cloudml-template Readme.md update
@@ -39,7 +39,6 @@ The examples show how the template is adapted given a dataset. The datasets are
|File Name| Purpose| Do You Need to Change?
|:---|:---|:---
-
|[metadata.py](template/trainer/metadata.py)|Defines: 1) task type, 2) input data header, 3) numeric and categorical feature names, 4) target feature name (and labels, for a classification task), and 5) unused feature names. | **Yes**, as you will need to specify the metadata of your dataset. **This might be the only module to change!**
|[input.py](template/trainer/input.py)| Includes: 1) data input functions to read data from csv and tfrecords files, 2) parsing functions to convert csv and tf.example to tensors, 3) function to implement your features custom processing and creation functionality, and 4) prediction functions (for serving the model) that accepts CSV, JSON, and tf.example instances. | **Maybe**, if you want to implement any custom pre-processing and feature creation during reading data.
|[featurizer.py](template/trainer/featurizer.py)| Creates: 1) tensorflow feature_column(s) based on the dataset metadata (and other extended feature columns, e.g. bucketisation, crossing, embedding, etc.), and 2) deep and wide feature column lists. | **Maybe**, if you want to change your feature_column(s) and/or change how deep and wide columns are defined (see next section).
