tf.contrib.layers.input_from_feature_columns

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

Defined in tensorflow/contrib/layers/python/layers/feature_column_ops.py.

See the guide: Layers (contrib) > Feature columns

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

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.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]

Args:

• columns_to_tensors: A mapping from feature column to tensors. 'string' key means a base feature (not-transformed). It can have FeatureColumn as a key too. That means that FeatureColumn is already transformed by input pipeline.
• feature_columns: A set containing all the feature columns. All items in the set should be instances of classes derived by FeatureColumn.
• weight_collections: List of graph collections to which weights are added.
• trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
• scope: Optional scope for variable_scope.
• cols_to_outs: Optional dict from feature column to output tensor, which is concatenated into the returned tensor.

Returns:

A Tensor which can be consumed by hidden layers in the neural network.

Raises:

• ValueError: if FeatureColumn cannot be consumed by a neural network.