{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install -q efficientnet","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras import models, layers\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\nfrom keras.optimizers import Adam\nimport os, cv2, json\nfrom efficientnet.tfkeras import EfficientNetB0\n\nimport warnings\nwarnings.simplefilter(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"WORK_DIR = \"../input/cassava-leaf-disease-classification/\"\nos.listdir(WORK_DIR)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with open('../input/cassava-leaf-disease-classification/label_num_to_disease_map.json', 'r') as file:\n    labels = json.load(file)\n    \nlabels","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = pd.read_csv(WORK_DIR + \"train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"IMG_SIZE = 512\nBATCH_SIZE = 18\nSTEPS_PER_EPOCH = len(data)*0.8/BATCH_SIZE\nVALIDATION_STEPS = len(data)*0.2/BATCH_SIZE\nEPOCHS = 20","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.label = data.label.astype(\"str\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_generator = ImageDataGenerator(\n                                    featurewise_center=True,                                    \n                                    featurewise_std_normalization=True, \n                                    rotation_range=45,\n                                    width_shift_range=0.1,\n                                    height_shift_range=0.1,\n                                    zoom_range=0.3,\n                                    fill_mode=\"nearest\",\n                                    horizontal_flip=True,\n                                    vertical_flip=True,\n                                    validation_split=0.2,\n) \\\n        .flow_from_dataframe(\n                            data,\n                            directory = WORK_DIR + \"train_images\",\n                            x_col = \"image_id\",\n                            y_col = \"label\",\n                            target_size = (IMG_SIZE, IMG_SIZE),\n                            class_mode = \"sparse\",\n                            batch_size = BATCH_SIZE,\n                            shuffle = True,\n                            subset = \"training\",\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"valid_generator = ImageDataGenerator(\n                                    validation_split = 0.2\n) \\\n        .flow_from_dataframe(\n                            data,\n                            directory = WORK_DIR + \"train_images\",\n                            x_col = \"image_id\",\n                            y_col = \"label\",\n                            target_size = (IMG_SIZE, IMG_SIZE),\n                            class_mode = \"sparse\",\n                            batch_size = BATCH_SIZE,\n                            shuffle = True,\n                            subset = \"validation\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_model():\n    conv_base = EfficientNetB0(include_top=False, input_tensor=None,\n                               pooling=None, input_shape=(IMG_SIZE, IMG_SIZE, 3))\n                               \n    model = conv_base.output\n    model = layers.GlobalAveragePooling2D()(model)\n    model = layers.Dense(5, activation = \"softmax\")(model)\n    model = models.Model(conv_base.input, model)\n\n    model.compile(optimizer = Adam(lr = 0.001),\n                  loss = \"sparse_categorical_crossentropy\",\n                  metrics = [\"acc\"])\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = create_model()\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_check = ModelCheckpoint(\n                            \"./EfficientNetB0_best_waights.h5\",\n                            monitor = \"val_loss\",\n                            verbose = 1,\n                            save_best_only = True,\n                            save_weights_only = True,\n                            mode = \"min\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"early_stop= EarlyStopping(\n                                monitor = \"val_loss\",\n                                min_delta=0.001,\n                                patience=5,\n                                verbose=1,\n                                mode=\"min\",\n                                restore_best_weights=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"reduce_lr = ReduceLROnPlateau(\n                                monitor=\"val_loss\",\n                                factor=0.3,\n                                patience=2,\n                                verbose=1,\n                                mode=\"min\",\n                                min_delta=0.001,\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer = Adam(lr = 0.001),\n            loss = \"sparse_categorical_crossentropy\",\n            metrics = [\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit_generator(train_generator,\n                              steps_per_epoch = STEPS_PER_EPOCH,\n                              epochs = EPOCHS,\n                              validation_data = valid_generator,\n                              callbacks = [model_check,early_stop,reduce_lr])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save('./EfficientNetB0.h5')","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}