我尝试使用四个基于相似架构构建的TensorFlow模型生成四种不同类型的预测。当我使用相同的特征四次调用model.predict()函数时，我收到了以下警告：
WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x2b18970e88c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.WARNING:tensorflow:6 out of the last 10 calls to <function Model.make_predict_function.<locals>.predict_function at 0x2b189734ab90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x2b189784be60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x2b1897e1c440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.这是我加载模型并进行预测时所做的操作。代码基于这个教程：https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/
model_1=load_model('./Models/model_1.hdf5')model_2=load_model('./Models/model_2.hdf5')model_3=load_model('./Models/model_3.hdf5')model_4=load_model('./Models/model_4.hdf5')models=[model_1,model_2,model_3,model_4]# get prediction outputs for the four modelsdef get_predictions(features,models): return np.array([i.predict(features) for i in models])preds=get_predictions(features,models)如何消除这些警告？我在这里做错了什么吗？谢谢！
回答：
不要使用
preds = m.predict(features)
请使用：
preds = m(features)
