TensorFlow文本分类示例-如何评估自己的文本？

Question

Does any one have full steps and example for TensorFlow example for passing in your own text files and getting them evaluated against the existing model that comes with examples - using train.py as documented?

Also, if I wanted to train on different input set of say 1000 text files of my own samples, and then use that model for new text files? I know there is documentation but is terse for someone who is not familiar with text classification process.

I was able to run image example against my own images as that was only requiring to swap out one image .jpg file name for myh new image file, but for text it seems to be more involved.

Thanks

Answer 1

Here is an example: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/skflow/text_classification.py

You can set the flag test_with_fake_data to use the fake data in text_train.csv (training samples) and text_test.csv (testing samples) here . Next, you can modify these two files to include whatever data you'd like to have. You will need to do some preprocessing if your existing text files are in a different format.

Answer 2

You need to load the vocabulary file saved during training and process your new text with that. See the eval.py file here

Change the data parameters with your input text and proceed.