tf.contrib.layers.input_from_feature_columns

A tf.contrib.layers style input layer builder based on FeatureColumns.

tf.contrib.layers.input_from_feature_columns(
    columns_to_tensors, feature_columns, weight_collections=None, trainable=True,
    scope=None, cols_to_outs=None
)

Generally a single example in training data is described with feature columns. At the first layer of the model, this column oriented data should be converted to a single tensor. Each feature column needs a different kind of operation during this conversion. For example sparse features need a totally different handling than continuous features.

Example:

# Building model for training
columns_to_tensor = tf.io.parse_example(...)
first_layer = input_from_feature_columns(
    columns_to_tensors=columns_to_tensor,
    feature_columns=feature_columns)
second_layer = fully_connected(inputs=first_layer, ...)
...

where feature_columns can be defined as follows:

sparse_feature = sparse_column_with_hash_bucket(
    column_name="sparse_col", ...)
sparse_feature_emb = embedding_column(sparse_id_column=sparse_feature, ...)
real_valued_feature = real_valued_column(...)
real_valued_buckets = bucketized_column(
    source_column=real_valued_feature, ...)

feature_columns=[sparse_feature_emb, real_valued_buckets]