tf.contrib.layers.sequence_input_from_feature_columns( *args, **kwargs )
Builds inputs for sequence models from
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
_DataFrameColumn. In addition, columns in
feature_columns may not be
constructed using any of the following:
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.
Truealso add variables to the graph collection
scope: Optional scope for variable_scope.
A Tensor which can be consumed by hidden layers in the neural network.
ValueError: if FeatureColumn cannot be consumed by a neural network.