numeric_columns と categorical_columns を結合し，これを tf.keras.layers.DenseFeatures に入力して，preprocessing_layer とする：
<![CDATA[>>> preprocessing_layer = \
... tf.keras.layers.DenseFeatures( categorical_columns + numeric_columns )]]>
preprocessing_layer の機能チェック：
<![CDATA[>>> for batch in packed_train_data.take(1):
... print( preprocessing_layer(batch).numpy()[0] )
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/home/pi/venv/lib/python3.7/site-packages/
tensorflow_core/python/keras/engine/base_layer.py",
line 913, in __call__outputs = self.call(cast_inputs, *args, **kwargs)
File "/home/pi/venv/lib/python3.7/site-packages/
tensorflow_core/python/feature_column/dense_features.py",
line 129, in call features)
ValueError: ('We expected a dictionary here. Instead we got: ',
(OrderedDict([
('sex', <tf.Tensor: shape=(5,), dtype=string, numpy=array([b'male', b'female', b'male', b'female', b'male'],
dtype=object)>),
('class', <tf.Tensor: shape=(5,), dtype=string, numpy=array([b'Third', b'First', b'Third', b'Second', b'First'],
dtype=object)>),
('deck', <tf.Tensor: shape=(5,), dtype=string, numpy=array([b'unknown', b'E', b'unknown', b'unknown', b'B'],
dtype=object)>),
('embark_town', <tf.Tensor: shape=(5,), dtype=string, numpy=array([b'Queenstown', b'Southampton',
b'Southampton', b'Southampton', b'Southampton'], dtype=object)>),
('alone', <tf.Tensor: shape=(5,), dtype=string, numpy=array([b'y', b'n', b'y', b'y', b'n'], dtype=object)>),
('numeric', <tf.Tensor: shape=(5, 4), dtype=float32, numpy=
array([[28. , 0. , 0. , 7.75],
[33. , 1. , 0. , 53.1 ],
[20. , 0. , 0. , 8.05],
[35. , 0. , 0. , 21. ],
[31. , 1. , 0. , 57. ]],
dtype=float32)>)
]),
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 0, 1, 1])>)]]>
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