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.