{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true,"collapsed":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\nfor 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.keras.layers import GlobalAveragePooling2D, Dense, Conv2D, Dropout, Flatten\nfrom tensorflow.keras.applications import EfficientNetB0\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import Adam\nimport tensorflow as tf\nfrom PIL import Image\nimport os, cv2, json\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.applications import EfficientNetB0\nfrom tensorflow.keras.models import  load_model\n# ignoring warnings\nimport warnings\nwarnings.simplefilter(\"ignore\")\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"general_path = '../input/cassava-leaf-disease-classification/'\nos.listdir(general_path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_model_ebnet0(learning_rate, img_width, img_height, dropout_rate):\n    model = Sequential()\n    optimizer = Adam(lr=learning_rate)\n    backbone = EfficientNetB0(include_top=False, \n                          weights=None, \n                          input_shape = (img_width, img_height, 3),\n                          pooling='avg')\n    model.add(backbone)\n    model.add(Dense(5, activation='softmax'))\n    model.compile(loss=\"categorical_crossentropy\", \n              optimizer=optimizer, \n              metrics=[\"accuracy\"])\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"LEARN_RATE = 0.001\nIMG_SIZE = 224\nDROP_RATE = 0.2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"saved_model = create_model_ebnet0(LEARN_RATE, IMG_SIZE, IMG_SIZE, DROP_RATE)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_weight_path = '../input/casavaleafdiseasemodels/EfficientNetB0.h5'\n#os.listdir(model_path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# Load the model\nsaved_model.load_weights(model_weight_path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"saved_model.compile(loss=\"categorical_crossentropy\", \n              optimizer=Adam(lr=LEARN_RATE), \n              metrics=[\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"saved_model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame(columns=['image_id','label'])\n\nfor image_name in os.listdir(general_path + '/test_images'):\n    image_path = os.path.join(general_path + '/test_images', image_name)\n    image = tf.keras.preprocessing.image.load_img(image_path)\n    resized_image = image.resize((224, 224))\n    numpied_image = np.expand_dims(resized_image, 0)\n    tensored_image = tf.cast(numpied_image, tf.float32)\n    print(saved_model.predict_classes(tensored_image))\n    submission = submission.append(pd.DataFrame({'image_id': image_name,'label': saved_model.predict_classes(tensored_image)}))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission\nsubmission.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}