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Builds inputs for sequence models from FeatureColumn
s. (experimental)
tf.contrib.layers.sequence_input_from_feature_columns(
*args,
**kwargs
)
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
: IfTrue
also add variables to the graph collectionGraphKeys.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.