# l.set_weights(res_img.layers[i + 1].get_weights()) return model # create callbacks list from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard from keras_callbacks import F1Metric from keras_metrics import f1, f1_02 from keras_losses import f1_loss epochs = [2, 120] batch_size = 16 # split data into train, valid mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=18) y = np.zeros((len(train_dataset_info), 28)) for i in range(len(train_dataset_info)): y[i][train_dataset_info[i]['labels']] = 1 mskf.get_n_splits(train_dataset_info, y) kf = mskf.split(train_dataset_info, y) fold_id = 0 train_indexes, valid_indexes = next(kf) train_generator = data_generator.create_train( train_dataset_info[train_indexes], batch_size, (SIZE, SIZE, 3), augument=True, heavy_augment_rares=False, oversample_factor=0)
# l.set_weights(res_img.layers[i + 1].get_weights()) return model # create callbacks list from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard from keras_callbacks import F1Metric from keras_metrics import f1, f1_02 from keras_losses import f1_loss epochs = [2, 100] batch_size = 32 # split data into train, valid mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=18) #y = np.zeros((len(train_dataset_info), 28)) #for i in range(len(train_dataset_info)): # y[i][train_dataset_info[i]['labels']] = 1 #mskf.get_n_splits(train_dataset_info, y) #kf = mskf.split(train_dataset_info, y) train_indexes, valid_indexes = get_fold_ids(fold_id, train_dataset_info) #train_indexes, valid_indexes = next(kf) train_generator = data_generator.create_train( train_dataset_info[train_indexes], batch_size, (SIZE, SIZE, 3), augument=True, oversample_factor=3)