使用TensorFlow的预测模型
我的目标是使用Java中的张量流进行预测模型，但我首先要确保我的目标是可以实现的。首先，如果我有一堆参数，并且为每个参数分配了一个输出，是否有可能训练模型以预测给定类似参数的输出？我能够获得数十万个样本（如果需要）来训练它，那么这是否有可能？
其次，在训练模型后，它可以实际生成结果的速度？
最后，假设所有内容迄今为止都可以检查Java张量流的最佳方法是用具有与结果相关的多个参数的数据训练模型的最佳方法？同样，在结果中，给定的数据满足了两个结果，这两者都可以返回，因为选项最少可能从最可能的选项订购。
还只是为了澄清我不是要某人为我做这个，我只是想确保存在解决方案并且很快（如果慢的话，我可以回去野蛮的强迫有点缓慢而资源密集型）。另外，如果您对开始解决这个问题有任何建议，我将非常感谢！
My goal is to generate a predictive model using tensor flow in Java but I first want to ensure that my goal is achievable. Firstly, if I have a bunch of parameters and each set of parameters is assigned an output is it possible to train a model to predict an output given similar parameters? I am able to get hundreds of thousands samples (if needed) in order to train it so is this possible?
Secondly, after the model is trained how fast can it actually generate results?
Lastly, assuming everything up until this point checks out what is the best method in Java’s tensor flow to train a model with data that has multiple parameters associated with an outcome? Also in the result a given piece of data satisfies two results both can be returned as options ordered from most likely to least.
Also just to clarify I am not asking someone to make this for me I am just trying to make sure that a solution exists and is quick (if it’s slow I could just go back to brute forcing which I am trying to move away from since is kinda slow and resource intensive). Also, if you have any pointers on getting started tackling this I would greatly appreciate it!
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您的问题非常非常非常，非常非常，但我会尝试提供一些见解：
采用一组参数（称为功能集x）并预测另一组参数（称为输出集y）是机器学习的主要目的。 的执行方式需要多个步骤，如何做得很好需要很多经验……但是，如果您询问原则上是否可能，这取决于特定功能集X，，和输出集Y。
机器学习的诀窍是数据必须具有足够的数量和质量。这需要特定的知识才能知道。
您是否能够提供有关数据的任何细节以帮助我们理解？
Your question is very, very general, but I'll try to offer some insight:
Taking a set of parameters (known as the feature set X) and making predictions of another set of parameters (known as the output set Y) is the primary purpose of machine learning. Exactly how to do this requires many steps, how to do it well takes a lot of experience... However if you are asking if it is possible in principle, that depends on the specific feature set X, and output set Y.
The trick to machine learning is the data must be of a sufficient quantity and quality. This takes domain specific knowledge to know.
Are you able to provide any specifics about your data to help us understand?