with pre_graph.as_default(): pre_model = getModel(model_type) pre_model.load_weights( 'logs/%s/kfold_%s/kfold_%s_dice_DA_K%d/kfold_%s_dice_DA_K%d_weights.h5' % (model_type, model_type, model_type, i, model_type, i)) exp_name = 'kfold_%s_BiCLSTM_K%d' % (model_type, i) #get parameters params = getParams(exp_name, model_type, is_lstm=True) #set common variables epochs = 10 batch_size = 10 verbose = 1 tr_images, tr_masks, te_images, te_masks = dh.getKFoldData( image_files, mask_files, kfold_indices[i]) train_generator = lstmGenerator(tr_images, tr_masks, batch_size, pre_model, pre_graph) val_generator = lstmGenerator(te_images, te_masks, batch_size, pre_model, pre_graph) #Get model and add weights lstm_graph = tf.get_default_graph() with lstm_graph.as_default(): model = lstmModel() model_json = model.to_json() with open(params['model_name'], "w") as json_file: json_file.write(model_json)