usage of tf.keras.layers.DenseFeatures

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Here is the official doc.

A layer that produces a dense Tensor based on given feature_columns.

Inherits From: DenseFeatures

tf.keras.layers.DenseFeatures(
    feature_columns, trainable=True, name=None, **kwargs
)

This is used in TF example and usually put in keras.Sequential(...) model construction. Like below:

model = tf.keras.Sequential([
  feature_layer,
  layers.Dense(128, activation='relu'),
  layers.Dense(128, activation='relu'),
  layers.Dropout(.1),
  layers.Dense(1)
])

In my case, I want to use it to transfer my dictionary data type into Tensor format and pass it into model. So I used code like below:

feature_columns = []
bins = [-125, -75, -50, -25, 0, 25, 50, 75, 125]
temp_num = feature_column.numeric_column('temp')
temp_buckets = feature_column.bucketized_column(temp_num, boundaries=bins)
feature_columns.append(temp_buckets)
feature_layer = layers.DenseFeatures(feature_columns) 
input = feature_layer(dict(dataframe))

And input is the training data I would feed into model. The question is whether my usage of this DenseFeatures() layer is reasonable. Or this feature_layer has to be in keras.Model class?

keras tensorflow
1 Answers

Yes, your idea is reasonable. And actually you are free to choose either Keras functional API or Keras Sequential API when specifying your deep learning architecture.

To complete your work, I would remove the last line and make some additional tweaks. What follows is a code snippet for completing the work you left by using Keras functional APIs:

feature_columns = []
bins = [-125, -75, -50, -25, 0, 25, 50, 75, 125]
temp_num = feature_column.numeric_column('temp')
temp_buckets = feature_column.bucketized_column(temp_num, boundaries=bins)
feature_columns.append(temp_buckets)
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)

# create a dictionary to associate column names with column values
inputs = {}
inputs["temp_num"] = tf.keras.Input(shape=(1,), name="temp_num") 

# convert FeatureColumns into a single tensor layer
x = feature_layer(inputs)

x = tf.keras.layers.Dense(128, activation='relu')(x)
x = tf.keras.layers.Dense(128, activation='relu')(x)
x = tf.keras.layers.Dropout(.1)(x)
out = tf.keras.layers.Dense(1)(x)

model = tf.keras.Model(inputs=dict(inputs), outputs=out)
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