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,
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
A set containing all the feature columns. All items in the
set should be instances of classes derived by FeatureColumn.
List of graph collections to which weights are added.
If True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
Optional scope for variable_scope.
A Tensor which can be consumed by hidden layers in the neural network.
if FeatureColumn cannot be consumed by a neural network.