contrib.layers.sequence_input_from_feature_columns

tf.contrib.layers.sequence_input_from_feature_columns

tf.contrib.layers.sequence_input_from_feature_columns

sequence_input_from_feature_columns(
    *args,
    **kwargs
)

Defined in tensorflow/contrib/framework/python/framework/experimental.py.

See the guide: Layers (contrib) > Feature columns

Builds inputs for sequence models from FeatureColumns. (experimental)

THIS FUNCTION IS EXPERIMENTAL. It may change or be removed at any time, and without warning.

See documentation for input_from_feature_columns. The following types of FeatureColumn are permitted in feature_columns: _OneHotColumn, _EmbeddingColumn, _ScatteredEmbeddingColumn, _RealValuedColumn, _DataFrameColumn. In addition, columns in feature_columns may not be constructed using any of the following: ScatteredEmbeddingColumn, BucketizedColumn, CrossedColumn.

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.

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.