cls_config['original_sentence_label'], name='recall_original_sentence') changed_sentence_recall = ClassWiseRecall(cls_config['changed_sentence_label'], name='recall_changed_sentence') model.compile(optimizer=tf.keras.optimizers.Adam( learning_rate=train_config['learning_rate']), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=[ tf.keras.metrics.BinaryAccuracy(threshold=0.0), original_sentence_precision, changed_sentence_precision, original_sentence_recall, changed_sentence_recall ]) INIT_EPOCH = train_config['starting_epoch'] NUM_EPOCHS = train_config['num_epochs'] model.fit(train_ds, validation_data=val_ds, epochs=INIT_EPOCH + NUM_EPOCHS, initial_epoch=INIT_EPOCH, callbacks=[sts_eval_callback, cp_callback]) """ Write model config into json file """ config_json_path = os.path.join(current_model_folder, "model_config.json") with open(config_json_path, 'w') as json_file: json.dump(get_config_dict(), json_file) end_time = datetime.now() print( f"============= Total Training Time: {end_time - train_start_datetime} ============" )