![]() Yes, handling categorical columns can be a little awkward. # to values as input to training and predicting. compile (.) # use `_dict('series')` to get a dictionary of feature names categorical_column_with_vocabulary_list ( 'product_id', df. numeric_column ( 'price' ), # same name as column in `df`įeature_product = fc. Import tensorflow.feature_columns as fc feature_price = fc. Include the DenseFeatures object as the first layer in the Sequential model. ![]() Use to wrap the feature columns together.Create a feature column for each feature you want to train on.Below we’ll basically recreate an example from the documentation, however we will demonstrate how to work the example with pandas instead of tensorflow DataSets. The official documentation contains some easy to follow examples. Working with tensorflow feature columns and the Sequential API is pretty straightforward. In this post we’ll demonstrate how to use the new feature columns with keras Sequential and functional APIs. Mainly because they weren’t compatible with keras even though tensorflow had already adopted the keras API.įortunately the implementation I posted about last July is, well, so last year, and tensorflow 2.0 introduces some considerable improvements. Last July I published a blog post which was enthusiastic about the idea of tensorflow's feature columns but disappointed by the actual implementation. Tensorflow 2.0 was just recently introduced and one of the most anticipated features, in my opinion, was the revamping its feature columns.
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