{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":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":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Flatten, Dense, Dropout\nfrom keras.preprocessing.image import ImageDataGenerator\nimport time\nfrom tensorflow.keras.applications import EfficientNetB0\n\n\nIMG_SIZE = 512 # Replace with the size of your images\nNB_CHANNELS = 3 # 3 for RGB images or 1 for grayscale images\nBATCH_SIZE = 32 # Typical values are 8, 16 or 32","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cnn_base = EfficientNetB0(include_top = False, weights = None,\n                               input_shape = (IMG_SIZE, IMG_SIZE, 3))\ncnn = Sequential()\ncnn.add(cnn_base)\ncnn.add(layers.GlobalAveragePooling2D())\ncnn.add(layers.Dense(5, activation = \"softmax\"))\ncnn.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), optimizer='adam', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cnn.load_weights('../input/weights/EffNetB0_512_8_best_weights.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# cnn.save_weights('./final_weights1.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_files = os.listdir('../input/cassava-leaf-disease-classification/test_images')\nimage_id = []\npredictions = []\nfor i in test_files:\n    image_id.append(i)\n    image = tf.keras.preprocessing.image.load_img('../input/cassava-leaf-disease-classification/test_images/'+i, target_size=(512,512))\n    input_arr = tf.keras.preprocessing.image.img_to_array(image)\n    input_arr = np.asarray(input_arr, dtype=np.uint8)\n    predict = cnn(np.array([input_arr/255]))\n    pred = tf.argmax(predict.numpy(), axis=1).numpy()[0]\n    predictions.append(pred)\n    print(i,tf.argmax(predict.numpy(), axis=1).numpy()[0])\n\nres = pd.DataFrame({'image_id': image_id, 'label':predictions})\nres.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}