Generally a single example in training data is described with FeatureColumns.
At the first layer of the model, this column oriented data should be converted
to a single Tensor.
A mapping from key to tensors. _FeatureColumns look up via these
keys. For example numeric_column('price') will look at 'price' key in
this dict. Values can be a SparseTensor or a Tensor depends on
corresponding _FeatureColumn.
feature_columns
An iterable containing the FeatureColumns to use as inputs
to your model. All items should be instances of classes derived from
_DenseColumn such as numeric_column, embedding_column,
bucketized_column, indicator_column. If you have categorical features,
you can wrap them with an embedding_column or indicator_column.
weight_collections
A list of collection names to which the Variable will be
added. Note that variables will also be added to collections
tf.GraphKeys.GLOBAL_VARIABLES and ops.GraphKeys.MODEL_VARIABLES.
trainable
If True also add the variable to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
cols_to_vars
If not None, must be a dictionary that will be filled with a
mapping from _FeatureColumn to list of Variables. For example, after
the call, we might have cols_to_vars = {_EmbeddingColumn(
categorical_column=_HashedCategoricalColumn( key='sparse_feature',
hash_bucket_size=5, dtype=tf.string), dimension=10): [
cols_to_output_tensors
If not None, must be a dictionary that will be
filled with a mapping from '_FeatureColumn' to the associated output
Tensors.
Returns
A Tensor which represents input layer of a model. Its shape
is (batch_size, first_layer_dimension) and its dtype is float32.
first_layer_dimension is determined based on given feature_columns.
Raises
ValueError
if an item in feature_columns is not a _DenseColumn.