Hello, there, I am currently working with Tensorflow for the first time and as far as I know Tensorflow uses Protocol Buffers to store the data. I work on IoT devices in a protected network and I have to deliver the data of a Tensorflow DNNClassifier to external systems. These may be databases, but I may also need to communicate with industrial controls as a byte stream. Especially in this case I can only transfer primitive data like Int, Float, Char etc. If I have now trained a Tensorflow DNNClassifier, a local directory with the stored data will be created for me and I would now like to read and process this data in native Python data types such as a Dict or similar. How can I do this? I also get a model as a native byte stream and would have to use it again to generate a DNNClassifier. How can I realize this?
I am currently quite "new" with Tensorflow and Protocol Buffers. Thank you very much for your help. Phil -- You received this message because you are subscribed to the Google Groups "Protocol Buffers" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/protobuf. For more options, visit https://groups.google.com/d/optout.
