', 'label']]\nfinal_csv.head()\n\nfinal_csv.to_csv('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}