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I wrote a code example demonstrating how to use the new Keras preprocessing layers for structured data: https://keras.io/examples/structured_data/structured_data_classification_from_scratch/ … With this workflow, you can have models that can process "raw" structured data -- mixing numerical features, integer categorical features, and strings.pic.twitter.com/EyAT8F94Hj
Structured data preprocessing layers include: - CategoryEncoding: do one-hot/multi-hot/count/tf-idf integer categorical feature encoding - StringLookup/IntegerLookup: build indexes of categorical features - Hashing: the hashing trick - Discretization: continuous feature binning
- Normalization: featurewise normalization - CategoryCrossing: generate co-occurence features of categorical features Together, these layers enable arbitrarily complex preprocessing pipelines that make your models truly end-to-end, from raw data to predictions.
This helps reduce the training/serving skew: since all the preprocessing is part of the model, you can load and use your model without having to be aware of how each feature is expected to be encoded & normalized.
This is fascinating! Traditionally, you would apply a weighting to the different variables/ parameters because not all elements are equal. I wonder what is the delta in prediction accuracy....
This is a neural network: the importance of each feature is learned by the model during training.
This looks like the UCI heart disease dataset
This is great. I really wish, though, that we had a centralized library for preprocessing that worked for TF, Keras, Scikit, PyTorch, big/medium/small data, everything...oh to dream...
In general, you can use these layers and extract the Numpy outputs to use with another framework, e.g. layer = CategoryEncoding() layer.adapt(np_data) # Learn the index preprocessed_np_data = layer(np_data).numpy() # Convert the input
Thx a lot. Please add works only with tf-nightly
Bookmarked for the weekend. This sounds so cool. Also that model plot is 
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