{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"TRAIN_IMG_LOC = \"../input/cassava-leaf-disease-classification/train_images\"\nTEST_IMG = \"../input/cassava-leaf-disease-classification/test_images/2216849948.jpg\"\nTRAIN_CSV = \"../input/cassava-leaf-disease-classification/train.csv\"\nSAMPLE_CSV = \"../input/cassava-leaf-disease-classification/sample_submission.csv\"\nMODELS_WEIGHTS = \"../input/cassavaeffentb7models/content/Models\"","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os\n\nimport tensorflow as tf\nfrom tensorflow.keras.applications import *\nfrom tensorflow.keras.optimizers import *\nfrom tensorflow.keras.losses import *\nfrom tensorflow.keras.preprocessing.image import *\nfrom tensorflow.keras.callbacks import *\nfrom tensorflow.keras.models import *\n\nfrom keras.utils.vis_utils import plot_model\n\nfrom tqdm import tqdm, tqdm_notebook\nimport cv2\nfrom PIL import Image\n\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.metrics import confusion_matrix, classification_report, accuracy_score\nimport tensorflow.keras.backend as K\n\nimport numpy as np\nimport pandas as pd\n\nprint(\"ALL Modules are successfully loaded\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = load_model(\"../input/ensemble-resnet50-effb0-effb4/resnet50_b0_b4.h5\")\nprint(\"Model Loading Complete\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_model(model, show_shapes = True, show_layer_names = True, to_file = \"ensemble_resnet50_effb0_effb4.png\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img = cv2.imread(\"../input/cassava-leaf-disease-classification/test_images/2216849948.jpg\")\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n# img = cv2.resize(img, (224, 224))\nprint(f\"Image Shape : {img.shape}\")\n\nplt.figure(figsize = (20, 12))\nplt.xticks([])\nplt.yticks([])\nplt.imshow(img);","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 = tf.keras.preprocessing.image.load_img('../input/cassava-leaf-disease-classification/test_images/' + image)\n    img = tf.keras.preprocessing.image.img_to_array(img)\n    img = tf.keras.preprocessing.image.smart_resize(img, (300, 300))\n    img = np.expand_dims(img, 0)\n    \n    prediction = model.predict([img]*3)\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) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"my_submission","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}