tf.contrib.layers.input_from_feature_columns(
columns_to_tensors,
feature_columns,
weight_collections=None,
trainable=True,
scope=None,
cols_to_outs=None
)
Defined in tensorflow/contrib/layers/python/layers/feature_column_ops.py
.
A tf.contrib.layers style input layer builder based on FeatureColumns.
Generally a single example in training data is described with feature columns. At the first layer of the model, this column oriented data should be converted to a single tensor. Each feature column needs a different kind of operation during this conversion. For example sparse features need a totally different handling than continuous features.
Example:
# Building model for training
columns_to_tensor = tf.parse_example(...)
first_layer = input_from_feature_columns(
columns_to_tensors=columns_to_tensor,
feature_columns=feature_columns)
second_layer = fully_connected(inputs=first_layer, ...)
...
where feature_columns can be defined as follows:
sparse_feature = sparse_column_with_hash_bucket(
column_name="sparse_col", ...)
sparse_feature_emb = embedding_column(sparse_id_column=sparse_feature, ...)
real_valued_feature = real_valued_column(...)
real_valued_buckets = bucketized_column(
source_column=real_valued_feature, ...)
feature_columns=[sparse_feature_emb, real_valued_buckets]
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.cols_to_outs
: Optional dict from feature column to output tensor, which is concatenated into the returned tensor.
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.