Returns a dense Tensor as input layer based on given feature_columns.

Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor.


price = numeric_column('price')
keywords_embedded = embedding_column(
    categorical_column_with_hash_bucket("keywords", 10K), dimensions=16)
columns = [price, keywords_embedded, ...]
features =, features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
for units in [128, 64, 32]:
  dense_tensor = tf.compat.v1.layers.dense(dense_tensor, units, tf.nn.relu)
prediction = tf.compat.v1.layers.dense(dense_tensor, 1)

features A mapping from key to tensors. _FeatureColumns look up via these keys. For example numeric_column('price') will look at 'price' key in this dict. Values can be a SparseTensor or a Tensor depends on corresponding _FeatureColumn.
feature_columns An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from _DenseColumn such as numeric_column, embedding_column, bucketized_column, indicator_column. If you have categorical features, you can wrap them with an embedding_column or indicator_column.
weight_collections A list of collection names to which th